Tue, 18 Jun 2019

Package ows4R updated to version 0.1-2 with previous version 0.1-1 dated 2019-05-26

Title: Interface to OGC Web-Services (OWS)
Description: Provides an Interface to Web-Services defined as standards by the Open Geospatial Consortium (OGC), including Web Feature Service (WFS) for vector data, Catalogue Service (CSW) for ISO/OGC metadata and associated standards such as the common web-service specification (OWS) and OGC Filter Encoding. The long-term purpose is to add support for additional OGC service standards such as Web Coverage Service (WCS) and Web Processing Service (WPS).
Author: Emmanuel Blondel [aut, cre] (<https://orcid.org/0000-0002-5870-5762>), Norbert Billet [ctb]
Maintainer: Emmanuel Blondel <emmanuel.blondel1@gmail.com>

Diff between ows4R versions 0.1-1 dated 2019-05-26 and 0.1-2 dated 2019-06-18

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Package fgeo.analyze updated to version 1.1.11 with previous version 1.1.10 dated 2019-05-20

Title: Analyze ForestGEO Data
Description: To help you access, transform, analyze, and visualize ForestGEO data, we developed a collection of R packages (<https://forestgeo.github.io/fgeo/>). This package, in particular, helps you to implement analyses of plot species distributions, topography, demography, and biomass. It also includes a torus translation test to determine habitat associations of tree species as described by Zuleta et al. (2018) <doi:10.1007/s11104-018-3878-0>. To learn more about ForestGEO visit <http://www.forestgeo.si.edu/>.
Author: Mauro Lepore [aut, ctr, cre] (<https://orcid.org/0000-0002-1986-7988>), Gabriel Arellano [aut, rev], Richard Condit [aut], Matteo Detto [aut], Kyle Harms [aut], Suzanne Lao [aut, rev], KangMin Ngo [rev], Haley Overstreet [rev], Sabrina Russo [aut, rev], Daniel Zuleta [aut, rev], CTFS-ForestGEO [cph]
Maintainer: Mauro Lepore <maurolepore@gmail.com>

Diff between fgeo.analyze versions 1.1.10 dated 2019-05-20 and 1.1.11 dated 2019-06-18

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Package CholWishart updated to version 1.0.1 with previous version 1.0.0 dated 2019-01-25

Title: Cholesky Decomposition of the Wishart Distribution
Description: Sampling from the Cholesky factorization of a Wishart random variable, sampling from the inverse Wishart distribution, sampling from the Cholesky factorization of an inverse Wishart random variable, sampling from the pseudo Wishart distribution, sampling from the generalized inverse Wishart distribution, computing densities for the Wishart and inverse Wishart distributions, and computing the multivariate gamma and digamma functions.
Author: Geoffrey Thompson [aut, cre] (<https://orcid.org/0000-0003-2436-8822>), R Core Team [ctb]
Maintainer: Geoffrey Thompson <gzthompson@gmail.com>

Diff between CholWishart versions 1.0.0 dated 2019-01-25 and 1.0.1 dated 2019-06-18

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New package treeplyr with initial version 0.1.6
Package: treeplyr
Type: Package
Title: 'dplyr' Functionality for Matched Tree and Data Objects
Version: 0.1.6
Date: 2019-06-18
Author: Josef Uyeda
Maintainer: Josef Uyeda <josef.uyeda@gmail.com>
Description: Matches phylogenetic trees and trait data, and allows simultaneous manipulation of the tree and data using 'dplyr'.
License: GPL-2 | GPL-3
Depends: ape (>= 3.0-6), dplyr (>= 0.8.0), R (>= 3.1.2)
Imports: Rcpp (>= 0.10.3), lazyeval, phytools, geiger
LinkingTo: Rcpp
URL: https://github.com/uyedaj/treeplyr
BugReports: https://github.com/uyedaj/treeplyr/issues
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-06-18 19:20:42 UTC; juyeda
Repository: CRAN
Date/Publication: 2019-06-18 21:50:03 UTC

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Package SimJoint updated to version 0.2.1 with previous version 0.1.1 dated 2019-06-13

Title: Simulate Joint Distribution
Description: Simulate multivariate correlated data given nonparametric marginal distributions and their covariance structure characterized by a correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover approach (1982) <doi:10.1080/03610918208812265> and the Ruscio-Kaczetow iteration (2008) <doi:10.1080/00273170802285693>. Package functions are carefully implemented in C++ for pushing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both outperform various CRAN packages to date by a substantial margin. Benchmarks are detailed in function examples. Additionally, a simple heuristic algorithm initially designed to optimize the simulated joint distribution demonstrated not only strong error reduction capability, but also the potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow as a primer, especially when the functional relationships between marginal distributions are highly nonlinear.
Author: Charlie Wusuo Liu
Maintainer: Charlie Wusuo Liu <liuwusuo@gmail.com>

Diff between SimJoint versions 0.1.1 dated 2019-06-13 and 0.2.1 dated 2019-06-18

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Package provSummarizeR updated to version 1.1 with previous version 1.0 dated 2019-01-25

Title: Summarizes Provenance Related to Inputs and Outputs of a Script or Console Commands
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>.
Author: Barbara Lerner [cre], Emery Boose [aut]
Maintainer: Barbara Lerner <blerner@mtholyoke.edu>

Diff between provSummarizeR versions 1.0 dated 2019-01-25 and 1.1 dated 2019-06-18

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Package LogicReg updated to version 1.5.12 with previous version 1.5.10 dated 2018-06-14

Title: Logic Regression
Description: Routines for fitting Logic Regression models. Logic Regression is described in Ruczinski, Kooperberg, and LeBlanc (2003) <DOI:10.1198/1061860032238>. Monte Carlo Logic Regression is described in and Kooperberg and Ruczinski (2005) <DOI:10.1002/gepi.20042>.
Author: Charles Kooperberg <clk@fredhutch.org> and Ingo Ruczinski <ingo@jhu.edu>
Maintainer: Charles Kooperberg <clk@fredhutch.org>

Diff between LogicReg versions 1.5.10 dated 2018-06-14 and 1.5.12 dated 2019-06-18

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More information about LogicReg at CRAN
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Package fgeo.plot updated to version 1.1.9 with previous version 1.1.8 dated 2019-05-31

Title: Plot ForestGEO Data
Description: To help you access, transform, analyze, and visualize ForestGEO data, we developed a collection of R packages (<https://forestgeo.github.io/fgeo/>). This package, in particular, helps you to plot ForestGEO data. To learn more about ForestGEO visit <http://www.forestgeo.si.edu/>.
Author: Mauro Lepore [aut, ctr, cre] (<https://orcid.org/0000-0002-1986-7988>), CTFS-ForestGEO [cph, fnd]
Maintainer: Mauro Lepore <maurolepore@gmail.com>

Diff between fgeo.plot versions 1.1.8 dated 2019-05-31 and 1.1.9 dated 2019-06-18

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Package drugCombo updated to version 1.1.0 with previous version 1.0.3 dated 2018-11-13

Title: Drug Interaction Modeling Based on Loewe Additivity Following Harbron's Approach
Description: Perform assessment of synergy/antagonism for drug combinations based on the Loewe additivity model, following Harbron's approach (Statistics in Medicine, 2010, <doi:10.1002/sim.3916>). The package allows flexible modeling of the drug interaction index and supports "2-stage" estimation in addition to "1-stage" estimation, including bootstrap-based confidence intervals. The method requires data on the monotherapy responses and the package accommodates both checkerboard and ray designs. Functions are available for graphical exploration of model goodness-of-fit and diagnostics, as well as for synergy/antagonism assessment in 2D and 3D.
Author: Maxim Nazarov, Nele Goeyvaerts, Chris Harbron (original article and code)
Maintainer: Maxim Nazarov <maxim.nazarov@openanalytics.eu>

Diff between drugCombo versions 1.0.3 dated 2018-11-13 and 1.1.0 dated 2019-06-18

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Package comparer updated to version 0.2.0 with previous version 0.1.1 dated 2018-01-08

Title: Compare Output and Run Time
Description: Quickly run experiments to compare the run time and output of code blocks. The function mbc() can make fast comparisons of code, and will calculate statistics comparing the resulting outputs. It can be used to compare model fits to the same data or see which function runs faster. The function ffexp() runs a function using all possible combinations of selected inputs. This is useful for comparing the effect of different parameter values. It can also run in parallel and automatically save intermediate results, which is very useful for long computations.
Author: Collin Erickson [aut, cre]
Maintainer: Collin Erickson <collinberickson@gmail.com>

Diff between comparer versions 0.1.1 dated 2018-01-08 and 0.2.0 dated 2019-06-18

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New package srm with initial version 0.1-166
Package: srm
Type: Package
Title: Structural Equation Modeling for the Social Relations Model
Version: 0.1-166
Date: 2019-06-18 10:32:43
Author: Steffen Nestler [aut], Alexander Robitzsch [aut, cre], Oliver Luedtke [aut]
Maintainer: Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>
Description: Provides functionality for structural equation modeling for the social relations model (Warner, Kenny, & Soto, 1979, <doi:10.1037/0022-3514.37.10.1742>). Maximum likelihood estimation (Gill & Swartz, 2001, <doi:10.2307/3316080>; Nestler, 2018, <doi:10.3102/1076998617741106>) and least squares estimation is supported (Bond & Malloy, 2018, <doi:10.1016/B978-0-12-811967-9.00014-X>).
Depends: R (>= 3.1)
Imports: Rcpp, stats, utils
Enhances: amen, TripleR
LinkingTo: Rcpp, RcppArmadillo
License: GPL (>= 2)
URL: https://github.com/alexanderrobitzsch/srm, https://sites.google.com/site/alexanderrobitzsch2/software
NeedsCompilation: yes
Packaged: 2019-06-18 08:33:56 UTC; sunpn563
Repository: CRAN
Date/Publication: 2019-06-18 16:10:02 UTC

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Package splithalf updated to version 0.5.2 with previous version 0.3.1 dated 2018-03-17

Title: Calculate Task Split Half Reliability Estimates
Description: Estimate the internal consistency of your tasks with a permutation based split-half reliability approach. Unofficial release name: "Fight Milk - re-brand".
Author: Sam Parsons [aut, cre]
Maintainer: Sam Parsons <sam.parsons@psy.ox.ac.uk>

Diff between splithalf versions 0.3.1 dated 2018-03-17 and 0.5.2 dated 2019-06-18

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Package poppr updated to version 2.8.3 with previous version 2.8.2 dated 2019-03-11

Title: Genetic Analysis of Populations with Mixed Reproduction
Description: Population genetic analyses for hierarchical analysis of partially clonal populations built upon the architecture of the 'adegenet' package. Originally described in Kamvar, Tabima, and Grünwald (2014) <doi:10.7717/peerj.281> with version 2.0 described in Kamvar, Brooks, and Grünwald (2015) <doi:10.3389/fgene.2015.00208>.
Author: Zhian N. Kamvar [cre, aut] (<https://orcid.org/0000-0003-1458-7108>), Javier F. Tabima [aut] (<https://orcid.org/0000-0002-3603-2691>), Sydney E. Everhart [ctb, dtc] (<https://orcid.org/0000-0002-5773-1280>), Jonah C. Brooks [aut], Stacy A. Krueger-Hadfield [ctb] (<https://orcid.org/0000-0002-7324-7448>), Erik Sotka [ctb], Brian J. Knaus [ctb] (<https://orcid.org/0000-0003-1665-4343>), Patrick G. Meirmans [ctb] (<https://orcid.org/0000-0002-6395-8107>), Frédéric D. Chevalier [ctb] (<https://orcid.org/0000-0003-2611-8106>), Niklaus J. Grünwald [ths] (<https://orcid.org/0000-0003-1656-7602>)
Maintainer: Zhian N. Kamvar <zkamvar@gmail.com>

Diff between poppr versions 2.8.2 dated 2019-03-11 and 2.8.3 dated 2019-06-18

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New package NonProbEst with initial version 0.1.0
Package: NonProbEst
Type: Package
Title: Estimation in Nonprobability Sampling
Version: 0.1.0
Author: Luis Castro Martín <luiscastro193@gmail.com>, Ramón Ferri García <rferri@ugr.es> and María del Mar Rueda <mrueda@ugr.es>
Maintainer: Luis Castro Martín <luiscastro193@gmail.com>
Description: Different inference procedures are proposed in the literature to correct for selection bias that might be introduced with non-random selection mechanisms. A class of methods to correct for selection bias is to apply a statistical model to predict the units not in the sample (super-population modeling). Other studies use calibration or Statistical Matching (statistically match nonprobability and probability samples). To date, the more relevant methods are weighting by Propensity Score Adjustment (PSA). The Propensity Score Adjustment method was originally developed to construct weights by estimating response probabilities and using them in Horvitz–Thompson type estimators. This method is usually used by combining a non-probability sample with a reference sample to construct propensity models for the non-probability sample. Calibration can be used in a posterior way to adding information of auxiliary variables. Propensity scores in PSA are usually estimated using logistic regression models. Machine learning classification algorithms can be used as alternatives for logistic regression as a technique to estimate propensities. The package 'NonProbEst' implements some of these methods and thus provides a wide options to work with data coming from a non-probabilistic sample.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Imports: caret, sampling, e1071
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-06-18 09:45:56 UTC; luis
Repository: CRAN
Date/Publication: 2019-06-18 16:10:13 UTC

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New package AlphaPart with initial version 0.6.0
Package: AlphaPart
Title: Partition/Decomposition of Additive Genetic Values by Paths of Information
Description: Additive genetic (breeding) value represents a sum of additive gene effects over whole genome and can be inferred from phenotype values of relatives via pedigree based mixed models. The partitioning method is described in Garcia-Cortes et al. (2008) <DOI:10.1017/S175173110800205X>.
Author: Gregor Gorjanc, Jana Obsteter
Maintainer: Gregor Gorjanc <gregor.gorjanc@roslin.ed.ac.uk>
License: GPL (>= 2)
LazyLoad: yes
Imports: directlabels (>= 1.1), gdata (>= 2.6.0), ggplot2 (>= 0.8.9), pedigree (>= 1.3.1), quadprog (>= 1.5-3), Rcpp (>= 0.9.4), reshape
Suggests: RColorBrewer (>= 1.0-2), truncnorm (>= 1.0-5), knitr, rmarkdown, testthat
LinkingTo: Rcpp
Version: 0.6.0
Date: 2019-05-20
NeedsCompilation: yes
Packaged: 2019-06-18 10:46:09 UTC; jana
VignetteBuilder: knitr
RoxygenNote: 6.1.1
Encoding: UTF-8
Repository: CRAN
Date/Publication: 2019-06-18 16:30:03 UTC

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New package websocket with initial version 1.0.0
Package: websocket
Version: 1.0.0
Title: 'WebSocket' Client Library
Description: Provides a 'WebSocket' client interface for R. 'WebSocket' is a protocol for low-overhead real-time communication: <https://en.wikipedia.org/wiki/WebSocket>.
Authors@R: c( person("Alan", "Dipert", role = c("aut", "cre"), email = "alan@rstudio.com"), person("Barbara", "Borges", role = c("aut")), person("Winston", "Chang", role = c("aut"), email = "winston@rstudio.com"), person("Joe", "Cheng", role = c("aut"), email = "joe@rstudio.com"), person(family = "RStudio", role = "cph"), person("Peter", "Thorson", role = c("ctb", "cph"), comment = "WebSocket++ library"), person("René", "Nyffenegger", role = c("ctb", "cph"), comment = "Base 64 library"), person("Micael", "Hildenborg", role = c("ctb", "cph"), comment = "SHA1 library"), person(family = "Aladdin Enterprises", role = "cph", comment = "MD5 library"), person("Bjoern", "Hoehrmann", role = c("ctb", "cph"), comment = "UTF8 Validation library"))
License: GPL-2
Encoding: UTF-8
LazyData: true
ByteCompile: true
Imports: Rcpp, R6, later
LinkingTo: Rcpp, BH, AsioHeaders
SystemRequirements: GNU make, OpenSSL >= 1.0.1
RoxygenNote: 6.1.1
Collate: 'RcppExports.R' 'websocket.R'
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-06-07 20:02:23 UTC; alan
Author: Alan Dipert [aut, cre], Barbara Borges [aut], Winston Chang [aut], Joe Cheng [aut], RStudio [cph], Peter Thorson [ctb, cph] (WebSocket++ library), René Nyffenegger [ctb, cph] (Base 64 library), Micael Hildenborg [ctb, cph] (SHA1 library), Aladdin Enterprises [cph] (MD5 library), Bjoern Hoehrmann [ctb, cph] (UTF8 Validation library)
Maintainer: Alan Dipert <alan@rstudio.com>
Repository: CRAN
Date/Publication: 2019-06-18 15:10:03 UTC

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New package RadioGx with initial version 0.0.1
Package: RadioGx
Type: Package
Title: Analysis of Large-Scale Radio-Genomic Data
Version: 0.0.1
Date: 2019-05-31
Authors@R: c( person("Venkata","Manem", email = "mail2mvskumar@gmail.com", role = c("aut")), person("Petr","Smirnov", email = "petr.smirnov@uhnresearch.ca", role = c("aut")), person("Ian","Smith", email = "ianc.smith@mail.utoronto.ca", role = c("aut")), person("Meghan","Lambie", email = "megan.lambie@mail.utoronto.ca", role = c("aut")), person("Christopher","Eeles", email = "christopher.eeles@uhnresearch.ca", role = c("aut")), person("Scott", "Bratman", email = "scott.bratman@rmp.uhn.ca", role = c("aut")), person("Benjamin","Haibe-Kains", email = "benjamin.haibe.kains@utoronto.ca", role = c("aut","cre")) )
Description: Computational tool box for radio-genomic analysis which integrates radio-response data, radio-biological modelling and comprehensive cell line annotations for hundreds of cancer cell lines. The 'RadioSet' class enables creation and manipulation of standardized datasets including information about cancer cells lines, radio-response assays and dose-response indicators. Included methods allow fitting and plotting dose-response data using established radio-biological models along with quality control to validate results. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, references, as well as: Manem, V. et al (2018) <doi:10.1101/449793>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0), CoreGx
Imports: PharmacoGx, Biobase, RColorBrewer, caTools, magicaxis, methods, reshape2, KernSmooth, cluster, Matrix, scales
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-06-17 17:21:50 UTC; chris
Author: Venkata Manem [aut], Petr Smirnov [aut], Ian Smith [aut], Meghan Lambie [aut], Christopher Eeles [aut], Scott Bratman [aut], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
Repository: CRAN
Date/Publication: 2019-06-18 15:50:02 UTC

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New package neatRanges with initial version 0.1.0
Package: neatRanges
Type: Package
Title: Tidy Up Date/Time Ranges
Version: 0.1.0
Author: Aljaz Jelenko [aut, cre]
Maintainer: Aljaz Jelenko <aljaz.jelenko@amis.net>
BugReports: https://github.com/arg0naut91/neatRanges/issues
Description: Collapse, partition, combine, fill gaps in and expand date/time ranges.
URL: https://github.com/arg0naut91/neatRanges
License: MIT + file LICENSE
Depends: R (>= 3.1.0)
Imports: data.table
Encoding: UTF-8
LazyData: true
Suggests: testthat
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-06-17 17:30:36 UTC; aljaz_000
Repository: CRAN
Date/Publication: 2019-06-18 15:30:03 UTC

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New package gausscov with initial version 0.0.1
Package: gausscov
Title: The Gaussian Covariate Method for Variable Selection
Version: 0.0.1
Authors@R: person("Laurie", "Davies", role = c("aut","cre"),email ="laurie.davies@uni-due.de")
Author: Laurie Davies [aut, cre]
Maintainer: Laurie Davies <laurie.davies@uni-due.de>
Description: Given the standard linear model the traditional way of deciding whether to include the jth covariate is to apply the F-test to decide whether the corresponding beta coefficient is zero. The Gaussian covariate method is completely different. The question as to whether the beta coefficient is or is not zero is replaced by the question as to whether the covariate is better or worse than i.i.d. Gaussian noise. The P-value for the covariate is the probability that Gaussian noise is better. Surprisingly this can be given exactly and it is the same a the P-value for the classical model based on the F-distribution. The Gaussian covariate P-value is model free, it is the same for any data set. Using the idea it is possible to do covariate selection for a small number of covariates 25 by considering all subsets. Post selection inference causes no problems as the P-values hold whatever the data. The idea extends to stepwise regression again with exact probabilities. In the simplest version the only parameter is a specified cut-off P-value which can be interpreted as the probability of a false positive being included in the final selection. For more information see the website below and the accompanying papers: L. Davies and L. Duembgen, "A Model-free Approach to Linear Least Squares Regression with Exact Probabilities and Applications to Covariate Selection", 2019, <arXiv:1906.01990>. L. Davies, "Lasso, Knockoff and Gaussian covariates: A comparison", 2018, <arXiv:1807.09633v4>.
License: GPL-3
Depends: R (>= 2.10), stats
Encoding: UTF-8
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-06-17 13:42:00 UTC; laurie
Repository: CRAN
Date/Publication: 2019-06-18 15:30:07 UTC

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New package FEprovideR with initial version 0.1.0
Package: FEprovideR
Title: Fixed Effects Logistic Model with High-Dimensional Parameters
Version: 0.1.0
Authors@R: c( person(given = "Kevin(Zhi)", family = "He", role = c("aut"), email = "kevinhe@umich.edu"), person(given = "Wenbo", family = "Wu", role = c("aut"), email = "wenbowu@umich.edu"), person(given = "Michael", family = "Kleinsasser", role = c("cre"), email = "mkleinsa@umich.edu"))
Description: A structured profile likelihood algorithm for the logistic fixed effects model and an approximate expectation maximization (EM) algorithm for the logistic mixed effects model. Based on He, K., Kalbfleisch, J.D., Li, Y. and Li, Y. (2013) <doi:10.1007/s10985-013-9264-6>.
License: GPL-2
Imports: ggplot2, Matrix, poibin
Encoding: UTF-8
LazyData: true
BugReports: https://github.com/umich-biostatistics/FEprovideR/issues
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-06-17 16:34:39 UTC; mkleinsa
Author: Kevin(Zhi) He [aut], Wenbo Wu [aut], Michael Kleinsasser [cre]
Maintainer: Michael Kleinsasser <mkleinsa@umich.edu>
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2019-06-18 15:20:03 UTC

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Package Compositional updated to version 3.5 with previous version 3.4 dated 2019-03-12

Title: Compositional Data Analysis
Description: Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include a) Tsagris M.T., Preston S. and Wood A.T.A. (2011) A data-based power transformation for compositional data. Fourth International International Workshop on Compositional Data Analysis. b) Tsagris M. (2014). The k-NN algorithm for compositional data: a revised approach with and without zero values present. Journal of Data Science, 12(3):519--534. c) Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2):47--57. d) Tsagris M., Preston S. and Wood A.T.A. (2016). Improved supervised classification for compositional data using the alpha-transformation. Journal of Classification, 33(2):243--261. <doi:10.1007/s00357-016-9207-5>. e) Tsagris M., Preston S. and Wood A.T.A. (2017). Nonparametric hypothesis testing for equality of means on the simplex. Journal of Statistical Computation and Simulation, 87(2): 406--422. <doi:10.1080/00949655.2016.1216554> f) Tsagris M. and Stewart C. (2018). A Dirichlet regression model for compositional data with zeros. Lobachevskii Journal of Mathematics, 39(3): 398--412. <doi:10.1134/S1995080218030198>. g) Alenazi A. (2019). Regression for compositional data with compositional data as predictor variables with or without zero values. Journal of Data Science, 17(1): 219--238. <doi:10.6339/JDS.201901_17(1).0010> Furher, we include functions for percentages (or proportions).
Author: Michail Tsagris [aut, cre], Giorgos Athineou [aut], Abdulaziz Alenazi [ctb]
Maintainer: Michail Tsagris <mtsagris@yahoo.gr>

Diff between Compositional versions 3.4 dated 2019-03-12 and 3.5 dated 2019-06-18

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 Compositional-3.5/Compositional/R/rcompnorm.R                |    2 
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New package rskey with initial version 0.4.1
Package: rskey
Title: Create Custom 'Rstudio' Keyboard Shortcuts
Version: 0.4.1
Date: 2019-05-24
Authors@R: person("Berry", "Boessenkool", email = "berry-b@gmx.de", role = c("aut", "cre"))
Description: Create custom keyboard shortcuts to examine code selected in the 'Rstudio' editor. F3 can for example yield 'str(selection)' and F7 open the source code of CRAN and base package functions on 'github'.
Imports: graphics, utils, shiny (>= 0.13), miniUI (>= 0.1.1), rstudioapi (>= 0.5), berryFunctions (>= 1.17.21)
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
BugReports: https://github.com/brry/rskey/issues
NeedsCompilation: no
Packaged: 2019-05-24 20:33:29 UTC; Berry
Author: Berry Boessenkool [aut, cre]
Maintainer: Berry Boessenkool <berry-b@gmx.de>
Repository: CRAN
Date/Publication: 2019-06-18 14:30:03 UTC

More information about rskey at CRAN
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New package dataesgobr with initial version 1.0.0
Package: dataesgobr
Title: Access and Use Spain Government's API
Version: 1.0.0
Authors@R: c(person("Adrian", "Perez", email = "adrianpelopez@gmail.com", role = "cre"), person("Francisco", "Charte", email = "francisco@fcharte.com", role = "aut"))
Description: Functions intended to work with the API of the Spain Government <https://datos.gob.es/en/apidata> that allows you to download, analyze and generate information through datasets obtained from the API. The API containing information about multiples topics from the municipalities and organizations of Spain, it is possible to find URIs corresponding to the primary sector taxonomy and the identification of geographical coverage defined in Annexes IV and V <https://www.boe.es/diario_boe/txt.php?id=BOE-A-2013-2380> (NTI).
License: LGPL (>= 3) | file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: stringr, dplyr, readr, httr, stringi, jsonlite, graphics, readxl, readODS, yaml, shiny, shinythemes, shinycssloaders, shinyjs, DT, shinyFiles
Suggests: testthat
NeedsCompilation: no
Packaged: 2019-06-17 08:18:49 UTC; adpl
Author: Adrian Perez [cre], Francisco Charte [aut]
Maintainer: Adrian Perez <adrianpelopez@gmail.com>
Repository: CRAN
Date/Publication: 2019-06-18 14:50:03 UTC

More information about dataesgobr at CRAN
Permanent link

Package prevtoinc updated to version 0.12.0 with previous version 0.11.0 dated 2019-03-10

Title: Prevalence to Incidence Calculations for Point-Prevalence Studies in a Nosocomial Setting
Description: Functions to simulate point prevalence studies (PPSs) of healthcare-associated infections (HAIs) and to convert prevalence to incidence in steady state setups. Companion package to the preprint Willrich et al., From prevalence to incidence - a new approach in the hospital setting; <doi:10.1101/554725> , where methods are explained in detail.
Author: Niklas Willrich [aut, cre]
Maintainer: Niklas Willrich <WillrichN@rki.de>

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More information about prevtoinc at CRAN
Permanent link

Package MortalityLaws updated to version 1.8.0 with previous version 1.7.0 dated 2018-11-06

Title: Parametric Mortality Models, Life Tables and HMD
Description: Fit the most popular human mortality 'laws', and construct full and abridge life tables given various input indices. A mortality law is a parametric function that describes the dying-out process of individuals in a population during a significant portion of their life spans. For a comprehensive review of the most important mortality laws see Tabeau (2001) <doi:10.1007/0-306-47562-6_1>. Practical functions for downloading data from various human mortality databases are provided as well.
Author: Marius D. Pascariu [aut, cre, cph] (<https://orcid.org/0000-0002-2568-6489>), Vladimir Canudas-Romo [ctb]
Maintainer: Marius D. Pascariu <mpascariu@outlook.com>

Diff between MortalityLaws versions 1.7.0 dated 2018-11-06 and 1.8.0 dated 2019-06-18

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Permanent link

Package HiResTEC updated to version 0.59 with previous version 0.58 dated 2019-06-13

Title: Non-Targeted Fluxomics on High-Resolution Mass-Spectrometry Data
Description: Identifying labeled compounds in a 13C-tracer experiment in non-targeted fashion is a cumbersome process. This package facilitates such type of analyses by providing high level quality control plots, deconvoluting and evaluating spectra and performing a multitude of tests in an automatic fashion. The main idea is to use changing intensity ratios of ion pairs from peak list generated with 'xcms' as candidates and evaluate those against base peak chromatograms and spectra information within the raw measurement data automatically. The functionality is described in Hoffmann et al. (2018) <doi:10.1021/acs.analchem.8b00356>.
Author: Jan Lisec [aut, cre], Friederike Hoffmann [aut]
Maintainer: Jan Lisec <jan.lisec@bam.de>

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Package gdtools updated to version 0.1.9 with previous version 0.1.8 dated 2019-04-02

Title: Utilities for Graphical Rendering
Description: Useful tools for writing vector graphics devices.
Author: David Gohel [aut, cre], Hadley Wickham [aut], Lionel Henry [aut], Jeroen Ooms [aut] (<https://orcid.org/0000-0002-4035-0289>), Yixuan Qiu [ctb], R Core Team [cph] (Cairo code from X11 device), RStudio [cph]
Maintainer: David Gohel <david.gohel@ardata.fr>

Diff between gdtools versions 0.1.8 dated 2019-04-02 and 0.1.9 dated 2019-06-18

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More information about gdtools at CRAN
Permanent link

Package bigchess updated to version 1.5.2 with previous version 1.3.2 dated 2019-05-15

Title: Read, Manipulate, Explore Chess PGN Files and R API to UCI Chess Engines
Description: Provides functions for reading *.PGN files with more than one game, including large files without copying it into RAM (using 'ff' package or 'RSQLite' package). Handle chess data and chess aggregated data, count figure moves statistics, create player profile, plot winning chances, browse openings. Set of functions of R API to communicate with UCI-protocol based chess engines.
Author: Wojciech Rosa
Maintainer: Wojciech Rosa <w.rosa@pollub.pl>

Diff between bigchess versions 1.3.2 dated 2019-05-15 and 1.5.2 dated 2019-06-18

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More information about bigchess at CRAN
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Package phytools updated to version 0.6-99 with previous version 0.6-60 dated 2018-09-28

Title: Phylogenetic Tools for Comparative Biology (and Other Things)
Description: A wide range of functions for phylogenetic analysis. Functionality is concentrated in phylogenetic comparative biology, but also includes a diverse array of methods for visualizing, manipulating, reading or writing, and even inferring phylogenetic trees and data. Included among the functions in phylogenetic comparative biology are various for ancestral state reconstruction, model-fitting, simulation of phylogenies and data, and multivariate analysis. There are a broad range of plotting methods for phylogenies and comparative data which include, but are not restricted to, methods for mapping trait evolution on trees, for projecting trees into phenotypic space or a geographic map, and for visualizing correlated speciation between trees. Finally, there are a number of functions for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data not covered by other packages. For instance, there are functions for randomly or non-randomly attaching species or clades to a phylogeny, for estimating supertrees or consensus phylogenies from a set, for simulating trees and phylogenetic data under a range of models, and for a wide variety of other manipulations and analyses that phylogenetic biologists might find useful in their research.
Author: Liam J. Revell
Maintainer: Liam J. Revell <liam.revell@umb.edu>

Diff between phytools versions 0.6-60 dated 2018-09-28 and 0.6-99 dated 2019-06-18

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Package multivariance updated to version 2.2.0 with previous version 2.1.0 dated 2019-03-19

Title: Measuring Multivariate Dependence Using Distance Multivariance
Description: Distance multivariance is a measure of dependence which can be used to detect and quantify dependence. The necessary functions are implemented in this packages, and examples are given. For the theoretic background we refer to the papers: B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using Distance Multivariance. <arXiv:1712.06532>. B. Böttcher, M. Keller-Ressel, R.L. Schilling, Detecting independence of random vectors: generalized distance covariance and Gaussian covariance. VMSTA, 2018, Vol. 5, No. 3, 353-383. <arXiv:1711.07778>. B. Böttcher, M. Keller-Ressel, R.L. Schilling, Distance multivariance: New dependence measures for random vectors. <arXiv:1711.07775>. G. Berschneider, B. Böttcher, On complex Gaussian random fields, Gaussian quadratic forms and sample distance multivariance. <arXiv:1808.07280>.
Author: Björn Böttcher [aut, cre], Martin Keller-Ressel [ctb]
Maintainer: Björn Böttcher <bjoern.boettcher@tu-dresden.de>

Diff between multivariance versions 2.1.0 dated 2019-03-19 and 2.2.0 dated 2019-06-18

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Permanent link

New package APFr with initial version 1.0.2
Package: APFr
Type: Package
Title: Multiple Testing Approach using Average Power Function (APF) and Bayes FDR Robust Estimation
Version: 1.0.2
Date: 2019-06-14
Authors@R: c(person("Nicolò", "Margaritella", email = "N.Margaritella@sms.ed.ac.uk", role = c("cre", "aut")), person("Piero", "Quatto", role = "aut"))
Author: Nicolò Margaritella [cre, aut], Piero Quatto [aut]
Maintainer: Nicolò Margaritella <N.Margaritella@sms.ed.ac.uk>
Depends: R (>= 3.5.0)
Imports: stats (>= 3.5.2), graphics (>= 3.5.2)
Description: Implements a multiple testing approach to the choice of a threshold gamma on the p-values using the Average Power Function (APF) and Bayes False Discovery Rate (FDR) robust estimation. Function apf_fdr() estimates both quantities from either raw data or p-values. Function apf_plot() produces smooth graphs and tables of the relevant results. Details of the methods can be found in Quatto P, Margaritella N, et al. (2019) <doi:10.1177/0962280219844288>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-06-17 07:49:35 UTC; Nico
Repository: CRAN
Date/Publication: 2019-06-18 12:20:07 UTC

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Package anytime updated to version 0.3.4 with previous version 0.3.3 dated 2018-11-14

Title: Anything to 'POSIXct' or 'Date' Converter
Description: Convert input in any one of character, integer, numeric, factor, or ordered type into 'POSIXct' (or 'Date') objects, using one of a number of predefined formats, and relying on Boost facilities for date and time parsing.
Author: Dirk Eddelbuettel
Maintainer: Dirk Eddelbuettel <edd@debian.org>

Diff between anytime versions 0.3.3 dated 2018-11-14 and 0.3.4 dated 2019-06-18

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Package mexhaz updated to version 1.6 with previous version 1.5 dated 2018-04-15

Title: Mixed Effect Excess Hazard Models
Description: Fit flexible (excess) hazard regression models with the possibility of including non-proportional effects of covariables and of adding a random effect at the cluster level (corresponding to a shared frailty).
Author: Hadrien Charvat, Aurelien Belot
Maintainer: Hadrien Charvat <chadrien@ncc.go.jp>

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Package jsmodule updated to version 0.9.2 with previous version 0.9.1 dated 2019-06-10

Title: 'RStudio' Addins and 'Shiny' Modules for Medical Research
Description: 'RStudio' addins and 'Shiny' modules for descriptive statistics, regression and survival analysis.
Author: Jinseob Kim [aut, cre] (<https://orcid.org/0000-0002-9403-605X>), Zarathu [cph, fnd]
Maintainer: Jinseob Kim <jinseob2kim@gmail.com>

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Package Directional updated to version 3.8 with previous version 3.7 dated 2019-04-19

Title: Directional Statistics
Description: A collection of functions for directional data (including massive, with millions of observations, data) analysis. Hypothesis testing, discriminant and regression analysis, MLE of distributions and more are included. The standard textbook for such data is the "Directional Statistics" by Mardia, K. V. and Jupp, P. E. (2000). Other references include Phillip J. Paine, Simon P. Preston Michail Tsagris and Andrew T. A. Wood (2018). An elliptically symmetric angular Gaussian distribution. Statistics and Computing 28(3): 689-697. <doi:10.1007/s11222-017-9756-4>, P. J. Paine, S. P. Preston, M. Tsagris and Andrew T. A. Wood (2019). Spherical regression models with general covariates and anisotropic errors. Statistics and Computing (to appear). <doi:10.1007/s11222-019-09872-2>.
Author: Michail Tsagris, Giorgos Athineou, Anamul Sajib, Eli Amson, Micah J. Waldstein
Maintainer: Michail Tsagris <mtsagris@yahoo.gr>

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Package CompareCausalNetworks updated to version 0.2.4 with previous version 0.2.2 dated 2018-05-18

Title: Interface to Diverse Estimation Methods of Causal Networks
Description: Unified interface for the estimation of causal networks, including the methods 'backShift' (from package 'backShift'), 'bivariateANM' (bivariate additive noise model), 'bivariateCAM' (bivariate causal additive model), 'CAM' (causal additive model) (from package 'CAM'), 'hiddenICP' (invariant causal prediction with hidden variables), 'ICP' (invariant causal prediction) (from package 'InvariantCausalPrediction'), 'GES' (greedy equivalence search), 'GIES' (greedy interventional equivalence search), 'LINGAM', 'PC' (PC Algorithm), 'FCI' (fast causal inference), 'RFCI' (really fast causal inference) (all from package 'pcalg') and regression.
Author: Christina Heinze-Deml <heinzedeml@stat.math.ethz.ch>, Nicolai Meinshausen <meinshausen@stat.math.ethz.ch>
Maintainer: Christina Heinze-Deml <heinzedeml@stat.math.ethz.ch>

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Package PolynomF updated to version 2.0-2 with previous version 2.0-0 dated 2019-04-24

Title: Polynomials in R
Description: Implements univariate polynomial operations in R, including polynomial arithmetic, finding zeros, plotting, and some operations on lists of polynomials.
Author: Bill Venables, with contribution by Kurt Hornik and Georgi Boshnakov
Maintainer: Bill Venables <Bill.Venables@gmail.com>

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Package adept updated to version 1.1.2 with previous version 1.0.1 dated 2019-05-24

Title: Adaptive Empirical Pattern Transformation
Description: Designed for optimal use in performing fast, accurate walking strides segmentation from high-density data collected from a wearable accelerometer worn during continuous walking activity.
Author: Marta Karas [aut, cre] (<https://orcid.org/0000-0001-5889-3970>), Jacek Urbanek [aut] (<https://orcid.org/0000-0002-1890-8899>), Ciprian Crainiceanu [aut], John Muschelli [ctb] (<https://orcid.org/0000-0001-6469-1750>), Adi Gherman [ctb]
Maintainer: Marta Karas <marta.karass@gmail.com>

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Package sudokuAlt updated to version 0.2-0 with previous version 0.1-11 dated 2017-05-13

Title: Tools for Making and Spoiling Sudoku Games
Description: Tools for making, retrieving, displaying and solving sudoku games. This package is an alternative to the earlier sudoku-solver package, 'sudoku'. The present package uses a slightly different algorithm, has a simpler coding and presents a few more sugar tools, such as plot and print methods. Solved sudoku games are of some interest in Experimental Design as examples of Latin Square designs with additional balance constraints.
Author: Bill Venables <Bill.Venables@gmail.com>
Maintainer: Bill Venables <Bill.Venables@gmail.com>

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Package future.apply updated to version 1.3.0 with previous version 1.2.0 dated 2019-03-07

Title: Apply Function to Elements in Parallel using Futures
Description: Implementations of apply(), by(), eapply(), lapply(), Map(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster. These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework.
Author: Henrik Bengtsson [aut, cre, cph], R Core Team [cph, ctb]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

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Package phuse updated to version 0.1.8 with previous version 0.1.7 dated 2019-01-02

Title: Web Application Framework for 'PhUSE' Scripts
Description: Make it easy to review, download and execute scripts stored in Github 'phuse-scripts' repository <https://github.com/phuse-org/phuse-scripts>. Some examples included show the web application framework using the script metadata. The 'PhUSE' is Pharmaceutical Users Software Exchange <http://www.phuse.eu>.
Author: Hanming Tu [aut, cre]
Maintainer: Hanming Tu <hanming.tu@gmail.com>

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Package MatTransMix updated to version 0.1.2 with previous version 0.1.1 dated 2019-04-06

Title: Clustering with Matrix Gaussian and Matrix Transformation Mixture Models
Description: Provides matrix Gaussian mixture models, matrix transformation mixture models and their model-based clustering results. The parsimonious models of the mean matrices and variance covariance matrices are implemented with a total of 196 variations.
Author: Xuwen Zhu [aut, cre], Volodymyr Melnykov [aut], Shuchismita Sarkar [ctb], Michael Hutt [ctb, cph], Stephen Moshier [ctb, cph], Rouben Rostamian [ctb, cph], Carl Edward Rasmussen [ctb, cph], Dianne Cook [ctb, cph]
Maintainer: Xuwen Zhu <xuwen.zhu@louisville.edu>

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