Fri, 09 Mar 2018

Package EpiILM updated to version 1.4.2 with previous version 1.4.1 dated 2018-02-18

Title: Spatial and Network Based Individual Level Models for Epidemics
Description: Provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR).
Author: Vineetha Warriyar. K. V. and Rob Deardon
Maintainer: Vineetha Warriyar. K. V. <vineethawarriyar.kod@ucalgary.ca>

Diff between EpiILM versions 1.4.1 dated 2018-02-18 and 1.4.2 dated 2018-03-09

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Package openEBGM updated to version 0.4.0 with previous version 0.3.0 dated 2017-11-15

Title: EBGM Scores for Mining Large Contingency Tables
Description: An implementation of DuMouchel's (1999) <doi:10.1080/00031305.1999.10474456> Bayesian data mining method for the market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and quantile scores from the posterior distribution using the Gamma-Poisson Shrinker (GPS) model to find unusually large cell counts in large, sparse contingency tables. Can be used to find unusually high reporting rates of adverse events associated with products. In general, can be used to mine any database where the co-occurrence of two variables or items is of interest. Also calculates relative and proportional reporting ratios. Builds on the work of the 'PhViD' package, from which much of the code is derived. Some of the added features include stratification to adjust for confounding variables and data squashing to improve computational efficiency. Now includes an implementation of the EM algorithm for hyperparameter estimation loosely derived from the 'mederrRank' package.
Author: John Ihrie [cre, aut], Travis Canida [aut], Ismaïl Ahmed [ctb] (author of 'PhViD' package (derived code)), Antoine Poncet [ctb] (author of 'PhViD' package), Sergio Venturini [ctb] (author of 'mederrRank' package (derived code))
Maintainer: John Ihrie <John.Ihrie@fda.hhs.gov>

Diff between openEBGM versions 0.3.0 dated 2017-11-15 and 0.4.0 dated 2018-03-09

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 NAMESPACE                                     |    2 
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Package micompr updated to version 1.1.0 with previous version 1.0.2 dated 2017-06-24

Title: Multivariate Independent Comparison of Observations
Description: A procedure for comparing multivariate samples associated with different groups. It uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. The procedure is independent of the distributional properties of samples and automatically selects features that best explain their differences, avoiding manual selection of specific points or summary statistics. It is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations.
Author: Nuno Fachada [aut, cre]
Maintainer: Nuno Fachada <faken@fakenmc.com>

Diff between micompr versions 1.0.2 dated 2017-06-24 and 1.1.0 dated 2018-03-09

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Package nlme updated to version 3.1-136 with previous version 3.1-135.5 dated 2018-02-16

Title: Linear and Nonlinear Mixed Effects Models
Description: Fit and compare Gaussian linear and nonlinear mixed-effects models.
Author: José Pinheiro [aut] (S version), Douglas Bates [aut] (up to 2007), Saikat DebRoy [ctb] (up to 2002), Deepayan Sarkar [ctb] (up to 2005), EISPACK authors [ctb] (src/rs.f), Siem Heisterkamp [ctb] (Author fixed sigma), Bert Van Willigen [ctb] (Programmer fixed sigma), R-core [aut, cre]
Maintainer: R-core <R-core@R-project.org>

Diff between nlme versions 3.1-135.5 dated 2018-02-16 and 3.1-136 dated 2018-03-09

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Package learnr updated to version 0.9.2 with previous version 0.9.1 dated 2017-11-16

Title: Interactive Tutorials for R
Description: Create interactive tutorials using R Markdown. Use a combination of narrative, figures, videos, exercises, and quizzes to create self-paced tutorials for learning about R and R packages.
Author: Barbara Borges [aut, cre], JJ Allaire [aut], RStudio, Inc. [cph], Ajax.org B.V. [ctb, cph] (Ace library), Zeno Rocha [ctb, cph] (clipboard.js library), Nick Payne [ctb, cph] (Bootbox library), Julie Cameron [ctb] (SlickQuiz library), Quicken Loans [cph] (SlickQuiz library), Mozilla [ctb, cph] (localforage library)
Maintainer: Barbara Borges <barbara@rstudio.com>

Diff between learnr versions 0.9.1 dated 2017-11-16 and 0.9.2 dated 2018-03-09

 DESCRIPTION                               |    8 ++---
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Package compound.Cox updated to version 3.8 with previous version 3.7 dated 2018-01-03

Title: Univariate Feature Selection and Compound Covariate for Predicting Survival under the Cox Model
Description: Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (gene expressions). Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>, statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627>, Emura & Chen (2016 Stat Methods Med Res) <DOI:10.1177/0962280214533378>, and Emura et al. (2018-)<submitted>. Algorithms for generating correlated gene expressions are also available.
Author: Takeshi Emura, Hsuan-Yu Chen, Shigeyuki Matsui, Yi-Hau Chen
Maintainer: Takeshi Emura <takeshiemura@gmail.com>

Diff between compound.Cox versions 3.7 dated 2018-01-03 and 3.8 dated 2018-03-09

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New package fluxweb with initial version 0.1.0
Package: fluxweb
Type: Package
Title: Estimate Energy Fluxes in Food Webs
Version: 0.1.0
Author: Benoit Gauzens
Maintainer: Benoit Gauzens <benoit.gauzens@gmail.com>
Description: Compute energy fluxes in trophic networks, from resources to their consumers, and can be applied to systems ranging from simple two-species interactions to highly complex food webs. It implements the approach described in Gauzens et al. (2017) <doi:10.1101/229450> to calculate energy fluxes, which are also used to calculate equilibrium stability.
License: GPL (>= 2.0)
Depends: stats
URL: https://www.biorxiv.org/content/early/2017/12/06/229450
LazyData: TRUE
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-03-09 13:14:31 UTC; bg33novu
Repository: CRAN
Date/Publication: 2018-03-09 13:47:22 UTC

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New package febr with initial version 1.0-0
Package: febr
Type: Package
Title: Free Brazilian Repository for Open Soil Data
Version: 1.0-0
Date: 2018-03-09
Authors@R: c( person("Alessandro", "Samuel-Rosa", role = c("aut", "cre"), email = "alessandrosamuelrosa@gmail.com", comment = c(ORCID = "0000-0003-0877-1320")), person("Universidade Federal de Santa Maria", role = "fnd", email = "ppgcs@ufsm.br"))
Description: Making the access to the Free Brazilian Repository for Open Soil Data <http://www.ufsm.br/febr/> as easy as possible.
License: GPL (>= 2)
Encoding: UTF-8
Imports: cellranger, dplyr, glue, googlesheets, knitr, pedometrics, readr, sp, stringr, xlsx
Suggests: bookdown, DT, lattice, latticeExtra, magrittr, pander, rgdal
VignetteBuilder: knitr
LazyData: true
RoxygenNote: 6.0.1
URL: https://github.com/febr-team/febr-package/
BugReports: https://github.com/febr-team/febr-package/issues/
NeedsCompilation: no
Packaged: 2018-03-09 12:43:23 UTC; alessandro
Author: Alessandro Samuel-Rosa [aut, cre] (<https://orcid.org/0000-0003-0877-1320>), Universidade Federal de Santa Maria [fnd]
Maintainer: Alessandro Samuel-Rosa <alessandrosamuelrosa@gmail.com>
Repository: CRAN
Date/Publication: 2018-03-09 13:04:31 UTC

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New package FASeg with initial version 0.1.9
Package: FASeg
Type: Package
Title: Joint Segmentation of Correlated Time Series
Version: 0.1.9
Date: 2018-03-09
Author: Xavier Collilieux, Emilie Lebarbier and Stephane Robin
Maintainer: Emilie Lebarbier <emilie.lebarbier@agroparistech.fr>
Description: It contains a function designed to the joint segmentation in the mean of several correlated series. The method is described in the paper X. Collilieux, E. Lebarbier and S. Robin. A factor model approach for the joint segmentation with between-series correlation (2015) <arXiv:1505.05660>.
License: GPL-2
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2018-03-09 13:49:27 UTC; lebarbier
Repository: CRAN
Date/Publication: 2018-03-09 12:59:37 UTC

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New package rdflib with initial version 0.1.0
Package: rdflib
Title: Tools to Manipulate and Query Semantic Data
Version: 0.1.0
Authors@R: person("Carl", "Boettiger", email = "cboettig@gmail.com", role = c("aut", "cre", "cph"), comment=c(ORCID = "0000-0002-1642-628X"))
Description: The Resource Description Framework, or 'RDF' is a widely used data representation model that forms the cornerstone of the Semantic Web. 'RDF' represents data as a graph rather than the familiar data table or rectangle of relational databases. The 'rdflib' package provides a friendly and concise user interface for performing common tasks on 'RDF' data, such as reading, writing and converting between the various serializations of 'RDF' data, including 'rdfxml', 'turtle', 'nquads', 'ntriples', and 'json-ld'; creating new 'RDF' graphs, and performing graph queries using 'SPARQL'. This package wraps the low level 'redland' R package which provides direct bindings to the 'redland' C library. Additionally, the package supports the newer and more developer friendly 'JSON-LD' format through the 'jsonld' package. The package interface takes inspiration from the Python 'rdflib' library.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
URL: https://github.com/ropensci/rdflib
BugReports: https://github.com/ropensci/rdflib/issues
Imports: redland, jsonld, methods, utils, stringi, readr
RoxygenNote: 6.0.1
Suggests: magrittr, covr, testthat, knitr, rmarkdown, jqr, DT, tidyverse, dplyr, tidyr, tibble, purrr, lubridate, httr, xml2, jsonlite, repurrrsive, nycflights13, codemetar
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-03-08 21:40:42 UTC; cboettig
Author: Carl Boettiger [aut, cre, cph] (<https://orcid.org/0000-0002-1642-628X>)
Maintainer: Carl Boettiger <cboettig@gmail.com>
Repository: CRAN
Date/Publication: 2018-03-09 12:56:58 UTC

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New package PP3 with initial version 1.2
Package: PP3
Type: Package
Title: Three-Dimensional Exploratory Projection Pursuit
Version: 1.2
Date: 2018-03-06
Authors@R: c(person("Guy", "Nason", role=c("aut", "cre"), email="G.P.Nason@bristol.ac.uk"), person("Robin", "Sibson", role=c("ctb", "ths")))
Depends: R (>= 3.0)
Imports: stats
Description: Exploratory projection pursuit is a method to discovers structure in multivariate data. At heart this package uses a projection index to evaluate how interesting a specific three-dimensional projection of multivariate data (with more than three dimensions) is. Typically, the main structure finding algorithm starts at a random projection and then iteratively changes the projection direction to move to a more interesting one. In other words, the projection index is maximised over the projection direction to find the most interesting projection. This maximum is, though, a local maximum. So, this code has the ability to restart the algorithm from many different starting positions automatically. Routines exist to plot a density estimate of projection indices over the runs, this enables the user to obtain an idea of the distribution of the projection indices, and, hence, which ones might be interesting. Individual projection solutions, including those identified as interesting, can be extracted and plotted individually. The package can make use of the mclapply() function to execute multiple runs in parallel to speed up index discovery. Projection pursuit is similar to independent component analysis. This package uses a projection index that maximises an entropy measure to look for projections that exhibit non-normality, and operates on sphered data. Hence, information from this package is different from that obtained from principal components analysis, but the rationale behind both methods is similar. Nason, G. P. (1995) <doi:10.2307/2986135>.
License: GPL (>= 2)
URL: http://www.stats.bris.ac.uk/~guy
NeedsCompilation: yes
Packaged: 2018-03-09 10:01:21 UTC; magpn
Author: Guy Nason [aut, cre], Robin Sibson [ctb, ths]
Maintainer: Guy Nason <G.P.Nason@bristol.ac.uk>
Repository: CRAN
Date/Publication: 2018-03-09 12:43:48 UTC

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New package pathfindR with initial version 1.0.0
Package: pathfindR
Type: Package
Title: Pathway Enrichment Analysis Utilizing Active Subnetworks
Version: 1.0.0
Author: Ege Ulgen, Ozan Ozisik
Maintainer: Ege Ulgen <egeulgen@gmail.com>
Description: Pathway enrichment analysis enables researchers to uncover mechanisms underlying the phenotype. pathfindR is a tool for pathway enrichment analysis utilizing active subnetworks. It identifies active subnetworks in a protein-protein interaction network using user-provided a list of genes. It performs pathway enrichment analyses on the identified subnetworks. pathfindR also offers functionality to cluster enriched pathways and identify representative pathways. The method is described in detail in Ulgen E, Ozisik O, Sezerman OU. 2018. pathfindR: An R Package for Pathway Enrichment Analysis Utilizing Active Subnetworks. bioRxiv. <doi:10.1101/272450>.
License: MIT + file LICENSE
URL: https://github.com/egeulgen/pathfindR
BugReports: https://github.com/egeulgen/pathfindR/issues
Encoding: UTF-8
LazyData: true
SystemRequirements: Java JVM 1.8
Imports: AnnotationDbi, DBI, doParallel, foreach, rmarkdown, org.Hs.eg.db
Depends: R (>= 3.4), pathview, knitr, shiny
RoxygenNote: 6.0.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-03-08 23:31:30 UTC; egeulgen
Repository: CRAN
Date/Publication: 2018-03-09 12:18:06 UTC

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New package joineRmeta with initial version 0.1.1
Package: joineRmeta
Type: Package
Title: Joint Modelling for Meta-Analytic (Multi-Study) Data
Version: 0.1.1
Authors@R: c( person("Maria", "Sudell", email = "mesudell@liverpool.ac.uk", role = c("cre", "aut"), comment = c(ORCID = "0000-0002-7919-4981")), person("Ruwanthi", "Kolamunnage-Dona", email = "kdrr@liverpool.ac.uk", role = "aut", comment = c(ORCID = "0000-0003-3886-6208")), person("Catrin", "Tudur Smith", email = "cat1@liverpool.ac.uk", role = "aut"))
Encoding: UTF-8
Description: Fits joint models of the type proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extends to the multi-study, meta-analytic case. Functions for meta-analysis of a single longitudinal and a single time-to-event outcome from multiple studies using joint models. Options to produce plots for multi study joint data, to pool joint model fits from 'JM' and 'joineR' packages in a two stage meta-analysis, and to model multi-study joint data in a one stage meta-analysis.
License: GPL-3 | file LICENSE
URL: https://github.com/mesudell/joineRmeta/
BugReports: https://github.com/mesudell/joineRmeta/issues
LazyData: true
Depends: R (>= 3.3.0), lme4, survival, JM
Imports: ggplot2, grid, gridExtra, gtools, joineR, MASS, meta, msm, nlme, statmod, stats, Matrix, utils
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-03-02 15:46:39 UTC; mesudell
Author: Maria Sudell [cre, aut] (<https://orcid.org/0000-0002-7919-4981>), Ruwanthi Kolamunnage-Dona [aut] (<https://orcid.org/0000-0003-3886-6208>), Catrin Tudur Smith [aut]
Maintainer: Maria Sudell <mesudell@liverpool.ac.uk>
Repository: CRAN
Date/Publication: 2018-03-09 12:41:26 UTC

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New package hdme with initial version 0.1.0
Package: hdme
Type: Package
Title: High-Dimensional Regression with Measurement Error
Version: 0.1.0
Encoding: UTF-8
Author: Oystein Sorensen
Maintainer: Oystein Sorensen <oystein.sorensen.1985@gmail.com>
Description: Penalized regression for generalized linear models for measurement error problems (aka. errors-in-variables). The package contains a version of the lasso (L1-penalization) which corrects for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). It also contains an implementation of the Generalized Matrix Uncertainty Selector, which is a version the (Generalized) Dantzig Selector for the case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).
License: GPL-3
LazyData: TRUE
RoxygenNote: 6.0.1
Imports: glmnet (>= 2.0-13), Rglpk (>= 0.6-1), ggplot2 (>= 2.2.1), Rdpack, Rcpp (>= 0.12.15)
URL: https://github.com/osorensen/hdme
RdMacros: Rdpack
Suggests: knitr, rmarkdown, testthat, flare, igraph, tidyverse, zeallot
VignetteBuilder: knitr
LinkingTo: Rcpp, RcppArmadillo
NeedsCompilation: yes
Packaged: 2018-03-09 11:33:40 UTC; oyste
Repository: CRAN
Date/Publication: 2018-03-09 12:54:30 UTC

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New package foreSIGHT with initial version 0.9.2
Package: foreSIGHT
Version: 0.9.2
Depends: R (>= 3.3.0),GA (>= 3.0.2), zoo
Imports: doParallel, ggplot2 (>= 2.1.0), directlabels, cowplot, stats, graphics, grDevices, utils, moments
Suggests: knitr (>= 1.8)
Title: Systems Insights from Generation of Hydroclimatic Timeseries
Authors@R: c(person("Bree", "Bennett", role=c("aut","cre"),email="bree.bennett@adelaide.edu.au", comment = c(ORCID = "0000-0002-2131-088X")), person("Sam", "Culley", role=c("aut"),email="sam.culley@adelaide.edu.au", comment = c(ORCID = "0000-0003-4798-8522")), person("Seth", "Westra", role=("aut"), email="seth.westra@adelaide.edu.au", comment = c(ORCID = "0000-0003-4023-6061")), person("Danlu", "Guo", role=("ctb"), email="danlu.guo@adelaide.edu.au", comment = c(ORCID = "0000-0003-1083-1214")), person("Holger", "Maier", role=("ths"), email="holger.maier@adelaide.edu.au", comment = c(ORCID = "0000-0002-0277-6887")))
Author: Bree Bennett [aut, cre] (<https://orcid.org/0000-0002-2131-088X>), Sam Culley [aut] (<https://orcid.org/0000-0003-4798-8522>), Seth Westra [aut] (<https://orcid.org/0000-0003-4023-6061>), Danlu Guo [ctb] (<https://orcid.org/0000-0003-1083-1214>), Holger Maier [ths] (<https://orcid.org/0000-0002-0277-6887>)
Maintainer: Bree Bennett <bree.bennett@adelaide.edu.au>
Description: A tool to create hydroclimate scenarios, stress test systems and visualize system performance in scenario-neutral climate change impact assessments. Scenario-neutral approaches 'stress-test' the performance of a modelled system by applying a wide range of plausible hydroclimate conditions (see Brown & Wilby (2012) <doi:10.1029/2012EO410001> and Prudhomme et al. (2010) <doi:10.1016/j.jhydrol.2010.06.043>). These approaches allow the identification of hydroclimatic variables that affect the vulnerability of a system to hydroclimate variation and change. This tool enables the generation of perturbed time series using a range of approaches including simple scaling of observed time series (e.g. Culley et al. (2016) <doi:10.1002/2015WR018253>) and stochastic simulation of perturbed time series via an inverse approach (see Guo et al. (2018) <doi:10.1016/j.jhydrol.2016.03.025>). It incorporates a number of stochastic weather models to generate hydroclimate variables on a daily basis (e.g. precipitation, temperature, potential evapotranspiration) and allows a variety of different hydroclimate variable properties, herein called attributes, to be perturbed. Options are included for the easy integration of existing system models both internally in R and externally for seamless 'stress-testing'. A suite of visualization options for the results of a scenario-neutral analysis (e.g. plotting performance spaces and overlaying climate projection information) are also included. As further developments in scenario-neutral approaches occur the tool will be updated to incorporate these advances.
License: GPL-3
NeedsCompilation: no
VignetteBuilder: knitr
Packaged: 2018-03-08 06:56:45 UTC; Phil
Repository: CRAN
Date/Publication: 2018-03-09 12:01:40 UTC

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Package defrostR (with last version 1.1.0) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2018-01-16 1.1.0
2017-08-09 1.0.0

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New package ProFit with initial version 1.1.1
Package: ProFit
Type: Package
Title: Fit Projected 2D Profiles to Galaxy Images
Version: 1.1.1
Date: 2018-03-08
Author: Aaron Robotham, Dan Taranu, Rodrigo Tobar
Maintainer: Aaron Robotham <aaron.robotham@uwa.edu.au>
Description: Get data / Define model / ??? / Profit! 'ProFit' is a Bayesian galaxy fitting tool that uses a fast 'C++' image generation library and a flexible interface to a large number of likelihood samplers.
License: LGPL-3
URL: https://github.com/ICRAR/ProFit
BugReports: https://github.com/ICRAR/ProFit/issues
Imports: fftw, R2Cuba, RColorBrewer, LaplacesDemon, methods
Depends: R (>= 3.0), Rcpp (>= 0.11.6), magicaxis (>= 2.0.3), celestial (>= 1.4.1), FITSio
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, ProFound, sn
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2018-03-09 01:33:17 UTC; aaron
Repository: CRAN
Date/Publication: 2018-03-09 11:13:18 UTC

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New package circglmbayes with initial version 1.2.3
Package: circglmbayes
Type: Package
Date: 2018-02-27
Title: Bayesian Analysis of a Circular GLM
Version: 1.2.3
Authors@R: person("Kees", "Mulder", email = "keestimmulder@gmail.com", role = c("aut", "cre"))
Maintainer: Kees Mulder <keestimmulder@gmail.com>
Description: Perform a Bayesian analysis of a circular outcome General Linear Model (GLM), which allows regressing a circular outcome on linear and categorical predictors. Posterior samples are obtained by means of an MCMC algorithm written in 'C++' through 'Rcpp'. Estimation and credible intervals are provided, as well as hypothesis testing through Bayes Factors. See Mulder and Klugkist (2017) <doi:10.1016/j.jmp.2017.07.001>.
License: GPL-3
Encoding: UTF-8
ByteCompile: true
URL: https://github.com/keesmulder/circglmbayes
BugReports: https://github.com/keesmulder/circglmbayes/issues
LazyData: true
Depends: R (>= 2.10)
LinkingTo: Rcpp, BH, RcppArmadillo
Imports: Rcpp, stats, graphics, shiny, grDevices, ggplot2, reshape2, coda
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-02-27 10:29:29 UTC; Kees Mulder
Author: Kees Mulder [aut, cre]
Repository: CRAN
Date/Publication: 2018-03-09 11:58:29 UTC

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New package IDetect with initial version 0.1.0
Package: IDetect
Type: Package
Title: Isolate-Detect Methodology for Multiple Change-Point Detection
Version: 0.1.0
Depends: R (>= 3.3.0)
Imports: splines
Authors@R: c(person("Andreas", "Anastasiou", email = "a.anastasiou@lse.ac.uk", role = c("aut", "cre")), person("Piotr", "Fryzlewicz", email = "p.fryzlewicz@lse.ac.uk", role = "aut"))
Description: Provides efficient implementation of the Isolate-Detect methodology for the consistent estimation of the number and location of multiple change-points in one-dimensional data sequences from the "deterministic + noise" model. For details on the Isolate-Detect methodology, please see Anastasiou and Fryzlewicz (2018) <https://docs.wixstatic.com/ugd/24cdcc_6a0866c574654163b8255e272bc0001b.pdf>. Currently implemented scenarios are: piecewise-constant signal with Gaussian noise, piecewise-constant signal with heavy-tailed noise, continuous piecewise-linear signal with Gaussian noise, continuous piecewise-linear signal with heavy-tailed noise.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: testthat
NeedsCompilation: no
Packaged: 2018-03-07 14:50:13 UTC; ANASTAS7
Author: Andreas Anastasiou [aut, cre], Piotr Fryzlewicz [aut]
Maintainer: Andreas Anastasiou <a.anastasiou@lse.ac.uk>
Repository: CRAN
Date/Publication: 2018-03-09 10:02:07 UTC

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New package daymetr with initial version 1.3
Package: daymetr
Title: Interface to the 'Daymet' Web Services
Version: 1.3
Authors@R: person("Hufkens","Koen", email="koen.hufkens@gmail.com", role=c("aut", "cre"))
Description: Programmatic interface to the 'Daymet' web services (<http://daymet.ornl.gov>). Allows for easy downloads of Daymet climate data directly to your R workspace or your computer. Routines for both single pixel data downloads and gridded (netCDF) data are provided.
Depends: R (>= 3.4.0)
Imports: sp, rgeos, raster, rgdal, ncdf4, httr, tools, utils
License: AGPL-3
LazyData: true
ByteCompile: true
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown, covr, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-03-07 15:29:18 UTC; khufkens
Author: Hufkens Koen [aut, cre]
Maintainer: Hufkens Koen <koen.hufkens@gmail.com>
Repository: CRAN
Date/Publication: 2018-03-09 10:13:11 UTC

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New package beadplexr with initial version 0.1.0
Package: beadplexr
Type: Package
Title: Analysis of Multiplex Cytometric Bead Assays
Version: 0.1.0
Authors@R: c( person("Ulrik", "Stervbo", , "ulrik.stervbo@gmail.com", role = c("aut", "cre")))
Description: Reproducible and automated analysis of multiplex bead assays such as CBA, LEGENDplex, and MACSPlex. The package provides functions for streamlined reading of fcs files, and identification of bead clusters and analyte expression. It eases the calculation of a standard curve and the subsequent calculation of the analyte concentration.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 2.10)
Suggests: knitr, rmarkdown, stringr, testthat, hexbin, igraph, gridExtra
VignetteBuilder: knitr
RoxygenNote: 6.0.1
Imports: magrittr, tibble, dplyr, ggplot2, flowCore, yaml, purrr, fpc, cluster, raster, lazyeval, tidyr, drc, mclust
NeedsCompilation: no
Packaged: 2018-03-07 16:51:18 UTC; ulrik
Author: Ulrik Stervbo [aut, cre]
Maintainer: Ulrik Stervbo <ulrik.stervbo@gmail.com>
Repository: CRAN
Date/Publication: 2018-03-09 10:15:37 UTC

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New package glm.deploy with initial version 1.0.4
Package: glm.deploy
Type: Package
Title: 'C' and 'Java' Source Code Generator for Fitted Glm Objects
Version: 1.0.4
Date: 2018-03-06
Authors@R: c(person("Oscar","Castro-Lopez", email="castroloj@gmail.com", role=c("cre", "aut")), person("Ines", "Vega-Lopez", email="ifvega@uas.edu.mx", role=c("aut")))
Author: Oscar Castro-Lopez [cre, aut], Ines Vega-Lopez [aut]
Maintainer: Oscar Castro-Lopez <castroloj@gmail.com>
License: GPL (>= 3) | file LICENSE
Description: Provides two functions that generate source code implementing the predict function of fitted glm objects. In this version, code can be generated for either 'C' or 'Java'. The idea is to provide a tool for the easy and fast deployment of glm predictive models into production. The source code generated by this package implements two function/methods. One of such functions implements the equivalent to predict(type="response"), while the second implements predict(type="link"). Source code is written to disk as a .c or .java file in the specified path. In the case of c, an .h file is also generated.
URL: https://github.com/oscarcastrolopez/glm.deploy
BugReports: https://github.com/oscarcastrolopez/glm.deploy/issues
Encoding: UTF-8
LazyData: true
Imports: Rcpp (>= 0.12.12), stats
LinkingTo: Rcpp
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown, testthat
NeedsCompilation: yes
Packaged: 2018-03-06 23:19:25 UTC; oscar
Repository: CRAN
Date/Publication: 2018-03-09 09:59:35 UTC

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New package fmlogcondens with initial version 1.0.0
Package: fmlogcondens
Type: Package
Title: Fast Multivariate Log-Concave Density Estimation
Version: 1.0.0
Authors@R: c(person("Fabian", "Rathke", role=c("aut", "cre"), email = "frathke@gmail.com"), person("Christoph", "Schnörr", role="aut"), person("Giovanni", "Garberoglio", role="cph"))
Description: A fast solver for the maximum likelihood estimator (MLE) of a multivariate log-concave probability function. Given a sample X, it estimates a non-parametric density function whose logarithm is a concave function. Many well-known parametric densities belong to that class, among them the normal density, the uniform density, the exponential distribution and many more. This package provides functions for the estimation of a log-concave density and a mixture of log-concave densities in multiple dimensions. While being similar to the package LogConcDEAD, fmlogcondens provides much fast run times for large samples (>= 250 points). As a reference see Fabian Rathke, Christoph Schnörr (2015), <doi:10.1515/auom-2015-0053>.
License: GPL (>= 2)
URL: https://github.com/FabianRathke/fmlogcondens
Depends: R (>= 3.2.4)
Imports: geometry, MASS, mclust, mvtnorm
Encoding: UTF-8
LazyData: true
Suggests: LogConcDEAD, knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-03-07 11:59:41 UTC; fabian
Author: Fabian Rathke [aut, cre], Christoph Schnörr [aut], Giovanni Garberoglio [cph]
Maintainer: Fabian Rathke <frathke@gmail.com>
Repository: CRAN
Date/Publication: 2018-03-09 09:43:16 UTC

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Package pedantics updated to version 1.6 with previous version 1.5 dated 2014-01-23

Title: Functions to Facilitate Power and Sensitivity Analyses for Genetic Studies of Natural Populations
Description: Functions for sensitivity and power analysis, for calculating statistics describing pedigrees from wild populations, and for viewing pedigrees.
Author: Michael Morrissey
Maintainer: Michael Morrissey <michael.morrissey@st-andrews.ac.uk>

Diff between pedantics versions 1.5 dated 2014-01-23 and 1.6 dated 2018-03-09

 pedantics-1.5/pedantics/R/rpederrBird.R      |only
 pedantics-1.5/pedantics/man/rpederrBird.Rd   |only
 pedantics-1.6/pedantics/DESCRIPTION          |   17 -
 pedantics-1.6/pedantics/MD5                  |   17 -
 pedantics-1.6/pedantics/NAMESPACE            |   13 +
 pedantics-1.6/pedantics/R/genomesim.R        |    2 
 pedantics-1.6/pedantics/R/phensim.R          |    2 
 pedantics-1.6/pedantics/man/drawPedigree.Rd  |   15 -
 pedantics-1.6/pedantics/man/rpederr.Rd       |    1 
 pedantics-1.6/pedantics/src/pedantics.cc     |  275 ---------------------------
 pedantics-1.6/pedantics/src/pedantics_init.c |only
 11 files changed, 45 insertions(+), 297 deletions(-)

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Package wgeesel updated to version 1.5 with previous version 1.4 dated 2017-07-23

Title: Weighted Generalized Estimating Equations and Model Selection
Description: Weighted generalized estimating equations (WGEE) is an extension of generalized linear models to longitudinal clustered data by incorporating the correlation within-cluster when data is missing at random (MAR). The parameters in mean, scale correlation structures are estimated based on quasi-likelihood. Multiple model selection criterion are provided for selection of mean model and working correlation structure based on WGEE/GEE.
Author: Cong Xu <congxu17@gmail.com>, Zheng Li <zheng.li@outlook.com>, Ming Wang <mwang@phs.psu.edu>
Maintainer: Zheng Li <zheng.li@outlook.com>

Diff between wgeesel versions 1.4 dated 2017-07-23 and 1.5 dated 2018-03-09

 DESCRIPTION         |   10 
 MD5                 |   22 +-
 NAMESPACE           |    3 
 R/QICW.gee.R        |  559 +++++++++++++++++++++++++---------------------------
 R/cluster_est.R     |    6 
 R/cs_con_str_est.R  |   14 +
 R/drgee.R           |only
 R/newton_raphson.R  |   41 ++-
 R/phi_con_str_est.R |    4 
 R/us_matrix.R       |    3 
 R/wgee.R            |   26 +-
 man/drgee.Rd        |only
 man/wgee.Rd         |   12 -
 13 files changed, 379 insertions(+), 321 deletions(-)

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