CRANberries

Tue, 19 Feb 2019

New package pgdraw with initial version 1.1
Package: pgdraw
Type: Package
Title: Generate Random Samples from the Polya-Gamma Distribution
Version: 1.1
Date: 2019-02-19
Author: Daniel F. Schmidt [aut, cph, cre], Enes Makalic [aut, cph]
Maintainer: Daniel F. Schmidt <daniel.schmidt@monash.edu>
Description: Generates random samples from the Polya-Gamma distribution using an implementation of the algorithm described in J. Windle's PhD thesis (2013) <https://repositories.lib.utexas.edu/bitstream/handle/2152/21842/WINDLE-DISSERTATION-2013.pdf>. The underlying implementation is in C.
License: GPL (>= 3)
Imports: Rcpp (>= 0.12.16)
NeedsCompilation: yes
LinkingTo: Rcpp
Authors@R: c( person("Daniel F. Schmidt", email="daniel.schmidt@monash.edu", role = c("aut","cph","cre")), person("Enes Makalic", email="emakalic@unimelb.edu.au", role=c("aut","cph")) )
RoxygenNote: 6.1.0
Packaged: 2019-02-19 01:26:45 UTC; dschmidt
Repository: CRAN
Date/Publication: 2019-02-19 13:00:03 UTC

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Mon, 18 Feb 2019

New package interactions with initial version 1.0.0
Package: interactions
Type: Package
Title: Comprehensive, User-Friendly Toolkit for Probing Interactions
Version: 1.0.0
Authors@R: person(c("Jacob","A."), "Long", email = "long.1377@osu.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-1582-6214"))
Description: A suite of functions for conducting and interpreting analysis of statistical interaction in regression models that was formerly part of the 'jtools' package. Functionality includes visualization of two- and three-way interactions among continuous and/or categorical variables as well as calculation of "simple slopes" and Johnson-Neyman intervals. These capabilities are implemented for generalized linear models in addition to the standard linear regression context.
URL: https://interactions.jacob-long.com
BugReports: https://github.com/jacob-long/interactions/issues
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: ggplot2, crayon, cli, jtools (>= 2.0.0), rlang
Suggests: brms, broom, cowplot, ggstance, glue, huxtable (>= 3.0.0), lme4, rstanarm, sandwich, survey, knitr, rmarkdown, rmdformats, testthat
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-09 14:06:52 UTC; jlong
Author: Jacob A. Long [aut, cre] (<https://orcid.org/0000-0002-1582-6214>)
Maintainer: Jacob A. Long <long.1377@osu.edu>
Repository: CRAN
Date/Publication: 2019-02-18 23:20:03 UTC

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New package segmenTier with initial version 0.1.2
Package: segmenTier
Type: Package
Title: Similarity-Based Segmentation of Multidimensional Signals
Version: 0.1.2
Author: Rainer Machne, Douglas B. Murray, Peter F. Stadler
URL: https://github.com/raim/segmenTier
BugReports: https://github.com/raim/segmenTier/issues
Maintainer: Rainer Machne <raim@tbi.univie.ac.at>
Description: A dynamic programming solution to segmentation based on maximization of arbitrary similarity measures within segments. The general idea, theory and this implementation are described in Machne, Murray & Stadler (2017) <doi:10.1038/s41598-017-12401-8>. In addition to the core algorithm, the package provides time-series processing and clustering functions as described in the publication. These are generally applicable where a `k-means` clustering yields meaningful results, and have been specifically developed for clustering of the Discrete Fourier Transform of periodic gene expression data (`circadian' or `yeast metabolic oscillations'). This clustering approach is outlined in the supplemental material of Machne & Murray (2012) <doi:10.1371/journal.pone.0037906>), and here is used as a basis of segment similarity measures. Notably, the time-series processing and clustering functions can also be used as stand-alone tools, independent of segmentation, e.g., for transcriptome data already mapped to genes.
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.7)
Suggests: flowMerge, flowClust, flowCore, knitr, rmarkdown
LinkingTo: Rcpp
Encoding: UTF-8
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-02-09 13:04:24 UTC; raim
Repository: CRAN
Date/Publication: 2019-02-18 23:00:03 UTC

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New package swephR with initial version 0.1.5
Package: swephR
Type: Package
Title: High Precision Swiss Ephemeris
Version: 0.1.5
Authors@R: c( person("Ralf", "Stubner", email = "ralf.stubner@gmail.com", role = c("aut", "cre")), person("Victor", "Reijs", role = "aut"), person("Authors and copyright holder of the Swiss Ephemeris", role = c("aut", "cph"), comment = "see LICENSE for details"))
Description: The Swiss Ephemeris is a high precision ephemeris based upon the DE431 ephemerides from NASA's JPL. It covers the time range 13201 BC to AD 17191. This package uses the semi-analytic theory by Steve Moshier. For faster and more accurate calculations, the compressed Swiss Ephemeris data is available in the 'swephRdata' package. To access this data package, run 'install.packages("swephRdata", repos = "https://rstub.github.io/drat/", type = "source")'. The size of the 'swephRdata' package is approximately 115 MB. The user can also use the original JPL DE431 data.
License: AGPL | file LICENSE
Imports: Rcpp (>= 0.12.18)
LinkingTo: Rcpp
RoxygenNote: 6.1.1
Suggests: testthat, swephRdata, knitr, rmarkdown
Encoding: UTF-8
URL: https://github.com/rstub/swephR/, http://www.astro.com/swisseph/
BugReports: https://github.com/rstub/swephR/issues/
Additional_repositories: https://rstub.github.io/drat
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-02-09 11:50:37 UTC; ralf
Author: Ralf Stubner [aut, cre], Victor Reijs [aut], Authors and copyright holder of the Swiss Ephemeris [aut, cph] (see LICENSE for details)
Maintainer: Ralf Stubner <ralf.stubner@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-18 15:00:03 UTC

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New package pcgen with initial version 0.2.0
Package: pcgen
Type: Package
Title: Reconstruction of Causal Networks for Data with Random Genetic Effects
Version: 0.2.0
Author: Willem Kruijer, Pariya Behrouzi, Maria Xose Rodriguez-Alvarez
Maintainer: Pariya Behrouzi <pariya.behrouzi@gmail.com>
Depends: R (>= 3.1.0)
Imports: pcalg, graph, Matrix, stats, MASS, utils, Hmisc, methods, lme4, sommer, ggm
Description: Implements the pcgen algorithm, which is a modified version of the standard pc-algorithm, with specific conditional independence tests and modified orientation rules. pcgen extends the approach of Valente et al. (2010) <doi:10.1534/genetics.109.112979> with reconstruction of direct genetic effects.
License: GPL-3
Date: 2019-02-18
NeedsCompilation: no
Packaged: 2019-02-18 13:27:37 UTC; behro001
Repository: CRAN
Date/Publication: 2019-02-18 14:50:03 UTC

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New package LongMemoryTS with initial version 0.1.0
Package: LongMemoryTS
Type: Package
Title: Long Memory Time Series
Version: 0.1.0
Date: 2019-01-18
Authors@R: c(person("Christian", "Leschinski", email="christian_leschinski@gmx.de", role=c("aut", "cre")), person("Michelle", "Voges", email="voges@statistik.uni-hannover.de", role="ctb"), person("Kai", "Wenger", email="wenger@statistik.uni-hannover.de", role="ctb"))
Description: Long Memory Time Series is a collection of functions for estimation, simulation and testing of long memory processes, spurious long memory processes and fractionally cointegrated systems.
License: GPL-2
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp, stats, longmemo, partitions, fracdiff, mvtnorm
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-02-09 13:31:27 UTC; Christian
Author: Christian Leschinski [aut, cre], Michelle Voges [ctb], Kai Wenger [ctb]
Maintainer: Christian Leschinski <christian_leschinski@gmx.de>
Repository: CRAN
Date/Publication: 2019-02-18 14:40:03 UTC

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New package imageviewer with initial version 0.1.0
Package: imageviewer
Title: Simple 'htmlwidgets' Image Viewer with WebGL Brightness/Contrast
Version: 0.1.0
Author: Iakov Pustilnik [aut, cre], Denis Rastegaev [aut]
Maintainer: Iakov Pustilnik <xyapus@gmail.com>
URL: https://github.com/yapus/imageviewer
BugReports: https://github.com/yapus/imageviewer/issues
Authors@R: c( person("Iakov", "Pustilnik", email = "xyapus@gmail.com", role = c("aut", "cre")), person("Denis", "Rastegaev", email = "leda82@gmail.com", role = "aut") )
Description: Display a 2D-matrix data as a interactive zoomable gray-scale image viewer, providing tools for manual data inspection. The viewer window shows cursor guiding lines and a corresponding data slices for both axes at the current cursor position. A tool-bar allows adjusting image display brightness/contrast through WebGL filters and performing basic high-pass/low-pass filtering.
Depends: R (>= 3.4), htmlwidgets
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-09 07:19:14 UTC; 2can
Repository: CRAN
Date/Publication: 2019-02-18 14:50:40 UTC

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New package DTAT with initial version 0.3-0
Package: DTAT
Type: Package
Title: Dose Titration Algorithm Tuning
Version: 0.3-0
Date: 2019-02-08
Authors@R: person("David C.", "Norris" , role = c("aut", "cre") , email = "david@precisionmethods.guru" )
Maintainer: David C. Norris <david@precisionmethods.guru>
Depends: R (>= 3.4.0), survival
Imports: km.ci, pomp, Hmisc, data.table, dplyr, r2d3, shiny, jsonlite, methods
Suggests: knitr, rmarkdown, lattice, latticeExtra, widgetframe, tidyr, RColorBrewer
Description: DTAT is a methodologic framework allowing dose individualization to be conceived as a continuous learning process that begins in early-phase clinical trials and continues throughout drug development, on into clinical practice. This package includes code that researchers may use to reproduce or extend key results of the DTAT research programme, plus tools for trialists to design and simulate a '3+3/PC' dose-finding study. Please see Norris (2017) <doi:10.12688/f1000research.10624.3> and Norris (2017) <doi:10.1101/240846>.
URL: https://osf.io/5479p/
License: MIT + file LICENSE
RoxygenNote: 6.1.1
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-02-08 12:05:34 UTC; david
Author: David C. Norris [aut, cre]
Repository: CRAN
Date/Publication: 2019-02-18 14:40:10 UTC

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New package Cascade with initial version 1.7
Package: Cascade
Type: Package
Title: Selection, Reverse-Engineering and Prediction in Cascade Networks
Version: 1.7
Date: 2019-02-09
Depends: R (>= 2.10)
Imports: abind, animation, cluster, grid, igraph, lars, lattice, limma, magic, methods, nnls, splines, stats4, survival, tnet, VGAM
Suggests: R.rsp, CascadeData, knitr
Authors@R: c( person(given = "Frederic", family= "Bertrand", role = c("cre", "aut"), email = "frederic.bertrand@math.unistra.fr", comment = c(ORCID = "0000-0002-0837-8281")), person(given = "Myriam", family= "Maumy-Bertrand", role = c("aut"), email = "myriam.maumy-bertrand@math.unistra.fr", comment = c(ORCID = "0000-0002-4615-1512")), person(given = "Laurent", family= "Vallat", role = c("ctb"), email = "vallat@unistra.fr"), person(given = "Nicolas", family= "Jung", role = c("ctb"), email = "nicolas.jung@unistra.fr"))
Author: Frederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>), Myriam Maumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>), Laurent Vallat [ctb], Nicolas Jung [ctb]
Maintainer: Frederic Bertrand <frederic.bertrand@math.unistra.fr>
Description: A modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.
License: GPL (>= 2)
Encoding: UTF-8
Collate: global.R micro_array.R network.R micro_array-network.R micropredict.R
Classification/MSC: 62J05, 62J07, 62J99, 92C42
VignetteBuilder: R.rsp
RoxygenNote: 6.1.1
URL: http://www-irma.u-strasbg.fr/~fbertran/, https://github.com/fbertran/Cascade
BugReports: https://github.com/fbertran/Cascade/issues
NeedsCompilation: no
Packaged: 2019-02-09 02:24:24 UTC; fbertran
Repository: CRAN
Date/Publication: 2019-02-18 14:50:52 UTC

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New package bdchecks with initial version 0.1.7
Package: bdchecks
Title: Biodiversity Data Checks
Description: Supplies a Shiny app and a set of functions to perform and managing data checks for biodiversity data.
Version: 0.1.7
Date: 2019-02-07
License: GPL-3 | file LICENSE
URL: https://github.com/bd-R/bdchecks
BugReports: https://github.com/bd-R/bdchecks/issues
Authors@R: c( person( "Povilas", "Gibas", email = "povilasgibas@gmail.com", role = c("aut", "cre"), comment = c(ORCID = '0000-0001-5311-6021')), person( "Tomer", "Gueta", email = "tomer.gu@gmail.com", role = c("aut"), comment = c(ORCID = '0000-0003-1557-8596')), person( "Vijay", "Barve", , email = "vijay.barve@gmail.com", role = c("aut"), comment = c(ORCID = '0000-0002-4852-2567')), person( "Thiloshon", "Nagarajah", email = "thiloshon@gmail.com", role = c("aut")), person( "Yohay", "Carmel", email = "yohay@cv.technion.ac.il", role = c("aut"), comment = c(ORCID = '0000-0002-5883-0184')) )
Depends: R (>= 2.10)
Imports: bdDwC, DT, data.table, finch, methods, knitr, rgbif, shiny, shinyBS, shinydashboard, shinyjs, spocc, yaml
LazyData: true
RoxygenNote: 6.1.1
Encoding: UTF-8
Suggests: testthat, covr
NeedsCompilation: no
Packaged: 2019-02-09 09:38:55 UTC; pogibas
Author: Povilas Gibas [aut, cre] (<https://orcid.org/0000-0001-5311-6021>), Tomer Gueta [aut] (<https://orcid.org/0000-0003-1557-8596>), Vijay Barve [aut] (<https://orcid.org/0000-0002-4852-2567>), Thiloshon Nagarajah [aut], Yohay Carmel [aut] (<https://orcid.org/0000-0002-5883-0184>)
Maintainer: Povilas Gibas <povilasgibas@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-18 15:00:07 UTC

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New package phenofit with initial version 0.2.0
Package: phenofit
Type: Package
Title: Extract Remote Sensing Vegetation Phenology
Version: 0.2.0
Authors@R: c( person("Dongdong", "Kong", role = c("aut", "cre"), email = "kongdd.sysu@gmail.com"), person("Mingzhong", "Xiao", role = c("aut"), email = "xmingzh@mail2.sysu.edu.cn"), person("Yongqiang", "Zhang", role = c("aut"), email = "yongqiang.zhang2014@gmail.com"), person("Xihui", "Gu", role = c("aut"), email = "guxh@cug.edu.cn"), person("Jianjian", "Cui", role = c("aut"), email = "cuijj6@mail2.sysu.edu.cn"))
Description: The merits of 'TIMESAT' and 'phenopix' are adopted. Besides, a simple and growing season dividing method and a practical snow elimination method based on Whittaker were proposed. 7 curve fitting methods and 4 phenology extraction methods were provided. Parameters boundary are considered for every curve fitting methods according to their ecological meaning. And 'optimx' is used to select best optimization method for different curve fitting methods.
License: GPL-2 | file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
LinkingTo: Rcpp, RcppArmadillo
Depends: R (>= 3.1)
Imports: Rcpp, tibble, dplyr, purrr, stringr, tidyr, ggplot2, lubridate, data.table, spam, grid, gridExtra, magrittr, plyr, reshape2, zoo, optimx, ucminf, numDeriv, grDevices, utils, stats, DT
Suggests: knitr, rmarkdown, testthat
URL: https://github.com/kongdd/phenofit
BugReports: https://github.com/kongdd/phenofit/issues
NeedsCompilation: yes
Packaged: 2019-02-08 23:37:39 UTC; kongdd
Author: Dongdong Kong [aut, cre], Mingzhong Xiao [aut], Yongqiang Zhang [aut], Xihui Gu [aut], Jianjian Cui [aut]
Maintainer: Dongdong Kong <kongdd.sysu@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-18 11:50:04 UTC

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New package telefit with initial version 1.0.1
Package: telefit
Type: Package
Title: Estimation and Prediction for Remote Effects Spatial Process Models
Version: 1.0.1
Date: 2019-02-15
Author: Joshua Hewitt
Maintainer: Joshua Hewitt <joshua.hewitt@colostate.edu>
Description: Implementation of the remote effects spatial process (RESP) model for teleconnection. The RESP model is a geostatistical model that allows a spatially-referenced variable (like average precipitation) to be influenced by covariates defined on a remote domain (like sea surface temperatures). The RESP model is introduced in Hewitt et al. (2018) <doi:10.1002/env.2523>. Sample code for working with the RESP model is available at <https://jmhewitt.github.io/research/resp_example>. This material is based upon work supported by the National Science Foundation under grant number AGS 1419558. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
License: GPL-3
Depends: R (>= 3.0.2)
Imports: abind, coda, cowplot, doRNG, dplyr, fields, itertools, mvtnorm, raster, scoringRules, stringr, foreach, ggplot2, gtable, reshape2, scales, sp, SDMTools
LinkingTo: Rcpp (>= 0.12.4), RcppArmadillo, RcppEigen (>= 0.3.3.3.1)
RoxygenNote: 6.1.1
Suggests: testthat
LazyData: true
SystemRequirements: A system with a recent-enough C++11 compiler (such as g++-4.8 or later).
NeedsCompilation: yes
Encoding: UTF-8
Packaged: 2019-02-15 22:34:18 UTC; pointdex
Repository: CRAN
Date/Publication: 2019-02-18 10:50:03 UTC

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New package orders with initial version 0.1.0
Package: orders
Type: Package
Title: Sampling from Order Statistics of New Families of Distributions
Version: 0.1.0
Author: Carlos Alberto Cardozo Delgado
Maintainer: Carlos Alberto Cardozo Delgado <cardozorpackages@gmail.com>
Description: Set of tools to generate samples of order statistics from new families of distributions. The main references for this package are: Gentle, J. (2009), Computational Statistics, Springer-Verlag and Naradajah, S. and Rocha, R. (2016), Newdistns: An R Package for New Families of Distributions, Journal of Statistical Software. The families of distributions are: Marshall Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modi???ed beta G distributions, and exponentiated exponential Poisson G distributions.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
Imports: Newdistns
NeedsCompilation: no
Packaged: 2019-02-08 23:26:30 UTC; Carlos
Repository: CRAN
Date/Publication: 2019-02-18 10:30:03 UTC

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New package fiberLD with initial version 0.1-6
Encoding: UTF-8
Package: fiberLD
Version: 0.1-6
Author: Natalya Pya Arnqvist[aut, cre], Sara Sjöstedt de Luna [aut] Konrad Abramowicz [aut]
Maintainer: Natalya Pya Arnqvist <nat.pya@gmail.com>
Title: Fiber Length Determination
Date: 2019-02-08
Description: Routines for estimating tree fiber (tracheid) length distributions in the standing tree based on increment core samples. Two types of data can be used with the package, increment core data measured by means of an optical fiber analyzer (OFA), e.g. such as the Kajaani Fiber Lab, or measured by microscopy. Increment core data analyzed by OFAs consist of the cell lengths of both cut and uncut fibres (tracheids) and fines (such as ray parenchyma cells) without being able to identify which cells are cut or if they are fines or fibres. The microscopy measured data consist of the observed lengths of the uncut fibres in the increment core. A censored version of a mixture of the fine and fiber length distributions is proposed to fit the OFA data, under distributional assumptions (Svensson et al., 2006) <doi:10.1111/j.1467-9469.2006.00501.x>. The package offers two choices for the assumptions of the underlying density functions of the true fiber (fine) lenghts of those fibers (fines) that at least partially appear in the increment core, being the generalized gamma and the log normal densities.
Depends: R (>= 2.15.0)
Imports: stats, graphics, Matrix, VGAM, parallel, foreach, doParallel
Suggests: MASS
License: GPL (>= 2)
LazyLoad: yes
NeedsCompilation: no
Packaged: 2019-02-08 21:07:16 UTC; natalya
Repository: CRAN
Date/Publication: 2019-02-18 10:30:10 UTC

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New package SPAS with initial version 2019.2
Package: SPAS
Type: Package
Title: Stratified-Petersen Analysis System
Version: 2019.2
Date: 2019-02-06
Author: Carl James Schwarz
Maintainer: Carl James Schwarz <cschwarz.stat.sfu.ca@gmail.com>
Depends:
Imports: BB, MASS, Matrix, msm, numDeriv, plyr
Description: The Stratified-Petersen Analysis System (SPAS) is designed to estimate abundance in two-sample capture-recapture experiments where the capture and recaptures are stratified. This is a generalization of the simple Lincoln-Petersen estimator. Strata may be defined in time or in space or both, and the s strata in which marking takes place may differ from the t strata in which recoveries take place. When s=t, SPAS reduces to the method described by Darroch (1961) <https://www.jstor.org/stable/2332748>. When s<t, SPAS implements the methods described in Plante, Rivest, and Tremblay (1988) <https://www.jstor.org/stable/2533994>. Schwarz and Taylor (1998) <doi:10.1139/f97-238> describe the use of SPAS in estimating return of salmon stratified by time and geography. A related package, BTSPAS, deals with temporal stratification where a spline is used to model the distribution of the population over time as it passes the second capture location. This is the R-version of the (now obsolete) standalone Windows program available at <http://www.cs.umanitoba.ca/~popan/spas/spas_home.html>.
License: GPL (>= 2)
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-07 06:25:06 UTC; carlschwarz
Repository: CRAN
Date/Publication: 2019-02-18 09:30:02 UTC

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New package SMITIDvisu with initial version 0.0.3
Package: SMITIDvisu
Type: Package
Title: Visualize Data for Host and Viral Population from 'SMITIDstruct' using HTMLwidgets
Version: 0.0.3
Authors@R: person("Jean-Francois", "Rey", email = "jean-francois.rey@inra.fr", role = c("aut", "cre"))
Description: Visualisation tools for 'SMITIDstruct' package. Allow to visualize host timeline, transmission tree, index diversities and variant graph using HTMLwidgets. It mainly using D3JS javascript framework.
Date: 2019-02-03
Depends: R (>= 3.4.0), utils
LinkingTo: Rcpp
NeedsCompilation: yes
SystemRequirements: C++11
Biarch: true
License: GPL (>= 3) | file LICENSE
URL: https://informatique-mia.inra.fr/biosp/anr-smitid-project, https://gitlab.paca.inra.fr/SMITID/visu
Encoding: UTF-8
LazyData: true
Imports: Rcpp (>= 0.11.0), htmlwidgets (>= 0.3.2), yaml (>= 2.1.16), jsonlite (>= 1.5.0), magrittr
Suggests: SMITIDstruct, knitr, shiny
RoxygenNote: 6.1.0
Packaged: 2019-02-08 13:24:25 UTC; jfrey
Author: Jean-Francois Rey [aut, cre]
Maintainer: Jean-Francois Rey <jean-francois.rey@inra.fr>
Repository: CRAN
Date/Publication: 2019-02-18 09:40:07 UTC

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New package PUPAIM with initial version 0.1.0
Package: PUPAIM
Type: Package
Title: 'A Collection of Physical and Chemical Adsorption Isotherm Models'
Version: 0.1.0
Author: John Ray V. Saroyda/Ranya Yyan S. Cruz/Roel Joseph C. Antonio/Chester C. Deocaris
Maintainer: Chester Deocaris <ccdeocaris@pup.edu.ph>
Description: Adsorption isotherm equations are linearized plots of different solid-liquid phase equilibria used in calculating different parameters related to the adsorption process. Isotherm equations deals with physical adsorption of gases and vapor and gives the most important characteristics of industrial adsorbents that include pore volume, pore size or energy distribution. PUPAIM has 28 documented adsorption isotherm models listed by Dabrowski (2001) <doi:10.1016/S0001-8686(00)00082-8> and Ayawei et al.(2017) <doi:10.1155/2017/3039817>. These models could be easily fitted in R using adsorption data (Ce and Qe) obtained from experiments.
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: R(>= 3.4.0)
Imports: FSA, Metrics, minpack.lm, stats, graphics
NeedsCompilation: no
Packaged: 2019-02-08 12:46:18 UTC; Joseph
Repository: CRAN
Date/Publication: 2019-02-18 09:40:10 UTC

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New package optimos.prime with initial version 0.1.0
Package: optimos.prime
Type: Package
Title: Optimos Prime Helps Calculate Autoecological Data for Biological Species
Version: 0.1.0
Authors@R: c( person("María Belén", "Sathicq", email = "mbelen@ilpla.edu.ar", role = "aut"), person("María Mercedes", "Nicolosi Gelis", email = "mercedesnicolosi@ilpla.edu.ar", role = "aut"), person("Joaquín", "Cochero", email = "jcochero@ilpla.edu.ar", role = "cre"))
Maintainer: Joaquín Cochero <jcochero@ilpla.edu.ar>
Description: Calculates autoecological data (optima and tolerance ranges) of a biological species given an environmental matrix. The package calculates by weighted averaging, using the number of occurrences to adjust the tolerance assigned to each taxon to estimate optima and tolerance range in cases where taxa have unequal occurrences. See the detailed methodology by Birks et al. (1990) <doi:10.1098/rstb.1990.0062>, and a case example by Potapova and Charles (2003) <doi:10.1046/j.1365-2427.2003.01080.x>.
License: GNU General Public License
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1.9000
Depends: ggplot2, tidyverse, plotly
NeedsCompilation: no
Packaged: 2019-02-08 17:18:13 UTC; Juaco
Author: María Belén Sathicq [aut], María Mercedes Nicolosi Gelis [aut], Joaquín Cochero [cre]
Repository: CRAN
Date/Publication: 2019-02-18 09:50:03 UTC

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New package opalr with initial version 1.0.0
Package: opalr
Version: 1.0.0
Title: Opal R and Datashield Utils
Authors@R: c(person(given = "Yannick", family = "Marcon", role = c("aut", "cre"), email = "yannick.marcon@obiba.org", comment = c(ORCID = "0000-0003-0138-2023")), person(given = "Amadou", family = "Gaye", role = "ctb", comment = c(ORCID = "0000-0002-1180-2792")), person("OBiBa group", role="cph", email="info@obiba.org"))
Depends: R (>= 3.1), httr
Imports: xml2, jsonlite, mime
Suggests: e1071, knitr, knitrBootstrap, rmarkdown
Description: Data integration Web application for biobanks by OBiBa. Opal is OBiBa's core database application for biobanks. Participant data, once collected from any data source, must be integrated and stored in a central data repository under a uniform model. Opal is such a central repository. It can import, process, validate, query, analyze, report, and export data. Opal is typically used in a research center to analyze the data acquired at assessment centres. Its ultimate purpose is to achieve seamless data-sharing among biobanks.
License: GPL-3
URL: https://www.obiba.org/ https://www.obiba.org/pages/products/opal/ https://academic.oup.com/ije/article/46/5/1372/4102813
BugReports: https://github.com/obiba/opalr
RoxygenNote: 6.1.1
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-02-08 16:46:59 UTC; yannick
Author: Yannick Marcon [aut, cre] (<https://orcid.org/0000-0003-0138-2023>), Amadou Gaye [ctb] (<https://orcid.org/0000-0002-1180-2792>), OBiBa group [cph]
Maintainer: Yannick Marcon <yannick.marcon@obiba.org>
Repository: CRAN
Date/Publication: 2019-02-18 10:00:08 UTC

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New package nsga3 with initial version 0.0.3
Package: nsga3
Type: Package
Title: An Implementation of Non-Dominated Sorting Genetic Algorithm III for Feature Selection
Version: 0.0.3
Author: Artem Shramko
Maintainer: Artem Shramko <art.shramko@gmail.com>
Description: An adaptation of Non-dominated Sorting Genetic Algorithm III for multi objective feature selection tasks. Non-dominated Sorting Genetic Algorithm III is a genetic algorithm that solves multiple optimization problems simultaneously by applying a non-dominated sorting technique. It uses a reference points based selection operator to explore solution space and preserve diversity. See the original paper by K. Deb and H. Jain (2014) <DOI:10.1109/TEVC.2013.2281534> for a detailed description.
Depends: R (>= 2.10.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: mlr, parallelMap, rPref, xgboost
Suggests: testthat, knitr, rmarkdown
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-08 16:47:37 UTC; user
Repository: CRAN
Date/Publication: 2019-02-18 10:00:15 UTC

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New package mixsqp with initial version 0.1-97
Encoding: UTF-8
Type: Package
Package: mixsqp
Version: 0.1-97
Date: 2019-02-08
Title: Sequential Quadratic Programming for Fast Maximum-Likelihood Estimation of Mixture Proportions
Authors@R: c(person("Youngseok","Kim",role="aut", email="youngseok@uchicago.edu"), person("Peter","Carbonetto",role=c("aut","cre"), email="peter.carbonetto@gmail.com"), person("Mihai","Anitescu",role="aut"), person("Matthew","Stephens",role="aut"), person("Jason","Willwerscheid",role="ctb"), person("Jean","Morrison",role="ctb"))
URL: https://github.com/stephenslab/mixsqp
BugReports: https://github.com/stephenslab/mixsqp/issues
SystemRequirements: C++11
Depends: R (>= 3.3.0)
Description: Provides optimization algorithms based on sequential quadratic programming (SQP) for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithms are expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver (called by function "KWDual" in the 'REBayes' package), and they are expected to arrive at solutions more quickly in large data sets. The algorithms are described in Y. Kim, P. Carbonetto, M. Stephens & M. Anitescu (2012) <arXiv:1806.01412>.
License: MIT + file LICENSE
Imports: stats, Rcpp (>= 0.12.15)
Suggests: REBayes, Rmosek, testthat, knitr, rmarkdown
LinkingTo: Rcpp, RcppArmadillo
LazyData: true
NeedsCompilation: yes
VignetteBuilder: knitr
RoxygenNote: 6.1.1
Packaged: 2019-02-08 13:57:28 UTC; pcarbo
Author: Youngseok Kim [aut], Peter Carbonetto [aut, cre], Mihai Anitescu [aut], Matthew Stephens [aut], Jason Willwerscheid [ctb], Jean Morrison [ctb]
Maintainer: Peter Carbonetto <peter.carbonetto@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-18 09:40:03 UTC

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Sat, 16 Feb 2019

New package mlbgameday with initial version 0.1.4
Package: mlbgameday
Title: Tools to Gather Data from Major League Baseball Advanced Media
Version: 0.1.4
Authors@R: person("Kris", "Eberwein", email = "eberwein@knights.ucf.edu", role = c("aut", "cre"))
Description: Multi-core processing of data from Major League Baseball Advanced Media <http://gd2.mlb.com/components/game/mlb/>. Additional tools to parallel process large data sets and write them to a database.
Depends: R (>= 3.3.0)
Imports: magrittr, xml2, dplyr, stringr, purrr, tidyr, utils, stats, foreach, iterators, parallel, doParallel, DBI
Suggests: testthat, knitr, rmarkdown, ggplot2, RSQLite, dbplyr
License: MIT + file LICENSE
URL: https://github.com/keberwein/mlbgameday
BugReports: https://github.com/keberwein/mlbgameday/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-16 19:18:45 UTC; mediacenter
Author: Kris Eberwein [aut, cre]
Maintainer: Kris Eberwein <eberwein@knights.ucf.edu>
Repository: CRAN
Date/Publication: 2019-02-16 23:30:08 UTC

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Thu, 14 Feb 2019

New package cranly with initial version 0.3
Package: cranly
Title: Package Directives and Collaboration Networks in CRAN
Version: 0.3
Authors@R: c(person(given = "Ioannis", family = "Kosmidis", role = c("aut", "cre"), email = "ioannis.kosmidis@warwick.ac.uk", comment = c(ORCID = "0000-0003-1556-0302")))
Description: Provides core visualisations and summaries for the CRAN package database. The package provides comprehensive methods for cleaning up and organising the information in the CRAN package database, for building package directives networks (depends, imports, suggests, enhances, linking to) and collaboration networks, producing package dependence trees, and for computing useful summaries and producing interactive visualisations from the resulting networks. The package also provides functions to coerce the networks to 'igraph' <https://CRAN.R-project.org/package=igraph> objects for further analyses and modelling.
URL: https://github.com/ikosmidis/cranly
BugReports: https://github.com/ikosmidis/cranly/issues
Depends: R (>= 3.4.0)
Imports: visNetwork, colorspace, igraph, magrittr, stringr, ggplot2, countrycode
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-14 17:13:18 UTC; yiannis
Author: Ioannis Kosmidis [aut, cre] (<https://orcid.org/0000-0003-1556-0302>)
Maintainer: Ioannis Kosmidis <ioannis.kosmidis@warwick.ac.uk>
Repository: CRAN
Date/Publication: 2019-02-14 21:42:14 UTC

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New package mRMRe with initial version 2.0.9
Package: mRMRe
Type: Package
Title: "Parallelized Minimum Redundancy, Maximum Relevance (mRMR) Ensemble Feature Selection"
Version: 2.0.9
Date: 2019-02-13
Author: Nicolas De Jay, Simon Papillon-Cavanagh, Catharina Olsen, Gianluca Bontempi, Benjamin Haibe-Kains
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
Description: "Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique with DOI: N De Jay et al. (2013) <doi:10.1093/bioinformatics/btt383>."
License: Artistic-2.0
Depends: R (>= 2.10), survival, igraph, methods
URL: http://www.pmgenomics.ca/bhklab/
NeedsCompilation: yes
Packaged: 2019-02-14 00:45:08 UTC; gangesh
Repository: CRAN
Date/Publication: 2019-02-14 09:20:14 UTC

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Wed, 13 Feb 2019

New package survivalAnalysis with initial version 0.1.1
Package: survivalAnalysis
Type: Package
Title: High-Level Interface for Survival Analysis and Associated Plots
Version: 0.1.1
Author: Marcel Wiesweg [aut, cre]
Authors@R: person("Marcel", "Wiesweg", email = "marcel.wiesweg@uk-essen.de", role = c("aut", "cre"))
Maintainer: Marcel Wiesweg <marcel.wiesweg@uk-essen.de>
Description: A high-level interface to perform survival analysis, including Kaplan-Meier analysis and log-rank tests and Cox regression. Aims at providing a clear and elegant syntax, support for use in a pipeline, structured output and plotting. Builds upon the 'survminer' package for Kaplan-Meier plots and provides a customizable implementation for forest plots. Kaplan & Meier (1958) <doi:10.1080/01621459.1958.10501452> Cox (1972) <JSTOR:2985181> Peto & Peto (1972) <doi:10.2307/2344317>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.3.0)
Imports: grDevices, graphics, stats, utils, survival, rlang, dplyr, forcats, magrittr, purrr, stringr, tibble, tidyr, gridExtra, ggplot2 (>= 2.2.1), scales, survminer (> 0.4.0), cowplot, tidytidbits
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown, tidyverse
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-12 15:23:56 UTC; wiesweg
Repository: CRAN
Date/Publication: 2019-02-13 09:40:04 UTC

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Tue, 12 Feb 2019

New package tidystopwords with initial version 0.9.0
Package: tidystopwords
Type: Package
Title: Customizable Lists of Stopwords in 53 Languages
Version: 0.9.0
Date: 2019-01-23
Author: Silvie Cinkova, Maciej Eder
Maintainer: Maciej Eder <maciejeder@gmail.com>
Depends: R (>= 2.14)
Imports: dplyr, stringr
Description: Functions to generate stopword lists in 53 languages, in a way consistent across all the languages supported. The generated lists are based on the morphological tagset from the Universal Dependencies.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: FALSE
LazyDataCompression: TRUE
SysDataCompression: TRUE
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-06 20:36:11 UTC; m
Repository: CRAN
Date/Publication: 2019-02-12 17:20:02 UTC

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New package ICD10gm with initial version 1.0.3
Package: ICD10gm
Title: Metadata Processing for the German Modification of the ICD-10 Coding System
Version: 1.0.3
Date: 2019-02-05
Authors@R: c( person("Ewan", "Donnachie", , "ewan@donnachie.net", c("aut", "cre"), , comment = c(ORCID = "0000-0002-0668-0049")) )
Description: Provides convenient access to the German modification of the International Classification of Diagnoses, 10th revision (ICD-10-GM). It provides functionality to aid in the identification, specification and historisation of ICD-10 codes. Its intended use is the analysis of routinely collected data in the context of epidemiology, medical research and health services research. The underlying metadata are released by the German Institute for Medical Documentation and Information <https://www.dimdi.de>, and are redistributed in accordance with their license.
Depends: R (>= 3.1.2)
License: MIT + file LICENSE
URL: https://github.com/edonnachie/ICD10gm, https://doi.org/10.5281/zenodo.2542833
BugReports: https://github.com/edonnachie/ICD10gm/issues
LazyData: true
RoxygenNote: 6.1.1
Imports: magrittr, dplyr, purrr, tidyr, stringi, rlang, tibble
Suggests: testthat, knitr, rmarkdown, rvest
VignetteBuilder: knitr
Encoding: UTF-8
Language: en-GB
NeedsCompilation: no
Packaged: 2019-02-07 13:04:35 UTC; ewan
Author: Ewan Donnachie [aut, cre] (<https://orcid.org/0000-0002-0668-0049>)
Maintainer: Ewan Donnachie <ewan@donnachie.net>
Repository: CRAN
Date/Publication: 2019-02-12 17:30:07 UTC

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New package fxtract with initial version 0.9.1
Package: fxtract
Type: Package
Title: Feature Extraction from Grouped Data
Date: 2019-02-07
Description: An R6 implementation for calculating features from grouped data. The output will be one row for each group. This functionality is often needed in the feature extraction process of machine learning problems. Very large datasets are supported, since data is only read into RAM when needed. Calculation can be done in parallel and the process can be monitored. Global error handling is supported. Results are available in one final dataframe.
Version: 0.9.1
Authors@R: c(person("Quay", "Au", email = "quayau@gmail.com", role = c("aut", "cre")), person("Clemens", "Stachl", email = "clemensstachl@gmail.com", role = "ctb"), person("Ramona", "Schoedel", email = "ramonaschoedel90@gmail.com", role = "ctb"), person("Theresa", "Ullmann", email = "theresa_ullmann@gmx.de", role = "ctb"), person("Andreas", "Hofheinz", email = "andreas.hofheinz@outlook.com", role = "ctb") )
URL: https://github.com/QuayAu/fxtract
BugReports: https://github.com/QuayAu/fxtract/issues
NeedsCompilation: yes
ByteCompile: yes
Depends: R (>= 3.4.0)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: dplyr, magrittr, checkmate, utils, data.table, future, future.apply, R6
Suggests: testthat, knitr, covr, pkgdown
VignetteBuilder: knitr
Packaged: 2019-02-07 13:39:02 UTC; auj
Author: Quay Au [aut, cre], Clemens Stachl [ctb], Ramona Schoedel [ctb], Theresa Ullmann [ctb], Andreas Hofheinz [ctb]
Maintainer: Quay Au <quayau@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-12 17:30:03 UTC

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New package rcitoid with initial version 0.1.0
Package: rcitoid
Type: Package
Title: Client for 'Citoid'
Description: Client for 'Citoid' (<https://www.mediawiki.org/wiki/Citoid>), an API for getting citations for various scholarly work identifiers found on 'Wikipedia'.
Version: 0.1.0
Authors@R: person("Scott", "Chamberlain", role = c("aut", "cre"), email = "sckott@protonmail.com", comment = c(ORCID = "0000-0003-1444-9135"))
License: MIT + file LICENSE
URL: https://github.com/ropenscilabs/rcitoid
BugReports: https://github.com/ropenscilabs/rcitoid/issues
Encoding: UTF-8
Imports: curl, crul (>= 0.7.0), fauxpas, jsonlite
Suggests: testthat
RoxygenNote: 6.1.1
X-schema.org-applicationCategory: Literature
X-schema.org-keywords: text-ming, literature, publications, citations, Wikipedia, Wikicite, Citoid
X-schema.org-isPartOf: https://ropensci.org
NeedsCompilation: no
Packaged: 2019-02-07 19:17:05 UTC; sckott
Author: Scott Chamberlain [aut, cre] (<https://orcid.org/0000-0003-1444-9135>)
Maintainer: Scott Chamberlain <sckott@protonmail.com>
Repository: CRAN
Date/Publication: 2019-02-12 15:20:02 UTC

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New package rosr with initial version 0.0.3
Package: rosr
Version: 0.0.3
Date: 2019-02-03
Title: Create Reproducible Research Projects
Authors@R: c( person("Peng", "Zhao", role = c("aut", "cre"), email = "pzhao@pzhao.net") )
Maintainer: Peng Zhao <pzhao@pzhao.net>
Depends: R (>= 3.1.0)
Imports: rmarkdown, bookdown, blogdown, tinytex, mindr, bookdownplus, rstudioapi, htmlwidgets, shiny, devtools
Suggests:
Description: Creates reproducible academic projects with integrated academic elements, including datasets, references, codes, images, manuscripts, dissertations, slides and so on. These elements are well connected so that they can be easily synchronized and updated.
License: GPL-3 | file LICENSE
URL: https://github.com/pzhaonet/rosr
BugReports: https://github.com/pzhaonet/rosr/issues
RoxygenNote: 6.1.1
NeedsCompilation: no
LazyData: true
Packaged: 2019-02-06 15:46:27 UTC; Peng.Zhao
Author: Peng Zhao [aut, cre]
Repository: CRAN
Date/Publication: 2019-02-12 14:10:03 UTC

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New package ptmixed with initial version 0.0.1
Package: ptmixed
Title: Poisson-Tweedie Generalized Linear Mixed Model
Version: 0.0.1
Authors@R: c(person("Mirko", "Signorelli", email = "m.signorelli@lumc.nl", role = c("aut", "cre")), person("Pietro", "Spitali", email = "p.spitali@lumc.nl", role = "aut"), person("Roula", "Tsonaka", email = "s.tsonaka@lumc.nl", role = c("aut")))
Description: Fits Poisson-Tweedie generalized linear mixed model. Likelihood approximation based on adaptive Gauss Hermite quadrature rule.
Depends: R (>= 3.4.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: GLMMadaptive, lme4, matrixcalc, moments, mvtnorm, numDeriv, tweeDEseq
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-07 08:32:30 UTC; msignorelli
Author: Mirko Signorelli [aut, cre], Pietro Spitali [aut], Roula Tsonaka [aut]
Maintainer: Mirko Signorelli <m.signorelli@lumc.nl>
Repository: CRAN
Date/Publication: 2019-02-12 14:50:03 UTC

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New package EGRETci with initial version 2.0.2
Package: EGRETci
Type: Package
Title: Exploration and Graphics for RivEr Trends Confidence Intervals
Version: 2.0.2
Authors@R: c(person("Laura", "DeCicco", role = c("aut","cre"), email = "ldecicco@usgs.gov", comment=c(ORCID="0000-0002-3915-9487")), person("Robert", "Hirsch", role = c("aut"), email = "rhirsch@usgs.gov", comment=c(ORCID="0000-0002-4534-075X")), person("Jennifer","Murphy", role = c("ctb")))
Description: Collection of functions to evaluate uncertainty of results from water quality analysis using the Weighted Regressions on Time Discharge and Season (WRTDS) method. This package is an add-on to the EGRET package that performs the WRTDS analysis. The WRTDS modeling method was initially introduced and discussed in Hirsch et al. (2010) <doi:10.1111/j.1752-1688.2010.00482.x>, and expanded in Hirsch and De Cicco (2015) <doi:10.3133/tm4A10>. The paper describing the uncertainty and confidence interval calculations is Hirsch et al. (2015) <doi:10.1016/j.envsoft.2015.07.017>.
License: CC0
Depends: R (>= 3.0)
Imports: EGRET(>= 3.0.0), binom, stats, graphics, utils
Suggests: knitr, testthat, doParallel, iterators, rmarkdown
LazyLoad: yes
LazyData: yes
VignetteBuilder: knitr
BuildVignettes: true
URL: https://github.com/USGS-R/EGRETci
BugReports: https://github.com/USGS-R/EGRETci/issues
Copyright: This software is in the public domain because it contains materials that originally came from the United States Geological Survey, an agency of the United States Department of Interior. For more information, see the official USGS copyright policy at http://www.usgs.gov/visual-id/credit_usgs.html#copyright
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-12 14:16:05 UTC; ldecicco
Author: Laura DeCicco [aut, cre] (<https://orcid.org/0000-0002-3915-9487>), Robert Hirsch [aut] (<https://orcid.org/0000-0002-4534-075X>), Jennifer Murphy [ctb]
Maintainer: Laura DeCicco <ldecicco@usgs.gov>
Repository: CRAN
Date/Publication: 2019-02-12 14:50:10 UTC

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New package seedwater with initial version 1.0
Package: seedwater
Type: Package
Title: Models for Drying and Soaking Kinetics of Seeds
Version: 1.0
Date: 2019-01-30
LazyLoad: yes
LazyData: yes
Author: Anderson Rodrigo da Silva
Maintainer: Anderson Rodrigo da Silva <anderson.silva@ifgoiano.edu.br>
Depends: rpanel, tcltk, tkrplot, stats, graphics, grDevices
Description: Bringing together tools for modeling drying and soaking (rehydration) kinetics of seeds. This package contains several widely used predictive models (e.g.: da Silva et al., 2018). As these are nonlinear, the functions are interactive-based and easy-to-use. Least squares estimates are obtained with just a few visual adjustments of the initial parameter values. Reference: da Silva AR et al. (2018) <doi:10.2134/agronj2017.07.0373>.
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2019-02-05 23:50:10 UTC; ander
Repository: CRAN
Date/Publication: 2019-02-12 14:00:03 UTC

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New package Claddis with initial version 0.3.0
Package: Claddis
Type: Package
Title: Measuring Morphological Diversity and Evolutionary Tempo
Version: 0.3.0
Date: 2019-02-12
Authors@R: c( person(given = "Graeme T.", family = "Lloyd", email = "graemetlloyd@gmail.com", role = c("aut", "cre", "cph")), person(given = "Thomas", family = "Guillerme", role = c("aut", "cph")), person(given = "Emma", family = "Sherratt", role = c("aut", "cph")), person(given = "Steve C.", family = "Wang", role = c("aut", "cph")) )
Maintainer: Graeme T. Lloyd <graemetlloyd@gmail.com>
Depends: ape, phytools, strap
Imports: gdata, graphics, grDevices, stats, utils
Suggests: rgl
Description: Measures morphological diversity from discrete character data and estimates evolutionary tempo on phylogenetic trees. Imports morphological data from #NEXUS (Maddison et al. (1997) <doi:10.1093/sysbio/46.4.590>) format with ReadMorphNexus(), and writes to both #NEXUS and TNT format (Goloboff et al. (2008) <doi:10.1111/j.1096-0031.2008.00217.x>). Main functions are DiscreteCharacterRate(), which implements likelihood ratio tests for discrete character rates introduced across Lloyd et al. (2012) <doi:10.1111/j.1558-5646.2011.01460.x>, Brusatte et al. (2014) <doi:10.1016/j.cub.2014.08.034>, Close et al. (2015) <doi:10.1016/j.cub.2015.06.047>, and Lloyd (2016) <doi:10.1111/bij.12746>, and MorphDistMatrix(), which implements multiple discrete character distance metrics from Gower (1971) <doi:10.2307/2528823>, Wills (1998) <doi:10.1006/bijl.1998.0255>, Lloyd (2016) <doi:10.1111/bij.12746>, and Hopkins and St John (2018) <doi:10.1098/rspb.2018.1784>. Multiple functions implement various morphospace plots: ChronoPhyloMorphospacePlot() implements Sakamoto and Ruta (2012) <doi:10.1371/journal.pone.0039752>, MorphospacePlot() implements Wills et al. (1994) <doi:10.1017/S009483730001263X>, PlotCharacterChanges() implements Wang and Lloyd (2016) <doi:10.1098/rspb.2016.0214>, and StackPlot() implements Foote (1993) <doi:10.1017/S0094837300015864>. Other functions include SafeTaxonomicReduction(), which implements Wilkinson (1995) <doi:10.1093/sysbio/44.4.501>, and DolloSCM() implements the Dollo stochastic character mapping of Tarver et al. (2018) <doi:10.1093/gbe/evy096>.
Encoding: UTF-8
License: GPL (>= 2)
LazyData: yes
ByteCompile: yes
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-12 12:17:26 UTC; eargtl
Author: Graeme T. Lloyd [aut, cre, cph], Thomas Guillerme [aut, cph], Emma Sherratt [aut, cph], Steve C. Wang [aut, cph]
Repository: CRAN
Date/Publication: 2019-02-12 13:19:36 UTC

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New package cgwtools with initial version 3.0.1
Package: cgwtools
Type: Package
Title: Miscellaneous Tools
Version: 3.0.1
Date: 2019-02-10
Author: Carl Witthoft
Maintainer: Carl Witthoft <carl@witthoft.com>
Description: Functions for performing quick observations or evaluations of data, including a variety of ways to list objects by size, class, etc. In addition, functions which mimic Unix shell commands, including 'head', 'tail' ,'pushd' ,and 'popd'. The functions 'seqle' and 'reverse.seqle' mimic the base 'rle' but can search for linear sequences. The function 'splatnd' allows the user to generate zero-argument commands without the need for 'makeActiveBinding' .
License: LGPL-3
Imports: methods
NeedsCompilation: no
Packaged: 2019-02-12 01:05:47 UTC; cgw
Repository: CRAN
Date/Publication: 2019-02-12 09:55:49 UTC

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Mon, 11 Feb 2019

New package rPACI with initial version 0.1.1
Package: rPACI
Title: Placido Analysis of Corneal Irregularity
Version: 0.1.1
Authors@R: c( person(given = "Darío", family = "Ramos-López", role = c("aut", "cre"), email = "dario.ramos.lopez@urjc.es"), person(given = "Ana D.", family = "Maldonado", role = c("aut"), email = "ana.d.maldonado@ual.es"))
Description: Analysis of corneal data obtained from a Placido disk corneal topographer with calculation of irregularity indices. A corneal topographer is an ophthalmic clinical device that obtains measurements in the cornea (the anterior part of the eye). A Placido disk corneal topographer makes use of the Placido disk (Rowsey et al. (1981), <doi:10.1001/archopht.1981.03930011093022>, Rand et al. (1997), <doi:10.1016/S0886-3350(99)00355-7>), which produce a circular pattern of measurement nodes. The raw information measured by such a topographer is used by practitioners to analyze curvatures, to study optical aberrations, or to diagnose specific conditions of the eye. The rPACI package allows the calculation of the corneal irregularity indices described in Ramos-Lopez et al. (2013), <doi:10.1097/OPX.0b013e3182843f2a>, and that were firstly introduced in Ramos-Lopez et al. (2011), <doi:10.1097/OPX.0b013e3182843f2a>. It provides a simple interface to read corneal topography data files as exported by a typical Placido disk topographer, to compute the irregularity indices mentioned before, and to display summary plots that are easy to interpret for a clinician.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1.9000
NeedsCompilation: no
Packaged: 2019-02-05 16:04:34 UTC; Usuario
Author: Darío Ramos-López [aut, cre], Ana D. Maldonado [aut]
Maintainer: Darío Ramos-López <dario.ramos.lopez@urjc.es>
Repository: CRAN
Date/Publication: 2019-02-11 21:40:03 UTC

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New package Ropj with initial version 0.1-1
Package: Ropj
Type: Package
Title: Import Origin(R) Project Files
Version: 0.1-1
Date: 2019-02-06
Authors@R: c(person('Miquel', 'Garriga', email = 'gbmiquel@gmail.com', role = c('aut', 'cph')), person('Stefan', 'Gerlach', email = 'stefan.gerlach@uni-konstanz.de', role = c('aut', 'cph')), person('Ion', 'Vasilief', email = 'ion_vasilief@yahoo.fr', role = c('aut', 'cph')), person('Alex', 'Kargovsky', email = 'kargovsky@yumr.phys.msu.su', role = c('aut', 'cph')), person('Knut', 'Franke', email = 'Knut.Franke@gmx.de', role = c('ctb', 'cph')), person('Alexander', 'Semke', email = 'alexander.semke@web.de', role = c('ctb', 'cph')), person('Tilman', 'Benkert', email = 'thzs@gmx.net', role = c('ctb', 'cph')), person('Kasper', 'Peeters', email = 'kasper.peeters@aei.mpg.de', role = c('ctb', 'cph')), person('Russell', 'Standish', role = c('ctb', 'cph')), person('Ivan', 'Krylov', email = 'krylov.r00t@gmail.com', role = c('cre', 'cph')))
Description: Read the data from Origin(R) project files ('*.opj') <https://www.originlab.com/doc/User-Guide/Origin-File-Types>. For now, only spreadsheet objects are imported as data frames and no export is planned. More object types may be available to be imported later.
License: GPL (>= 3)
Imports: Rcpp (>= 1.0.0)
LinkingTo: Rcpp
URL: https://github.com/aitap/Ropj
BugReports: https://github.com/aitap/Ropj/issues
NeedsCompilation: yes
Packaged: 2019-02-06 15:12:05 UTC; aitap
Author: Miquel Garriga [aut, cph], Stefan Gerlach [aut, cph], Ion Vasilief [aut, cph], Alex Kargovsky [aut, cph], Knut Franke [ctb, cph], Alexander Semke [ctb, cph], Tilman Benkert [ctb, cph], Kasper Peeters [ctb, cph], Russell Standish [ctb, cph], Ivan Krylov [cre, cph]
Maintainer: Ivan Krylov <krylov.r00t@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-11 21:33:22 UTC

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New package HRW with initial version 1.0-3
Package: HRW
Version: 1.0-3
Date: 2019-02-06
Title: Datasets, Functions and Scripts for Semiparametric Regression Supporting Harezlak, Ruppert & Wand (2018)
Authors@R: c(person("Jaroslaw", "Harezlak", role = "aut", email = "harezlak@iu.edu"), person("David", "Ruppert", role = "aut", email = "dr24@cornell.edu"), person("Matt P.", "Wand", role = c("aut","cre"), email = "matt.wand@uts.edu.au"))
Maintainer: Matt P. Wand <matt.wand@uts.edu.au>
Depends: R (>= 2.10), KernSmooth, grDevices, graphics, splines, stats
Suggests: fields, lattice, mgcv
Description: The book "Semiparametric Regression with R" by J. Harezlak, D. Ruppert & M.P. Wand (2018, Springer; ISBN: 978-1-4939-8851-8) makes use of datasets and scripts to explain semiparametric regression concepts. Each of the book's scripts are contained in this package as well as datasets that are not within other R packages. Functions that aid semiparametric regression analysis are also included.
License: GPL (>= 2)
LazyData: TRUE
NeedsCompilation: no
Packaged: 2019-02-06 11:25:33 UTC; mwand
Author: Jaroslaw Harezlak [aut], David Ruppert [aut], Matt P. Wand [aut, cre]
Repository: CRAN
Date/Publication: 2019-02-11 21:33:31 UTC

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New package uclust with initial version 0.1.0
Package: uclust
Title: Clustering and Classification Inference with U-Statistics
Version: 0.1.0
Authors@R: c(person("Gabriela","Cybis",email="gcybis@gmail.com",role=c("aut","cre")), person("Marcio","Valk",email="marciovalk@gmail.com",role="aut"), person("Kazuki","Yokoyama",email="gcybis@gmail.com",role="ctb"))
Description: Clustering and classification inference for high dimension low sample size (HDLSS) data with U-statistics. The package contains implementations of nonparametric statistical tests for sample homogeneity, group separation, clustering, and classification of multivariate data. The methods have high statistical power and are tailored for data in which the dimension L is much larger than sample size n. See Gabriela B. Cybis, Marcio Valk and Sílvia RC Lopes (2018) <doi:10.1080/00949655.2017.1374387> and Marcio Valk and Gabriela B. Cybis (2018) <arXiv:1805.12179>.
Depends: R (>= 3.4.0),dendextend,robcor
Imports:
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
Suggests: testthat
NeedsCompilation: no
Packaged: 2019-02-05 13:26:44 UTC; Gabriela
Author: Gabriela Cybis [aut, cre], Marcio Valk [aut], Kazuki Yokoyama [ctb]
Maintainer: Gabriela Cybis <gcybis@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-11 16:00:03 UTC

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New package rrum with initial version 0.2.0
Package: rrum
Type: Package
Title: Bayesian Estimation of the Reduced Reparameterized Unified Model with Gibbs Sampling
Version: 0.2.0
Authors@R: c( person("Steven Andrew", "Culpepper", email = "sculpepp@illinois.edu", role = c("aut", "cph"), comment = c(ORCID = "0000-0003-4226-6176") ), person("Aaron", "Hudson", email = "awhudson@uw.edu", role = c("aut", "cph"), comment = c(ORCID = "0000-0002-9731-2224") ), person("James Joseph", "Balamuta", email = "balamut2@illinois.edu", role = c("aut", "cph", "cre"), comment = c(ORCID = "0000-0003-2826-8458") ) )
Description: Implementation of Gibbs sampling algorithm for Bayesian Estimation of the Reduced Reparameterized Unified Model ('rrum'), described by Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.
License: GPL (>= 2)
Depends: R (>= 3.4.0), simcdm (>= 0.1.0)
Imports: Rcpp (>= 1.0.0)
LinkingTo: Rcpp, RcppArmadillo (>= 0.9.200), rgen, simcdm
RoxygenNote: 6.1.1
Suggests: testthat, covr
SystemRequirements: C++11
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2019-02-05 22:55:59 UTC; ronin
Author: Steven Andrew Culpepper [aut, cph] (<https://orcid.org/0000-0003-4226-6176>), Aaron Hudson [aut, cph] (<https://orcid.org/0000-0002-9731-2224>), James Joseph Balamuta [aut, cph, cre] (<https://orcid.org/0000-0003-2826-8458>)
Maintainer: James Joseph Balamuta <balamut2@illinois.edu>
Repository: CRAN
Date/Publication: 2019-02-11 16:10:05 UTC

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New package metaMix with initial version 0.3
Package: metaMix
Title: Bayesian Mixture Analysis for Metagenomic Community Profiling
Version: 0.3
Author: Sofia Morfopoulou <sofia.morfopoulou.10@ucl.ac.uk>
Maintainer: Sofia Morfopoulou <sofia.morfopoulou.10@ucl.ac.uk>
Depends: R (>= 3.2)
Imports: data.table (>= 1.9.2), Matrix, gtools, Rmpi, ggplot2
Suggests: knitr
VignetteBuilder: knitr
Description: Resolves complex metagenomic mixtures by analysing deep sequencing data, using a mixture model based approach. The use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species more likely to contribute to the mixture.
License: GPL-3
LazyData: true
SystemRequirements: Open MPI (>=1.4.3)
RoxygenNote: 6.1.1
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2019-02-07 15:41:40 UTC; sophia
Repository: CRAN
Date/Publication: 2019-02-11 16:20:03 UTC

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New package matsindf with initial version 0.3.0
Package: matsindf
Type: Package
Title: Matrices in data frames
Version: 0.3.0
Date: 2019-01-26
Authors@R: c(person("Matthew", "Heun", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7438-214X"), email = "matthew.heun@me.com"))
Maintainer: Matthew Heun <matthew.heun@me.com>
Description: Provides functions to collapse a tidy data frame into matrices in a data frame and expand a data frame of matrices into a tidy data frame.
License: MIT + file LICENSE
Language: en-US
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: R (>= 2.10)
Imports: dplyr, magrittr, matsbyname, purrr, rlang, rlist, tibble, tidyr
Suggests: ggplot2, knitr, rmarkdown, testthat, covr
VignetteBuilder: knitr
URL: https://github.com/MatthewHeun/matsindf
BugReports: https://github.com/MatthewHeun/matsindf/issues
NeedsCompilation: no
Packaged: 2019-02-05 14:59:35 UTC; mkh2
Author: Matthew Heun [aut, cre] (<https://orcid.org/0000-0002-7438-214X>)
Repository: CRAN
Date/Publication: 2019-02-11 16:00:11 UTC

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New package leiden with initial version 0.2.3
Package: leiden
Type: Package
Title: Implementation of the 'Python leidenalg' Module
Version: 0.2.3
Date: 2019-02-05
Author: Tom Kelly <tom.kelly@riken.jp>
Maintainer: Tom Kelly <tom.kelly@riken.jp>
Description: Implements the 'Python leidenalg' module to be called in R. Enables clustering using the leiden algorithm for partition a graph into communities. See the 'Python' repository for more details: <https://github.com/vtraag/leidenalg> Traag et al (2018) From Louvain to Leiden: guaranteeing well-connected communities. <arXiv:1810.08473>.
License: GPL-3
URL: https://github.com/TomKellyGenetics/leiden
Imports: reticulate
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: covr, testthat, spelling, knitr, rmarkdown, igraph, RColorBrewer
Language: en-US
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-05 12:54:48 UTC; tom
Repository: CRAN
Date/Publication: 2019-02-11 16:53:22 UTC

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New package CB2 with initial version 1.0
Package: CB2
Type: Package
Title: CRISPR Pooled Screen Analysis using Beta-Binomial Test
Version: 1.0
Date: 2019-01-30
Authors@R: c(person(given = "Hyun-Hwan", family = "Jeong", role = c("aut", "cre"), email = "hyun-hwan.jeong@bcm.edu"))
Description: Provides functions of a statistical hypothesis test for hit gene identification and a mapping algorithm to quantify sgRNA (single-guided RNA) abundances for CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) pooled screen data analysis. Details are in Jeong et al. (2018) <doi:10.1101/309302>.
Depends: R (>= 3.5.0)
License: MIT + file LICENSE
Imports: Rcpp (>= 0.12.16), metap, magrittr, dplyr, tibble, stringr, ggplot2, tidyr, glue
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown
RoxygenNote: 6.1.1
Encoding: UTF-8
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-02-05 17:57:58 UTC; hyunhwan
Author: Hyun-Hwan Jeong [aut, cre]
Maintainer: Hyun-Hwan Jeong <hyun-hwan.jeong@bcm.edu>
Repository: CRAN
Date/Publication: 2019-02-11 16:23:19 UTC

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New package STMotif with initial version 1.0.2
Package: STMotif
Type: Package
Title: Discovery of Motifs in Spatial-Time Series
Version: 1.0.2
Authors@R: c( person("Amin", "Bazaz", role = c("aut"), comment = "Polytech'Montpellier"), person("Heraldo", "Borges", email = "stmotif@eic.cefet-rj.br", role = c("aut", "cre"), comment = "CEFET/RJ"), person("Eduardo", "Ogasawara", role = c("aut"), comment = "CEFET/RJ"))
Maintainer: Heraldo Borges <stmotif@eic.cefet-rj.br>
Description: Allow to identify motifs in spatial-time series. A motif is a previously unknown subsequence of a (spatial) time series with relevant number of occurrences. For this purpose, the Combined Series Approach (CSA) is used.
License: GPL-2 | GPL-3
Encoding: UTF-8
LazyData: true
Imports: stats, ggplot2, reshape2, scales, grDevices, RColorBrewer, shiny
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-10 18:07:36 UTC; heraldoborges
Author: Amin Bazaz [aut] (Polytech'Montpellier), Heraldo Borges [aut, cre] (CEFET/RJ), Eduardo Ogasawara [aut] (CEFET/RJ)
Depends: R (>= 2.10)
Repository: CRAN
Date/Publication: 2019-02-11 15:13:14 UTC

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New package Rfssa with initial version 0.0.1
Package: Rfssa
Type: Package
Title: Functional Singular Spectrum Analysis
Version: 0.0.1
Authors@R: c( person("Hossein", "Haghbin", email = "haghbinh@gmail.com", role = c("aut", "cre")), person("Seyed Morteza", "Najibi", email = "mnajibi@shirazu.ac.ir", role = "aut"))
Maintainer: Hossein Haghbin <haghbinh@gmail.com>
URL: https://github.com/haghbinh/Rfssa.git
Description: Methods and tools for implementing functional singular spectrum analysis for functional time series as described in Haghbin H., Najibi, S.M., Mahmoudvand R., Maadooliat M. (2019). Functional singular spectrum Analysis. Manuscript submitted for publication.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: TRUE
RoxygenNote: 6.1.1
Imports: Rcpp, fda, lattice, plot3D,
LinkingTo: Rcpp, RcppArmadillo,
Suggests: knitr,
Depends: R (>= 2.10)
NeedsCompilation: yes
Packaged: 2019-02-05 06:12:25 UTC; Hossein
Author: Hossein Haghbin [aut, cre], Seyed Morteza Najibi [aut]
Repository: CRAN
Date/Publication: 2019-02-11 14:13:16 UTC

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New package IrregLong with initial version 0.1.0
Package: IrregLong
Type: Package
Title: Analysis of Longitudinal Data with Irregular Observation Times
Version: 0.1.0
Date: 2019-01-28
Author: Eleanor Pullenayegum
Maintainer: Eleanor Pullenayegum <eleanor.pullenayegum@sickkids.ca>
Description: Analysis of longitudinal data for which the times of observation are random variables that are potentially associated with the outcome process. The package includes inverse-intensity weighting methods (Lin H, Scharfstein DO, Rosenheck RA (2004) <doi:10.1111/j.1467-9868.2004.b5543.x>) and multiple outputation (Pullenayegum EM (2016) <doi:10.1002/sim.6829>).
Depends: R (>= 2.10)
Imports: survival, geepack, frailtypack
License: GPL-3
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown, nlme, MEMSS
VignetteBuilder: knitr
LazyData: true
Language: en-GB
NeedsCompilation: no
Packaged: 2019-02-05 01:13:03 UTC; Eleanor Pullenayegum
Repository: CRAN
Date/Publication: 2019-02-11 14:03:20 UTC

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New package expSBM with initial version 1.0
Package: expSBM
Type: Package
Title: An Exponential Stochastic Block Model for Interaction Lengths
Version: 1.0
Date: 2019-02-04
Authors@R: c(person("Riccardo", "Rastelli", role = c("aut", "cre"), email = "riccardoras@gmail.com", comment = c(ORCID = "0000-0003-0982-2935")), person("Michael", "Fop", role = "aut", comment = c(ORCID = "0000-0003-3936-2757")))
Description: Given a continuous-time dynamic network, this package allows one to fit a stochastic blockmodel where nodes belonging to the same group create interactions and non-interactions of similar lengths. This package implements the methodology described by R. Rastelli and M. Fop (2019) <arXiv:1901.09828>.
License: GPL-3
Imports: Rcpp (>= 0.12.10), mclust, gtools
LinkingTo: Rcpp, RcppArmadillo
LazyData: TRUE
NeedsCompilation: yes
Packaged: 2019-02-04 22:41:52 UTC; riccardo
Author: Riccardo Rastelli [aut, cre] (<https://orcid.org/0000-0003-0982-2935>), Michael Fop [aut] (<https://orcid.org/0000-0003-3936-2757>)
Maintainer: Riccardo Rastelli <riccardoras@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-11 14:03:17 UTC

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New package TmCalculator with initial version 1.0.0
Package: TmCalculator
Type: Package
Title: Melting Temperature of Nucleic Acid Sequences
Version: 1.0.0
Date: 2019-01-25
Author: Junhui Li
Maintainer: Junhui Li <junhuili@cau.edu.cn>
Description: The melting temperature of nucleic acid sequences can be calculated in three method, the Wallace rule (Thein & Wallace (1986) <doi:10.1016/S0140-6736(86)90739-7>), empirical formulas based on G and C content (Marmur J. (1962) <doi:10.1016/S0022-2836(62)80066-7>, Schildkraut C. (2010) <doi:10.1002/bip.360030207>, Wetmur J G (1991) <10.3109/10409239109114069>, Untergasser,A. (2012) <doi:10.1093/nar/gks596>, von Ahsen N (2001) <PMID:11673362>) and nearest neighbor thermodynamics (Breslauer K J (1986) <doi:10.1073/pnas.83.11.3746>, Sugimoto N (1996) <doi:10.1093/nar/24.22.4501>, Allawi H (1998) <doi:10.1093/nar/26.11.2694>, SantaLucia J (2004) <doi:10.1146/annurev.biophys.32.110601.141800>, Freier S (1986) <doi:10.1073/pnas.83.24.9373>, Xia T (1998) <doi:10.1021/bi9809425>, Chen JL (2012) <doi:10.1021/bi3002709>, Bommarito S (2000) <doi:10.1093/nar/28.9.1929>, Turner D H (2010) <doi:10.1093/nar/gkp892>, Sugimoto N (1995) <doi:10.1016/S0048-9697(98)00088-6>, Allawi H T (1997) <doi:10.1021/bi962590c>, Santalucia N (2005) <doi:10.1093/nar/gki918>), and it can also be corrected with salt ions and chemical compound (SantaLucia J (1996) <doi:10.1021/bi951907q>, SantaLucia J(1998) <doi:10.1073/pnas.95.4.1460>, Owczarzy R (2004) <doi:10.1021/bi034621r>, Owczarzy R (2008) <doi:10.1021/bi702363u>).
License: GPL (>= 2)
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2019-02-05 05:10:32 UTC; lenovo
Repository: CRAN
Date/Publication: 2019-02-11 13:53:18 UTC

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New package calendar with initial version 0.0.1
Package: calendar
Title: Create, Read, Write, and Work with 'iCalander' Files, Calendars and Scheduling Data
Version: 0.0.1
Authors@R: c( person("Robin", "Lovelace", email = "rob00x@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-5679-6536")), person("Layik", "Hama", email = "layik.hama@gmail.com", role = c("aut"), comment = c(ORCID = "0000-0003-1912-4890")) )
Description: Provides function to create, read, write, and work with 'iCalander' files (which typically have '.ics' or '.ical' extensions), and the scheduling data, calendars and timelines of people, organisations and other entities that they represent. 'iCalendar' is an open standard for exchanging calendar and scheduling information between users and computers, described at <https://icalendar.org/>.
Depends: R (>= 3.4.0)
License: Apache License (>= 2.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: testthat, knitr, rmarkdown
Imports: methods, tibble, lubridate
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-05 11:59:18 UTC; robin
Author: Robin Lovelace [aut, cre] (<https://orcid.org/0000-0001-5679-6536>), Layik Hama [aut] (<https://orcid.org/0000-0003-1912-4890>)
Maintainer: Robin Lovelace <rob00x@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-11 13:53:13 UTC

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New package sparsenet with initial version 1.3
Package: sparsenet
Type: Package
Title: Fit Sparse Linear Regression Models via Nonconvex Optimization
Version: 1.3
Date: 2019-02-01
Author: Rahul Mazumder [aut, cre], Trevor Hastie [aut, cre], Jerome Friedman [aut, cre]
Maintainer: Trevor Hastie <hastie@stanford.edu>
Description: Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <DOI: 10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
Depends: glmnet, Matrix (>= 1.0-6), shape
Imports: methods
License: GPL-2
NeedsCompilation: yes
URL: http://www.stanford.edu/~hastie/Papers/Sparsenet/jasa_MFH_final.pdf
Packaged: 2019-02-11 00:57:44 UTC; hastie
Repository: CRAN
Date/Publication: 2019-02-11 08:40:02 UTC

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New package sdpt3r with initial version 0.3
Package: sdpt3r
Type: Package
Title: Semi-Definite Quadratic Linear Programming Solver
Version: 0.3
Date: 2019-02-08
Author: Kim-Chuan Toh (Matlab/C), Micheal Todd (Matlab/C), Reha Tutunco (Matlab/C), Adam Rahman (R/C Headers), Timothy A. Davis (symamd C code), Stefan I. Larimore (symamd C code)
Maintainer: Adam Rahman <a45rahma@uwaterloo.ca>
Description: Solves the general Semi-Definite Linear Programming formulation using an R implementation of SDPT3 (K.C. Toh, M.J. Todd, and R.H. Tutuncu (1999) <doi:10.1080/10556789908805762>). This includes problems such as the nearest correlation matrix problem (Higham (2002) <doi:10.1093/imanum/22.3.329>), D-optimal experimental design (Smith (1918) <doi:10.2307/2331929>), Distance Weighted Discrimination (Marron and Todd (2012) <doi:10.1198/016214507000001120>), as well as graph theory problems including the maximum cut problem. Technical details surrounding SDPT3 can be found in R.H Tutuncu, K.C. Toh, and M.J. Todd (2003) <doi:10.1007/s10107-002-0347-5>.
License: GPL-2 | GPL-3
Depends: Matrix, R (>= 2.10)
RoxygenNote: 6.1.1
NeedsCompilation: yes
Imports: methods, stats
Packaged: 2019-02-09 00:45:34 UTC; adamr
Repository: CRAN
Date/Publication: 2019-02-11 08:50:03 UTC

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Sat, 09 Feb 2019

New package kangar00 with initial version 1.3
Package: kangar00
Type: Package
Title: Kernel Approaches for Nonlinear Genetic Association Regression
Version: 1.3
Date: 2019-02-02
Author: Juliane Manitz [aut], Stefanie Friedrichs [aut], Patricia Burger [aut], Benjamin Hofner [aut], Ngoc Thuy Ha [aut], Saskia Freytag [ctb], Heike Bickeboeller [ctb]
Maintainer: Juliane Manitz <r@manitz.org>
Description: Methods to extract information on pathways, genes and various single-nucleotid polymorphisms (SNPs) from online databases. It provides functions for data preparation and evaluation of genetic influence on a binary outcome using the logistic kernel machine test (LKMT). Three different kernel functions are offered to analyze genotype information in this variance component test: A linear kernel, a size-adjusted kernel and a network-based kernel (Friedrichs et al., 2017, <doi:10.1155/2017/6742763>).
License: GPL-2
Collate: 'pathway.r' 'GWASdata.r' 'data.R' 'kernel.r' 'lkmt.r'
Depends: R (>= 3.1.0)
Imports: methods, biomaRt, bigmemory, sqldf, CompQuadForm, data.table, lattice, igraph
LazyData: true
Suggests: testthat
NeedsCompilation: no
RoxygenNote: 6.1.1
Packaged: 2019-02-09 20:51:06 UTC; jmanitz
Repository: CRAN
Date/Publication: 2019-02-10 03:43:16 UTC

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Fri, 08 Feb 2019

New package EGRET with initial version 3.0.2
Package: EGRET
Type: Package
Title: Exploration and Graphics for RivEr Trends
Version: 3.0.2
Authors@R: c( person("Robert", "Hirsch", role = c("aut"), email = "rhirsch@usgs.gov", comment=c(ORCID="0000-0002-4534-075X")), person("Laura", "DeCicco", role = c("aut","cre"), email = "ldecicco@usgs.gov", comment=c(ORCID="0000-0002-3915-9487")), person("David", "Watkins", role = c("ctb")), person("Lindsay","Carr", role = c("ctb")), person("Jennifer","Murphy", role = c("ctb")))
Description: Statistics and graphics for streamflow history, water quality trends, and the statistical modeling algorithm: Weighted Regressions on Time, Discharge, and Season (WRTDS). The modeling method is introduced and discussed in Hirsch et al. (2010) <doi:10.1111/j.1752-1688.2010.00482.x>, and expanded in Hirsch and De Cicco (2015) <doi:10.3133/tm4A10>.
License: CC0
Depends: R (>= 3.0)
Imports: dataRetrieval (>= 2.0.1), survival, fields, methods, utils, graphics, stats, grDevices, truncnorm, foreach
Suggests: EGRETci, xtable, knitr, rmarkdown, extrafont, testthat, rkt, doParallel, parallel
LazyLoad: yes
LazyData: yes
BugReports: https://github.com/USGS-R/EGRET/issues
VignetteBuilder: knitr
BuildVignettes: true
URL: https://pubs.usgs.gov/tm/04/a10/, https://github.com/USGS-R/EGRET/wiki
Copyright: This software is in the public domain because it contains materials that originally came from the United States Geological Survey, an agency of the United States Department of Interior. For more information, see the official USGS copyright policy at https://www.usgs.gov/visual-id/credit_usgs.html#copyright
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-08 16:51:49 UTC; ldecicco
Author: Robert Hirsch [aut] (<https://orcid.org/0000-0002-4534-075X>), Laura DeCicco [aut, cre] (<https://orcid.org/0000-0002-3915-9487>), David Watkins [ctb], Lindsay Carr [ctb], Jennifer Murphy [ctb]
Maintainer: Laura DeCicco <ldecicco@usgs.gov>
Repository: CRAN
Date/Publication: 2019-02-08 22:43:33 UTC

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New package TestCor with initial version 0.0.0.9
Package: TestCor
Title: FWER and FDR Controlling Procedures for Multiple Correlation Tests
Version: 0.0.0.9
Date: 2019-01-20
Authors@R: c( person("Gannaz", "Irène", email = "irene.gannaz@insa-lyon.fr", role = c("aut","cre")), person("Roux", "Marine", email = "marine.roux@gipsa-lab.fr", role = "aut"))
Maintainer: Gannaz Irène <irene.gannaz@insa-lyon.fr>
Description: Different multiple testing procedures for correlation tests are implemented. These procedures were shown to theoretically control asymptotically the Family Wise Error Rate (Roux (2018) <https://tel.archives-ouvertes.fr/tel-01971574v1>) or the False Discovery Rate (Cai & Liu (2016) <doi:10.1080/01621459.2014.999157>). The package gather four test statistics used in correlation testing, four FWER procedures with either single step or stepdown versions, and four FDR procedures.
Depends: R (>= 3.4)
Encoding: UTF-8
License: GPL (>= 2)
Imports: Rcpp, MASS, stats
LinkingTo: Rcpp
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-02-04 22:35:53 UTC; igannaz
Author: Gannaz Irène [aut, cre], Roux Marine [aut]
Repository: CRAN
Date/Publication: 2019-02-08 17:33:24 UTC

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New package StempCens with initial version 0.1.0
Package: StempCens
Type: Package
Title: Spatio-Temporal Estimation and Prediction for Censored/Missing Responses
Version: 0.1.0
Author: Katherine A. L. Valeriano, Victor H. Lachos and Larissa Avila Matos
Maintainer: Larissa Avila Matos <larissa.amatos@gmail.com>
Description: It estimates the parameters of a censored or missing data in spatio-temporal models using the SAEM algorithm (Delyon et al., 1999 <doi:10.1214/aos/1018031103>). This algorithm is a stochastic approximation of the widely used EM algorithm and an important tool for models in which the E-step does not have an analytic form. Besides the expressions obtained to estimate the parameters to the proposed model, we include the calculations for the observed information matrix using the method developed by Louis (1982) <https://www.jstor.org/stable/2345828>. To examine the performance of the fitted model, case-deletion measure are provided.
License: GPL (>= 2)
RoxygenNote: 6.1.1
Imports: ssym, optimx, Matrix, sp, spTimer, mvtnorm, tmvtnorm, MCMCglmm, ggplot2, grid, distances, gridExtra
Suggests: testthat
NeedsCompilation: no
Packaged: 2019-02-04 13:51:30 UTC; larissam
Repository: CRAN
Date/Publication: 2019-02-08 17:43:18 UTC

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New package Spectrum with initial version 0.1
Package: Spectrum
Title: Versatile Ultra-Fast Spectral Clustering for Single and Multi-View Data
Version: 0.1
Author: Christopher R John
Maintainer: Christopher R John <chris.r.john86@gmail.com>
Description: A versatile ultra-fast spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens local connections in dense regions in the graph. For integrating multi-view data and reducing noise we use a recently developed tensor product graph data integration and diffusion system. 'Spectrum' contains two techniques for finding the number of clusters (K); the classical eigengap method and a novel multimodality gap procedure. The multimodality gap analyses the distribution of the eigenvectors of the graph Laplacian to decide K and tune the kernel. 'Spectrum' is suited for clustering a wide range of complex data.
Depends: R (>= 3.5.0)
License: AGPL-3
Encoding: UTF-8
LazyData: true
Imports: ggplot2, Rtsne, ClusterR, umap, Rfast, RColorBrewer, diptest
Suggests: knitr
VignetteBuilder: knitr
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-04 08:36:33 UTC; christopher
Repository: CRAN
Date/Publication: 2019-02-08 16:03:24 UTC

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New package qs with initial version 0.12
Package: qs
Type: Package
Title: Quick Serialization of R Objects
Version: 0.12
Date: 2019-2-1
Authors@R: c( person("Travers", "Ching", email = "traversc@gmail.com", role = c("aut", "cre", "cph")), person("Yann", "Collet", role = c("aut", "ctb", "cph"), comment = "Yann Collet is the author of the bundled zstd code"), person("Facebook, Inc.", role = "cph", comment = "Facebook is the copyright holder of the bundled zstd code"), person("Reichardt", "Tino", role = c("ctb", "cph"), comment = "Contributor/copyright holder of zstd packaged code"), person("Skibinski", "Przemyslaw", role = c("ctb", "cph"), comment = "Contributor/copyright holder of zstd packaged code"), person("Mori", "Yuta", role = c("ctb", "cph"), comment = "Contributor/copyright holder of zstd packaged code"), person("Francois", "Romain", role = c("ctb", "cph"), comment = "Derived example/tutorials for Alt-Rep structures"))
Maintainer: Travers Ching <traversc@gmail.com>
Description: Provides functions for quickly writing and reading any R object to and from disk. This package makes use of the 'zstd' library for compression and decompression. 'zstd' is created by Yann Collet and owned by Facebook, Inc.
License: AGPL-3 | file LICENSE
Imports: Rcpp, RApiSerialize
LinkingTo: Rcpp, RApiSerialize
Depends: R (>= 3.5.0)
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Copyright: This package includes code from the 'zstd' library owned by Facebook, Inc. and created by Yann Collet.
URL: https://github.com/traversc/qs
BugReports: https://github.com/traversc/qs/issues
NeedsCompilation: yes
Packaged: 2019-02-02 23:32:06 UTC; tching
Author: Travers Ching [aut, cre, cph], Yann Collet [aut, ctb, cph] (Yann Collet is the author of the bundled zstd code), Facebook, Inc. [cph] (Facebook is the copyright holder of the bundled zstd code), Reichardt Tino [ctb, cph] (Contributor/copyright holder of zstd packaged code), Skibinski Przemyslaw [ctb, cph] (Contributor/copyright holder of zstd packaged code), Mori Yuta [ctb, cph] (Contributor/copyright holder of zstd packaged code), Francois Romain [ctb, cph] (Derived example/tutorials for Alt-Rep structures)
Repository: CRAN
Date/Publication: 2019-02-08 16:10:02 UTC

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New package expperm with initial version 0.1.0
Package: expperm
Type: Package
Title: Computing Expectations and Marginal Likelihoods for Permutations
Version: 0.1.0
Author: Ben Powell
Maintainer: Ben Powell <ben.powell@york.ac.uk>
Description: A set of functions for computing expected permutation matrices given a matrix of likelihoods for each individual assignment. It has been written to accompany the forthcoming paper 'Computing expectations and marginal likelihoods for permutations'. Publication details will be updated as soon as they are finalized.
Depends: R (>= 2.10)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-04 15:24:53 UTC; ben
Repository: CRAN
Date/Publication: 2019-02-08 16:03:21 UTC

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New package ClinReport with initial version 0.9.1.1
Package: ClinReport
Type: Package
Title: Statistical Reporting in Clinical Trials
Version: 0.9.1.1
Date: 2019-01-23
Author: JF COLLIN
Maintainer: JF COLLIN <jfcollin@live.fr>
Description: It enables to create easily formatted statistical tables in 'Microsoft Word' documents in pretty formats according to 'clinical standards'. It can be used also outside the scope of clinical trials, for any statistical reporting in 'Word'. Descriptive tables for quantitative statistics (mean, median, max etc..) and/or qualitative statistics (frequencies and percentages) are available and formatted tables of Least Square Means of Linear Models, Linear Mixed Models and Generalized Linear Mixed Models coming from emmeans() function are also available. The package works with 'officer' and 'flextable' packages to export the outputs into 'Microsoft Word' documents.
License: GPL (>= 2)
Imports: stats, ggplot2, reshape2, dplyr, emmeans, utils, officer, flextable
Suggests: lme4, nlme
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-04 13:26:18 UTC; jfcollin
Repository: CRAN
Date/Publication: 2019-02-08 16:03:28 UTC

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New package rosetta with initial version 0.0.1
Package: rosetta
Title: Parallel Use of Statistical Packages in Teaching
Version: 0.0.1
Authors@R: c( person(given = "Gjalt-Jorn", family = "Peters", role = c("aut", "cre"), email = "gjalt-jorn@userfriendlyscience.com"), person(given = "Ron", family = "Pat-El", role = c("ctb"), email = "ron.pat-el@ou.nl"), person(given = "Peter", family = "Verboon", role = c("ctb"), email = "peters.verboon@ou.nl") )
Description: When teaching statistics, it can often be desirable to uncouple the content from specific software packages. To easy such efforts, the Rosetta Stats website (<https://rosettastats.com>) allows comparing analyses in different packages. This package is the companion to the Rosetta Stats website, aiming to provide functions that produce output that is similar to output from other statistical packages, thereby facilitating 'software-agnostic' teaching of statistics.
Maintainer: Gjalt-Jorn Peters <gjalt-jorn@userfriendlyscience.com>
License: GPL (>= 3)
URL: https://rosetta.opens.science
BugReports: https://github.com/psytext/rosetta
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: R (>= 3.0.0)
Imports: car (>= 3.0.2), ggplot2 (>= 2.2.1), gridExtra (>= 2.3), methods (>= 3.0.0), lme4 (>= 1.1.19), multcompView (>= 0.1-0), pander (>= 0.6.3), psych (>= 1.8.4), rio (>= 0.5.10), ufs (>= 0.1.0), userfriendlyscience (>= 0.7.1)
NeedsCompilation: no
Packaged: 2019-02-04 12:37:13 UTC; micro
Author: Gjalt-Jorn Peters [aut, cre], Ron Pat-El [ctb], Peter Verboon [ctb]
Repository: CRAN
Date/Publication: 2019-02-08 15:50:03 UTC

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New package ICcforest with initial version 0.5.0
Package: ICcforest
Version: 0.5.0
Date: 2019-01-09
Title: An Ensemble Method for Interval-Censored Survival Data
Authors@R: c(person(given = "Weichi", family = "Yao", role = c("aut", "cre"), email = "wy635@stern.nyu.edu"), person(given = "Halina", family = "Frydman", role = "aut", email = "hf2@stern.nyu.edu"), person(given = "Jeffrey S.", family = "Simonoff", role = "aut", email = "jss2@stern.nyu.edu"))
Author: Weichi Yao [aut, cre], Halina Frydman [aut], Jeffrey S. Simonoff [aut]
Maintainer: Weichi Yao <wy635@stern.nyu.edu>
Depends: R (>= 3.4.0), partykit
Imports: stats, utils, graphics, survival, icenReg, ipred, parallel
Suggests: LTRCtrees, inum, bayesSurv
Description: Implements the conditional inference forest approach to modeling interval-censored survival data. It also provides functions to tune the parameters and evaluate the model fit. See Yao et al. (2019) <arXiv:1901.04599>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-03 20:10:53 UTC; wyao
Repository: CRAN
Date/Publication: 2019-02-08 15:53:24 UTC

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New package GVARX with initial version 1.1
Package: GVARX
Type: Package
Title: Perform Stationary Global Vector Autoregression Estimation and Inference
Version: 1.1
Date: 2019-02-05
Author: Ho Tsung-wu
Maintainer: Ho Tsung-wu <tsungwu@ntnu.edu.tw>
Description: Perform the estimation and inference of stationary Global Vector Autoregression model (GVAR) of Pesaran, Schuermann and Weiner (2004) <DOI:10.1198/073500104000000019> and Dees, di Mauro, Pesaran and Smith (2007) <DOI:10.1002/jae.932>.
License: GPL (>= 2)
LazyData: TRUE
LazyLoad: yes
Depends: R (>= 2.10),vars,xts
Imports: lmtest, lubridate, urca, sandwich, strucchange
NeedsCompilation: no
Packaged: 2019-02-04 06:26:08 UTC; tsungwu
Repository: CRAN
Date/Publication: 2019-02-08 15:53:28 UTC

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New package ropendata with initial version 0.1.0
Package: ropendata
Type: Package
Title: Query and Download 'Rapid7' 'Cybersecurity' Data Sets
Version: 0.1.0
Date: 2019-01-20
Authors@R: c( person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-5670-2640")), person("Rapid7", role = c("cph", "fnd")) )
Maintainer: Bob Rudis <bob@rud.is>
Description: 'Rapid7' collects 'cybersecurity' data and makes it available via their 'Open Data' <http://opendata.rapid7.com> portal which has an API. Tools are provided to assist in querying for available data sets and downloading any data set authorized to a free, registered account.
URL: https://github.com/brudis-r7/ropendata
BugReports: https://github.com/brudis-r7/ropendata/issues
Encoding: UTF-8
License: MIT + file LICENSE
Suggests: testthat, covr
Depends: R (>= 3.2.0)
Imports: httr, utils, jsonlite
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-03 22:09:32 UTC; hrbrmstr
Author: Bob Rudis [aut, cre] (<https://orcid.org/0000-0001-5670-2640>), Rapid7 [cph, fnd]
Repository: CRAN
Date/Publication: 2019-02-08 12:00:03 UTC

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New package robFitConGraph with initial version 0.1.0
Package: robFitConGraph
Type: Package
Title: Graph-Constrained Robust Covariance Estimation
Version: 0.1.0
Author: Stuart Watt, Daniel Vogel
Maintainer: Stuart Watt <s.watt.15@aberdeen.ac.uk>
Description: Contains a single function named robFitConGraph() which includes two algorithms for robust estimation of scatter matrices subject to zero-constraints in its inverse. The methodology is described in Vogel & Tyler (2014) <doi:10.1093/biomet/asu041>. See robFitConGraph() function documentation for further details.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
LinkingTo: Rcpp
Imports: Rcpp, mvtnorm, MASS
NeedsCompilation: yes
Packaged: 2019-02-03 23:56:37 UTC; stuar
Repository: CRAN
Date/Publication: 2019-02-08 12:30:03 UTC

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New package noaaoceans with initial version 0.1.0
Package: noaaoceans
Type: Package
Title: Collect Ocean Data from NOAA
Version: 0.1.0
Authors@R: person("Sean", "Warlick", email = "warlick.sean@gmail.com", role = c("aut", "cre"))
Maintainer: Sean Warlick <warlick.sean@gmail.com>
Description: Provides a small set of tools for collecting data from National Oceanic and Atmospheric Administration (NOAA) data sources. The functions provided in the package are wrappers around NOAA's existing APIs which is found at <https://tidesandcurrents.noaa.gov/api/>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Suggests: testthat, covr, knitr, rmarkdown, dplyr, httptest, ggplot2, maps, mapdata
RoxygenNote: 6.1.1
Imports: httr, jsonlite, rvest, xml2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-03 22:01:11 UTC; SeanWarlick
Author: Sean Warlick [aut, cre]
Repository: CRAN
Date/Publication: 2019-02-08 12:30:06 UTC

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New package ivmte with initial version 1.0.0
Package: ivmte
Title: Instrumental Variables: Extrapolation by Marginal Treatment Effects
Version: 1.0.0
Authors@R: c(person("Alexander", "Torgovitsky", email = "atorgovitsky@gmail.com", role = c("aut")), person("Joshua", "Shea", email = "jkcshea@uchicago.edu", role = c("aut", "cre")))
Maintainer: Joshua Shea <jkcshea@uchicago.edu>
Description: The marginal treatment effect was introduced by Heckman and Vytlacil (2005) <doi:10.1111/j.1468-0262.2005.00594.x> to provide a choice-theoretic interpretation to instrumental variables models that maintain the monotonicity condition of Imbens and Angrist (1994) <doi:10.2307/2951620>. This interpretation can be used to extrapolate from the compliers to estimate treatment effects for other subpopulations. This package provides a flexible set of methods for conducting this extrapolation. It allows for parametric or nonparametric sieve estimation, and allows the user to maintain shape restrictions such as monotonicity. The package operates in the general framework developed by Mogstad, Santos and Torgovitsky (2018) <doi:10.3982/ECTA15463>, and accommodates either point identification or partial identification (bounds). In the partially identified case, bounds are computed using linear programming. Support for three linear programming solvers is provided. Gurobi and the Gurobi R API can be obtained from <http://www.gurobi.com/index>. CPLEX can be obtained from <https://www.ibm.com/analytics/cplex-optimizer>. CPLEX R APIs 'Rcplex' and 'cplexAPI' are available from CRAN. The lp_solve library is freely available from <http://lpsolve.sourceforge.net/5.5/>, and is included when installing either of its R APIs, 'lpSolve' or 'lpSolveAPI', which are available from CRAN.
Depends: R (>= 3.4.0)
Imports: polynom (>= 1.3-9), Formula, methods, stats, utils
Suggests: gurobi (>= 7.5-1), slam (>= 0.1-42), Rcplex (>= 0.3.3), cplexAPI (>= 1.3.3), lpSolve (>= 5.6.13), lpSolveAPI (>= 3.4.4), testthat (>= 2.0.0), data.table (>= 1.11.2), splines2 (>= 0.2.8), Matrix
License: GPL-2 | GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-03 22:23:27 UTC; joshua
Author: Alexander Torgovitsky [aut], Joshua Shea [aut, cre]
Repository: CRAN
Date/Publication: 2019-02-08 12:33:27 UTC

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New package TextForecast with initial version 0.1.0
Package: TextForecast
Type: Package
Title: Regression Analysis and Forecasting Using Textual Data from a Time-Varying Dictionary
Version: 0.1.0
Authors@R: c( person("Luiz Renato", "Lima", email = "llima@utk.edu", role = "aut"), person("Lucas", "Godeiro", email = "lucas.godeiro@hotmail.com", role = c("aut","cre")))
Description: Provides functionalities based on the paper "Time Varying Dictionary and the Predictive Power of FED Minutes" (Lima, 2018) <doi:10.2139/ssrn.3312483>. It selects the most predictive terms, that we call time-varying dictionary using unsupervised machine learning techniques as lasso and elastic net.
Depends: R (>= 3.1.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: SnowballC, forecast, rpart, stats, text2vec, tidyr, tidytext, tm, tsDyn, wordcloud, dplyr, plyr, udpipe, class, lars, tau, RColorBrewer, forcats, ggplot2, glmnet, pdftools, parallel, doParallel
URL: https://github.com/lucasgodeiro/TextForecast
BugReports: https://github.com/lucasgodeiro/TextForecast/issues
Suggests: knitr, rmarkdown, covr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-07 18:06:38 UTC; lucasgodeirolucas
Author: Luiz Renato Lima [aut], Lucas Godeiro [aut, cre]
Maintainer: Lucas Godeiro <lucas.godeiro@hotmail.com>
Repository: CRAN
Date/Publication: 2019-02-08 09:53:20 UTC

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Thu, 07 Feb 2019

New package whitebox with initial version 0.1.0
Package: whitebox
Type: Package
Title: 'WhiteboxTools' R Frontend
Version: 0.1.0
Description: An R frontend of the 'WhiteboxTools' library, which is an advanced geospatial data analysis platform developed by Prof. John Lindsay at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. 'WhiteboxTools' can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. 'WhiteboxTools' also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. Suggested citation: Lindsay (2016) <doi:10.1016/j.cageo.2016.07.003>.
Authors@R: person("Qiusheng", "Wu", email = "giswqs@gmail.com", role = c("aut", "cre"))
Maintainer: Qiusheng Wu <giswqs@gmail.com>
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
URL: https://github.com/giswqs/whiteboxR
BugReports: https://github.com/giswqs/whiteboxR/issues
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-03 18:05:55 UTC; qiusheng
Author: Qiusheng Wu [aut, cre]
Repository: CRAN
Date/Publication: 2019-02-07 17:40:02 UTC

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New package OutlierDetection with initial version 0.1.0
Package: OutlierDetection
Type: Package
Title: Outlier Detection
Version: 0.1.0
Author: Vinay Tiwari, Akanksha Kashikar
Maintainer: Vinay Tiwari <vinaystiwari786@gmail.com>
Description: To detect outliers using different methods namely model based outlier detection (Barnett, V. 1978 <https://www.jstor.org/stable/2347159>), distance based outlier detection (Hautamaki, V., Karkkainen, I., and Franti, P. 2004 <http://cs.uef.fi/~franti/papers.html>), dispersion based outlier detection (Jin, W., Tung, A., and Han, J. 2001 <https://link.springer.com/chapter/10.1007/0-387-25465-X_7>), depth based outlier detection (Johnson, T., Kwok, I., and Ng, R.T. 1998 <http://www.aaai.org/Library/KDD/1998/kdd98-038.php>) and density based outlier detection (Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. 1996 <https://dl.acm.org/citation.cfm?id=3001507>). This package provides labelling of observations as outliers and outlierliness of each outlier. For univariate and bivariate data, visualization is also provided.
Imports: ggplot2, DDoutlier, depth, depthTools,ldbod
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-03 18:56:31 UTC; Galaxy
Repository: CRAN
Date/Publication: 2019-02-07 17:43:36 UTC

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New package heemod with initial version 0.9.3
Package: heemod
Title: Markov Models for Health Economic Evaluations
Version: 0.9.3
Authors@R: c( person("Kevin", "Zarca", email = "kevin.zarca@gmail.com", role = c("aut", "cre")), person("Antoine", "Filipovic-Pierucci", role = "aut"), person("Matthew", "Wiener", role = "ctb"), person("Zdenek", "Kabat", role = "ctb"), person("Vojtech", "Filipec", role = "ctb"), person("Jordan", "Amdahl", role=c("ctb")), person("Yonatan", "Carranza Alarcon", role=c("ctb")), person("Vince", "Daniels", role=c("ctb")) )
Description: An implementation of the modelling and reporting features described in reference textbook and guidelines (Briggs, Andrew, et al. Decision Modelling for Health Economic Evaluation. Oxford Univ. Press, 2011; Siebert, U. et al. State-Transition Modeling. Medical Decision Making 32, 690-700 (2012).): deterministic and probabilistic sensitivity analysis, heterogeneity analysis, time dependency on state-time and model-time (semi-Markov and non-homogeneous Markov models), etc.
Depends: R (>= 3.3.0)
Imports: dplyr (>= 0.7.2), ggplot2 (>= 2.2.0), lazyeval (>= 0.2.0), memoise (>= 1.1.0), mvnfast (>= 0.2.2), plyr (>= 1.8.0), pryr (>= 0.1.2), tibble (>= 1.3.0)
License: GPL (>= 3)
LazyData: true
VignetteBuilder: knitr
RoxygenNote: 6.1.1
Suggests: BCEA, diagram, flexsurv, knitr, logitnorm, lpSolve, mgcv, optimx, parallel, readxl, rgho, rmarkdown, stringr, survival, testthat, triangle, XLConnect
URL: https://github.com/pierucci/heemod, https://pierucci.org
BugReports: https://github.com/pierucci/heemod/issues
NeedsCompilation: no
Packaged: 2019-02-07 12:52:36 UTC; kevin
Author: Kevin Zarca [aut, cre], Antoine Filipovic-Pierucci [aut], Matthew Wiener [ctb], Zdenek Kabat [ctb], Vojtech Filipec [ctb], Jordan Amdahl [ctb], Yonatan Carranza Alarcon [ctb], Vince Daniels [ctb]
Maintainer: Kevin Zarca <kevin.zarca@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-07 14:03:20 UTC

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New package dartR with initial version 1.1.11
Package: dartR
Type: Package
Title: Importing and Analysing SNP and Silicodart Data Generated by Genome-Wide Restriction Fragment Analysis
Version: 1.1.11
Date: 2019-02-07
Authors@R: c( person("Bernd", "Gruber", email="bernd.gruber@canberra.edu.au", role=c("aut","cre")), person("Arthur", "Georges", email="georges@aerg.edu.au", role="aut"), person("Peter J.", "Unmack", email="peter.mail2@unmack.net", rol="ctb"), person("Lindsay V.", "Clark", email="lvclark@illinois.edu", rol="ctb"), person("Oliver", "Berry", email="oliver.berry@csiro.au", rol="ctb" ) )
Description: Functions are provided that facilitate the import and analysis of SNP (single nucleotide polymorphism) and silicodart (presence/absence) data. The main focus is on data generated by DarT (Diversity Arrays Technology). However, once SNP or related fragment presence/absence data from any source is imported into a genlight object many of the functions can be used. Functions are available for input and output of SNP and silicodart data, for reporting on and filtering on various criteria (e.g. CallRate, Heterozygosity, Reproducibility, maximum allele frequency). Advanced filtering is based on Linkage Disequilibrium and HWE (Hardy-Weinberg equilibrium). Other functions are available for visualization after PCoA (Principle Coordinate Analysis), or to facilitate transfer of data between genlight/genind objects and newhybrids, related, phylip, structure, faststructure packages.
VignetteBuilder: knitr
Encoding: UTF-8
Depends: R (>= 3.1.1), adegenet (>= 2.0.0)
biocViews:
Imports: plyr, tidyr, reshape2, MASS, ggplot2, directlabels, pca3d, utils, seqinr, pegas, SNPassoc, methods, doParallel, stats, data.table, parallel, foreach, stringr, ape, vegan, SNPRelate, StAMPP, dismo, qvalue, sp, rgdal, igraph, rrBLUP, leaflet, mmod, PopGenReport, gdistance, hierfstat
Suggests: knitr, rmarkdown, rgl, plotly
License: GPL-2
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-07 02:24:27 UTC; s425824
Author: Bernd Gruber [aut, cre], Arthur Georges [aut], Peter J. Unmack [ctb], Lindsay V. Clark [ctb], Oliver Berry [ctb]
Maintainer: Bernd Gruber <bernd.gruber@canberra.edu.au>
Repository: CRAN
Date/Publication: 2019-02-07 14:13:23 UTC

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Wed, 06 Feb 2019

New package CascadeData with initial version 1.2
Package: CascadeData
Type: Package
Title: Experimental Data of Cascade Experiments in Genomics
Version: 1.2
Date: 2019-02-06
Depends: R (>= 2.10)
Imports:
Suggests:
Authors@R: c( person(given = "Frederic", family= "Bertrand", role = c("cre", "aut"), email = "frederic.bertrand@math.unistra.fr", comment = c(ORCID = "0000-0002-0837-8281")), person(given = "Myriam", family= "Maumy-Bertrand", role = c("aut"), email = "myriam.maumy-bertrand@math.unistra.fr", comment = c(ORCID = "0000-0002-4615-1512")), person(given = "Laurent", family= "Vallat", role = c("ctb"), email = "vallat@unistra.fr"), person(given = "Nicolas", family= "Jung", role = c("ctb"), email = "nicolas.jung@unistra.fr"))
Author: Frederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>), Myriam Maumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>), Laurent Vallat [ctb], Nicolas Jung [ctb]
Maintainer: Frederic Bertrand <frederic.bertrand@math.unistra.fr>
Description: These experimental expression data (5 leukemic 'CLL' B-lymphocyte of aggressive form from 'GSE39411', <doi:10.1073/pnas.1211130110>), after B-cell receptor stimulation, are used as examples by packages such as the 'Cascade' one, a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.
License: GPL (>= 2)
Encoding: UTF-8
RoxygenNote: 6.1.1
URL: http://www-irma.u-strasbg.fr/~fbertran/, https://github.com/fbertran/CascadeData
BugReports: https://github.com/fbertran/CascadeData/issues
NeedsCompilation: no
Packaged: 2019-02-06 18:51:03 UTC; fbertran
Repository: CRAN
Date/Publication: 2019-02-07 00:03:21 UTC

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New package POD with initial version 0.99.0
Package: POD
Type: Package
Title: Probability of Detection for Qualitative PCR Methods
Version: 0.99.0
Date: 2019-02-06
Author: Markus Boenn (State Office for Consumer Protection Saxony-Anhalt, Germany)
Maintainer: Markus Boenn <markus.boenn.sf@gmail.com>
Description: This tool computes the probability of detection (POD) curve and the limit of detection (LOD), i.e. the number of copies of the target DNA sequence required to ensure a 95 % probability of detection (LOD95). Other quantiles of the LOD can be specified. This is a reimplementation of the mathematical-statistical modelling of the validation of qualitative polymerase chain reaction (PCR) methods within a single laboratory as provided by the commercial tool 'PROLab' <http://quodata.de/>. The modelling itself has been described by Uhlig et al. (2015) <doi:10.1007/s00769-015-1112-9>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.4.0)
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-06 06:34:18 UTC; boenn
Repository: CRAN
Date/Publication: 2019-02-06 23:53:27 UTC

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New package insurancerating with initial version 0.4.0
Package: insurancerating
Type: Package
Title: Analytic Insurance Rating Techniques
Version: 0.4.0
Author: Martin Haringa
Maintainer: Martin Haringa <mtharinga@gmail.com>
Description: Methods for insurance rating. It provides a data driven strategy for the construction of insurance tariff classes. This strategy is based on the work by Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>. The package also adds functionality showing additional lines for the reference categories in the levels of the coefficients in the output of a generalized linear regression analysis. In addition it implements a procedure determining the level of a factor with the largest exposure, and thereafter changing the base level of the factor to this level.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: classInt, ggplot2, mgcv, rpart
Depends: R (>= 3.3)
Suggests: testthat
NeedsCompilation: no
Packaged: 2019-02-06 16:24:27 UTC; haringa
Repository: CRAN
Date/Publication: 2019-02-06 23:11:02 UTC

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New package bain with initial version 0.2.0
Package: bain
Type: Package
Date: 2019-01-23
Title: Bayes Factors for Informative Hypotheses
Version: 0.2.0
Authors@R: c( person(c("Xin"), "Gu", role = c("aut"), email = "guxin57@hotmail.com"), person(c("Herbert"), "Hoijtink", role = c("aut"), email = "h.hoijtink@uu.nl"), person(c("Joris"), "Mulder", role = c("aut"), email = "jomulder@gmail.com"), person(c("Caspar", "J"), "van Lissa", role = c("aut", "cre"), email = "c.j.vanlissa@uu.nl"), person(c("Jeff"), "Jones", role = "ctb"), person(c("Niels"), "Waller", role = "ctb"), person("The R Core Team", role = "cph") )
Description: Computes approximated adjusted fractional Bayes factors for equality, inequality, and about equality constrained hypotheses. S3 methods are available for specific types of lm() models, namely ANOVA, ANCOVA, and multiple regression, and for the t_test(). The statistical underpinnings are described in Hoijtink, Mulder, van Lissa, and Gu, (2018) <doi:10.31234/osf.io/v3shc>, Gu, Mulder, and Hoijtink, (2018) <doi:10.1111/bmsp.12110>, Hoijtink, Gu, and Mulder, (2018) <doi:10.1111/bmsp.12145>, and Hoijtink, Gu, Mulder, and Rosseel, (2018) <doi:10.1037/met0000187>.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
URL: https://informative-hypotheses.sites.uu.nl/software/bain/
NeedsCompilation: yes
RoxygenNote: 6.1.1
Depends: R (>= 3.0.0), stats
Suggests: MASS, testthat, knitr, rmarkdown
VignetteBuilder: knitr
Packaged: 2019-02-02 13:55:32 UTC; Lissa102
Author: Xin Gu [aut], Herbert Hoijtink [aut], Joris Mulder [aut], Caspar J van Lissa [aut, cre], Jeff Jones [ctb], Niels Waller [ctb], The R Core Team [cph]
Maintainer: Caspar J van Lissa <c.j.vanlissa@uu.nl>
Repository: CRAN
Date/Publication: 2019-02-06 23:25:00 UTC

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New package geosample with initial version 0.2.1
Package: geosample
Type: Package
Title: Construction of Geostatistical Sampling Designs
Version: 0.2.1
Author: Michael G Chipeta, Peter J Diggle
Maintainer: Michael G Chipeta <mgchipeta@mlw.mw>
Imports: splancs, pdist, graphics, stats
Depends: R (>= 3.0.0), sf, sp
Description: Functions for constructing sampling designs, including spatially random, inhibitory (simple or with close pairs), both discrete and continuous, and adaptive designs. For details on the methods, see the following references: Chipeta et al. (2016) <doi:10.1016/j.spasta.2015.12.004> and Chipeta et al. (2016) <doi:10.1002/env.2425>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: geoR, dplyr, PrevMap, rmarkdown, testthat, viridisLite, raster, knitr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-02 12:15:45 UTC; admin
Repository: CRAN
Date/Publication: 2019-02-06 16:23:22 UTC

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New package tsfeatures with initial version 1.0.0
Package: tsfeatures
Title: Time Series Feature Extraction
Version: 1.0.0
Authors@R: c( person("Rob", "Hyndman", email = "Rob.Hyndman@monash.edu", role = c("aut","cre"), comment = c(ORCID = "0000-0002-2140-5352")), person("Yanfei", "Kang", role = "aut", comment = c(ORCID = "0000-0001-8769-6650")), person("Pablo", "Montero-Manso", email="p.montero.manso@udc.es", role="aut"), person("Thiyanga", "Talagala", role = "aut", comment=c(ORCID = "0000-0002-0656-9789")), person("Earo", "Wang", role = "aut", comment=c(ORCID = "0000-0001-6448-5260")), person("Yangzhuoran", "Yang", email = "Fin.Yang@monash.edu", role = "aut"), person("Souhaib", "Ben Taieb", role = "ctb"), person("Cao", "Hanqing", role="ctb"), person("D K", "Lake", email="dlake@virginia.edu", role="ctb"), person("Nikolay", "Laptev", role="ctb"), person("J R", "Moorman", role="ctb"), person("Mitchell", "O'Hara-Wild", role="ctb", comment=c(ORCID = "0000-0001-6729-7695")))
Description: Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.
Depends: R (>= 3.2.3)
Imports: ForeCA, fracdiff, forecast (>= 8.3), purrr, RcppRoll (>= 0.2.2), stats, tibble, tseries, urca, future, furrr
Suggests: testthat, knitr, rmarkdown, ggplot2, tidyr, dplyr, Mcomp, GGally
License: GPL-3
LazyData: true
ByteCompile: true
URL: https://pkg.robjhyndman.com/tsfeatures/
BugReports: https://github.com/robjhyndman/tsfeatures/issues/
RoxygenNote: 6.1.1
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-02-02 01:26:37 UTC; hyndman
Author: Rob Hyndman [aut, cre] (<https://orcid.org/0000-0002-2140-5352>), Yanfei Kang [aut] (<https://orcid.org/0000-0001-8769-6650>), Pablo Montero-Manso [aut], Thiyanga Talagala [aut] (<https://orcid.org/0000-0002-0656-9789>), Earo Wang [aut] (<https://orcid.org/0000-0001-6448-5260>), Yangzhuoran Yang [aut], Souhaib Ben Taieb [ctb], Cao Hanqing [ctb], D K Lake [ctb], Nikolay Laptev [ctb], J R Moorman [ctb], Mitchell O'Hara-Wild [ctb] (<https://orcid.org/0000-0001-6729-7695>)
Maintainer: Rob Hyndman <Rob.Hyndman@monash.edu>
Repository: CRAN
Date/Publication: 2019-02-06 15:50:03 UTC

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New package Repliscope with initial version 1.0.0
Package: Repliscope
Type: Package
Language: en-GB
Title: Replication Timing Profiling using DNA Copy Number
Version: 1.0.0
Author: Dzmitry G Batrakou
Maintainer: Dzmitry G Batrakou <d.batrakou@gmail.com>
Description: Create, Plot and Compare Replication Timing Profiles. The method is described in Muller et al., (2014) <doi: 10.1093/nar/gkt878>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: R (>= 2.10), ggplot2, shiny
Imports: stats, utils, grDevices, colourpicker
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-01 19:22:56 UTC; dzmitry
Repository: CRAN
Date/Publication: 2019-02-06 15:53:37 UTC

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New package mstknnclust with initial version 0.1.0
Package: mstknnclust
Type: Package
Title: MST-kNN Clustering Algorithm
Version: 0.1.0
Date: 2019-01-25
Authors@R: c(person("Jorge Parraga-Alava", email = "jorge.parraga@usach.cl", role = c("aut", "cre")), person("Pablo Moscato", email = "pablo.moscato@newcastle.edu.au", role=c("aut")), person("Mario Inostroza-Ponta", email = "mario.inostroza@usach.cl", role = c("aut")))
Author: Jorge Parraga-Alava [aut, cre], Pablo Moscato [aut], Mario Inostroza-Ponta [aut]
Maintainer: Jorge Parraga-Alava <jorge.parraga@usach.cl>
Description: Implements the MST-kNN clustering algorithm which was proposed by Inostroza-Ponta, M. (2008) <https://trove.nla.gov.au/work/28729389?selectedversion=NBD44634158>.
Depends: R (>= 3.2.5)
License: GPL-2
Encoding: UTF-8
Imports: igraph, amap
RoxygenNote: 6.1.1
VignetteBuilder: knitr
Suggests: knitr, rmarkdown
NeedsCompilation: no
Packaged: 2019-02-01 19:12:01 UTC; jorgeklz
Repository: CRAN
Date/Publication: 2019-02-06 15:50:09 UTC

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New package IPPP with initial version 1.0
Package: IPPP
Type: Package
Title: Inhomogeneous Poisson Point Processes
Version: 1.0
Date: 2019-01-13
Author: Niklas Hohmann
Maintainer: Niklas Hohmann <Niklas.Hohmann@fau.de>
Description: Generates random numbers corresponding to the events on a Poisson point process with changing event rates. This includes the possibility to incorporate additional information such as the number of events occurring or the location of an already known event. It can also generate the probability density functions of specific events in the cases where additional information is available. Based on Hohmann (2019) <arXiv:1901.10754>.
License: CC BY 4.0
Depends: stats
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-01 18:44:31 UTC; nick
Repository: CRAN
Date/Publication: 2019-02-06 15:53:41 UTC

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New package bayfoxr with initial version 0.0.1
Package: bayfoxr
Title: Global Bayesian Foraminifera Core Top Calibration
Version: 0.0.1
Authors@R: person("Steven", "Malevich", email = "malevich@email.arizona.edu", role = c("aut", "cre"))
Description: A Bayesian, global planktic foraminifera core top calibration to modern sea-surface temperatures. Includes four calibration models, considering species-specific calibration parameters and seasonality.
URL: https://github.com/brews/bayfoxr/
BugReports: https://github.com/brews/bayfoxr/issues
Depends: R (>= 3.4)
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Suggests: testthat, knitr, rmarkdown
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-01 21:15:18 UTC; sbm
Author: Steven Malevich [aut, cre]
Maintainer: Steven Malevich <malevich@email.arizona.edu>
Repository: CRAN
Date/Publication: 2019-02-06 15:53:33 UTC

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New package mfx with initial version 1.2-2
Package: mfx
Type: Package
Title: Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs
Version: 1.2-2
Date: 2019-02-06
Authors@R: c( person( "Alan", "Fernihough", role = c("aut", "cre"), email = "alan.fernihough@gmail.com" ), person( "Arne", "Henningsen", role = "ctb", email = "arne.henningsen@gmail.com" ) )
Description: Estimates probit, logit, Poisson, negative binomial, and beta regression models, returning their marginal effects, odds ratios, or incidence rate ratios as an output. Greene (2008, pp. 780-7) provides a textbook introduction to this topic.
License: GPL-2 | GPL-3
Depends: stats, sandwich, lmtest, MASS, betareg
NeedsCompilation: no
Packaged: 2019-02-06 07:08:01 UTC; gsl324
Author: Alan Fernihough [aut, cre], Arne Henningsen [ctb]
Maintainer: Alan Fernihough <alan.fernihough@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-06 11:20:07 UTC

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Tue, 05 Feb 2019

New package tfio with initial version 0.2.0
Package: tfio
Type: Package
Title: Interface to 'TensorFlow IO'
Version: 0.2.0
Authors@R: c( person("TensorFlow IO Contributors", role = c("aut", "cph"), email = "io@tensorflow.org", comment = "Full list of contributors can be found at <https://github.com/tensorflow/io/graphs/contributors>"), person("Yuan", "Tang", role = c("aut", "cre"), email = "terrytangyuan@gmail.com", comment = c(ORCID = "0000-0001-5243-233X")), person(family = "TensorFlow Authors", role = c("cph")), person("Ant Financial", role = c("cph")), person("RStudio", role = c("cph")) )
Description: Interface to 'TensorFlow IO', datasets and filesystem extensions maintained by 'TensorFlow SIG-IO' <https://github.com/tensorflow/community/blob/master/sigs/io/CHARTER.md>.
License: Apache License 2.0
URL: https://github.com/tensorflow/io
BugReports: https://github.com/tensorflow/io/issues
SystemRequirements: TensorFlow >= 1.4 (https://www.tensorflow.org/)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.1)
Imports: reticulate (>= 1.10), tensorflow (>= 1.9), tfdatasets (>= 1.9), forge, magrittr
RoxygenNote: 6.1.0
Suggests: testthat, knitr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-01 16:51:39 UTC; yuan.tang
Author: TensorFlow IO Contributors [aut, cph] (Full list of contributors can be found at <https://github.com/tensorflow/io/graphs/contributors>), Yuan Tang [aut, cre] (<https://orcid.org/0000-0001-5243-233X>), TensorFlow Authors [cph], Ant Financial [cph], RStudio [cph]
Maintainer: Yuan Tang <terrytangyuan@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-05 22:50:03 UTC

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New package tfdeploy with initial version 0.6.0
Package: tfdeploy
Type: Package
Title: Deploy 'TensorFlow' Models
Version: 0.6.0
Authors@R: c( person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut", "cre")), person(family = "RStudio", role = c("cph")) )
Maintainer: Javier Luraschi <javier@rstudio.com>
Description: Tools to deploy 'TensorFlow' <https://www.tensorflow.org/> models across multiple services. Currently, it provides a local server for testing 'cloudml' compatible services.
License: Apache License 2.0
Encoding: UTF-8
LazyData: true
Imports: httpuv, httr, jsonlite, magrittr, reticulate, swagger, tensorflow
Suggests: cloudml, knitr, pixels, processx, testthat, yaml
RoxygenNote: 6.1.0
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-31 17:56:07 UTC; javierluraschi
Author: Javier Luraschi [aut, cre], RStudio [cph]
Repository: CRAN
Date/Publication: 2019-02-05 22:40:04 UTC

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New package shinyMatrix with initial version 0.1.0
Package: shinyMatrix
Title: Shiny Matrix Input Field
Version: 0.1.0
Author: Andreas Neudecker
Maintainer: Andreas Neudecker <andreas.neudecker@inwt-statistics.de>
Description: Implements a custom matrix input field.
Depends: R (>= 3.5)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: shiny, jsonlite
Suggests: testthat, covr
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2019-01-30 15:57:49 UTC; aneudecker
Repository: CRAN
Date/Publication: 2019-02-05 22:00:03 UTC

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New package missCompare with initial version 1.0.1
Package: missCompare
Type: Package
Title: Intuitive Missing Data Imputation Framework
Version: 1.0.1
Authors@R: c(person("Tibor", "V. Varga", email = "tirgit@hotmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-2383-699X")), person("David", "Westergaard", email = "david.westergaard@cpr.ku.dk", role= c("aut"), comment = c(ORCID = "0000-0003-0128-8432")))
Maintainer: Tibor V. Varga <tirgit@hotmail.com>
Description: Offers a convenient pipeline to test and compare various missing data imputation algorithms on simulated and real data. The central assumption behind missCompare is that structurally different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation algorithms. missCompare takes measurements of your dataset and sets up a sandbox to try a curated list of standard and sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns. missCompare will also impute your real-life dataset for you after the selection of the best performing algorithm in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you assess imputation performance.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
BugReports: https://github.com/Tirgit/missCompare/issues
Depends: R (>= 3.5.0)
biocViews:
Imports: Amelia, data.table, dplyr, ggdendro, ggplot2, Hmisc, ltm, magrittr, MASS, Matrix, mi, mice, missForest, missMDA, pcaMethods, plyr, rlang, stats, utils, tidyr, VIM
Suggests: testthat, knitr, rmarkdown, devtools
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-31 07:55:03 UTC; med-tv_
Author: Tibor V. Varga [aut, cre] (<https://orcid.org/0000-0002-2383-699X>), David Westergaard [aut] (<https://orcid.org/0000-0003-0128-8432>)
Repository: CRAN
Date/Publication: 2019-02-05 22:22:07 UTC

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New package CongreveLamsdell2016 with initial version 1.0.0
Package: CongreveLamsdell2016
Version: 1.0.0
Date: 2019-01-28
Title: Distance metrics for trees generated by Congreve and Lamsdell (2016)
Description: Includes the 100 datasets simulated by Congreve and Lamsdell (2016) <doi:10.1111/pala.12236>, and analyses of the partition and quartet distance of reconstructed trees from the generative tree, as analysed by Smith (2019) <doi:10.1098/rsbl.2018.0632>.
URL: https://github.com/ms609/CongreveLamsdell2016
BugReports: https://github.com/ms609/Quartet/issues
Authors@R: c(person("Martin R.", 'Smith', email='martin.smith@durham.ac.uk', role=c("aut", "cre", "cph"), comment=c(ORCID = "0000-0001-5660-1727")), person(given = "Curtis R.", family="Congreve", role = c("cph", "dtc")), person('James C.', 'Lamsdell', role=c("cph", "dtc")) )
Copyright: Data from Congreve & Lamsdell (2016) released under a CC0 license <doi:10.5061/dryad.7dq0j/1>.
License: GPL (>= 2)
Encoding: UTF-8
Language: en-GB
Depends: R (>= 3.4.0)
Imports: Ternary
Suggests: ape, bookdown, knitr, phangorn, Quartet, rmarkdown, TreeSearch (> 0.2.0), usethis
LazyData: true
ByteCompile: true
VignetteBuilder: knitr
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-01-28 14:11:20 UTC; ms609
Author: Martin R. Smith [aut, cre, cph] (<https://orcid.org/0000-0001-5660-1727>), Curtis R. Congreve [cph, dtc], James C. Lamsdell [cph, dtc]
Maintainer: Martin R. Smith <martin.smith@durham.ac.uk>
Repository: CRAN
Date/Publication: 2019-02-05 22:04:07 UTC

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New package bayesdistreg with initial version 0.1.0
Package: bayesdistreg
Type: Package
Title: Bayesian Distribution Regression
Version: 0.1.0
Authors@R: c( person("Emmanuel", "Tsyawo", email = "estsyawo@temple.edu", role = c("aut","cre")), person("Weige", "Huang", email = "weige.huang@temple.edu", role=c("aut")))
Maintainer: Emmanuel Tsyawo <estsyawo@temple.edu>
Description: Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) <doi:10.2139/ssrn.3048658> which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference.
Depends: R (>= 2.1.0)
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: MASS, sandwich, stats
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-01-31 20:45:33 UTC; Selorm
Author: Emmanuel Tsyawo [aut, cre], Weige Huang [aut]
Repository: CRAN
Date/Publication: 2019-02-05 22:44:13 UTC

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New package NSO1212 with initial version 1.0.0
Package: NSO1212
Title: National Statistical Office of Mongolia's Open Data API Handler
Version: 1.0.0
Date: 2019-01-24
Description: National Statistical Office of Mongolia (NSO) is the national statistical service and an organization of Mongolian government. NSO provides open access and official data via its web site <http://www.1212.mn/> and API <http://opendata.1212.mn/en/doc>. The package NSO1212 has functions for accessing the API service. The functions are compatible with the API v2.0 and get data sets and its detailed informations from the API.
Authors@R: person("Makhgal", "Ganbold", email = "makhgal@seas.num.edu.mn", role = c("aut", "cre"))
Maintainer: Makhgal Ganbold <makhgal@seas.num.edu.mn>
URL: https://github.com/galaamn/NSO1212
BugReports: https://github.com/galaamn/NSO1212/issues
Depends: R (>= 3.5.0)
Imports: httr, jsonlite
License: GPL-3
Encoding: UTF-8
LazyData: true
ByteCompile: true
NeedsCompilation: no
RoxygenNote: 6.1.1
Packaged: 2019-01-31 09:58:25 UTC; galaa
Author: Makhgal Ganbold [aut, cre]
Repository: CRAN
Date/Publication: 2019-02-05 21:56:01 UTC

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New package anapuce with initial version 2.3
Package: anapuce
Title: Tools for Microarray Data Analysis
Version: 2.3
Authors@R: person("Julie", "Aubert", email = "julie.aubert@agroparistech.fr", role = c("aut", "cre"))
Description: Functions for normalisation, differentially analysis of microarray data and local False Discovery Rate.
Depends: R (>= 3.4.0)
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2019-02-05 14:36:42 UTC; aubert
Author: Julie Aubert [aut, cre]
Maintainer: Julie Aubert <julie.aubert@agroparistech.fr>
Repository: CRAN
Date/Publication: 2019-02-05 15:13:20 UTC

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Mon, 04 Feb 2019

New package safetyGraphics with initial version 0.7.3
Package: safetyGraphics
Title: Create Interactive Graphics Related to Clinical Trial Safety
Version: 0.7.3
Authors@R: c( person("Jeremy", "Wildfire", email = "jeremy_wildfire@rhoworld.com", role = c("cre","aut")), person("Becca", "Krouse", role="aut"), person("Preston","Burns", role="aut"), person("Xiao","Ni", role = "aut"), person("James","Buchanan", role="aut"), person("Susan","Duke", role="aut"), person("Rho Inc.", role = "cph"))
Maintainer: Jeremy Wildfire <jeremy_wildfire@rhoworld.com>
Description: A framework for evaluation of clinical trial safety. Users can interactively explore their data using the 'Shiny' application or create standalone 'htmlwidget' charts. Interactive charts are built using 'd3.js' and 'webcharts.js' 'JavaScript' libraries.
Depends: R (>= 3.5)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: testthat, shinytest, knitr
Imports: dplyr, htmlwidgets, purrr, stringr, jsonlite, shiny, magrittr, DT, shinyjs, rmarkdown, rlang
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-31 05:57:42 UTC; jeremy
Author: Jeremy Wildfire [cre, aut], Becca Krouse [aut], Preston Burns [aut], Xiao Ni [aut], James Buchanan [aut], Susan Duke [aut], Rho Inc. [cph]
Repository: CRAN
Date/Publication: 2019-02-04 20:40:03 UTC

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New package fedregs with initial version 0.1.1
Package: fedregs
Type: Package
Title: Text Analysis of the US Code of Federal Regulations
Version: 0.1.1
Authors@R: person("Scott", "Large", email = "scott.large@noaa.gov", role = c("cre", "aut"))
Description: The Code of Federal Regulations (CFR) annual edition is the codification of the general and permanent rules published in the Federal Register by the departments and agencies of the Federal Government of the United States of America. Simply, the 'fedregs' package facilitates word processing and sentiment analysis of the CFR using tidy principles. Note: According to the Code of Federal Regulations XML Rendition User Guide Document: "In general, there are no restrictions on re-use of information in Code of Federal Regulations material because U.S. Government works are not subject to copyright. OFR and GPO do not restrict downstream uses of Code of Federal Regulations data, except that independent providers should be aware that only the OFR and GPO are entitled to represent that they are the providers of the official versions of the Code of Federal Regulations and related Federal Register publications."
License: GPL-3
BugReports: https://github.com/slarge/fedregs/issues
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.1.0),
Imports: dplyr (>= 0.7.4), magrittr (>= 1.5), xml2 (>= 1.2.0), purrr(>= 0.2.5), rvest(>= 0.3.2), stringi(>= 1.1.7), httr (>= 1.3.1), tidytext (>= 0.1.9)
Suggests: ggplot2 (>= 2.2.1), tidyr (>= 0.8.0), testthat (>= 2.0.0)
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2019-01-28 20:19:55 UTC; scott.large
Author: Scott Large [cre, aut]
Maintainer: Scott Large <scott.large@noaa.gov>
Repository: CRAN
Date/Publication: 2019-02-04 15:13:30 UTC

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New package PopGenReport with initial version 3.0.4
Package: PopGenReport
Type: Package
Title: A Simple Framework to Analyse Population and Landscape Genetic Data
Version: 3.0.4
Date: 2019-02-04
Authors@R: c( person("Bernd", "Gruber", email="bernd.gruber@canberra.edu.au", role=c("aut","cre")), person("Aaron", "Adamack", email="aaron.adamack@canberra.edu.au", role="aut"))
Description: Provides beginner friendly framework to analyse population genetic data. Based on 'adegenet' objects it uses 'knitr' to create comprehensive reports on spatial genetic data. For detailed information how to use the package refer to the comprehensive tutorials or visit <http://www.popgenreport.org/>.
License: GPL
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.0.0), knitr, adegenet (>= 2.0.0)
Imports: lattice, RgoogleMaps, gap, calibrate, xtable, plyr, dismo, reshape, ggplot2, R.utils, ade4, pegas, genetics, rgdal, gdistance, vegan, sp, mmod, GGally, data.table, graphics, grDevices, methods, stats, utils, raster
VignetteBuilder: knitr
URL: https://github.com/green-striped-gecko/PopGenReport
Suggests:
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-04 03:40:39 UTC; s425824
Author: Bernd Gruber [aut, cre], Aaron Adamack [aut]
Maintainer: Bernd Gruber <bernd.gruber@canberra.edu.au>
Repository: CRAN
Date/Publication: 2019-02-04 12:13:23 UTC

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New package MCMC.qpcr with initial version 1.2.3
Package: MCMC.qpcr
Type: Package
Title: Bayesian Analysis of qRT-PCR Data
Version: 1.2.3
Date: 2016-11-07
Author: Mikhail V. Matz
Maintainer: Mikhail V. Matz <matz@utexas.edu>
Description: Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: <http://bit.ly/1Nwo4CB>.
License: GPL-3
Depends: MCMCglmm,ggplot2,coda
NeedsCompilation: no
Packaged: 2016-11-09 18:51:30 UTC; c-monstr
Repository: CRAN
Date/Publication: 2016-11-09 23:56:55

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New package MCMC.OTU with initial version 1.0.10
Package: MCMC.OTU
Type: Package
Title: Bayesian Analysis of Multivariate Counts Data in DNA Metabarcoding and Ecology
Version: 1.0.10
Date: 2016-02-10
Author: Mikhail V. Matz
Maintainer: Mikhail V. Matz <matz@utexas.edu>
Description: Poisson-lognormal generalized linear mixed model analysis of multivariate counts data using MCMC, aiming to infer the changes in relative proportions of individual variables. The package was originally designed for sequence-based analysis of microbial communities ("metabarcoding", variables = operational taxonomic units, OTUs), but can be used for other types of multivariate counts, such as in ecological applications (variables = species). The results are summarized and plotted using 'ggplot2' functions. Includes functions to remove sample and variable outliers and reformat counts into normalized log-transformed values for correlation and principal component/coordinate analysis. Walkthrough and examples: http://www.bio.utexas.edu/research/matz_lab/matzlab/Methods_files/walkthroughExample_mcmcOTU_R.txt.
License: GPL-3
Depends: MCMCglmm,ggplot2,coda
NeedsCompilation: no
Packaged: 2016-02-11 19:23:29 UTC; c-monstr
Repository: CRAN
Date/Publication: 2016-02-12 00:53:04

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New package glmmsr with initial version 0.2.3
Package: glmmsr
Title: Fit a Generalized Linear Mixed Model
Version: 0.2.3
Authors@R: person("Helen", "Ogden", , "heogden12@gmail.com", role = c("aut", "cre"))
Description: Conduct inference about generalized linear mixed models, with a choice about which method to use to approximate the likelihood. In addition to the Laplace and adaptive Gaussian quadrature approximations, which are borrowed from 'lme4', the likelihood may be approximated by the sequential reduction approximation, or an importance sampling approximation. These methods provide an accurate approximation to the likelihood in some situations where it is not possible to use adaptive Gaussian quadrature.
Depends: R (>= 3.2.0)
LinkingTo: Rcpp, RcppEigen, BH
Imports: lme4 (>= 1.1-8), Matrix, R6, Rcpp, methods, stats, utils, numDeriv
URL: http://github.com/heogden/glmmsr
BugReports: http://github.com/heogden/glmmsr/issues
License: GPL (>= 2)
LazyData: true
Suggests: BradleyTerry2, knitr, mdhglm, rmarkdown, testthat
VignetteBuilder: knitr
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-01-29 16:16:52 UTC; helen
Author: Helen Ogden [aut, cre]
Maintainer: Helen Ogden <heogden12@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-04 11:09:22 UTC

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Sun, 03 Feb 2019

New package simone with initial version 1.0-4
Package: simone
Version: 1.0-4
Date: 2019-02-06
Title: Statistical Inference for MOdular NEtworks (SIMoNe)
Authors@R: c( person("Julien", "Chiquet", role = c("aut", "cre"), email = "julien.chiquet@inra.fr", comment = c(ORCID = "0000-0002-3629-3429")), person("Gilles", "Grasseau", role = "aut", email = "grasseau@llr.in2p3.fr"), person("Christophe", "Ambroise", role = "aut", email = "christophe.ambroise@genopole.cnrs.fr"), person("Camille", "Charbonnier", role = "ctb", email = "miyu.cc@gmail.com"), person("Alexander", "Smith", role = "ctb", email = "aatsmith@orange.fr"), person("Catherine", "Matias", role = "ctb", email = "Catherine.Matias@math.cnrs.fr") )
Maintainer: Julien Chiquet <julien.chiquet@inra.fr>
Depends: R (>= 3.1.1), blockmodels
Description: Implements the inference of co-expression networks based on partial correlation coefficients from either steady-state or time-course transcriptomic data. Note that with both type of data this package can deal with samples collected in different experimental conditions and therefore not identically distributed. In this particular case, multiple but related networks are inferred on one simone run.
License: GPL (>= 2)
URL: http://julien.cremeriefamily.info/simone.html
Encoding: UTF-8
Packaged: 2019-02-03 20:08:45 UTC; jchiquet
Repository: CRAN
Date/Publication: 2019-02-03 23:10:03 UTC
RoxygenNote: 5.0.1
NeedsCompilation: yes
Author: Julien Chiquet [aut, cre] (<https://orcid.org/0000-0002-3629-3429>), Gilles Grasseau [aut], Christophe Ambroise [aut], Camille Charbonnier [ctb], Alexander Smith [ctb], Catherine Matias [ctb]

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New package expp with initial version 1.2.4
Package: expp
Type: Package
Title: Spatial Analysis of Extra-Pair Paternity
Version: 1.2.4
Authors@R: c( person("Mihai", "Valcu", ,"valcu@orn.mpg.de", c("aut", "cre")), person("Lotte", "Schlicht", role = "ctb") )
Depends: R(>= 3.5.0)
Imports: graphics, methods, sp, stats, spdep, rgeos, deldir, spatstat
Suggests: roxygen2, knitr, lme4
VignetteBuilder: knitr
Description: Tools and data to accompany Schlicht, L., Valcu, M., & Kempenaers, B. (2015) <doi:10.1111/1365-2656.12293>. Spatial patterns of extra-pair paternity: beyond paternity gains and losses. Journal of Animal Ecology, 84(2), 518-531.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 6.1.1
Collate: 'AAA.R' 'eppMatrix.R' 'SpatialPointsBreeding.R' 'DirichletPolygons.R' 'epp.R' 'eppSimDat.R' 'expp-package.R' 'neighborsDataFrame.R'
NeedsCompilation: no
Packaged: 2019-01-28 11:11:18 UTC; mihai
Author: Mihai Valcu [aut, cre], Lotte Schlicht [ctb]
Maintainer: Mihai Valcu <valcu@orn.mpg.de>
Repository: CRAN
Date/Publication: 2019-02-03 23:13:46 UTC

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New package dpcR with initial version 0.5
Package: dpcR
Type: Package
Title: Digital PCR Analysis
Version: 0.5
LazyData: true
Date: 2019-02-01
Authors@R: c( person("Michal", "Burdukiewicz", email = "michalburdukiewicz@gmail.com", role = c("cre", "aut"), comment = c(ORCID = "0000-0001-8926-582X")), person("Stefan", "Roediger", email = "stefan.roedigier@b-tu.de", role = "aut", comment = c(ORCID = "0000-0002-1441-6512")), person("Bart", "Jacobs", email = "BartKM.Jacobs@ugent.be", role = "aut"), person("Piotr", "Sobczyk", email = "pj.sobczyk@gmail.com", role = "ctb"), person("Andrej-Nikolai", "Spiess", email = "draspiess@gmail.com", role = "ctb"))
Description: Analysis, visualisation and simulation of digital polymerase chain reaction (dPCR) (Burdukiewicz et al. (2016) <doi:10.1016/j.bdq.2016.06.004>). Supports data formats of commercial systems (Bio-Rad QX100 and QX200; Fluidigm BioMark) and other systems.
License: GPL-3
URL: https://github.com/michbur/dpcR
BugReports: https://github.com/michbur/dpcR/issues
Depends: R (>= 3.0.0), methods
Imports: binom, chipPCR, e1071, evd, dgof, multcomp, qpcR, pracma, rateratio.test, readxl, signal, shiny, spatstat
Suggests: digest, DT, ggplot2, knitr, markdown, rhandsontable, rmarkdown, shinythemes, xtable
VignetteBuilder: knitr
Packaged: 2019-02-03 21:07:59 UTC; michal
NeedsCompilation: no
Repository: CRAN
Encoding: UTF-8
RoxygenNote: 6.1.1
Maintainer: Michal Burdukiewicz <michalburdukiewicz@gmail.com>
Collate: 'AUCtest.R' 'BioradCNV.R' 'White.R' 'adpcr2panel.R' 'adpcr2ppp.R' 'binarize.R' 'classes.R' 'bind_dpcr.R' 'bioamp.R' 'calc_breaks.R' 'calc_coordinates.R' 'calc_lambda.R' 'compare_dens.R' 'count_test-class.R' 'create_dpcr.R' 'dPCRmethyl.R' 'ddpcRquant.R' 'df2dpcr.R' 'dpcR-package.R' 'dpcReport_gui.R' 'dpcr2df.R' 'dpcr_calculator.R' 'dpcr_density.R' 'dpcr_density_gui.R' 'dpcr_density_table.R' 'extract_run.R' 'fit_adpcr.R' 'fl.R' 'get_k_n.R' 'hsm.R' 'limit_cq.R' 'many_peaks.R' 'modlist.R' 'moments.R' 'num2int.R' 'pds.R' 'pds_raw.R' 'plot_distr.R' 'plot_panel.R' 'plot_vf_circ.R' 'plot_vic_fam.R' 'print_summary.R' 'qdpcr.R' 'qpcr2pp.R' 'qpcr_analyser.R' 'read_amp.R' 'read_dpcr.R' 'rename_dpcr.R' 'rtadpcr.R' 'safe_efficiency.R' 'show_dpcr.R' 'sim_adpcr.R' 'sim_ddpcr_bkm.R' 'sim_dpcr.R' 'simulations.R' 'six_panels.R' 'summary_dpcr.R' 'test_counts.R' 'test_counts_gui.R' 'test_panel.R' 'test_peaks.R' 'test_pooled.R' 'valid_amp.R' 'y_val_conf.R'
Author: Michal Burdukiewicz [cre, aut] (<https://orcid.org/0000-0001-8926-582X>), Stefan Roediger [aut] (<https://orcid.org/0000-0002-1441-6512>), Bart Jacobs [aut], Piotr Sobczyk [ctb], Andrej-Nikolai Spiess [ctb]
Date/Publication: 2019-02-03 23:13:49 UTC

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New package RobinHood with initial version 1.0.1
Package: RobinHood
Type: Package
Title: Interface for the RobinHood.com No Commission Investing Platform
Version: 1.0.1
Date: 2019-01-17
Author: Joseph Blubaugh
Maintainer: Joseph Blubaugh <jestonblu@gmail.com>
Description: Execute API calls to the RobinHood <https://robinhood.com> investing platform. Functionality includes accessing account data and current holdings, retrieving investment statistics and quotes, placing and canceling orders, getting market trading hours, searching investments by popular tag, and interacting with watch lists.
Imports: curl, jsonlite, magrittr, lubridate, profvis
License: GPL-3
URL: https://github.com/JestonBlu/RobinHood
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-03 05:09:08 UTC; jeston
Repository: CRAN
Date/Publication: 2019-02-03 17:24:28 UTC

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New package phase1PRMD with initial version 1.0.1
Package: phase1PRMD
Type: Package
Date: 2019-01-29
Title: Personalized Repeated Measurement Design for Phase I Clinical Trials
Version: 1.0.1
Author: Lu Zhang, Jun Yin
Maintainer: Lu Zhang <luzhangstat@gmail.com>
Description: Implements Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints and longitudinal efficacy measure from multiple treatment cycles. The package provides flags to fit a variety of model-based phase I design, including 1 stage models with or without individualized dose modification, 3-stage models with or without individualized dose modification, etc. Functions are provided to recommend dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.
SystemRequirements: JAGS (http://mcmc-jags.sourceforge.net)
Depends: R (>= 3.0.0), coda (>= 0.13), ggplot2, stats
Encoding: UTF-8
LazyData: true
Imports: rjags, arrayhelpers, phase1RMD, MASS, reshape2, dplyr, plyr, RColorBrewer, gridExtra, kableExtra, knitr
RoxygenNote: 6.1.1
Suggests:
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2019-01-30 04:42:11 UTC; luzhang
Repository: CRAN
Date/Publication: 2019-02-03 17:00:03 UTC

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New package cbsem with initial version 1.0.0
Package: cbsem
Type: Package
Title: Simulation, Estimation and Segmentation of Composite Based Structural Equation Models
Version: 1.0.0
Author: Rainer Schlittgen
Maintainer: Rainer Schlittgen <R.Schlittgen@t-online.de>
Description: The composites are linear combinations of their indicators in composite based structural equation models. Structural models are considered consisting of two blocks. The indicators of the exogenous composites are named by X, the indicators of the endogenous by Y. Reflective relations are given by arrows pointing from the composite to their indicators. Their values are called loadings. In a reflective-reflective scenario all indicators have loadings. Arrows are pointing to their indicators only from the endogenous composites in the formative-reflective scenario. There are no loadings at all in the formative-formative scenario. The covariance matrices are computed for these three scenarios. They can be used to simulate these models. These models can also be estimated and a segmentation procedure is included as well.
Depends: R (>= 2.10)
License: GPL
Encoding: UTF-8
LazyData: true
Suggests: R.rsp
VignetteBuilder: R.rsp
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2019-01-30 10:55:47 UTC; Gigabyte
Repository: CRAN
Date/Publication: 2019-02-03 17:05:33 UTC

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New package rblt with initial version 0.2.3.6
Package: rblt
Type: Package
Title: Bio-Logging Toolbox
Version: 0.2.3.6
Authors@R: person(given = "Sebastien", family = "Geiger", role = c("aut", "cre"), email = "sebastien.geiger@iphc.cnrs.fr")
Description: An R-shiny application to plot datalogger time series at a microsecond precision as Acceleration, Temperature, Pressure, Light intensity from CATS, AXY-TREK and WACU bio-loggers. It is possible to link behavioral labels extracted from 'BORIS' software <http://www.boris.unito.it> or manually written in a csv file. CATS bio-logger are manufactured by <http://www.cats.is>, AXY-TREK are manufactured by <http://www.technosmart.eu> and WACU are manufactured by <http://www.iphc.cnrs.fr/-MIBE-.html>.
Maintainer: Sebastien Geiger <sebastien.geiger@iphc.cnrs.fr>
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
SystemRequirements: libhdf5 (>= 1.8.12)
Depends: R (>= 3.2), tools, h5 (>= 0.9), xts, dygraphs, shiny, methods
Imports: data.table
URL: https://github.com/sg4r/rblt
BugReports: https://github.com/sg4r/rblt/issues
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-29 18:12:37 UTC; seb
Author: Sebastien Geiger [aut, cre]
Repository: CRAN
Date/Publication: 2019-02-03 16:40:02 UTC

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New package MMLR with initial version 0.1.0
Package: MMLR
Type: Package
Title: Fitting Markov-Modulated Linear Regression Models
Version: 0.1.0
Authors@R: c( person("Nadezda", "Spiridovska", email = "Spiridovska.N@tsi.lv", role = c("aut", "cre")), person("Diana", "Santalova", role = "ctb"))
Maintainer: Nadezda Spiridovska <Spiridovska.N@tsi.lv>
Description: A set of tools for fitting Markov-modulated linear regression, where responses Y(t) are time-additive, and model operates in the external environment, which is described as a continuous time Markov chain with finite state space. Model is proposed by Alexander Andronov (2012) <arXiv:1901.09600v1> and algorithm of parameters estimation is based on eigenvalues and eigenvectors decomposition. Also, package will provide a set of data simulation tools for Markov-modulated linear regression (for academical/research purposes). Research project No. 1.1.1.2/VIAA/1/16/075.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Imports: matlib
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-01-29 14:25:08 UTC; Nadezda Spiridovska
Author: Nadezda Spiridovska [aut, cre], Diana Santalova [ctb]
Repository: CRAN
Date/Publication: 2019-02-03 16:33:17 UTC

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New package ctsem with initial version 2.8.2
Package: ctsem
Type: Package
Title: Continuous Time Structural Equation Modelling
Version: 2.8.2
Date: 2019-1-31
Authors@R: c(person("Charles", "Driver", role = c("aut","cre","cph"),email="driver@mpib-berlin.mpg.de"), person("Manuel", "Voelkle", role = c("aut","cph")), person("Han", "Oud", role = c("aut","cph") ))
Description: A hierarchical, multivariate, continuous (and discrete) time dynamic modelling package for panel and time series data, using stochastic differential equations. Contains a faster frequentist set of functions using OpenMx for single subject and mixed-effects (random intercepts only) structural equation models, or a hierarchical Bayesian implementation using Stan that allows for random effects and non-linearity over all model parameters. Allows for modelling of multiple noisy measurements of multiple stochastic processes, time varying input / event covariates, and time invariant covariates used to predict the parameters. Bayesian formulation not available on 32 bit Windows systems.
License: GPL-3
Depends: R (>= 3.4.0), Rcpp (>= 0.12.16), OpenMx (>= 2.3.0)
URL: https://github.com/cdriveraus/ctsem
Imports: rstan (>= 2.17.1), rstantools (>= 1.5.0), plyr, tools, data.table, Matrix, datasets, stats, graphics, grDevices, parallel, shiny, MASS, methods, utils, corrplot, mvtnorm, DEoptim, KernSmooth
Encoding: UTF-8
LazyData: true
ByteCompile: true
LinkingTo: StanHeaders (>= 2.17.0), rstan (>= 2.17.1), BH (>= 1.66.0-1), Rcpp (>= 0.12.16), RcppEigen (>= 0.3.3.4.0)
SystemRequirements: GNU make
NeedsCompilation: yes
Suggests: knitr, testthat
VignetteBuilder: knitr
RoxygenNote: 6.1.1
Packaged: 2019-02-01 20:48:00 UTC; driver
Author: Charles Driver [aut, cre, cph], Manuel Voelkle [aut, cph], Han Oud [aut, cph]
Maintainer: Charles Driver <driver@mpib-berlin.mpg.de>
Repository: CRAN
Date/Publication: 2019-02-03 16:13:16 UTC

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New package shutterstock with initial version 0.1.0
Package: shutterstock
Version: 0.1.0
Title: Access 'Shutterstock' REST API
Description: Access 'Shutterstock' API from R. The 'Shutterstock' API presents access to search, view, license and download the media and information from the 'Shutterstock's library <https://api-reference.shutterstock.com/>.
Authors@R: person("Metin", "Yazici", email = "stradivariusboul@gmail.com", role = c("aut", "cre"))
License: MIT + file LICENSE
URL: https://github.com/strboul/shutterstock-r
BugReports: https://github.com/strboul/shutterstock-r/issues
Depends: R (>= 3.4.0)
Imports: httr, jsonlite, utils
Suggests: testthat, httptest, knitr, rmarkdown
LazyData: true
Encoding: UTF-8
RoxygenNote: 6.1.1
Language: en-US
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-26 16:12:55 UTC; metin
Author: Metin Yazici [aut, cre]
Maintainer: Metin Yazici <stradivariusboul@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-03 15:20:02 UTC

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New package MMeM with initial version 0.1.0
Package: MMeM
Title: Multivariate Mixed Effects Model
Version: 0.1.0
Depends: R (>= 3.3.0)
Authors@R: c(person(given = "Luyao", family = "Peng", role = c("aut", "cre"), email = "luyaopeng.cn@gmail.com"), person(given = "Rui", family = "Yang", role = c("aut"), email = "rkzyang@gmail.com"))
Maintainer: Luyao Peng <luyaopeng.cn@gmail.com>
Description: Analyzing data under multivariate mixed effects model using multivariate REML and multivariate Henderson3 methods. See Meyer (1985) <doi:10.2307/2530651> and Wesolowska Janczarek (1984) <doi:10.1002/bimj.4710260613>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: stats, MASS, Matrix, jointDiag, lme4, matrixcalc, psych, stringr
BugReports: https://github.com/pengluyaoyao/MMeM/issues
NeedsCompilation: no
Packaged: 2019-01-25 18:16:00 UTC; pengluyao
Author: Luyao Peng [aut, cre], Rui Yang [aut]
Repository: CRAN
Date/Publication: 2019-02-03 15:03:15 UTC

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New package isoband with initial version 0.1.0
Package: isoband
Title: Generate Isolines and Isobands from Regularly Spaced Elevation Grids
Version: 0.1.0
Authors@R: person(given = "Claus", family = "Wilke", role = c("aut", "cre"), email = "wilke@austin.utexas.edu")
Description: A fast C++ implementation to generate contour lines (isolines) and contour polygons (isobands) from regularly spaced grids containing elevation data.
URL: https://github.com/clauswilke/isoband
BugReports: https://github.com/clauswilke/isoband/issues
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp, testthat
Imports: Rcpp, grid
RoxygenNote: 6.1.0
Suggests: ggplot2, sf, testthat, covr
SystemRequirements: C++11
NeedsCompilation: yes
Packaged: 2019-01-26 07:02:07 UTC; wilke
Author: Claus Wilke [aut, cre]
Maintainer: Claus Wilke <wilke@austin.utexas.edu>
Repository: CRAN
Date/Publication: 2019-02-03 15:13:14 UTC

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New package hutilscpp with initial version 0.1.0
Package: hutilscpp
Title: Miscellaneous Functions in C++
Version: 0.1.0
Authors@R: person(given = "Hugh", family = "Parsonage", role = c("aut", "cre"), email = "hugh.parsonage@gmail.com")
Description: Provides utility functions that are simply, frequently used, but may require higher performance that what can be obtained from base R. Incidentally provides support for 'reverse geocoding', such as matching a point with its nearest neighbour in another array. Used as a complement to package 'hutils' by sacrificing compilation or installation time for higher running speeds. The name is a portmanteau of the author and 'Rcpp'.
URL: https://github.com/hughparsonage/hutilscpp
BugReports: https://github.com/hughparsonage/hutilscpp/issues
License: GPL-2
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp
Imports: Rcpp, data.table, hutils, utils
RoxygenNote: 6.1.1
Suggests: bench, testthat, TeXCheckR, covr
NeedsCompilation: yes
Packaged: 2019-01-28 17:14:07 UTC; hughp
Author: Hugh Parsonage [aut, cre]
Maintainer: Hugh Parsonage <hugh.parsonage@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-03 15:43:19 UTC

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New package guardianapi with initial version 0.1.0
Package: guardianapi
Title: Access 'The Guardian' Newspaper Open Data API
Version: 0.1.0
Authors@R: person(given = "Evan", family = "Odell", role = c("aut", "cre"), email = "evanodell91@gmail.com", comment = c(ORCID = "0000-0003-1845-808X"))
Description: Access to the 'Guardian' open API <https://open-platform.theguardian.com/>, containing all articles published in 'The Guardian' from 1999 to the present, including article text, metadata, tags and contributor information. An API key and registration is required.
URL: https://docs.evanodell.com/guardianapi
BugReports: https://github.com/evanodell/guardianapi/issues
License: MIT + file LICENSE
RoxygenNote: 6.1.1
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.4.0)
Imports: dplyr, httr, jsonlite, tibble, rlang
Suggests: testthat, knitr, rmarkdown, lubridate, ggplot2, covr, scales
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-28 12:17:29 UTC; EOdell
Author: Evan Odell [aut, cre] (<https://orcid.org/0000-0003-1845-808X>)
Maintainer: Evan Odell <evanodell91@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-03 15:33:14 UTC

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New package countfitteR with initial version 1.0
Package: countfitteR
Type: Package
Title: Comprehensive Automatized Evaluation of Distribution Models for Count Data
Version: 1.0
Authors@R: c(person("Jaroslaw", "Chilimoniuk", email = "jaroslaw.chilimoniuk@gmail.com", comment = c(ORCID = "0000-0001-5467-018X"), role = c("cre", "ctb")), person("Michal", "Burdukiewicz", email = "michalburdukiewicz@gmail.com", comment = c(ORCID = "0000-0001-8926-582X"), role = c("aut")), person("Stefan", "Roediger", email = "stefan.roedigier@b-tu.de", comment = c(ORCID = "0000-0002-1441-6512"), role = ("ctb")) )
Maintainer: Jaroslaw Chilimoniuk <jaroslaw.chilimoniuk@gmail.com>
Description: A large number of measurements generate count data. This is a statistical data type that only assumes non-negative integer values and is generated by counting. Typically, counting data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. The number of DNA double-strand breaks can be counted in individual cells using various bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. Journal of Laboratory and Precision Medicine. <doi:10.21037/jlpm.2018.04.10>). The software offers functions for a comprehensive automated evaluation of distribution models of count data. In addition to programmatic interaction, a graphical user interface (web server) is included, which enables fast and interactive data-scientific analyses. The user is supported in selecting the most suitable counting distribution for his own data set.
License: GPL-3
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
Suggests: dplyr, utils, testthat, shinythemes, rhandsontable, gridExtra, pscl, DT, rmarkdown, knitr
Date: 2019-01-18
URL: https://github.com/jarochi/countfitteR
BugReports: https://github.com/jarochi/countfitteR/issues
RoxygenNote: 6.1.1
Imports: ggplot2, MASS, shiny, stats, tools
NeedsCompilation: no
Packaged: 2019-01-27 20:52:36 UTC; jarek
Author: Jaroslaw Chilimoniuk [cre, ctb] (<https://orcid.org/0000-0001-5467-018X>), Michal Burdukiewicz [aut] (<https://orcid.org/0000-0001-8926-582X>), Stefan Roediger [ctb] (<https://orcid.org/0000-0002-1441-6512>)
Repository: CRAN
Date/Publication: 2019-02-03 15:33:19 UTC

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Sat, 02 Feb 2019

New package clogitL1 with initial version 1.5
Package: clogitL1
Type: Package
Title: Fitting Exact Conditional Logistic Regression with Lasso and Elastic Net Penalties
Version: 1.5
Date: 2019-02-01
Author: Stephen Reid and Robert Tibshirani
Maintainer: Stephen Reid <sreid1652@gmail.com>
Description: Tools for the fitting and cross validation of exact conditional logistic regression models with lasso and elastic net penalties. Uses cyclic coordinate descent and warm starts to compute the entire path efficiently.
License: GPL-2
Depends: Rcpp (>= 0.10.2)
LinkingTo: Rcpp
Packaged: 2019-02-02 21:42:59 UTC; stephen
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2019-02-02 22:33:36 UTC

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Fri, 01 Feb 2019

New package SAR with initial version 1.0.0
Package: SAR
Title: Smart Adaptive Recommendations
Version: 1.0.0
Authors@R: c( person("Hong", "Ooi", , "hongooi@microsoft.com", role = c("aut", "cre")), person("Microsoft Product Recommendations team", role = "ctb", comment="source for MS sample datasets"), person("Microsoft", role="cph") )
Description: 'Smart Adaptive Recommendations' (SAR) is the name of a fast, scalable, adaptive algorithm for personalized recommendations based on user transactions and item descriptions. It produces easily explainable/interpretable recommendations and handles "cold item" and "semi-cold user" scenarios. This package provides two implementations of 'SAR': a standalone implementation, and an interface to a web service in Microsoft's 'Azure' cloud: <https://github.com/Microsoft/Product-Recommendations/blob/master/doc/sar.md>. The former allows fast and easy experimentation, and the latter provides robust scalability and extra features for production use.
URL: https://github.com/Hong-Revo/SAR
BugReports: https://github.com/Hong-Revo/SAR/issues
License: MIT + file LICENSE
Depends: R (>= 3.3)
Imports: AzureRMR, AzureStor, dplyr (>= 0.7), httr, jsonlite, Matrix, R6, parallel, Rcpp (>= 0.12), RcppParallel
Suggests: testthat
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
SystemRequirements: GNU make
RoxygenNote: 6.1.0.9000
NeedsCompilation: yes
Packaged: 2019-01-25 19:30:05 UTC; hongooi
Author: Hong Ooi [aut, cre], Microsoft Product Recommendations team [ctb] (source for MS sample datasets), Microsoft [cph]
Maintainer: Hong Ooi <hongooi@microsoft.com>
Repository: CRAN
Date/Publication: 2019-02-01 18:23:18 UTC

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New package gratia with initial version 0.2-1
Package: gratia
Version: 0.2-1
Date: 2019-01-25
Title: Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'
Authors@R: c(person(given = "Gavin L.", family = "Simpson", email = "ucfagls@gmail.com", role = c("aut","cre"), comment = c(ORCID = "0000-0002-9084-8413")))
Maintainer: Gavin L. Simpson <ucfagls@gmail.com>
Depends: R (>= 3.5.0)
Imports: mgcv, ggplot2, tibble, dplyr, tidyr, cowplot, grid, mvtnorm, stats, tools, grDevices
Suggests: testthat, vdiffr, MASS, scam, datasets
Description: Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.
License: MIT + file LICENSE
LazyData: true
URL: https://gavinsimpson.github.io/gratia
BugReports: https://github.com/gavinsimpson/gratia/issues
RoxygenNote: 6.1.1
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-01-25 17:18:41.050 UTC; gavin
Author: Gavin L. Simpson [aut, cre] (<https://orcid.org/0000-0002-9084-8413>)
Repository: CRAN
Date/Publication: 2019-02-01 18:33:19 UTC

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New package SMITIDstruct with initial version 0.0.3
Package: SMITIDstruct
Type: Package
Encoding: UTF-8
Title: Data Structure and Manipulations Tool for Host and Viral Population
Version: 0.0.3
Date: 2019-01-14
Authors@R: c(person("Jean-Francois", "Rey", role = c("aut", "cre"), email = "jean-francois.rey@inra.fr"))
Author: Jean-Francois Rey [aut, cre]
Maintainer: Jean-Francois Rey <jean-francois.rey@inra.fr>
Description: Statistical Methods for Inferring Transmissions of Infectious Diseases from deep sequencing data (SMITID). It allow sequence-space-time host and viral population data storage, indexation and querying.
License: GPL (>= 2) | file LICENSE
LazyData: true
BuildVignettes: true
NeedsCompilation: no
Biarch: true
URL: https://informatique-mia.inra.fr/biosp/anr-smitid-project, https://gitlab.paca.inra.fr/SMITID/structR
BugReports: https://gitlab.paca.inra.fr/SMITID/structR/issues
Depends: methods, utils, grDevices (>= 3.0.0), graphics (>= 3.0.0), R (>= 3.3.0)
DependsNote: BioC (>= 3.0)
Imports: ggplot2, sf (>= 0.6.3), stats (>= 3.0.2), Biostrings (>= 2.0.0)
ImportsNote: BioC (>= 3.0), Recommended: Biostrings
Collate: 'Class-Host.R' 'Class-ViralPop.R' 'Methods-Host.R' 'Methods-ViralPop.R' 'demo.R' 'diversity.R' 'index.R' 'SMITIDstruct.R'
RoxygenNote: 6.1.0
Packaged: 2019-01-25 13:24:33 UTC; jfrey
Repository: CRAN
Date/Publication: 2019-02-01 17:33:23 UTC

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New package NMAoutlier with initial version 0.1.13
Package: NMAoutlier
Title: Detecting Outliers in Network Meta-Analysis
Version: 0.1.13
Date: 2019-01-09
Depends: R (>= 3.0.0)
Imports: netmeta (>= 0.9-7), stats (>= 3.4.3), parallel (>= 3.4.1), MASS (>= 7.3-47), reshape2 (>= 1.4.3), ggplot2 (>= 3.0.0), gridExtra (>= 2.3)
Authors@R: c(person("Maria", "Petropoulou", role = c("aut", "cre"), email = "mpetrop@cc.uoi.gr", comment = c(ORCID = "0000-0002-7147-3644")), person("Guido", "Schwarzer", role = "aut", comment = c(ORCID = "0000-0001-6214-9087")), person("Agapios", "Panos", role = "aut"), person("Dimitris", "Mavridis", role = "aut", comment = c(ORCID = "0000-0003-1041-4592")))
Maintainer: Maria Petropoulou <mpetrop@cc.uoi.gr>
URL: https://github.com/petropouloumaria/NMAoutlier
Description: A set of functions providing the forward search algorithm for detecting outlying studies (i.e., studies with extreme findings) in network meta-analysis: - provides the length of the initial subset for forward search algorithm; - iterations of forward search algorithm; - basic set of studies in each step of forward search algorithm; - summary estimates and their confidence intervals in each step of forward search algorithm; - outlying case diagnostics measures; - ranking measures; - heterogeneity and inconsistency measures; - forward plot for summary estimates and their confidence intervals; - forward plots for monitored measures: outlying case diagnostics measures, ranking measures, heterogeneity, and inconsistency measures.
License: GPL (>= 2)
Encoding: UTF-8
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-01-24 19:46:33 UTC; marak
Author: Maria Petropoulou [aut, cre] (<https://orcid.org/0000-0002-7147-3644>), Guido Schwarzer [aut] (<https://orcid.org/0000-0001-6214-9087>), Agapios Panos [aut], Dimitris Mavridis [aut] (<https://orcid.org/0000-0003-1041-4592>)
Repository: CRAN
Date/Publication: 2019-02-01 17:33:27 UTC

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New package mephas with initial version 0.1.0
Package: mephas
Type: Package
Title: Medical and Pharmaceutical Statistics Shiny Application
Version: 0.1.0
Date: 2019-01-23
Authors@R: person("Yi", "Zhou", email = "zhou-y@phs.osaka-u.ac.jp", role = c("aut", "cre"))
Description: This is a shiny application which facilitates researchers to analyze medical and pharmaceutical and related data.
License: MIT + file LICENSE
LazyData: true
URL: https://github.com/mephas/mephas_pkg
Depends: R (>= 3.1.0),
Imports: shiny, ggplot2, survival, survminer, ggfortify, DescTools, gridExtra, reshape, pastecs, RVAideMemoire, xtable, ROCR, plotROC, psych, stargazer, Rmisc, mixOmics
RoxygenNote: 6.1.1
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-01-23 11:50:21 UTC; yi
Author: Yi Zhou [aut, cre]
Maintainer: Yi Zhou <zhou-y@phs.osaka-u.ac.jp>
Repository: CRAN
Date/Publication: 2019-02-01 17:30:02 UTC

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New package localICE with initial version 0.1.0
Package: localICE
Type: Package
Title: Local Individual Conditional Expectation
Version: 0.1.0
Authors@R: c(person(given = "Martin", family = "Walter", role = c("aut", "cre"), email = "mf-walter@web.de"))
Maintainer: Martin Walter <mf-walter@web.de>
Description: Local Individual Conditional Expectation is as an extension to Individual Conditional Expectation (ICE) and provides three-dimensional local explanations for particular data instances. The three dimension are two features at the horizontal and vertical axes as well as the target that is represented by different colors. The approach is applicable for classification and regression problems to explain interactions of two features towards the target. The plot for discrete targets looks similar to plots of cluster algorithms like k-means, where different clusters represent different predictions. Reference to the ICE approach: Alex Goldstein, Adam Kapelner, Justin Bleich, Emil Pitkin (2013) <arXiv:1309.6392>.
URL: https://github.com/viadee/localICE
BugReports: https://github.com/viadee/localICE/issues
License: BSD_3_clause + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: ggplot2, checkmate
Suggests: covr, h2o, mlbench, randomForest, stats, testthat, utils
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-01-25 18:18:31 UTC; Martin
Author: Martin Walter [aut, cre]
Repository: CRAN
Date/Publication: 2019-02-01 17:40:03 UTC

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New package jstable with initial version 0.7.6
Package: jstable
Title: Create Tables from Different Types of Regression
Version: 0.7.6
Date: 2019-01-26
Authors@R: c(person("Jinseob", "Kim", email = "jinseob2kim@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9403-605X")), person("Anpanman", role = c("cph", "fnd")) )
Description: Create regression tables from generalized linear model(GLM), Generalized estimating equation(GEE), generalized linear mixed-effects model(GLMM), Cox proportional hazards model, survey-weighted generalized linear model(svyglm) and survey-weighted Cox model results for publication.
Depends: R (>= 3.4.0)
License: Apache License 2.0
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: geepack, lme4, stats, data.table, labelled, tableone, coxme, epiDisplay, survival, DT, survey, methods
URL: https://github.com/jinseob2kim/jstable
BugReports: https://github.com/jinseob2kim/jstable/issues
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-26 02:54:15 UTC; js
Author: Jinseob Kim [aut, cre] (<https://orcid.org/0000-0002-9403-605X>), Anpanman [cph, fnd]
Maintainer: Jinseob Kim <jinseob2kim@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-01 17:43:25 UTC

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New package jskm with initial version 0.2.7
Package: jskm
Title: Kaplan-Meier Plot with 'ggplot2'
Version: 0.2.7
Date: 2019-01-17
Authors@R: c(person("Jinseob", "Kim", email = "jinseob2kim@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9403-605X")), person("Anpanman", role = c("cph", "fnd")) )
Description: The function 'jskm()' creates publication quality Kaplan-Meier plot with at risk tables below. 'svyjskm()' provides plot for weighted Kaplan-Meier estimator.
Depends: R (>= 3.4.0)
License: Apache License 2.0
Encoding: UTF-8
LazyData: true
Imports: ggplot2, gridExtra, plyr, survival, survey, scales
RoxygenNote: 6.1.1
URL: https://github.com/jinseob2kim/jskm
BugReports: https://github.com/jinseob2kim/jstable/issues
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-26 00:59:32 UTC; js
Author: Jinseob Kim [aut, cre] (<https://orcid.org/0000-0002-9403-605X>), Anpanman [cph, fnd]
Maintainer: Jinseob Kim <jinseob2kim@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-01 17:43:29 UTC

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New package CalSim with initial version 0.2.2
Package: CalSim
Type: Package
Title: The Calibration Simplex
Version: 0.2.2
Author: Johannes Resin
Maintainer: Johannes Resin <johannes.resin@h-its.org>
Depends: R (>= 3.3), spatstat
Suggests: scoring
Description: Generates the calibration simplex (a generalization of the reliability diagram) for three-category probability forecasts, as proposed by Wilks (2013) <doi:10.1175/WAF-D-13-00027.1>.
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2019-01-24 16:39:44 UTC; resinjs
Repository: CRAN
Date/Publication: 2019-02-01 17:33:35 UTC

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New package bigMap with initial version 2.0.0
Package: bigMap
Type: Package
Title: Big Data Mapping
Version: 2.0.0
Date: 2019-01-04
Authors@R: c( person(given="Joan", family="Garriga", email="jgarriga@ceab.csic.es", role=c("aut", "cre")), person(given="Frederic", family="Bartumeus", email="fbartu@ceab.csic.es", role=c("aut")) )
Description: Unsupervised clustering protocol for large scale structured data, based on a low dimensional representation of the data. Dimensionality reduction is performed using a parallelized implementation of the t-Stochastic Neighboring Embedding algorithm (Garriga J. and Bartumeus F. (2018), <arXiv:1812.09869>).
License: GPL-3
Depends: R (>= 3.4.0)
Imports: Rcpp (>= 0.12.0), bigmemory (>= 4.5.0), parallel (>= 3.5.0), RColorBrewer, colorspace,
Suggests: snow (>= 0.4-2), Rmpi (>= 0.6-7), knitr, rmarkdown
LinkingTo: Rcpp, RcppArmadillo, BH, bigmemory
LazyData: FALSE
VignetteBuilder: knitr
RoxygenNote: 6.1.1
SystemRequirements: GNU make
NeedsCompilation: yes
Packaged: 2019-01-23 16:57:27 UTC; jgarriga
Author: Joan Garriga [aut, cre], Frederic Bartumeus [aut]
Maintainer: Joan Garriga <jgarriga@ceab.csic.es>
Repository: CRAN
Date/Publication: 2019-02-01 17:05:25 UTC

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New package SCAT with initial version 0.5.0
Package: SCAT
Type: Package
Title: Summary Based Conditional Association Test
Version: 0.5.0
Date: 2019-02-01
Author: Han Zhang, Kai Yu
Maintainer: Han Zhang <han.zhang2@nih.gov>
Depends: stats, utils
Description: Conditional association test based on summary data from genome-wide association study (GWAS). SCAT adjusts for heterogeneity in SNP coverage that exists in summary data if SNPs are not present in all of the participating studies of a GWAS meta-analysis. This commonly happens when different reference panels are used in participating studies for genotype imputation. This could happen when ones simply do not have data for some SNPs (e.g. different array, or imputated data is not available). Without properly adjusting for this kind of heterogeneity leads to inflated false positive rate. SCAT can also be used to conduct conventional conditional analysis when coverage heterogeneity is absent. For more details, refer to Zhang et al. (2018) Brief Bioinform. 19(6):1337-1343. <doi: 10.1093/bib/bbx072>.
License: GPL-2 | GPL-3
LazyData: TRUE
SystemRequirements: C++11
NeedsCompilation: yes
Packaged: 2019-02-01 15:47:57 UTC; zhangh12
Repository: CRAN
Date/Publication: 2019-02-01 16:43:27 UTC

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New package RiverLoad with initial version 1.0
Package: RiverLoad
Type: Package
Title: Load Estimation of River Compounds with Different Methods
Version: 1.0
Date: 2019-01-23
Author: Veronica Nava [aut, cre], Martina Patelli [ctb], Marco Rotiroti [ctb], Barbara Leoni [ctb]
Maintainer: Veronica Nava <veronicanava245@gmail.com>
Description: Implements several of the most popular load estimation procedures, including averaging methods, ratio estimators and regression methods. The package provides an easy-to-use tool to rapidly calculate the load for various compounds and to compare different methods. The package also supplies additional functions to easily organize and analyze the data.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Depends: R (>= 2.10)
Imports: graphics, grDevices, stats
NeedsCompilation: no
Packaged: 2019-01-23 11:49:57 UTC; Veronica
Repository: CRAN
Date/Publication: 2019-02-01 16:53:22 UTC

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New package ltsk with initial version 1.0.7
Package: ltsk
Type: Package
Title: Local Time Space Kriging
Version: 1.0.7
Date: 2019-01-01
Author: Naresh Kumar, Dong Liang, Jin Chen, Jun Chen
Maintainer: Dong Liang <dliang@umces.edu>
Description: Implements local spatial and local spatiotemporal Kriging based on local spatial and local spatiotemporal variograms, respectively. The method is documented in Kumar et al (2013) <https://www.nature.com/articles/jes201352)>.
License: GPL-2
Depends: parallel,R (>= 2.10)
Imports: fields,gstat,sp
Packaged: 2019-02-01 15:41:13 UTC; dliang
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2019-02-01 16:30:03 UTC

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New package dosearch with initial version 1.0
Package: dosearch
Type: Package
Title: Causal Effect Identification from Multiple Incomplete Data Sources
Version: 1.0
Date: 2019-01-23
Author: Santtu Tikka, Antti Hyttinen, Juha Karvanen
Maintainer: Santtu Tikka <santtuth@gmail.com>
Description: Identification of causal effects from arbitrary observational and experimental probability distributions via do-calculus and standard probability manipulations using a search-based algorithm. Allows for the presence of mechanisms related to selection bias (Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>), transportability (Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>) and missing data (Mohan, K. and Pearl, J. and Tian., J. (2013) <http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf>).
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.19)
LinkingTo: Rcpp
SystemRequirements: C++11
NeedsCompilation: yes
Packaged: 2019-01-23 16:27:27 UTC; Santtu
Repository: CRAN
Date/Publication: 2019-02-01 16:53:19 UTC

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New package readsdmx with initial version 0.2.2
Package: readsdmx
Type: Package
Title: Read SDMX-XML Data
Version: 0.2.2
Authors@R: c(person("Matthew", "de Queljoe", email = "matthew.dequeljoe@gmail.com", role = c("aut", "cre")), person("Marcin", "Kalicinski", role = c("ctb", "cph"), comment = "Author of RapidXML library"), person("Emmanuel", "Blondel", role = c("ctb", "cph"), comment = c(ORCID = "0000-0002-5870-5762", "SDMX-ML test files")))
Maintainer: Matthew de Queljoe <matthew.dequeljoe@gmail.com>
Description: Read Statistical Data and Metadata Exchange (SDMX) XML data. This the main transmission format used in official statistics. Data can be imported from local SDMX-ML files or a SDMX web-service and will be read in 'as is' into a dataframe object. The 'RapidXML' C++ library <http://rapidxml.sourceforge.net> is used to parse the XML data.
URL: https://github.com/mdequeljoe/readsdmx
BugReports: https://github.com/mdequeljoe/readsdmx/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: Rcpp (>= 0.12.18), utils
LinkingTo: Rcpp
RoxygenNote: 6.1.1
Suggests: testthat
NeedsCompilation: yes
Packaged: 2019-01-21 18:36:56 UTC; matthew
Author: Matthew de Queljoe [aut, cre], Marcin Kalicinski [ctb, cph] (Author of RapidXML library), Emmanuel Blondel [ctb, cph] (<https://orcid.org/0000-0002-5870-5762>, SDMX-ML test files)
Repository: CRAN
Date/Publication: 2019-02-01 15:10:07 UTC

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New package bootstrapFP with initial version 0.4.2
Package: bootstrapFP
Type: Package
Title: Bootstrap Algorithms for Finite Population Inference
Version: 0.4.2
Date: 2019-01-22
Authors@R: person("Roberto", "Sichera", email = "roberto.sichera@unipa.it", role = c("aut", "cre"))
Description: Finite Population bootstrap algorithms to estimate the variance of the Horvitz-Thompson estimator for single-stage sampling. For a survey of bootstrap methods for finite populations, see Mashreghi et Al. (2016) <doi:10.1214/16-SS113>.
License: GPL-3
Encoding: UTF-8
LazyData: true
BugReports: https://github.com/rhobis/bootstrapFP/issues
RoxygenNote: 6.1.1
Imports: sampling
NeedsCompilation: no
Packaged: 2019-01-22 17:26:16 UTC; roberto
Author: Roberto Sichera [aut, cre]
Maintainer: Roberto Sichera <roberto.sichera@unipa.it>
Repository: CRAN
Date/Publication: 2019-02-01 15:53:22 UTC

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Wed, 30 Jan 2019

New package ROCit with initial version 1.1.1
Package: ROCit
Type: Package
Title: Performance Assessment of Binary Classifier with Visualization
Version: 1.1.1
Date: 2019-01-21
Authors@R: c(person("Md Riaz Ahmed", "Khan", email = "mdriazahmed.khan@jacks.sdstate.edu", role = c("aut", "cre")), person("Thomas", "Brandenburger", email = "thomas.brandenburger@sdstate.edu", role = "aut" ))
Description: Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.
Imports: stats, graphics
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-21 16:38:04 UTC; Riaz
Author: Md Riaz Ahmed Khan [aut, cre], Thomas Brandenburger [aut]
Maintainer: Md Riaz Ahmed Khan <mdriazahmed.khan@jacks.sdstate.edu>
Repository: CRAN
Date/Publication: 2019-01-30 23:23:35 UTC

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New package plot3logit with initial version 1.0.0
Package: plot3logit
Type: Package
Title: Ternary Plots for Trinomial Regression Models
Version: 1.0.0
Authors@R: c( person('Flavio', 'Santi', email = 'flavio.santi@univr.it', role = c('cre', 'aut'), comment = c(ORCID = "0000-0002-2014-1981")), person('Maria Michela', 'Dickson', email = 'mariamichela.dickson@unitn.it', role = c('aut'), comment = c(ORCID = "0000-0002-4307-0469")), person('Giuseppe', 'Espa', email = 'giuseppe.espa@unitn.it', role = c('aut'), comment = c(ORCID = "0000-0002-0331-3630")))
Author: Flavio Santi [cre, aut] (<https://orcid.org/0000-0002-2014-1981>), Maria Michela Dickson [aut] (<https://orcid.org/0000-0002-4307-0469>), Giuseppe Espa [aut] (<https://orcid.org/0000-0002-0331-3630>)
Maintainer: Flavio Santi <flavio.santi@univr.it>
BugReports: https://github.com/f-santi/plot3logit
Description: An implementation of the ternary plot for interpreting regression coefficients of trinomial regression models, as proposed in Santi, Dickson and Espa (2018) <doi:10.1080/00031305.2018.1442368>. Ternary plots are drawn using either 'ggtern' package (based on 'ggplot2') or 'Ternary' package (based on standard graphics).
Depends: R (>= 3.1), ggtern (>= 3.1.0), Ternary (>= 1.0.1)
Imports: magrittr (>= 1.5), ggplot2 (>= 3.1.0), graphics, reshape2 (>= 1.4.3), stats, utils
Suggests: MASS (>= 7.3-51), mlogit, nnet
License: GPL (>= 2)
LazyData: true
NeedsCompilation: no
Encoding: UTF-8
RoxygenNote: 6.1.0
Repository: CRAN
Date: 2019-01-21
Packaged: 2019-01-21 17:00:51 UTC; flavio
Date/Publication: 2019-01-30 23:10:10 UTC

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New package ordinalLBM with initial version 1.0
Package: ordinalLBM
Title: Co-Clustering of Ordinal Data via Latent Continuous Random Variables
Version: 1.0
Author: Marco Corneli, Charles Bouveyron and Pierre Latouche
Maintainer: Marco Corneli <marcogenni@gmail.com>
Description: It implements functions for simulation and estimation of the ordinal latent block model (OLBM), as described in Corneli, Bouveyron and Latouche (2019).
Imports: reshape2, RColorBrewer
Depends: R (>= 3.4.0)
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2019-01-21 16:23:25 UTC; marco
Repository: CRAN
Date/Publication: 2019-01-30 23:20:04 UTC

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New package nhdR with initial version 0.5.0
Package: nhdR
Title: Tools for working with the National Hydrography Dataset
Version: 0.5.0
Authors@R: person("Joseph", "Stachelek", email = "stachel2@msu.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-5924-2464"))
Description: Tools for working with the National Hydrography Dataset, with functions for querying, downloading, and networking both the NHD <https://www.usgs.gov/core-science-systems/ngp/national-hydrography> and NHDPlus <http://www.horizon-systems.com/nhdplus> datasets.
URL: https://github.com/jsta/nhdR
BugReports: https://github.com/jsta/nhdR/issues
Depends: R (>= 3.3), maps
License: GPL
Imports: rappdirs, rgdal, sf, httr, rvest, xml2, foreign, ggplot2, gdalUtils, rlang, dplyr, curl, units, stringr, memoise, purrr
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown, wikilake, sp, rgeos, testthat, covr, RCurl
VignetteBuilder: knitr
SystemRequirements: 7-zip command line tool (7z)
Language: en-US
NeedsCompilation: no
Packaged: 2019-01-21 17:37:55 UTC; jose
Author: Joseph Stachelek [aut, cre] (<https://orcid.org/0000-0002-5924-2464>)
Maintainer: Joseph Stachelek <stachel2@msu.edu>
Repository: CRAN
Date/Publication: 2019-01-30 23:11:04 UTC

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New package MetabolicSurv with initial version 1.0.0
Package: MetabolicSurv
Type: Package
Title: A Biomarker Validation Approach for Classification and Predicting Survival Using Metabolomics Signature
Version: 1.0.0
Authors@R: c( person("Olajumoke Evangelina","Owokotomo",email="olajumoke.owokotomo@uhasselt.be",role=c("aut","cre")), person("Ziv","Shkedy",email="ziv.shkedy@uhasselt.be",role="aut"))
Maintainer: Olajumoke Evangelina Owokotomo <olajumoke.owokotomo@uhasselt.be>
Description: An approach to identifies metabolic biomarker signature for metabolic data by discovering predictive metabolite for predicting survival and classifying patients into risk groups. Classifiers are constructed as a linear combination of predictive/important metabolites, prognostic factors and treatment effects if necessary. Several methods were implemented to reduce the metabolomics matrix such as the principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9> , the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, the elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>. Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected predictive metabolites and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.
URL: https://github.com/OlajumokeEvangelina/MetabolicSurv
BugReports: https://github.com/OlajumokeEvangelina/MetabolicSurv/issues/new
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.1.0)
Imports: superpc, glmnet, matrixStats, survminer, survival, rms, tidyr, pls, Rdpack, methods, stats, gplots, ggplot2
RdMacros: Rdpack
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-21 06:25:34 UTC; Olajumoke
Author: Olajumoke Evangelina Owokotomo [aut, cre], Ziv Shkedy [aut]
Repository: CRAN
Date/Publication: 2019-01-30 23:16:23 UTC

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New package cleanerR with initial version 0.1.0
Package: cleanerR
Type: Package
Title: How to Handle your Missing Data
Version: 0.1.0
Author: Rafael Silva Pereira
Maintainer: Rafael Silva Pereira <r.s.p.models@gmail.com>
Description: How to deal with missing data?Based on the concept of almost functional dependencies, a method is proposed to fill missing data, as well as help you see what data is missing. The user can specify a measure of error and how many combinations he wish to test the dependencies against, the closer to the length of the dataset, the more precise. But the higher the number, the more time it will take for the process to finish. If the program cannot predict with the accuracy determined by the user it shall not fill the data, the user then can choose to increase the error or deal with the data another way.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0.9000
Imports: plyr
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-21 16:50:49 UTC; rpereira
Repository: CRAN
Date/Publication: 2019-01-30 23:23:32 UTC

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New package agriwater with initial version 1.0.0

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New package vegawidget with initial version 0.1.0
Package: vegawidget
Version: 0.1.0
Title: Htmlwidget Renderer for Vega and Vega-Lite
Description: Vega and Vega-Lite parse text in JSON notation to render chart-specifications into HTML. This package is used to facilitate the rendering. It also provides a means to interact with signals, events, and datasets in a Vega chart using JavaScript or Shiny.
Authors@R: c( person( given = "Ian", family = "Lyttle", email = "ian.lyttle@schneider-electric.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-9962-4849") ), person("Vega/Vega-Lite Developers", role = c("aut")), person( given = "Alicia", family = "Schep", email = "aschep@gmail.com", role = c("ctb"), comment = c(ORCID = "orcid.org/0000-0002-3915-0618") ), person( given = "Stuart", family = "Lee", email = "lee.s@wehi.edu.au", role = c("ctb") ), person("Hadley", "Wickham", , "hadley@rstudio.com", role = c("ctb"), comment = c(ORCID = "0000-0003-4757-117X") ), person("Kanit", "Wongsuphasawat", comment = "Vega/Vega-Lite library", role = c("ctb")), person("Dominik", "Moritz", comment = "Vega/Vega-Lite library", role = c("ctb")), person("Arvind", "Satyanarayan", comment = "Vega/Vega-Lite library", role = c("ctb")), person("Jeffrey", "Heer", comment = "Vega/Vega-Lite library", role = c("ctb")), person("Mike", "Bostock", comment = "D3 library", role = c("ctb")), person("David", "Frank", comment = "node-fetch library", role = c("ctb")) )
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
ByteCompile: true
URL: https://github.com/vegawidget/vegawidget
BugReports: https://github.com/vegawidget/vegawidget/issues
RoxygenNote: 6.1.1
Depends: R (>= 2.10)
Imports: jsonlite, htmlwidgets, assertthat, rlang, glue, magrittr, shiny, htmltools
Suggests: knitr, rmarkdown, listviewer, httr, testthat, yaml, fs, gistr, webshot, magick, usethis, whisker, crayon, clipr, desc, clisymbols, shinytest, readr, tibble, lubridate, learnr, processx, rsvg, dplyr, png, conflicted, here
NeedsCompilation: no
Packaged: 2019-01-21 01:43:00 UTC; ijlyttle
Author: Ian Lyttle [aut, cre] (<https://orcid.org/0000-0001-9962-4849>), Vega/Vega-Lite Developers [aut], Alicia Schep [ctb] (<https://orcid.org/0000-0002-3915-0618>), Stuart Lee [ctb], Hadley Wickham [ctb] (<https://orcid.org/0000-0003-4757-117X>), Kanit Wongsuphasawat [ctb] (Vega/Vega-Lite library), Dominik Moritz [ctb] (Vega/Vega-Lite library), Arvind Satyanarayan [ctb] (Vega/Vega-Lite library), Jeffrey Heer [ctb] (Vega/Vega-Lite library), Mike Bostock [ctb] (D3 library), David Frank [ctb] (node-fetch library)
Maintainer: Ian Lyttle <ian.lyttle@schneider-electric.com>
Repository: CRAN
Date/Publication: 2019-01-30 18:20:03 UTC

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New package unifed with initial version 1.0
Package: unifed
Title: The Unifed Distribution
Version: 1.0
Date: 2018-12-23
Description: Introduced in Quijano Xacur (2018) <arXiv:1812.00251>. This package contains the density, distribution, quantile and random generation functions for the unifed. It also contains functions for the unifed family and quasifamily that can be used with the glm() function.
Depends: R (>= 3.1), methods
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Author: Oscar Alberto Quijano Xacur [aut,cre]
Maintainer: Oscar Alberto Quijano Xacur <oscar.quijano@use.startmail.com>
NeedsCompilation: yes
Packaged: 2019-01-21 15:22:43 UTC; oscar
Repository: CRAN
Date/Publication: 2019-01-30 18:40:03 UTC

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New package swephR with initial version 0.1.3
Package: swephR
Type: Package
Title: High Precision Swiss Ephemeris
Version: 0.1.3
Authors@R: c( person("Ralf", "Stubner", email = "ralf.stubner@gmail.com", role = c("aut", "cre")), person("Victor", "Reijs", role = "aut"), person("Authors and copyright holder of the Swiss Ephemeris", role = c("aut", "cph"), comment = "see LICENSE for details"))
Description: The Swiss Ephemeris is a high precision ephemeris based upon the DE431 ephemerides from NASA's JPL. It covers the time range 13201 BC to AD 17191. This package uses the semi-analytic theory by Steve Moshier. For faster and more accurate calculations, the compressed Swiss Ephemeris data is available in the 'swephRdata' package. To access this data package, run 'install.packages("swephRdata", repos = "https://rstub.github.io/drat/", type = "source")'. The size of the 'swephRdata' package is approximately 115 MB. The user can also use the original JPL DE431 data.
License: AGPL | file LICENSE
Imports: Rcpp (>= 0.12.18)
LinkingTo: Rcpp
RoxygenNote: 6.1.1
Suggests: testthat, swephRdata, knitr, rmarkdown
Encoding: UTF-8
URL: https://github.com/rstub/swephR/, http://www.astro.com/swisseph/
BugReports: https://github.com/rstub/swephR/issues/
Additional_repositories: https://rstub.github.io/drat
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-01-20 22:34:38 UTC; ralf
Author: Ralf Stubner [aut, cre], Victor Reijs [aut], Authors and copyright holder of the Swiss Ephemeris [aut, cph] (see LICENSE for details)
Maintainer: Ralf Stubner <ralf.stubner@gmail.com>
Repository: CRAN
Date/Publication: 2019-01-30 18:20:08 UTC

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New package simcdm with initial version 0.0.5
Package: simcdm
Type: Package
Title: Simulate Cognitive Diagnostic Model (CDM) Data
Version: 0.0.5
Authors@R: c(person("James Joseph", "Balamuta", email = "balamut2@illinois.edu", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0003-2826-8458")), person("Steven Andrew", "Culpepper", email = "sculpepp@illinois.edu", role = c("aut", "cph"), comment = c(ORCID = "0000-0003-4226-6176") ), person("Aaron", "Hudson", email = "awhudson@uw.edu", role = c("ctb", "cph") ) )
Description: Provides efficient R and 'C++' routines to simulate cognitive diagnostic model data for Deterministic Input, Noisy "And" Gate (DINA) and reduced Reparameterized Unified Model (rRUM) from Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>, Culpepper (2015) <doi:10.3102/1076998615595403>, and de la Torre (2009) <doi:10.3102/1076998607309474>.
Depends: R (>= 3.5.0)
Imports: Rcpp (>= 1.0.0)
LinkingTo: Rcpp, RcppArmadillo (>= 0.9.200)
URL: https://github.com/tmsalab/simcdm
BugReports: https://github.com/tmsalab/simcdm/issues
License: GPL (>= 2)
RoxygenNote: 6.1.1
Encoding: UTF-8
Suggests: testthat, covr, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-01-20 22:07:36 UTC; ronin
Author: James Joseph Balamuta [aut, cre, cph] (<https://orcid.org/0000-0003-2826-8458>), Steven Andrew Culpepper [aut, cph] (<https://orcid.org/0000-0003-4226-6176>), Aaron Hudson [ctb, cph]
Maintainer: James Joseph Balamuta <balamut2@illinois.edu>
Repository: CRAN
Date/Publication: 2019-01-30 18:20:11 UTC

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New package secsse with initial version 1.0.0
Package: secsse
Title: Several Examined and Concealed States-Dependent Speciation and Extinction
Version: 1.0.0
Date: 2019-01-19
License: GPL-3
Authors@R: c( person("Leonel", "Herrera Alsina", email = "leonelhalsina@gmail.com", role = "cre"), person("Paul", "van Els", email = "paulvanels@gmail.com", role = "aut"), person("Rampal", "Etienne", email = "r.s.etienne@rug.nl", role = "aut"))
Description: Combines the features of HiSSE and MuSSE to simultaneously infer state-dependent diversification across two or more traits or states while accounting for the role of a possible concealed trait. See Herrera-Alsina et al. Systematic Biology, in press <DOI:10.1093/sysbio/syy057>.
Depends: R (>= 3.5.0)
Imports: utils, DDD (> 3.0), ape, foreach, doParallel, apTreeshape, phylobase, geiger, deSolve
Suggests: testthat, testit, knitr, rmarkdown
Enhances: doMC
NeedsCompilation: yes
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
RoxygenNote: 6.1.1
Packaged: 2019-01-19 16:12:13 UTC; rampa
Author: Leonel Herrera Alsina [cre], Paul van Els [aut], Rampal Etienne [aut]
Maintainer: Leonel Herrera Alsina <leonelhalsina@gmail.com>
Repository: CRAN
Date/Publication: 2019-01-30 18:40:06 UTC

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New package pointdexter with initial version 0.1.0
Package: pointdexter
Type: Package
Title: Labels Points Inside Polygons
Date: 2019-01-20
Version: 0.1.0
Authors@R: person("Cristian E.", "Nuno", email = "cenuno@syr.edu", role = c("aut", "cre"))
Maintainer: Cristian E. Nuno <cenuno@syr.edu>
Description: Labels longitudinal and latitudinal coordinates located inside a polygon. For a singular polygon, the label is a logical vector of TRUE and FALSE values. For multiple polygons, the label is a character vector based on the names of each polygon. The package is designed to work with both sf and SpatialPolygonsDataFrame objects.
URL: https://cenuno.github.io/pointdexter/, https://github.com/cenuno/pointdexter
License: GPL-3
Encoding: UTF-8
Depends: R (>= 3.3.0)
Imports: sp (>= 1.3-1), splancs (>= 2.01-40)
Suggests: knitr (>= 1.21), rmarkdown (>= 1.11), testthat (>= 2.0.1)
Enhances: sf (>= 0.7)
VignetteBuilder: knitr
RoxygenNote: 6.1.1
Language: en-US
BugReports: https://github.com/cenuno/pointdexter/issues
NeedsCompilation: no
Packaged: 2019-01-20 19:14:24 UTC; cristiannuno
Author: Cristian E. Nuno [aut, cre]
Repository: CRAN
Date/Publication: 2019-01-30 18:10:06 UTC

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New package GMMAT with initial version 1.0.3
Package: GMMAT
Version: 1.0.3
Date: 2019-01-20
Title: Generalized Linear Mixed Model Association Tests
Author: Han Chen, Matthew P. Conomos
Maintainer: Han Chen <Han.Chen.2@uth.tmc.edu>
Description: Perform association tests using generalized linear mixed models (GLMMs) in genome-wide association studies (GWAS) and sequencing association studies. First, GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant as proposed in Chen et al. (2016) <DOI:10.1016/j.ajhg.2016.02.012>. For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT) as proposed in Chen et al. (2019) <DOI:10.1016/j.ajhg.2018.12.012>, including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.
License: GPL-3
Copyright: See COPYRIGHTS for details.
Imports: Rcpp, SeqArray, SeqVarTools, CompQuadForm, foreach, parallel
Suggests: doMC, testthat
LinkingTo: Rcpp, RcppArmadillo
Encoding: UTF-8
NeedsCompilation: yes
Depends: R (>= 3.2.0)
Packaged: 2019-01-20 18:29:35 UTC; hchen
Repository: CRAN
Date/Publication: 2019-01-30 18:23:42 UTC

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New package FlickrAPI with initial version 0.1.0.0
Package: FlickrAPI
Title: Access to Flickr API
Version: 0.1.0.0
Authors@R: person("Koki", "Ando", email = "koki.25.ando@gmail.com", role = c("aut", "cre"))
Description: Provides an interface to the Flickr API <https://www.flickr.com/services/api/> and allows R users to download data on Flickr.
Depends: R (>= 3.1)
License: GPL-2 | file LICENSE
Encoding: UTF-8
LazyData: true
Imports: magrittr, RCurl, jsonlite, stringr
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-01-21 03:36:54 UTC; KokiAndo
Author: Koki Ando [aut, cre]
Maintainer: Koki Ando <koki.25.ando@gmail.com>
Repository: CRAN
Date/Publication: 2019-01-30 18:13:32 UTC

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New package ezplot with initial version 0.2.2
Package: ezplot
Type: Package
Title: Functions for Common Chart Types
Version: 0.2.2
Author: Wojtek Kostelecki
Maintainer: Wojtek Kostelecki <wojtek.kostelecki@gmail.com>
Description: Wrapper for the ggplot2 package that creates a variety of common charts (e.g. bar, line, area, ROC, waterfall, pie) while aiming to reduce typing.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 2.10)
Imports: dplyr, forcats, ggplot2, magrittr, rlang
RoxygenNote: 6.1.1
Suggests: knitr, lubridate, methods, rmarkdown, ROCR, testthat, tibble, tidyr, covr
NeedsCompilation: no
Packaged: 2019-01-20 19:55:16 UTC; wkostelecki
Repository: CRAN
Date/Publication: 2019-01-30 18:03:17 UTC

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New package dominanceanalysis with initial version 1.0.0
Package: dominanceanalysis
Title: Dominance Analysis
Date: 2019-01-12
Description: Dominance analysis is a method that allows to compare the relative importance of predictors in multiple regression models: ordinary least squares, generalized linear models and hierarchical linear models. The main principles and methods of dominance analysis are described in Budescu, D. V. (1993) <doi:10.1037/0033-2909.114.3.542> and Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129> for ordinary least squares regression. Subsequently, the extensions for multivariate regression, logistic regression and hierarchical linear models were described in Azen, R., & Budescu, D. V. (2006) <doi:10.3102/10769986031002157>, Azen, R., & Traxel, N. (2009) <doi:10.3102/1076998609332754> and Luo, W., & Azen, R. (2013) <doi:10.3102/1076998612458319>, respectively.
Authors@R: c( person("Claudio", "Bustos Navarrete", email = "clbustos@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-3478-9858")), person("Filipa", "Coutinho Soares", email = "filipa.mco.soares@gmail.com", role = c("aut")))
Version: 1.0.0
Depends: R (>= 3.0.2)
License: GPL-2
LazyData: true
Imports: methods, stats
Suggests: lme4, boot, testthat, car, heplots, covr, knitr,rmarkdown,caTools,pscl
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-20 17:02:27 UTC; cdx
Author: Claudio Bustos Navarrete [aut, cre] (<https://orcid.org/0000-0003-3478-9858>), Filipa Coutinho Soares [aut]
Maintainer: Claudio Bustos Navarrete <clbustos@gmail.com>
Repository: CRAN
Date/Publication: 2019-01-30 18:03:21 UTC

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New package concurve with initial version 1.0.1
Package: concurve
Type: Package
Title: Computes and Plots Confidence (Consonance) Intervals, P-Values, and S-Values to Form Consonance Curves (Functions)
Version: 1.0.1
Authors@R: c( person("Zad", "Chow", , "zad@lesslikely.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1545-8199") ), person("Andrew", "Vigotsky", role = "aut", comment = c(ORCID = "0000-0003-3166-0688") ) )
Maintainer: Zad Chow <zad@lesslikely.com>
Description: Allows one to compute confidence (compatibility/consonance) intervals for various statistical tests along with their corresponding P-values and S-values. The intervals can be plotted to create consonance functions allowing one to see what effect sizes are compatible with the test model at various compatibility levels rather than being limited to one interval estimate such as 95%. These methods are discussed by Poole C. (1987) <doi:10.2105/AJPH.77.2.195>, Schweder T, Hjort NL. (2002) <doi:10.1111/1467-9469.00285>, Singh K, Xie M, Strawderman WE. (2007) <arXiv:0708.0976>, Rothman KJ, Greenland S, Lash TL. (2012, ISBN:9781451190052), and Amrhein V, Trafimow D, Greenland S. (2018) <doi:10.7287/peerj.preprints.26857v4>.
Imports: ggplot2, metafor
Suggests: testthat, knitr
License: GPL-3 | file LICENSE
URL: https://data.lesslikely.com/concurve/, https://github.com/Zadchow/concurve, https://www.lesslikely.com/
BugReports: https://github.com/Zadchow/concurve/issues
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-01-21 04:40:39 UTC; Zad
Author: Zad Chow [aut, cre] (<https://orcid.org/0000-0003-1545-8199>), Andrew Vigotsky [aut] (<https://orcid.org/0000-0003-3166-0688>)
Repository: CRAN
Date/Publication: 2019-01-30 18:23:38 UTC

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New package bayesCT with initial version 0.99.0
Package: bayesCT
Type: Package
Title: Simulation and Analysis of Adaptive Bayesian Clinical Trials
Version: 0.99.0
Authors@R: c(person(given = "Thevaa", family = "Chandereng", role = c("aut", "cre", "cph"), email = "chandereng@wisc.edu", comment = c(ORCID = "0000-0003-4078-9176")), person("Donald", "Musgrove", email = "donald.r.musgrove@medtronic.com", role = c("aut", "cph")), person("Tarek", "Haddad", email = "tarek.d.haddad@medtronic.com", role = c("aut", "cph")), person("Graeme", "Hickey", email = "graeme.l.hickey@medtronic.com", role = c("aut", "cph")), person("Timothy", "Hanson", email = "tim.hanson2@medtronic.com", role = c("aut", "cph")), person("Theodore", "Lystig", email = "theodore.lystig@medtronic.com", role = c("aut", "cph")))
Description: Simulation and analysis of Bayesian adaptive clinical trial, incorporates historical data and allows early stopping for futility or early success.
LazyLoad: yes
License: GPL-3
NeedsCompilation: no
URL: https://github.com/thevaachandereng/bayesCT/
BugReports: https://github.com/thevaachandereng/bayesCT/issues/
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: testthat, rmarkdown, pkgdown
VignetteBuilder: knitr
Imports: tidyr, dplyr, knitr, bayesDP, parallel, purrr, msm, magrittr, devtools
Packaged: 2019-01-20 20:46:53 UTC; thevaasiinenchandereng
Author: Thevaa Chandereng [aut, cre, cph] (<https://orcid.org/0000-0003-4078-9176>), Donald Musgrove [aut, cph], Tarek Haddad [aut, cph], Graeme Hickey [aut, cph], Timothy Hanson [aut, cph], Theodore Lystig [aut, cph]
Maintainer: Thevaa Chandereng <chandereng@wisc.edu>
Repository: CRAN
Date/Publication: 2019-01-30 18:13:27 UTC

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New package bitsqueezr with initial version 0.1.0
Package: bitsqueezr
Type: Package
Title: Quantize Floating-Point Numbers for Improved Compressibility
Version: 0.1.0
Author: Daniel Baston <dbaston@gmail.com>
Maintainer: Daniel Baston <dbaston@gmail.com>
Description: Provides a implementation of floating-point quantization algorithms for use in precision-preserving compression, similar to the approach taken in the 'netCDF operators' (NCO) software package and described in Zender (2016) <doi:10.5194/gmd-2016-63>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Suggests: testthat
NeedsCompilation: yes
Packaged: 2019-01-20 17:45:47 UTC; dan
Repository: CRAN
Date/Publication: 2019-01-30 17:50:03 UTC

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New package HHG with initial version 2.3.1
Package: HHG
Type: Package
Title: Heller-Heller-Gorfine Tests of Independence and Equality of Distributions
Version: 2.3.1
Date: 2019-01-30
Author: Barak Brill & Shachar Kaufman, based in part on an earlier implementation by Ruth Heller and Yair Heller.
Maintainer: Barak Brill <barakbri@mail.tau.ac.il>
Depends: R (>= 3.1.0)
Suggests: MASS,knitr
Description: Heller-Heller-Gorfine tests are a set of powerful statistical tests of multivariate k-sample homogeneity and independence (Heller et. al., 2013, <doi:10.1093/biomet/ass070>). For the univariate case, the package also offers implementations of the 'MinP DDP' and 'MinP ADP' tests by Heller et. al. (2016), which are consistent against all continuous alternatives but are distribution-free, and are thus much faster to apply.
License: GPL-3
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-01-30 13:08:02 UTC; barak
RoxygenNote: 5.0.1
Imports: Rcpp (>= 0.12.9)
LinkingTo: Rcpp
SystemRequirements: C++11
URL: https://github.com/barakbri/HHG
BugReports: https://github.com/barakbri/HHG/issues
Repository: CRAN
Date/Publication: 2019-01-30 15:20:11 UTC

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New package GpGp with initial version 0.1.1
Package: GpGp
Type: Package
Title: Fast Gaussian Process Computation Using Vecchia's Approximation
Version: 0.1.1
Date: 2019-01-27
Authors@R: c( person("Joseph", "Guinness", email = "joeguinness@gmail.com", role = c("aut", "cre")), person("Matthias", "Katzfuss", email = "katzfuss@gmail.com", role = "aut" ) )
Maintainer: Joseph Guinness <joeguinness@gmail.com>
Description: Functions for reordering input locations, finding ordered nearest neighbors (with help from 'FNN' package), grouping operations, approximate likelihood evaluations, profile likelihoods, Gaussian process predictions, and conditional simulations. Covariance functions for spatial and spatial-temporal data on Euclidean domains and spheres are provided. The original approximation is due to Vecchia (1988) <http://www.jstor.org/stable/2345768>, and the reordering and grouping methods are from Guinness (2018) <doi:10.1080/00401706.2018.1437476>.
Depends: R (>= 2.10)
License: MIT + file LICENSE
Imports: Rcpp (>= 0.12.13), FNN
Suggests: fields, knitr, rmarkdown, testthat, maps, maptools
LinkingTo: Rcpp
RoxygenNote: 6.1.0
VignetteBuilder: knitr
LazyData: true
NeedsCompilation: yes
Packaged: 2019-01-29 14:15:23 UTC; guinness
Author: Joseph Guinness [aut, cre], Matthias Katzfuss [aut]
Repository: CRAN
Date/Publication: 2019-01-30 09:20:03 UTC

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Tue, 29 Jan 2019

New package ceterisParibus with initial version 0.3.1
Package: ceterisParibus
Title: Ceteris Paribus Profiles
Version: 0.3.1
Authors@R: person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8423-1823"))
Description: Ceteris Paribus Profiles (What-If Plots) are designed to present model responses around selected points in a feature space. For example around a single prediction for an interesting observation. Plots are designed to work in a model-agnostic fashion, they are working for any predictive Machine Learning model and allow for model comparisons. Ceteris Paribus Plots supplement the Break Down Plots from 'breakDown' package.
Depends: R (>= 3.3), ggplot2, gower
Suggests: randomForest, ggiraph, e1071, testthat, rpart
Imports: DALEX, knitr
License: GPL-2
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
URL: https://pbiecek.github.io/ceterisParibus/
BugReports: https://github.com/pbiecek/ceterisParibus/issues
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-01-29 18:41:13 UTC; pbiecek
Author: Przemyslaw Biecek [aut, cre] (<https://orcid.org/0000-0001-8423-1823>)
Maintainer: Przemyslaw Biecek <przemyslaw.biecek@gmail.com>
Repository: CRAN
Date/Publication: 2019-01-29 23:20:08 UTC

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New package HHG with initial version 2.3
Package: HHG
Type: Package
Title: Heller-Heller-Gorfine Tests of Independence and Equality of Distributions
Version: 2.3
Date: 2019-01-29
Author: Barak Brill & Shachar Kaufman, based in part on an earlier implementation by Ruth Heller and Yair Heller.
Maintainer: Barak Brill <barakbri@mail.tau.ac.il>
Depends: R (>= 3.1.0)
Suggests: MASS,knitr
Description: Heller-Heller-Gorfine ('HHG') tests are a set of powerful statistical tests of multivariate k-sample homogeneity and independence. For the univariate case, the package also offers implementations of the 'MinP DDP' and 'MinP ADP' tests, which are consistent against all continuous alternatives but are distribution-free, and are thus much faster to apply.
License: GPL-3
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-01-29 10:26:59 UTC; Barak
RoxygenNote: 5.0.1
Imports: Rcpp (>= 0.12.9)
LinkingTo: Rcpp
SystemRequirements: C++11
URL: https://github.com/barakbri/HHG
BugReports: https://github.com/barakbri/HHG/issues
Repository: CRAN
Date/Publication: 2019-01-29 15:30:06 UTC

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Mon, 28 Jan 2019

New package dr4pl with initial version 1.1.7.5
Package: dr4pl
Type: Package
Date: 2019-1-28
Title: Dose Response Data Analysis using the 4 Parameter Logistic (4pl) Model
Version: 1.1.7.5
Depends: R (>= 3.1.0)
Authors@R: c(person("Justin T.", "Landis", rol = c("aut", "cre"), email = "jtlandis314@gmail.com"), person("Hyowon", "An", role = "aut", email = "ahwbest@gmail.com"), person("Aubrey G.", "Bailey", rol = "aut", email = "aubreybailey@gmail.com"), person("Dirk P.", "Dittmer", rol = "aut", email = "dirk_dittmer@med.unc.edu"), person("James S.", "Marron", rol = "aut", email = "marron@unc.edu"))
Description: Models the relationship between dose levels and responses in a pharmacological experiment using the 4 Parameter Logistic model. Traditional packages on dose-response modelling such as 'drc' and 'nplr' often draw errors due to convergence failure especially when data have outliers or non-logistic shapes. This package provides robust estimation methods that are less affected by outliers and other initialization methods that work well for data lacking logistic shapes. We provide the bounds on the parameters of the 4PL model that prevent parameter estimates from diverging or converging to zero and base their justification in a statistical principle. These methods are used as remedies to convergence failure problems. Gadagkar, S. R. and Call, G. B. (2015) <doi:10.1016/j.vascn.2014.08.006> Ritz, C. and Baty, F. and Streibig, J. C. and Gerhard, D. (2015) <doi:10.1371/journal.pone.0146021>.
License: GPL (>= 2)
LazyData: TRUE
URL: https://bitbucket.org/dittmerlab/dr4pl
BugReports: https://bitbucket.org/dittmerlab/dr4pl/issues?status=new&status=open
RdMacros: Rdpack
RoxygenNote: 6.1.1
Imports: ggplot2, Matrix, matrixcalc, tensor, Rdpack
Suggests: drc, devtools, roxygen2, testthat, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-28 15:54:57 UTC; travis
Author: Justin T. Landis [aut, cre], Hyowon An [aut], Aubrey G. Bailey [aut], Dirk P. Dittmer [aut], James S. Marron [aut]
Maintainer: Justin T. Landis <jtlandis314@gmail.com>
Repository: CRAN
Date/Publication: 2019-01-28 19:10:07 UTC

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New package hisse with initial version 1.9.0
Package: hisse
Version: 1.9.0
Date: 2019-1-26
Title: Hidden State Speciation and Extinction
Authors@R: c(person("Jeremy", "Beaulieu", role=c("aut", "cre"), email = "jbeaulieu@nimbios.org"), person("Brian", "O'Meara", role=c("aut")), person("Daniel", "Caetano", role=c("aut")))
Maintainer: Jeremy Beaulieu <jbeaulieu@nimbios.org>
Depends: ape, deSolve, GenSA, subplex, nloptr
Suggests: testthat, knitr
Imports: parallel, phytools, data.table, methods, diversitree, plotrix
Description: Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification. Beaulieu and O'Meara (2016) <doi:10.1093/sysbio/syw022>.
License: GPL (>= 2)
VignetteBuilder: knitr
RoxygenNote: 6.1.0
NeedsCompilation: yes
Packaged: 2019-01-28 04:29:47 UTC; jeremy
Author: Jeremy Beaulieu [aut, cre], Brian O'Meara [aut], Daniel Caetano [aut]
Repository: CRAN
Date/Publication: 2019-01-28 16:40:03 UTC

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New package cdfquantreg with initial version 1.2.1
Package: cdfquantreg
Type: Package
Title: Quantile Regression for Random Variables on the Unit Interval
Version: 1.2.1
Date: 2019-01-25
Authors@R: c(person(given = "Yiyun", family = "Shou", email = "yiyun.shou@anu.edu.au", role = c("aut", "cre")), person(given = "Michael", family = "Smithson", email = "Michael.Smithson@anu.edu.au", role = "aut"))
Description: Employs a two-parameter family of distributions for modelling random variables on the (0, 1) interval by applying the cumulative distribution function (cdf) of one parent distribution to the quantile function of another.
BugReports: https://cloudyshou.wordpress.com/cdfquantreg-bugs-report
Depends: R (>= 3.1.0)
License: GPL-3
Imports: pracma (>= 1.8), Formula (>= 1.2), stats, MASS
Suggests: knitr, R2OpenBUGS, rmarkdown
VignetteBuilder: knitr
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2019-01-25 05:32:17 UTC; cici_blue
Author: Yiyun Shou [aut, cre], Michael Smithson [aut]
Maintainer: Yiyun Shou <yiyun.shou@anu.edu.au>
Repository: CRAN
Date/Publication: 2019-01-28 07:00:03 UTC

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Sun, 27 Jan 2019

New package shar with initial version 0.1
Type: Package
Package: shar
Title: Species-Habitat Associations
Version: 0.1
Authors@R: c(person("Maximillian H.K.", "Hesselbarth", email = "maximilian.hesselbarth@uni-goettingen.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1125-9918")), person("Marco", "Sciaini", email = "sciaini.marco@gmail.com", role = "aut", comment = c(ORCID = '0000-0002-3042-5435')))
Maintainer: Maximillian H.K. Hesselbarth <maximilian.hesselbarth@uni-goettingen.de>
Description: Analyse species-habitat associations in R. Therefore, information about the location of the species is needed and about the environmental conditions. To test for significance habitat associations, one of the two components is randomized. Methods are mainly based on Plotkin et al. (2000) <doi:10.1006/jtbi.2000.2158> and Harms et al. (2001) <doi:10.1111/j.1365-2745.2001.00615.x>.
License: GPL-3
URL: https://r-spatialecology.github.io/shar
BugReports: https://github.com/r-spatialecology/shar/issues
Depends: R (>= 3.1)
Imports: classInt, dplyr, raster, spatstat
RoxygenNote: 6.1.1
Suggests: covr, testthat, knitr, rmarkdown
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2019-01-19 15:19:17 UTC; Maximilian
Author: Maximillian H.K. Hesselbarth [aut, cre] (<https://orcid.org/0000-0003-1125-9918>), Marco Sciaini [aut] (<https://orcid.org/0000-0002-3042-5435>)
Repository: CRAN
Date/Publication: 2019-01-27 23:50:02 UTC

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New package nonneg.cg with initial version 0.1
Package: nonneg.cg
Type: Package
Title: Non-Negative Conjugate-Gradient Minimizer
Version: 0.1
Date: 2019-01-03
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera@gmail.com>
Description: Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0), using a Conjugate-Gradient algorithm based on a modified Polak-Rubiere-Polyak formula as described in (Li, Can, 2013, <https://www.hindawi.com/journals/jam/2013/986317/abs/>).
License: BSD_2_clause + file LICENSE
Imports: Rcpp (>= 0.12.19)
LinkingTo: Rcpp
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-01-19 22:06:11 UTC; david
Repository: CRAN
Date/Publication: 2019-01-28 00:00:03 UTC

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New package neo4r with initial version 0.1.0
Package: neo4r
Title: A 'Neo4J' Driver
Version: 0.1.0
Authors@R: c(person(given = "Colin", family = "Fay", role = c("cre", "aut"), email = "contact@colinfay.me", comment = structure("0000-0001-7343-1846", .Names = "ORCID")), person(given = "ThinkR", role = "cph"), person(given = "Neo4J", role = "spn"))
Description: A Modern and Flexible 'Neo4J' Driver, allowing you to query data on a 'Neo4J' server and handle the results in R. It's modern in the sense it provides a driver that can be easily integrated in a data analysis workflow, especially by providing an API working smoothly with other data analysis and graph packages. It's flexible in the way it returns the results, by trying to stay as close as possible to the way 'Neo4J' returns data. That way, you have the control over the way you will compute the results. At the same time, the result is not too complex, so that the "heavy lifting" of data wrangling is not left to the user.
License: MIT + file LICENSE
URL: https://github.com/neo4j-rstats/neo4r
BugReports: https://github.com/neo4j-rstats/neo4r/issues
Imports: attempt, data.table, glue, httr, igraph, jsonlite, magrittr, purrr, R6, rlang, rstudioapi, shiny, tibble, tidyr, tidyselect, utils
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2019-01-18 22:59:45 UTC; colin
Author: Colin Fay [cre, aut] (<https://orcid.org/0000-0001-7343-1846>), ThinkR [cph], Neo4J [spn]
Maintainer: Colin Fay <contact@colinfay.me>
Repository: CRAN
Date/Publication: 2019-01-27 23:20:03 UTC

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New package mme with initial version 0.1-6
Package: mme
Type: Package
Title: Multinomial Mixed Effects Models
Version: 0.1-6
Date: 2019-01-27
Author: E. Lopez-Vizcaino, M.J. Lombardia and D. Morales
Maintainer: E. Lopez-Vizcaino <esther.lopez@ige.eu>
Depends: R (>= 1.8.0), MASS
Imports: Matrix, methods
Description: Fit Gaussian Multinomial mixed-effects models for small area estimation: Model 1, with one random effect in each category of the response variable (Lopez-Vizcaino,E. et al., 2013) <doi:10.1177/1471082X13478873>; Model 2, introducing independent time effect; Model 3, introducing correlated time effect. mme calculates direct and parametric bootstrap MSE estimators (Lopez-Vizcaino,E et al., 2014) <doi:10.1111/rssa.12085>.
License: GPL (>= 2)
LazyData: yes
Packaged: 2019-01-27 12:43:14 UTC; Oscar Iglesias
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2019-01-27 15:40:13 UTC

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Fri, 25 Jan 2019

New package es.dif with initial version 1.0.0
Package: es.dif
Type: Package
Title: Compute Effect Sizes of the Difference
Version: 1.0.0
Author: Satoshi Aoki
Maintainer: Satoshi Aoki <aokis1ll1@gmail.com>
Date: 2019-01-18
Description: Computes various effect sizes of the difference, their variance, and confidence interval. This package treats Cohen's d, Hedges' d, biased/unbiased c (an effect size between a mean and a constant) and e (an effect size between means without assuming the variance equality).
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2019-01-18 03:33:47 UTC; akaka
Repository: CRAN
Date/Publication: 2019-01-25 23:30:02 UTC

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New package DEVis with initial version 1.0.1
Package: DEVis
Type: Package
Title: A Differential Expression Analysis Toolkit for Visual Analytics and Data Aggregation
Version: 1.0.1
Author: Adam Price
Maintainer: Adam Price <ap3637@cumc.columbia.edu>
Description: Differential expression analysis tools for data aggregation, visualization, exploratory analysis, and project organization.
License: LGPL
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
Suggests: knitr, rmarkdown, kableExtra
VignetteBuilder: knitr
Depends: DESeq2
Imports: ggplot2, pheatmap, RColorBrewer, ggthemes, MASS, plyr, ggsci, SummarizedExperiment, methods, ggdendro, PoiClaClu, reshape2, grDevices, stats, utils, gridExtra
NeedsCompilation: no
Packaged: 2019-01-25 17:18:47 UTC; adam
Repository: CRAN
Date/Publication: 2019-01-25 23:10:03 UTC

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New package ssev with initial version 0.1.0
Package: ssev
Title: Sample Size Computation for Fixed N with Optimal Reward
Version: 0.1.0
Authors@R: person("Maurits", "Kaptein", email = "maurits@mauritskaptein.com", role = c("aut", "cre"))
Description: Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. Sample sizes and expected rewards for standard t- and z- tests are also provided.
Depends: R (>= 3.4)
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: pwr, MESS, stats
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-01-18 15:43:33 UTC; mauritskate
Author: Maurits Kaptein [aut, cre]
Maintainer: Maurits Kaptein <maurits@mauritskaptein.com>
Repository: CRAN
Date/Publication: 2019-01-25 17:30:03 UTC

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New package rehydratoR with initial version 0.5.1
Package: rehydratoR
Type: Package
Title: Downloads Tweets from a List of Tweet IDs
Version: 0.5.1
Authors@R: c( person("Kevin", "Coakley", email = "kcoakley@sdsc.edu", role = c("aut", "cre")), person("Zachary", "Steinert-Threlkeld", email = "zst@luskin.ucla.edu", role = "ctb"))
Author: Kevin Coakley [aut, cre], Zachary Steinert-Threlkeld [ctb]
URL: https://kevincoakley.github.io/rehydratoR/
BugReports: https://github.com/kevincoakley/rehydratoR/issues
Description: Facilitates replication of Twitter-based research by handling common programming tasks needed when downloading tweets. Specifically, it ensures a user does not exceed Twitter’s rate limits, and it saves tweets in moderately sized files. While a user could perform these tasks in their own code, doing so may be beyond the capabilities of many users.
Maintainer: Kevin Coakley <kcoakley@sdsc.edu>
License: BSD_3_clause + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: rtweet (>= 0.6.7), tibble (>= 1.3.4), dplyr (>= 0.7.6), jsonlite (>= 1.5)
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2019-01-18 22:23:36 UTC; kcoakley
Repository: CRAN
Date/Publication: 2019-01-25 17:10:03 UTC

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New package NetLogoR with initial version 0.3.5
Package: NetLogoR
Title: Build and Run Spatially Explicit Agent-Based Models
Description: Build and run spatially explicit agent-based models using only the R platform. 'NetLogoR' follows the same framework as the 'NetLogo' software (Wilensky, 1999 <http://ccl.northwestern.edu/netlogo/>) and is a translation in R of the structure and functions of 'NetLogo'. 'NetLogoR' provides new R classes to define model agents and functions to implement spatially explicit agent-based models in the R environment. This package allows benefiting of the fast and easy coding phase from the highly developed 'NetLogo' framework, coupled with the versatility, power and massive resources of the R software. Examples of three models (Ants <http://ccl.northwestern.edu/netlogo/models/Ants>, Butterfly (Railsback and Grimm, 2012) and Wolf-Sheep-Predation <http://ccl.northwestern.edu/netlogo/models/WolfSheepPredation>) written using 'NetLogoR' are available. The 'NetLogo' code of the original version of these models is provided alongside. A programming guide inspired from the 'NetLogo' Programming Guide (<https://ccl.northwestern.edu/netlogo/docs/programming.html>) and a dictionary of 'NetLogo' primitives (<https://ccl.northwestern.edu/netlogo/docs/dictionary.html>) equivalences are also available. NOTE: To increment 'time', these functions can use a for loop or can be integrated with a discrete event simulator, such as 'SpaDES' (<https://cran.r-project.org/package=SpaDES>). The suggested package 'fastshp' can be installed with 'install.packages("fastshp", repos = "https://rforge.net", type = "source")'.
URL: http://netlogor.predictiveecology.org, https://github.com/PredictiveEcology/NetLogoR/
Version: 0.3.5
Date: 2019-01-25
Authors@R: c( person("Sarah", "Bauduin", email = "sarahbauduin@hotmail.fr", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-3252-5894")), person("Eliot J B", "McIntire", email = "eliot.mcintire@canada.ca", role = c("aut"), comment=c(ORCID = "0000-0002-6914-8316")), person("Alex M", "Chubaty", email = "alex.chubaty@gmail.com", role = c("aut"), comment = c(ORCID = "0000-0001-7146-8135")), person("Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada", role = "cph") )
Depends: R (>= 3.3.0), raster
Imports: abind, car, CircStats, data.table, grDevices, Hmisc, matrixStats, methods, plyr, quickPlot (>= 0.1.2), sp, SpaDES.tools, stats, rgeos
Suggests: fastshp, knitr, magrittr, microbenchmark, rmarkdown, sf, SpaDES.core, testthat
License: GPL-3
Encoding: UTF-8
VignetteBuilder: knitr, rmarkdown
BugReports: https://github.com/PredictiveEcology/NetLogoR/issues
ByteCompile: yes
LazyData: true
RoxygenNote: 6.1.1
Additional_repositories: https://rforge.net
Collate: 'Agent-classes.R' 'Classes.R' 'NetLogoR-package.R' 'worldNLR-classes-methods.R' 'agentMatrix-Class-methods.R' 'agentset-functions.R' 'function-arguments.R' 'patch-functions.R' 'plot.R' 'world-functions.R' 'quickPlot.R' 'spades-functions.R' 'turtle-functions.R'
NeedsCompilation: no
Packaged: 2019-01-25 16:26:49 UTC; bauduin
Author: Sarah Bauduin [aut, cre] (<https://orcid.org/0000-0002-3252-5894>), Eliot J B McIntire [aut] (<https://orcid.org/0000-0002-6914-8316>), Alex M Chubaty [aut] (<https://orcid.org/0000-0001-7146-8135>), Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada [cph]
Maintainer: Sarah Bauduin <sarahbauduin@hotmail.fr>
Repository: CRAN
Date/Publication: 2019-01-25 17:20:07 UTC

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New package matsbyname with initial version 0.4.9
Package: matsbyname
Type: Package
Title: An Implementation of Matrix Mathematics
Version: 0.4.9
Date: 2019-01-17
Authors@R: c(person("Matthew", "Heun", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7438-214X"), email = "matthew.heun@me.com"))
Maintainer: Matthew Heun <matthew.heun@me.com>
Description: An implementation of matrix mathematics wherein operations are performed "by name."
License: MIT + file LICENSE
Language: en-US
Encoding: UTF-8
RoxygenNote: 6.1.1
Imports: dplyr, Hmisc, magrittr
Suggests: testthat, knitr, rmarkdown, tidyr, covr
VignetteBuilder: knitr
URL: https://github.com/MatthewHeun/matsbyname
BugReports: https://github.com/MatthewHeun/matsbyname/issues
NeedsCompilation: no
Packaged: 2019-01-18 21:13:10 UTC; mkh2
Author: Matthew Heun [aut, cre] (<https://orcid.org/0000-0002-7438-214X>)
Repository: CRAN
Date/Publication: 2019-01-25 17:30:14 UTC

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New package BENMMI with initial version 4.3-6
Package: BENMMI
Type: Package
Title: Benthic Multi-Metric Index
Description: Analysis tool for evaluating benthic multimetric indices (BENMMIs). It generates reproducible reports on the analysis of benthic data, e.g., validation and correction of species names, sample pooling, automatic conversion of genus to species names, outlier detection, benthic indicator calculation, optimization of single and multimetric indicators against a pressure gradient, and spatial aggregation of benthic indicators. One of its use cases was the development of a common benthic indicator for <https://www.ospar.org> (publication accepted by Ecological Indicators). See Van Loon et al. (2018) <doi:10.1016/j.ecolind.2017.09.029> for details.
Version: 4.3-6
Date: 2019-01-18
Authors@R: c(person(given = "Dennis", family = "Walvoort", email = "dennis.Walvoort@wur.nl", role = c("aut", "cre")), person(given = "Willem", family = "van Loon", email = "willem.van.loon@rws.nl", role = c("aut", "cph")))
Depends: R (>= 3.2.0)
Imports: benthos (>= 1.3-5), readr, purrr, knitr, markdown, jsonlite, xtable, dplyr (>= 0.7.0), tidyr, ggplot2 (>= 2.0.0)
Suggests: DEoptim
License: GPL (>= 3)
RoxygenNote: 6.1.1
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-01-18 07:56:47 UTC; dennis
Author: Dennis Walvoort [aut, cre], Willem van Loon [aut, cph]
Maintainer: Dennis Walvoort <dennis.Walvoort@wur.nl>
Repository: CRAN
Date/Publication: 2019-01-25 17:30:26 UTC

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New package timeR with initial version 1.0.0
Package: timeR
Type: Package
Title: Time Your Codes
Version: 1.0.0
Author: Yifu Yan
Maintainer: Yifu Yan <yanyifu94@hotmail.com>
Description: Provides a 'timer' class that makes timing codes easier. One can create 'timer' objects and use them to record all timings, and extract recordings as data frame for later use.
URL: https://github.com/yusuzech/timeR
BugReports: https://github.com/yusuzech/timeR/issues
Depends: R (>= 3.1.0)
Imports: R6
License: Apache License (== 2.0) | file LICENSE
LazyData: true
Encoding: UTF-8
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-17 19:32:04 UTC; yanyi
Repository: CRAN
Date/Publication: 2019-01-25 17:00:03 UTC

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New package surveysd with initial version 1.0.0
Package: surveysd
Type: Package
Title: Survey Standard Error Estimation for Cumulated Estimates and their Differences in Complex Panel Designs
Version: 1.0.0
Date: 2019-01-07
Authors@R: c(person("Johannes", "Gussenbauer", role = c("aut", "cre"), email = "Johannes.Gussenbauer@statistik.gv.at"), person("Alexander", "Kowarik", role = "aut", email = "Alexander.Kowarik@statistik.gv.at", comment=c(ORCID="0000-0001-8598-4130")), person("Gregor", "de Cillia", role = "aut", email = "Gregor.deCillia@statistik.gv.at"), person("Matthias", "Till", role = "ctb", email = "Matthias.Till@statistik.gv.at"))
Maintainer: Johannes Gussenbauer <Johannes.Gussenbauer@statistik.gv.at>
Description: Calculate point estimates and their standard errors in complex household surveys using bootstrap replicates. Bootstrapping considers survey design with a rotating panel. A comprehensive description of the methodology can be found under <https://statistikat.github.io/surveysd/articles/methodology.html>.
Encoding: UTF-8
LazyData: true
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.12),data.table,matrixStats, ggplot2, laeken, methods, dplyr
LinkingTo: Rcpp
URL: https://github.com/statistikat/surveysd
BugReports: https://github.com/statistikat/surveysd/issues
RoxygenNote: 6.1.1
Suggests: testthat, simPop
NeedsCompilation: yes
Packaged: 2019-01-17 15:49:59 UTC; gussenbauer
Author: Johannes Gussenbauer [aut, cre], Alexander Kowarik [aut] (<https://orcid.org/0000-0001-8598-4130>), Gregor de Cillia [aut], Matthias Till [ctb]
Repository: CRAN
Date/Publication: 2019-01-25 16:40:03 UTC

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New package provSummarizeR with initial version 1.0
Package: provSummarizeR
Title: Summarizes Provenance Related to Inputs and Outputs of a Script or Console Commands
Version: 1.0
Date: 2019-01-09
Authors@R: c( person("Barbara", "Lerner", role = "cre", email = "blerner@mtholyoke.edu"), person("Emery", "Boose", role = "aut", email = "boose@fas.harvard.edu") )
Copyright: President and Fellows of Harvard College, Trustees of Mount Holyoke College
Description: Reads the provenance collected by the 'rdt' or 'rdtLite' packages, or other tools providing compatible PROV JSON output created by the execution of a script, and provides a human-readable summary identifying the input and output files, the script used (if any), errors and warnings produced, and the environment in which it was executed. It can also optionally package all the files into a zip file. The exact format of the JSON created by 'rdt' and 'rdtLite' is described in <https://github.com/End-to-end-provenance/ExtendedProvJson>. More information about 'rdtLite' and associated tools is available at <https://github.com/End-to-end-provenance/> and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, <doi: 10.3390/informatics5010012>.
Depends: R (>= 3.5)
License: GPL-3 | file LICENSE
Encoding: UTF-8
LazyData: true
Imports: dplyr, provParseR
Suggests: rdtLite, rdt, testthat
Additional_repositories: https://end-to-end-provenance.github.io/drat/
URL: https://github.com/End-to-end-provenance
BugReports: https://github.com/End-to-end-provenance/provSummarizeR/issues
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-01-16 22:22:49 UTC; blerner
Author: Barbara Lerner [cre], Emery Boose [aut]
Maintainer: Barbara Lerner <blerner@mtholyoke.edu>
Repository: CRAN
Date/Publication: 2019-01-25 16:50:03 UTC

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New package overture with initial version 0.1-0
Package: overture
Type: Package
Title: Tools for Writing MCMC
Version: 0.1-0
Authors@R: person("Kurtis", "Shuler", role=c("aut", "cre"), email="kurtis.s.1122+CRAN@gmail.com")
Description: Simplifies MCMC setup by automatically looping through sampling functions and saving the results. Reduces the memory footprint of running MCMC and saves samples to disk as the chain runs. Allows samples from the chain to be analyzed while the MCMC is still running. Provides functions for commonly performed operations such as calculating Metropolis acceptance ratios and creating adaptive Metropolis samplers. References: Roberts and Rosenthal (2009) <doi:10.1198/jcgs.2009.06134>.
License: LGPL-3
URL: https://github.com/kurtis-s/overture
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
Suggests: testthat, mockery, covr
Imports: bigmemory
NeedsCompilation: no
Packaged: 2019-01-18 07:26:28 UTC; kws
Author: Kurtis Shuler [aut, cre]
Maintainer: Kurtis Shuler <kurtis.s.1122+CRAN@gmail.com>
Repository: CRAN
Date/Publication: 2019-01-25 17:00:06 UTC

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New package ICDS with initial version 0.1.1
Package: ICDS
Type: Package
Title: Identification of Cancer Dysfunctional Subpathway by Integrating DNA Methylation, Copy Number Variation, and Gene Expression Data
Version: 0.1.1
Author: Junwei Han,Baotong Zheng,Siyao Liu
Maintainer: Junwei Han <hanjunwei1981@163.com>
Description: Identify Cancer Dysfunctional Sub-pathway by integrating gene expression, DNA methylation and copy number variation, and pathway topological information. 1)We firstly calculate the gene risk scores by integrating three kinds of data: DNA methylation, copy number variation, and gene expression. 2)Secondly, we perform a greedy search algorithm to identify the key dysfunctional sub-pathways within the pathways for which the discriminative scores were locally maximal. 3)Finally, the permutation test was used to calculate statistical significance level for these key dysfunctional sub-pathways.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Suggests: knitr, rmarkdown, prettydoc
VignetteBuilder: knitr
Imports: igraph, graphite, metap, methods, org.Hs.eg.db
Depends: R (>= 2.10)
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-01-18 08:22:40 UTC; zbt
Repository: CRAN
Date/Publication: 2019-01-25 17:00:10 UTC

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New package catseyes with initial version 0.2.3
Package: catseyes
Type: Package
Title: Create Catseye Plots Illustrating the Normal Distribution of the Means
Version: 0.2.3
Authors@R: person("Clark", "Andersen", email = "clanders@utmb.edu", role=c("cre","aut"))
Description: Provides the tools to produce catseye plots, principally by catseyesplot() function which calls R's standard plot() function internally, or alternatively by the catseyes() function to overlay the catseye plot onto an existing R plot window. Catseye plots illustrate the normal distribution of the mean (picture a normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence interval; they are an intuitive way of illustrating and comparing normally distributed estimates, and are arguably a superior alternative to standard confidence intervals, since they show the full distribution rather than fixed quantile bounds. The catseyesplot and catseyes functions require pre-calculated means and standard errors (or standard deviations), provided as numeric vectors; this allows the flexibility of obtaining this information from a variety of sources, such as direct calculation or prediction from a model. Catseye plots, as illustrations of the normal distribution of the means, are described in Cumming (2013 & 2014). Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 27, 7-29. <doi:10.1177/0956797613504966> pmid:24220629.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
Suggests: emmeans
NeedsCompilation: no
Packaged: 2019-01-17 18:52:35 UTC; clanders
Author: Clark Andersen [cre, aut]
Maintainer: Clark Andersen <clanders@utmb.edu>
Repository: CRAN
Date/Publication: 2019-01-25 16:50:06 UTC

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New package IMaGES with initial version 0.1
Package: IMaGES
Version: 0.1
Date: 2019-01-04
Title: Independent Multiple-Sample Greedy Equivalence Search Implementation
Description: Functions for the implementation of Independent Multiple-sample Greedy Equivalence Search (IMaGES), a causal inference algorithm for creating aggregate graphs and structural equation modeling data for one or more datasets. This package is useful for time series data with specific regions of interest. This implementation is inspired by the paper "Six problems for causal inference from fMRI" by Ramsey, Hanson, Hanson, Halchenko, Poldrack, and Glymour (2010) <DOI:10.1016/j.neuroimage.2009.08.065>. The IMaGES algorithm uses a modified BIC score to compute goodness of fit of edge additions, subtractions, and turns across all datasets and returns a representative graph, along with structural equation modeling data for the global graph and individual datasets, means, and standard errors. Functions for plotting the resulting graph(s) are provided. This package is built upon the 'pcalg' package.
Maintainer: Noah Frazier-Logue <n.frazier.logue@nyu.edu>
Authors@R: c(person("Noah","Frazier-Logue", email="n.frazier.logue@nyu.edu", role=c("aut","cre")), person("Stephen Jose","Hanson", email="jose@rubic.rutgers.edu", role=c("aut")), person("Markus","Kalisch", role=c("ctb")), person("Alain", "Hauser", role="ctb"), person("Martin","Maechler", role="ctb"), person("Diego", "Colombo", role="ctb"), person("Doris", "Entner", role="ctb"), person("Patrik","Hoyer", role="ctb"), person("Antti", "Hyttinen", role="ctb"), person("Jonas", "Peters", role="ctb"), person("Nicoletta", "Andri", role="ctb"), person("Emilija", "Perkovic", role="ctb"), person("Preetam", "Nandy", role="ctb"), person("Philipp", "Ruetimann", role="ctb"), person("Daniel", "Stekhoven", role="ctb"), person("Manuel", "Schuerch", role="ctb"))
Author: Noah Frazier-Logue [aut, cre], Stephen Jose Hanson [aut], Markus Kalisch [ctb], Alain Hauser [ctb], Martin Maechler [ctb], Diego Colombo [ctb], Doris Entner [ctb], Patrik Hoyer [ctb], Antti Hyttinen [ctb], Jonas Peters [ctb], Nicoletta Andri [ctb], Emilija Perkovic [ctb], Preetam Nandy [ctb], Philipp Ruetimann [ctb], Daniel Stekhoven [ctb], Manuel Schuerch [ctb]
Depends: R (>= 3.2.3)
LinkingTo: Rcpp (>= 0.11.0), RcppArmadillo, BH
Imports: stats, graphics, utils, methods, graph, igraph, ggm, Rcpp, sfsmisc, lavaan, Rgraphviz
Suggests: knitr, rmarkdown
LazyData: true
VignetteBuilder: knitr
ByteCompile: yes
NeedsCompilation: yes
Encoding: UTF-8
License: GPL (>= 2)
Packaged: 2019-01-04 16:04:57 UTC; noahfl
Repository: CRAN
RoxygenNote: 6.1.0
Date/Publication: 2019-01-25 15:50:03 UTC

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New package imagerExtra with initial version 1.3.2
Package: imagerExtra
Type: Package
Title: Extra Image Processing Library Based on 'imager'
Version: 1.3.2
Authors@R: c( person("Shota", "Ochi", email = "shotaochi1990@gmail.com", role = c("aut", "cre")), person("Guoshen", "Yu", email = "yu@cmap.polytechnique.fr", role = c("ctb", "cph")), person("Guillermo", "Sapiro", email = "guille@umn.edu", role = c("ctb", "cph")), person("Catalina", "Sbert", email = "catalina.sbert@uib.es", role = c("ctb", "cph")), person("Image Processing On Line", role = "cph"), person("Pascal", "Getreuer", email = "getreuer@gmail.com", role = c("ctb", "cph")))
Maintainer: Shota Ochi <shotaochi1990@gmail.com>
Description: Provides advanced functions for image processing based on the package 'imager'.
License: GPL-3
Depends: R (>= 2.10.0), imager (>= 0.40.2)
Imports: fftwtools, magrittr, Rcpp (>= 0.12.14)
Suggests: testthat (>= 2.0.0), knitr, rmarkdown, tesseract
URL: https://github.com/ShotaOchi/imagerExtra
BugReports: https://github.com/ShotaOchi/imagerExtra/issues
LinkingTo: Rcpp
LazyData: true
RoxygenNote: 6.1.1
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2019-01-24 16:05:36 UTC; shota
Author: Shota Ochi [aut, cre], Guoshen Yu [ctb, cph], Guillermo Sapiro [ctb, cph], Catalina Sbert [ctb, cph], Image Processing On Line [cph], Pascal Getreuer [ctb, cph]
Repository: CRAN
Date/Publication: 2019-01-25 13:50:02 UTC

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Thu, 24 Jan 2019

New package ri2 with initial version 0.1.2
Package: ri2
Type: Package
Title: Randomization Inference for Randomized Experiments
Version: 0.1.2
Authors@R: person("Alexander", "Coppock", email = "acoppock@gmail.com", role = c("aut", "cre"))
Description: Randomization inference procedures for simple and complex randomized designs, including multi-armed trials, as described in Gerber and Green (2012, ISBN: 978-0393979954). Users formally describe their randomization procedure and test statistic. The randomization distribution of the test statistic under some null hypothesis is efficiently simulated.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: generics, ggplot2, pbapply
Depends: randomizr (>= 0.16.0), estimatr
Suggests: testthat, knitr, rmarkdown, ri
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-24 18:25:12 UTC; ac2595
Author: Alexander Coppock [aut, cre]
Maintainer: Alexander Coppock <acoppock@gmail.com>
Repository: CRAN
Date/Publication: 2019-01-24 22:00:03 UTC

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New package rdhs with initial version 0.6.1
Package: rdhs
Type: Package
Title: API Client and Dataset Management for the Demographic and Health Survey (DHS) Data
Version: 0.6.1
Authors@R: c(person(given = "OJ", family = "Watson", role = c("aut", "cre"), email = "oj.watson@hotmail.co.uk", comment = c(ORCID = "0000-0003-2374-0741")), person(given = "Jeff", family = "Eaton", role = "aut", comment = c(ORCID = "0000-0001-7728-728X")), person(given = "Lucy", family = "D'Agostino McGowan", role = "rev", comment = c(ORCID = "0000-0001-7297-9359")), person(given = "Duncan", family = "Gillespie", role = "rev"))
Maintainer: OJ Watson <oj.watson@hotmail.co.uk>
URL: https://ropensci.github.io/rdhs/
BugReports: https://github.com/ropensci/rdhs/issues
Description: Provides a client for (1) querying the DHS API for survey indicators and metadata (<https://api.dhsprogram.com/#/index.html>), (2) identifying surveys and datasets for analysis, (3) downloading survey datasets from the DHS website, (4) loading datasets and associate metadata into R, and (5) extracting variables and combining datasets for pooled analysis.
LazyData: TRUE
Depends: R (>= 3.3.0)
Imports: R6, httr, jsonlite, foreign, magrittr, rappdirs, digest, storr, xml2, qdapRegex, rgdal, getPass, haven, iotools
Suggests: testthat, knitr, rmarkdown, ggplot2, survey, data.table, microbenchmark
License: MIT + file LICENSE
RoxygenNote: 6.1.1
VignetteBuilder: knitr
Language: en-GB
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-01-24 18:14:20 UTC; oj
Author: OJ Watson [aut, cre] (<https://orcid.org/0000-0003-2374-0741>), Jeff Eaton [aut] (<https://orcid.org/0000-0001-7728-728X>), Lucy D'Agostino McGowan [rev] (<https://orcid.org/0000-0001-7297-9359>), Duncan Gillespie [rev]
Repository: CRAN
Date/Publication: 2019-01-24 22:00:07 UTC

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Wed, 23 Jan 2019

New package ZVCV with initial version 1.0.0
Package: ZVCV
Type: Package
Title: Zero-Variance Control Variates
Version: 1.0.0
Date: 2018-12-20
Authors@R: c(person("Leah F.","South",role=c("aut","cre"),email="leah.south@hdr.qut.edu.au",comment = c(ORCID = "0000-0002-5646-2963")))
Description: Zero-variance control variates (ZV-CV, Mira et al. (2013) <doi:10.1007/s11222-012-9344-6>) is a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target. Once the derivatives are available, the only additional computational effort is in solving a linear regression problem. Recently, this method has been extended to higher dimensions using regularisation (South et al., 2018 <arXiv:1811.05073>). This package can be used to easily perform ZV-CV or regularised ZV-CV when a set of samples, derivatives and function evaluations are available. Additional functions for applying ZV-CV to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied.
License: GPL (>= 2)
LazyLoad: yes
Imports: Rcpp (>= 0.11.0), glmnet, abind, mvtnorm, partitions, stats
LinkingTo: Rcpp, RcppArmadillo
LazyData: true
Encoding: UTF-8
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-01-16 02:03:43 UTC; n8001626
Author: Leah F. South [aut, cre] (<https://orcid.org/0000-0002-5646-2963>)
Maintainer: Leah F. South <leah.south@hdr.qut.edu.au>
Repository: CRAN
Date/Publication: 2019-01-24 00:00:02 UTC

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New package portalr with initial version 0.2.1
Package: portalr
Title: Create Useful Summaries of the Portal Data
Version: 0.2.1
Authors@R: c(person(c("Glenda", "M."), "Yenni", role = c("aut", "cre"), email = "glenda@weecology.org", comment = c(ORCID = "0000-0001-6969-1848")), person("Hao", "Ye", role = c("aut"), comment = c(ORCID = "0000-0002-8630-1458")), person(c("Erica", "M."), "Christensen", role = c("aut"), comment = c(ORCID = "0000-0002-5635-2502")), person(c("Juniper", "L."), "Simonis", role = c("aut"), comment = c(ORCID = "0000-0001-9798-0460")), person(c("Ellen", "K."), "Bledsoe", role = c("aut"), comment = c(ORCID = "0000-0002-3629-7235")), person(c("Renata", "M."), "Diaz", role = c("aut"), comment = c(ORCID = "0000-0003-0803-4734")), person(c("Shawn", "D."), "Taylor", role = c("aut"), comment = c(ORCID = "0000-0002-6178-6903")), person(c("Ethan", "P,"), "White", role = c("aut"), comment = c(ORCID = "0000-0001-6728-7745")), person(c("S.K.", "Morgan"), "Ernest", role = c("aut"), comment = c(ORCID = "0000-0002-6026-8530")))
Description: Download and generate summaries for the rodent, plant, ant, and weather data from the Portal Project. Portal is a long-term (and ongoing) experimental monitoring site in the Chihuahua desert. The raw data files can be found at <https://github.com/weecology/portaldata>.
License: MIT + file LICENSE
URL: https://weecology.github.io/portalr/, https://github.com/weecology/portalr
BugReports: https://github.com/weecology/portalr/issues
LazyData: true
Depends: R (>= 3.2.3)
Imports: dplyr, ggplot2, tidyr, zoo, lubridate, magrittr, httr, rlang, forecast, lunar, jsonlite, tibble
Suggests: httptest, digest, tidyverse, cowplot, knitr, rmarkdown, pkgdown, covr
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-23 19:54:53 UTC; GlendaYenni
Author: Glenda M. Yenni [aut, cre] (<https://orcid.org/0000-0001-6969-1848>), Hao Ye [aut] (<https://orcid.org/0000-0002-8630-1458>), Erica M. Christensen [aut] (<https://orcid.org/0000-0002-5635-2502>), Juniper L. Simonis [aut] (<https://orcid.org/0000-0001-9798-0460>), Ellen K. Bledsoe [aut] (<https://orcid.org/0000-0002-3629-7235>), Renata M. Diaz [aut] (<https://orcid.org/0000-0003-0803-4734>), Shawn D. Taylor [aut] (<https://orcid.org/0000-0002-6178-6903>), Ethan P, White [aut] (<https://orcid.org/0000-0001-6728-7745>), S.K. Morgan Ernest [aut] (<https://orcid.org/0000-0002-6026-8530>)
Maintainer: Glenda M. Yenni <glenda@weecology.org>
Repository: CRAN
Date/Publication: 2019-01-23 22:50:35 UTC

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New package blscrapeR with initial version 3.1.5
Package: blscrapeR
Type: Package
Title: An API Wrapper for the Bureau of Labor Statistics (BLS)
Version: 3.1.5
Authors@R: person("Kris", "Eberwein", email = "eberwein@knights.ucf.edu", role = c("aut", "cre"))
Description: Scrapes various data from <https://www.bls.gov/>. The U.S. Bureau of Labor Statistics is the statistical branch of the United States Department of Labor. The package has additional functions to help parse, analyze and visualize the data.
Depends: R (>= 3.3.0)
Imports: httr, jsonlite, ggplot2, magrittr, utils, stats, grDevices, dplyr, purrr, tibble, stringr
Suggests: testthat, knitr, rmarkdown
License: MIT + file LICENSE
URL: https://github.com/keberwein/blscrapeR
BugReports: https://github.com/keberwein/blscrapeR/issues
LazyData: true
RoxygenNote: 6.1.0
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-23 20:20:57 UTC; mediacenter
Author: Kris Eberwein [aut, cre]
Maintainer: Kris Eberwein <eberwein@knights.ucf.edu>
Repository: CRAN
Date/Publication: 2019-01-23 22:50:59 UTC

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New package woeR with initial version 0.2.1
Package: woeR
Title: Weight of Evidence Based Segmentation of a Variable
Version: 0.2.1
Author: Kashish Soien
Maintainer: Kashish Soien <kashish.soien19@gmail.com>
Description: Segment a numeric variable based on a dichotomous dependent variable by using the weight of evidence (WOE) approach (Ref: Siddiqi, N. (2006) <doi:10.1002/9781119201731.biblio>). The underlying algorithm adopts a recursive approach to create segments that are diverse in respect of their WOE values and meet the demands of user-defined parameters. The algorithm also aims to maintain a monotonic trend in WOE values of consecutive segments. As such, it can be particularly helpful in improving robustness of linear and logistic regression models.
Imports: dplyr
Suggests: smbinning
Depends: R (>= 3.4.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
BugReports: https://github.com/kraken19/woeR/issues
NeedsCompilation: no
Packaged: 2019-01-23 00:15:53 UTC; kashish
Repository: CRAN
Date/Publication: 2019-01-23 08:30:04 UTC

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Tue, 22 Jan 2019

New package wsyn with initial version 1.0.0
Package: wsyn
Version: 1.0.0
Type: Package
Title: Wavelet Approaches to Studies of Synchrony in Ecology and Other Fields
Authors@R: c( person("Daniel C.", "Reuman", email = "reuman@ku.edu", role = c("aut", "cre")), person("Thomas L.", "Anderson", email="anderstl@gmail.com", role=c("aut")), person("Jonathan A.", "Walter", email = "jaw3es@virginia.edu", role=c("aut")), person("Lei", "Zhao", email = "lei.zhao@cau.edu.cn", role=c("aut")), person("Lawrence W.", "Sheppard", email="lwsheppard@ku.edu", role=c("aut")) )
Description: Tools for a wavelet-based approach to analyzing spatial synchrony, principally in ecological data. Some tools will be useful for studying community synchrony.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: fields (>= 9.6), graphics (>= 3.4.4), grDevices (>= 3.4.4), MASS (>= 7.3-47), stats (>= 3.4.4)
Suggests: knitr, mvtnorm, rmarkdown, testthat, vdiffr
VignetteBuilder: knitr
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2019-01-15 16:57:35 UTC; dreuman
Author: Daniel C. Reuman [aut, cre], Thomas L. Anderson [aut], Jonathan A. Walter [aut], Lei Zhao [aut], Lawrence W. Sheppard [aut]
Maintainer: Daniel C. Reuman <reuman@ku.edu>
Repository: CRAN
Date/Publication: 2019-01-22 18:10:03 UTC

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Mon, 21 Jan 2019

New package RcppHNSW with initial version 0.1.0
Package: RcppHNSW
Title: 'Rcpp' Bindings for 'hnswlib', a Library for Approximate Nearest Neighbors
Version: 0.1.0
Authors@R: c(person("James", "Melville", email = "jlmelville@gmail.com", role = c("aut", "cre")), person("Aaron", "Lun", role = "ctb"))
Description: 'Hnswlib' is a C++ library for Approximate Nearest Neighbors. This package provides a minimal R interface by relying on the 'Rcpp' package. See <https://github.com/nmslib/hnswlib> for more on 'hnswlib'. 'hnswlib' is released under Version 2.0 of the Apache License.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: methods, Rcpp (>= 0.11.3)
LinkingTo: Rcpp
RoxygenNote: 6.1.1
Suggests: testthat, covr
NeedsCompilation: yes
Packaged: 2019-01-15 03:34:10 UTC; jlmel
Author: James Melville [aut, cre], Aaron Lun [ctb]
Maintainer: James Melville <jlmelville@gmail.com>
Repository: CRAN
Date/Publication: 2019-01-21 23:30:06 UTC

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New package ensr with initial version 0.1.0
Package: ensr
Title: Elastic Net SearcheR
Version: 0.1.0
Authors@R: c( person("Peter", "DeWitt", , "peter.dewitt@ucdenver.edu", role = c("aut", "cre")), person("Tell", "Bennett", , "tell.bennett@ucdenver.edu", role = c("ctb")) )
Description: Elastic net regression models are controlled by two parameters, lambda, a measure of shrinkage, and alpha, a metric defining the model's location on the spectrum between ridge and lasso regression. glmnet provides tools for selecting lambda via cross validation but no automated methods for selection of alpha. Elastic Net SearcheR automates the simultaneous selection of both lambda and alpha. Developed, in part, with support by NICHD R03 HD094912.
License: GPL-2
Encoding: UTF-8
URL: https://github.com/dewittpe/ensr
LazyData: true
Depends: R (>= 3.5.0), glmnet
Imports: data.table, ggplot2
Suggests: digest, ggforce, gridExtra, knitr, magrittr, microbenchmark, qwraps2 (>= 0.4.0), R.rsp, rmarkdown
RoxygenNote: 6.1.1
VignetteBuilder: R.rsp
NeedsCompilation: no
Packaged: 2019-01-14 22:51:48 UTC; dewittp
Author: Peter DeWitt [aut, cre], Tell Bennett [ctb]
Maintainer: Peter DeWitt <peter.dewitt@ucdenver.edu>
Repository: CRAN
Date/Publication: 2019-01-21 23:30:03 UTC

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New package eventstudies with initial version 1.2.1
Package: eventstudies
Version: 1.2.1
Date: 2019-01-08
Type: Package
Title: Event Study Analysis
Authors@R: c( person("Chirag", "Anand", role = c("aut", "cre"), email = "anand.chirag@gmail.com"), person("Vikram", "Bahure", role = "aut", email = "economics.vikram@gmail.com"), person("Vimal", "Balasubramaniam", role = "aut", email = "vimsaa@gmail.com"), person("Shekhar", "Harikumar", role = "ctb", email = "shekhar.harikumar@gmail.com"), person("Sargam", "Jain", role = "ctb", email = "sargamjain13@gmail.com"), person("Ajay", "Shah", role = "aut", email = "ajayshah@mayin.org") )
Maintainer: Chirag Anand <anand.chirag@gmail.com>
Depends: R (>= 3.1), zoo, xts
Description: A platform for conducting event studies (Fama, Fisher, Jensen, Roll (1969) <doi:10.2307/2525569>) and for methodological research on event studies. The package supports market model, augmented market model, and excess returns methods for data modelling along with Wilcox, classical t-test, and Bootstrap as inference procedures.
VignetteBuilder: knitr
Suggests: knitr
Imports: boot, graphics, methods, testthat, sandwich, stats, utils
License: GPL-2
URL: https://github.com/nipfpmf/eventstudies
BugReports: https://github.com/nipfpmf/eventstudies/issues
LazyLoad: yes
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2019-01-21 06:58:41.50401 UTC; chirag
Author: Chirag Anand [aut, cre], Vikram Bahure [aut], Vimal Balasubramaniam [aut], Shekhar Harikumar [ctb], Sargam Jain [ctb], Ajay Shah [aut]
Repository: CRAN
Date/Publication: 2019-01-21 09:10:15 UTC

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New package CluMix with initial version 2.3.1
Package: CluMix
Type: Package
Title: Clustering and Visualization of Mixed-Type Data
Version: 2.3.1
Date: 2019-01-20
Author: M. Hummel, D. Edelmann, A. Kopp-Schneider
Maintainer: Manuela Hummel <manuela.hummel@web.de>
Description: Provides utilities for clustering subjects and variables of mixed data types (Hummel, Edelmann, Kopp-Schneider (2017) <doi: 10.1371/journal.pone.0188274>). Similarities between subjects are measured by Gower's general similarity coefficient with an extension of Podani for ordinal variables. Similarities between variables can be assessed i) by combination of appropriate measures of association for different pairs of data types or ii) based on distance correlation. Alternatively, variables can also be clustered by the 'ClustOfVar' approach. The main feature of the package is the generation of a mixed-data heatmap. For visualizing similarities between either subjects or variables, a heatmap of the corresponding distance matrix can be drawn. Associations between variables can be explored by a 'confounder plot', which allows visual detection of possible confounding, collinear, or surrogate factors for some variables of primary interest. Distance matrices and dendrograms for subjects and variables can be derived and used for further visualizations and applications. This work was supported by BMBF grant 01ZX1609B, Germany.
License: GPL (>= 2)
Depends:
Imports: ClustOfVar, Hmisc, DescTools, extracat, marray, FD, gplots, Matrix, Biobase
Suggests:
NeedsCompilation: no
Packaged: 2019-01-20 22:01:33 UTC; Humpas
Repository: CRAN
Date/Publication: 2019-01-21 09:10:22 UTC

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Sun, 20 Jan 2019

New package bender with initial version 0.1.0
Package: bender
Type: Package
Title: Bender Client
Version: 0.1.0
Author: Dylan Heirstraeten
Maintainer: Valentin Thorey <valentin@dreem.com>
Description: R client for Bender Hyperparameters optimizer : <https://bender.dreem.com> The R client allows you to communicate with the Bender API and therefore submit some new trials within your R script itself.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: R6, jsonlite, httr
NeedsCompilation: no
Packaged: 2019-01-15 08:42:05 UTC; dylan
Repository: CRAN
Date/Publication: 2019-01-20 18:30:03 UTC

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New package RGCxGC with initial version 1.0.0
Package: RGCxGC
Type: Package
Title: Preprocessing and Multivariate Analysis of Bidimensional Gas Chromatography Data
Version: 1.0.0
Date: 2019-01-09
Authors@R: c( person("Cristian", "Quiroz-Moreno", email = "cristianquirozd1997@gmail.com", role = c("aut", "cre")), person("Guilherme", "L. Alexandrino", email = "Alexandrinogl@gmail.com", role = "aut"), person("Noroska", "G.S. Mogollón", email = "gaby867@gmail.com", role = "aut"))
Author: Cristian Quiroz-Moreno [aut, cre], Guilherme L. Alexandrino [aut], Noroska G.S. Mogollón [aut]
Maintainer: Cristian Quiroz-Moreno <cristianquirozd1997@gmail.com>
Description: Implementation of chemometrics analysis for bidimensional gas chromatography data. This package can handle data for common scientific data format (netCDF) and fold it to 2D chromatogram. Then, it can perform preprocessing and multivariate analysis. In the preprocessing algorithms, baseline correction, smoothing, and peak alignment are available. While in multivariate analysis, multiway principal component analysis is incorporated.
License: MIT + file LICENSE
Imports: colorRamps (>= 2.3), Rdpack (>= 0.7), prettydoc (>= 0.2)
RdMacros: Rdpack
Depends: R (>= 3.5.0), stats, methods, graphics, RNetCDF (>= 1.9-1), ptw (>= 1.9-13)
LazyData: true
RoxygenNote: 6.1.1
Encoding: UTF-8
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-12 22:56:13 UTC; cizquime
Repository: CRAN
Date/Publication: 2019-01-20 17:30:03 UTC

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New package abstractr with initial version 0.1.0
Package: abstractr
Type: Package
Title: An R-Shiny Application for Creating Visual Abstracts
Version: 0.1.0
Author: Matthew Kumar <mattkumar@gmail.com>
Maintainer: Matthew Kumar <mattkumar@gmail.com>
Description: An R-Shiny application to create visual abstracts for original research. A variety of user defined options and formatting are included.
URL: https://matt-kumar.shinyapps.io/portfolio
Imports: shiny (>= 1.2.0), ggplot2 (>= 3.0.0), gridExtra (>= 2.3.0), colourpicker, shinythemes, emojifont, rintrojs
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-14 22:41:00 UTC; Kumar
Repository: CRAN
Date/Publication: 2019-01-20 17:20:03 UTC

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New package trendchange with initial version 0.1.0
Package: trendchange
Type: Package
Title: Innovative Trend Analysis and Time-Series Change Point Analysis
Version: 0.1.0
Author: Sandeep Kumar Patakamuri <sandeep.patakamuri@gmail.com>
Maintainer: Sandeep Kumar Patakamuri <sandeep.patakamuri@gmail.com>
Description: Innovative Trend Analysis is a graphical method to examine the trends in time series data. Sequential Mann-Kendall test uses the intersection of prograde and retrograde series to indicate the possible change point in time series data. Distribution free cumulative sum charts indicate location and significance of the change point in time series. Zekai, S. (2011). <doi:10.1061/(ASCE)HE.1943-5584.0000556>. Grayson, R. B. et al. (1996). Hydrological Recipes: Estimation Techniques in Australian Hydrology. Cooperative Research Centre for Catchment Hydrology, Australia, p. 125. Sneyers, S. (1990). On the statistical analysis of series of observations. Technical note no 5 143, WMO No 725 415. Secretariat of the World Meteorological Organization, Geneva, 192 pp.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: testthat, knitr, rmarkdown,covr
NeedsCompilation: no
Packaged: 2019-01-13 04:23:08 UTC; Deepu
Repository: CRAN
Date/Publication: 2019-01-20 16:10:03 UTC

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New package dapr with initial version 0.0.2
Package: dapr
Title: 'purrr'-Like Apply Functions Over Input Elements
Version: 0.0.2
Authors@R: person(given = "Michael W.", family = "Kearney", role = c("aut", "cre"), email = "kearneymw@missouri.edu", comment = c(ORCID = "0000-0002-0730-4694"))
Description: An easy-to-use, dependency-free set of functions for iterating over elements of various input objects. Functions are wrappers around base apply()/lapply()/vapply() functions but designed to have similar functionality to the mapping functions in the 'purrr' package <https://purrr.tidyverse.org/>. Specifically, function names more explicitly communicate the expected class of the output and functions also allow for the convenient shortcut of '~ .x' instead of the more verbose 'function(.x) .x'.
License: GPL-3
Encoding: UTF-8
Language: en
LazyData: true
RoxygenNote: 6.1.1
URL: https://github.com/mkearney/dapr
BugReports: https://github.com/mkearney/dapr/issues
Suggests: testthat, covr
NeedsCompilation: no
Packaged: 2019-01-13 20:48:34 UTC; mwk
Author: Michael W. Kearney [aut, cre] (<https://orcid.org/0000-0002-0730-4694>)
Maintainer: Michael W. Kearney <kearneymw@missouri.edu>
Repository: CRAN
Date/Publication: 2019-01-20 16:20:03 UTC

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New package cmenet with initial version 0.1.0
Package: cmenet
Type: Package
Title: Bi-Level Selection of Conditional Main Effects
Version: 0.1.0
Author: Simon Mak
Maintainer: Simon Mak <smak6@gatech.edu>
Description: Provides functions for implementing cmenet - a bi-level variable selection method for conditional main effects (see Mak and Wu (2018) <doi:10.1080/01621459.2018.1448828>). CMEs are reparametrized interaction effects which capture the conditional impact of a factor at a fixed level of another factor. Compared to traditional two-factor interactions, CMEs quantify more interpretable interaction effects in many problems of interest (e.g., genomics, molecular engineering, personalized medicine). The current implementation performs variable selection on only binary CMEs, but we are working on an extension for the continuous setting. This work was supported by USARO grant W911NF-14-1-0024.
License: GPL (>= 2)
LazyData: FALSE
Imports: Rcpp (>= 0.12.4), MASS, glmnet, hierNet
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2019-01-13 03:13:15 UTC; smak6
Repository: CRAN
Date/Publication: 2019-01-20 16:10:06 UTC

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New package Bayesrel with initial version 0.1.0
Package: Bayesrel
Type: Package
Title: Bayesian Reliability Estimation
Version: 0.1.0
Author: Julius Pfadt and Don van den Bergh
Maintainer: Julius Pfadt <julius.pfadt@gmail.com>
Description: So far, it provides the most common single test reliability estimates, being: Coefficient Alpha, Guttman's lambda-2/-4/-6, greatest lower bound and Mcdonald's Omega. The Bayesian estimates are provided with credible intervals. The method for the Bayesian estimates, except for omega, is sampling from the posterior inverse Wishart for the covariance matrix based measures. See Murphy (2007) <https://www.seas.harvard.edu/courses/cs281/papers/murphy-2007.pdf>. Gibbs Sampling from the joint conditional distributions of a single factor model in the case of omega. See Lee (2007, ISBN:978-0-470-02424-9). Methods for the glb are from Moltner and Revelle (2018) <https://www.rdocumentation.org/packages/psych/versions/1.8.10/topics/glb.algebraic>; lambda-4 is from Benton (2015) <doi:10.1007/978-3-319-07503-7_19>; the principal factor analysis is from Schlegel (2017) <https://www.r-bloggers.com/iterated-principal-factor-method-of-factor-analysis-with-r/>; and the analytic alpha interval is from Bonnett and Wright (2014) <doi:10.1002/job.1960>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: LaplacesDemon, Rcsdp, MASS, ggplot2, ggridges, lavaan, plotrix, coda, methods, stats, graphics, Rdpack
RdMacros: Rdpack
RoxygenNote: 6.1.0
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2019-01-14 13:09:08 UTC; julius
Repository: CRAN
Date/Publication: 2019-01-20 16:40:02 UTC

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New package odds.n.ends with initial version 0.1.0
Package: odds.n.ends
Title: Odds Ratios, Contingency Table, and Model Significance from a Generalized Linear Model Object
Version: 0.1.0
Date: 2019-01-07
Author: Jenine Harris <harrisj@wustl.edu>
Maintainer: Jenine Harris <harrisj@wustl.edu>
Description: Computes odds ratios and 95% confidence intervals from a generalized linear model object. It also computes model significance with the chi-squared statistic and p-value and it computes model fit using a contingency table to determine the percent of observations for which the model correctly predicts the value of the outcome. Calculates model sensitivity and specificity.
License: CC0
Encoding: UTF-8
LazyData: true
Imports: MASS
NeedsCompilation: no
Packaged: 2019-01-11 23:33:15 UTC; jenine
Repository: CRAN
Date/Publication: 2019-01-20 15:40:03 UTC

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Sat, 19 Jan 2019

New package shinyrecap with initial version 0.1.0
Package: shinyrecap
Type: Package
Title: Shiny User Interface for Multiple Source Capture Recapture Models
Version: 0.1.0
Author: Ian E. Fellows
Maintainer: Ian E. Fellows <ian@fellstat.com>
Description: Implements user interfaces for log-linear models, Bayesian model averaging and Bayesian Dirichlet process mixture models.
License: MIT + file LICENCE
Imports: Rcapture, shiny, shinycssloaders, conting, ggplot2, reshape, CARE1, dga, LCMCR, ipc, future, promises, coda, testthat
URL: https://fellstat.github.io/shinyrecap/
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2019-01-11 23:11:06 UTC; ianfellows
Repository: CRAN
Date/Publication: 2019-01-19 23:40:03 UTC

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New package iva with initial version 0.1.0
Package: iva
Type: Package
Title: Instrumental Variable Analysis in Case-Control Association Studies
Version: 0.1.0
Author: Han Zhang, Kai Yu
Maintainer: Han Zhang <han.zhang2@nih.gov>
Imports: ucminf, Formula
Description: Mendelian randomization (MR) analysis is a special case of instrumental variable analysis with genetic instruments. It is used to estimate the unconfounded causal effect of an exposure. This package implements estimating and testing methods in Zhang et al. (2019) for MR analysis in case-control studies. It (1) estimates the causal effect of a quantitative exposure by the quasi empirical likelihood approach; (2) uses Lagrange multiplier test for testing the presence of causal; (3) provides a test for the presence of confounder.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2019-01-12 01:02:36 UTC; zhangh12
Repository: CRAN
Date/Publication: 2019-01-19 23:50:03 UTC

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New package hcandersenr with initial version 0.2.0
Type: Package
Package: hcandersenr
Title: H.C. Andersens Fairy Tales
Version: 0.2.0
Authors@R: person(given = "Emil", family = "Hvitfeldt", role = c("aut", "cre"), email = "emilhhvitfeldt@gmail.com")
Description: Texts for H.C. Andersens fairy tales, ready for text analysis. Fairy tales in German, Danish, English, Spanish and French.
License: MIT + file LICENSE
URL: https://github.com/EmilHvitfeldt/hcandersenr
Depends: R (>= 3.0.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-01-12 05:21:33 UTC; emilhvitfeldthansen
Author: Emil Hvitfeldt [aut, cre]
Maintainer: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Repository: CRAN
Date/Publication: 2019-01-19 23:40:06 UTC

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New package handyplots with initial version 1.1.3
Package: handyplots
Type: Package
Title: Handy Plots
Version: 1.1.3
Date: 2019-01-11
Author: Jonathan Schwartz
Maintainer: Jonathan Schwartz <jzs1986@gmail.com>
Description: Several handy plots for quickly looking at the relationship between two numeric vectors of equal length. Quickly visualize scatter plots, residual plots, qq-plots, box plots, confidence intervals, and prediction intervals.
License: GPL (>= 2)
Imports: stats, graphics
Depends: R (>= 3.4)
NeedsCompilation: no
Packaged: 2019-01-11 20:39:12 UTC; schwartstack
Repository: CRAN
Date/Publication: 2019-01-19 23:40:10 UTC

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New package germinationmetrics with initial version 0.1.3
Package: germinationmetrics
Title: Seed Germination Indices and Curve Fitting
Version: 0.1.3
Authors@R: c( person(given = "J.", family = "Aravind", email = "j.aravind@icar.gov.in", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-4791-442X")), person(given = "S.", family = "Vimala Devi", email = "vimala.devi@icar.gov.in", role = "aut"), person(given = "J.", family = "Radhamani", email = "jalli.radhamani@icar.gov.in", role = "aut"), person(given = c("Sherry", "Rachel"), family = "Jacob", email = "sherry.jacob@icar.gov.in", role = "aut"), person(given = c("Kalyani", "Srinivasan"), email = "kalyani.srinivasan@icar.gov.in", role = "aut"), person("ICAR-NBGPR", role = "cph", comment = c(url = "www.nbpgr.ernet.in")))
Description: Provides functions to compute various germination indices such as germinability, median germination time, mean germination time, mean germination rate, speed of germination, Timson's index, germination value, coefficient of uniformity of germination, uncertainty of germination process, synchrony of germination etc. from germination count data. Includes functions for fitting cumulative seed germination curves using four-parameter hill function and computation of associated parameters. See the vignette for more, including full list of citations for the methods implemented.
Copyright: 2017-2018, ICAR-NBPGR
License: GPL-2 | GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0)
VignetteBuilder: knitr
RoxygenNote: 6.1.1
Imports: broom, data.table, ggplot2, ggrepel, minpack.lm, plyr, Rdpack, utils, stats
Suggests: knitr, rmarkdown, reshape2, pander, XML, httr, RCurl
RdMacros: Rdpack
URL: https://github.com/aravind-j/germinationmetrics, https://aravind-j.github.io/germinationmetrics/ https://CRAN.R-project.org/package=germinationmetrics https://doi.org/10.5281/zenodo.1219630
BugReports: https://github.com/aravind-j/germinationmetrics/issues
NeedsCompilation: no
Packaged: 2019-01-19 18:44:38 UTC; J. Aravind
Author: J. Aravind [aut, cre] (<https://orcid.org/0000-0002-4791-442X>), S. Vimala Devi [aut], J. Radhamani [aut], Sherry Rachel Jacob [aut], Kalyani Srinivasan [aut], ICAR-NBGPR [cph] (www.nbpgr.ernet.in)
Maintainer: J. Aravind <j.aravind@icar.gov.in>
Repository: CRAN
Date/Publication: 2019-01-19 22:30:03 UTC

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New package dsm with initial version 2.2.17
Package: dsm
Maintainer: Laura Marshall <lhm@st-andrews.ac.uk>
License: GPL (>= 2)
Title: Density Surface Modelling of Distance Sampling Data
LazyLoad: yes
Author: David L. Miller, Eric Rexstad, Louise Burt, Mark V. Bravington, Sharon Hedley.
Description: Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.
Version: 2.2.17
URL: http://github.com/DistanceDevelopment/dsm
BugReports: https://github.com/DistanceDevelopment/dsm/issues
Depends: R (>= 3.0), mgcv (>= 1.8-23), mrds (>= 2.1.16), numDeriv
Imports: nlme, ggplot2, plyr, statmod
Suggests: Distance, sp, tweedie, testthat
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-01-19 21:46:29 UTC; lhm
Repository: CRAN
Date/Publication: 2019-01-19 22:30:20 UTC

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