Sat, 11 May 2019

Package PopGenome updated to version 2.7.1 with previous version 2.6.1 dated 2018-05-20

Title: An Efficient Swiss Army Knife for Population Genomic Analyses
Description: Provides efficient tools for population genomics data analysis, able to process individual loci, large sets of loci, or whole genomes. PopGenome <DOI:10.1093/molbev/msu136> not only implements a wide range of population genetics statistics, but also facilitates the easy implementation of new algorithms by other researchers. PopGenome is optimized for speed via the seamless integration of C code.
Author: Bastian Pfeifer [aut, cre], Ulrich Wittelsbuerger [ctb], Heng Li [ctb], Bob Handsaker [ctb]
Maintainer: Bastian Pfeifer <bastianxpfeifer@gmail.com>

Diff between PopGenome versions 2.6.1 dated 2018-05-20 and 2.7.1 dated 2019-05-11

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Package jsmodule updated to version 0.8.3 with previous version 0.8.2 dated 2019-05-11

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 insight updated to version 0.3.0 with previous version 0.2.0 dated 2019-03-29

Title: Easy Access to Model Information for Various Model Objects
Description: A tool to provide an easy, intuitive and consistent access to information contained in various R models, like model formulas, model terms, information about random effects, data that was used to fit the model or data from response variables. 'insight' mainly revolves around two types of functions: Functions that find (the names of) information, starting with 'find_', and functions that get the underlying data, starting with 'get_'. The package has a consistent syntax and works with many different model objects, where otherwise functions to access these information are missing.
Author: Daniel Lüdecke [aut, cre] (<https://orcid.org/0000-0002-8895-3206>), Dominique Makowski [aut, ctb] (<https://orcid.org/0000-0001-5375-9967>)
Maintainer: Daniel Lüdecke <d.luedecke@uke.de>

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More information about insight at CRAN
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Package heatmaply updated to version 0.16.0 with previous version 0.15.2 dated 2018-07-06

Title: Interactive Cluster Heat Maps Using 'plotly'
Description: Create interactive cluster 'heatmaps' that can be saved as a stand- alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and available in the 'RStudio' viewer pane. Hover the mouse pointer over a cell to show details or drag a rectangle to zoom. A 'heatmap' is a popular graphical method for visualizing high-dimensional data, in which a table of numbers are encoded as a grid of colored cells. The rows and columns of the matrix are ordered to highlight patterns and are often accompanied by 'dendrograms'. 'Heatmaps' are used in many fields for visualizing observations, correlations, missing values patterns, and more. Interactive 'heatmaps' allow the inspection of specific value by hovering the mouse over a cell, as well as zooming into a region of the 'heatmap' by dragging a rectangle around the relevant area. This work is based on the 'ggplot2' and 'plotly.js' engine. It produces similar 'heatmaps' as 'heatmap.2' or 'd3heatmap', with the advantage of speed ('plotly.js' is able to handle larger size matrix), the ability to zoom from the 'dendrogram' panes, and the placing of factor variables in the sides of the 'heatmap'.
Author: Tal Galili [aut, cre, cph] (https://www.r-statistics.com), Alan O'Callaghan [aut] (https://github.com/Alanocallaghan), Jonathan Sidi [ctb] (https://github.com/yonicd), Yoav Benjamini [ths]
Maintainer: Tal Galili <tal.galili@gmail.com>

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More information about heatmaply at CRAN
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Package analogue updated to version 0.17-3 with previous version 0.17-1 dated 2018-10-23

Title: Analogue and Weighted Averaging Methods for Palaeoecology
Description: Fits Modern Analogue Technique and Weighted Averaging transfer function models for prediction of environmental data from species data, and related methods used in palaeoecology.
Author: Gavin L. Simpson [aut, cre] (<https://orcid.org/0000-0002-9084-8413>), Jari Oksanen [aut]
Maintainer: Gavin L. Simpson <ucfagls@gmail.com>

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New package AnaCoDa with initial version 0.1.3.0
Package: AnaCoDa
Type: Package
Title: Analysis of Codon Data under Stationarity using a Bayesian Framework
Version: 0.1.3.0
Date: 2019-05-11
Authors@R: c(person("Cedric", "Landerer", role = c("aut", "cre"), email = "cedric.landerer@gmail.com"), person("Gabriel", "Hanas", role = "ctb"), person("Jeremy", "Rogers", role = "ctb"), person("Alex", "Cope", role="ctb"), person("Denizhan", "Pak", role="ctb"))
Maintainer: Cedric Landerer <cedric.landerer@gmail.com>
URL: https://github.com/clandere/AnaCoDa
VignetteBuilder: knitr
NeedsCompilation: yes
Depends: R (>= 3.3.0), Rcpp (>= 0.11.3), methods, mvtnorm
Suggests: knitr, Hmisc, VGAM, coda, testthat, lmodel2
RcppModules: Test_mod, Trace_mod, CovarianceMatrix_mod, MCMCAlgorithm_mod, Model_mod, Parameter_mod, Genome_mod, Gene_mod, SequenceSummary_mod
Description: Is a collection of models to analyze genome scale codon data using a Bayesian framework. Provides visualization routines and checkpointing for model fittings. Currently published models to analyze gene data for selection on codon usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist et al. (2015) <doi:10.1093/gbe/evv087>), and ROC with phi (Wallace & Drummond (2013) <doi:10.1093/molbev/mst051>). In addition 'AnaCoDa' contains three currently unpublished models. The FONSE (First order approximation On NonSense Error) model analyzes gene data for selection on codon usage against of nonsense error rates. The PA (PAusing time) and PANSE (PAusing time + NonSense Error) models use ribosome footprinting data to analyze estimate ribosome pausing times with and without nonsense error rate from ribosome footprinting data.
License: GPL (>= 2)
Imports:
LinkingTo: Rcpp
LazyLoad: yes
LazyData: yes
RoxygenNote: 6.1.1
Packaged: 2019-05-11 15:01:40 UTC; landerer
Author: Cedric Landerer [aut, cre], Gabriel Hanas [ctb], Jeremy Rogers [ctb], Alex Cope [ctb], Denizhan Pak [ctb]
Repository: CRAN
Date/Publication: 2019-05-11 22:12:44 UTC

More information about AnaCoDa at CRAN
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Package welchADF updated to version 0.3.1 with previous version 0.3 dated 2018-06-01

Title: Welch-James Statistic for Robust Hypothesis Testing under Heterocedasticity and Non-Normality
Description: Implementation of Johansen's general formulation of Welch-James's statistic with Approximate Degrees of Freedom, which makes it suitable for testing any linear hypothesis concerning cell means in univariate and multivariate mixed model designs when the data pose non-normality and non-homogeneous variance. Some improvements, namely trimmed means and Winsorized variances, and bootstrapping for calculating an empirical critical value, have been added to the classical formulation. The code departs from a previous SAS implementation by L.M. Lix and H.J. Keselman, available at <http://supp.apa.org/psycarticles/supplemental/met_13_2_110/SAS_Program.pdf> and published in Keselman, H.J., Wilcox, R.R., and Lix, L.M. (2003) <DOI:10.1111/1469-8986.00060>.
Author: Pablo J. Villacorta <pjvi@decsai.ugr.es>
Maintainer: Pablo J. Villacorta <pjvi@decsai.ugr.es>

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Package cocorresp updated to version 0.4-0 with previous version 0.3-0 dated 2016-02-28

Title: Co-Correspondence Analysis Methods
Description: Fits predictive and symmetric co-correspondence analysis (CoCA) models to relate one data matrix to another data matrix. More specifically, CoCA maximises the weighted covariance between the weighted averaged species scores of one community and the weighted averaged species scores of another community. CoCA attempts to find patterns that are common to both communities.
Author: Original Matlab routines by C.J.F. ter Braak and A.P. Schaffers. R port by Gavin L. Simpson. Function simpls based on simpls.fit (package pls) by Ron Wehrens and Bjorn-Helge Mevik.
Maintainer: Gavin L. Simpson <ucfagls@gmail.com>

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Package mlrCPO updated to version 0.3.4-3 with previous version 0.3.4-2 dated 2019-01-10

Title: Composable Preprocessing Operators and Pipelines for Machine Learning
Description: Toolset that enriches 'mlr' with a diverse set of preprocessing operators. Composable Preprocessing Operators ("CPO"s) are first-class R objects that can be applied to data.frames and 'mlr' "Task"s to modify data, can be attached to 'mlr' "Learner"s to add preprocessing to machine learning algorithms, and can be composed to form preprocessing pipelines.
Author: Martin Binder [aut, cre], Bernd Bischl [ctb], Michel Lang [ctb], Lars Kotthoff [ctb]
Maintainer: Martin Binder <developer.mb706@doublecaret.com>

Diff between mlrCPO versions 0.3.4-2 dated 2019-01-10 and 0.3.4-3 dated 2019-05-11

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Package miceadds updated to version 3.3-33 with previous version 3.2-48 dated 2019-04-15

Title: Some Additional Multiple Imputation Functions, Especially for 'mice'
Description: Contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, <doi:10.18637/jss.v045.i03>) are included. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, <doi:10.1007/BF02294457>), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, Ch.7, <doi:10.1201/9780429492259>), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, <doi:10.1111/1467-9574.00217>) and substantive model compatible imputation (Bartlett et al., 2015, <doi:10.1177/0962280214521348>).
Author: Alexander Robitzsch [aut, cre], Simon Grund [aut], Thorsten Henke [aut]
Maintainer: Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>

Diff between miceadds versions 3.2-48 dated 2019-04-15 and 3.3-33 dated 2019-05-11

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Package pillar updated to version 1.4.0 with previous version 1.3.1 dated 2018-12-15

Title: Coloured Formatting for Columns
Description: Provides 'pillar' and 'colonnade' generics designed for formatting columns of data using the full range of colours provided by modern terminals.
Author: Kirill Müller [aut, cre], Hadley Wickham [aut], RStudio [cph]
Maintainer: Kirill Müller <krlmlr+r@mailbox.org>

Diff between pillar versions 1.3.1 dated 2018-12-15 and 1.4.0 dated 2019-05-11

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More information about pillar at CRAN
Permanent link

Package suddengains updated to version 0.2.0 with previous version 0.1.0 dated 2019-05-03

Title: Identify Sudden Gains in Longitudinal Data
Description: Identify sudden gains based on the three criteria outlined by Tang and DeRubeis (1999) <doi:10.1037/0022-006X.67.6.894> to a selection of repeated measures. Sudden losses, defined as the opposite of sudden gains can also be identified. Two different datasets can be created, one including all sudden gains/losses and one including one selected sudden gain/loss for each case. It can extract scores around sudden gains/losses. It can plot the average change around sudden gains/losses and trajectories of individual cases.
Author: Milan Wiedemann [aut, cre] (<https://orcid.org/0000-0003-1991-282X>), Graham M Thew [ctb] (<https://orcid.org/0000-0003-2851-1315>), Richard Stott [ctb] (<https://orcid.org/0000-0003-2533-5504>), Anke Ehlers [ctb, ths] (<https://orcid.org/0000-0002-8742-0192>)
Maintainer: Milan Wiedemann <milan.wiedemann@gmail.com>

Diff between suddengains versions 0.1.0 dated 2019-05-03 and 0.2.0 dated 2019-05-11

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Package RcppArmadillo updated to version 0.9.400.3.0 with previous version 0.9.400.2.0 dated 2019-04-29

Title: 'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra Library
Description: 'Armadillo' is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. The 'RcppArmadillo' package includes the header files from the templated 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Note that Armadillo requires a fairly recent compiler; for the g++ family at least version 4.6.* is required.
Author: Dirk Eddelbuettel, Romain Francois, Doug Bates and Binxiang Ni
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package fmri updated to version 1.9 with previous version 1.7-2 dated 2016-09-15

Title: Analysis of fMRI Experiments
Description: Contains R-functions to perform an fMRI analysis as described in Tabelow et al. (2006) <DOI:10.1016/j.neuroimage.2006.06.029>, Polzehl et al. (2010) <DOI:10.1016/j.neuroimage.2010.04.241>, Tabelow and Polzehl (2011) <DOI:10.18637/jss.v044.i11>.
Author: Karsten Tabelow [aut, cre], Joerg Polzehl [aut], Brandon Whitcher [ctb], Dames Sibylle [ctb]
Maintainer: Karsten Tabelow <tabelow@wias-berlin.de>

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Package dodgr updated to version 0.1.4 with previous version 0.1.3 dated 2019-03-20

Title: Distances on Directed Graphs
Description: Distances on dual-weighted directed graphs using priority-queue shortest paths (Padgham (2019) <doi:10.32866/6945>). Weighted directed graphs have weights from A to B which may differ from those from B to A. Dual-weighted directed graphs have two sets of such weights. A canonical example is a street network to be used for routing in which routes are calculated by weighting distances according to the type of way and mode of transport, yet lengths of routes must be calculated from direct distances.
Author: Mark Padgham [aut, cre], Andreas Petutschnig [aut], Robin Lovelace [ctb], Andrew Smith [ctb], Malcolm Morgan [ctb], Shane Saunders [cph] (Original author of included code for priority heaps)
Maintainer: Mark Padgham <mark.padgham@email.com>

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Package clubSandwich updated to version 0.3.4 with previous version 0.3.3 dated 2019-01-24

Title: Cluster-Robust (Sandwich) Variance Estimators with Small-Sample Corrections
Description: Provides several cluster-robust variance estimators (i.e., sandwich estimators) for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator introduced by Bell and McCaffrey (2002) <http://www.statcan.gc.ca/pub/12-001-x/2002002/article/9058-eng.pdf> and developed further by Pustejovsky and Tipton (2017) <DOI:10.1080/07350015.2016.1247004>. The package includes functions for estimating the variance- covariance matrix and for testing single- and multiple- contrast hypotheses based on Wald test statistics. Tests of single regression coefficients use Satterthwaite or saddle-point corrections. Tests of multiple- contrast hypotheses use an approximation to Hotelling's T-squared distribution. Methods are provided for a variety of fitted models, including lm() and mlm objects, glm(), ivreg() (from package 'AER'), plm() (from package 'plm'), gls() and lme() (from 'nlme'), robu() (from 'robumeta'), and rma.uni() and rma.mv() (from 'metafor').
Author: James Pustejovsky [aut, cre] (<https://orcid.org/0000-0003-0591-9465>)
Maintainer: James Pustejovsky <jepusto@gmail.com>

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Package CLA updated to version 0.95-1 with previous version 0.95-0 dated 2019-03-13

Title: Critical Line Algorithm in Pure R
Description: Implements 'Markovitz' Critical Line Algorithm ('CLA') for classical mean-variance portfolio optimization, see Markovitz (1952) <doi:10.2307/2975974>. Care has been taken for correctness in light of previous buggy implementations.
Author: Yanhao Shi <syhelena@163.com>, Martin Maechler <maechler@stat.math.ethz.ch>
Maintainer: Martin Maechler <maechler@stat.math.ethz.ch>

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Package dendextend updated to version 1.12.0 with previous version 1.10.0 dated 2019-03-15

Title: Extending 'dendrogram' Functionality in R
Description: Offers a set of functions for extending 'dendrogram' objects in R, letting you visualize and compare trees of 'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters - the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different 'dendrograms' to one another.
Author: Tal Galili [aut, cre, cph] (https://www.r-statistics.com), Yoav Benjamini [ths], Gavin Simpson [ctb], Gregory Jefferis [aut, ctb] (imported code from his dendroextras package), Marco Gallotta [ctb] (a.k.a: marcog), Johan Renaudie [ctb] (https://github.com/plannapus), The R Core Team [ctb] (Thanks for the Infastructure, and code in the examples), Kurt Hornik [ctb], Uwe Ligges [ctb], Andrej-Nikolai Spiess [ctb], Steve Horvath [ctb], Peter Langfelder [ctb], skullkey [ctb], Mark Van Der Loo [ctb] (https://github.com/markvanderloo d3dendrogram), Andrie de Vries [ctb] (ggdendro author), Zuguang Gu [ctb] (circlize author), Cath [ctb] (https://github.com/CathG), John Ma [ctb] (https://github.com/JohnMCMa), Krzysiek G [ctb] (https://github.com/storaged), Manuela Hummel [ctb] (https://github.com/hummelma), Chase Clark [ctb] (https://github.com/chasemc), Lucas Graybuck [ctb] (https://github.com/hypercompetent), jdetribol [ctb] (https://github.com/jdetribol), Ben Ho [ctb] (https://github.com/SplitInf), Samuel Perreault [ctb] (https://github.com/samperochkin), Christian Hennig [ctb] (http://www.homepages.ucl.ac.uk/~ucakche/)
Maintainer: Tal Galili <tal.galili@gmail.com>

Diff between dendextend versions 1.10.0 dated 2019-03-15 and 1.12.0 dated 2019-05-11

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Package smurf updated to version 1.0.1 with previous version 1.0.0 dated 2018-12-09

Title: Sparse Multi-Type Regularized Feature Modeling
Description: Implementation of the SMuRF algorithm of Devriendt et al. (2018) <arXiv:1810.03136> to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood.
Author: Tom Reynkens [aut, cre] (<https://orcid.org/0000-0002-5516-5107>), Sander Devriendt [aut], Katrien Antonio [aut]
Maintainer: Tom Reynkens <tomreynkens@hotmail.com>

Diff between smurf versions 1.0.0 dated 2018-12-09 and 1.0.1 dated 2019-05-11

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Package BradleyTerry2 updated to version 1.1-0 with previous version 1.0-9 dated 2019-02-25

Title: Bradley-Terry Models
Description: Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.
Author: Heather Turner [aut, cre], David Firth [aut]
Maintainer: Heather Turner <ht@heatherturner.net>

Diff between BradleyTerry2 versions 1.0-9 dated 2019-02-25 and 1.1-0 dated 2019-05-11

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Package sfsmisc updated to version 1.1-4 with previous version 1.1-3 dated 2018-11-27

Title: Utilities from 'Seminar fuer Statistik' ETH Zurich
Description: Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, some of which were ported from S-plus in the 1990's. For graphics, have pretty (Log-scale) axes, an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole().
Author: Martin Maechler [aut, cre] (<https://orcid.org/0000-0002-8685-9910>), Werner Stahel [ctb] (Functions: compresid2way(), f.robftest(), last(), p.scales(), p.dnorm()), Andreas Ruckstuhl [ctb] (Functions: p.arrows(), p.profileTraces(), p.res.2x()), Christian Keller [ctb] (Functions: histBxp(), p.tachoPlot()), Kjetil Halvorsen [ctb] (Functions: KSd(), ecdf.ksCI()), Alain Hauser [ctb] (Functions: cairoSwd(), is.whole(), toLatex.numeric()*), Christoph Buser [ctb] (to function Duplicated()), Lorenz Gygax [ctb] (to function p.res.2fact()), Bill Venables [ctb] (Functions: empty.dimnames(), primes()), Tony Plate [ctb] (to inv.seq()), Isabelle Flckiger [ctb], Marcel Wolbers [ctb], Markus Keller [ctb], Sandrine Dudoit [ctb], Jane Fridlyand [ctb], Greg Snow [ctb] (to loessDemo()), Henrik Aa. Nielsen [ctb] (to loessDemo()), Vincent Carey [ctb], Ben Bolker [ctb], Philippe Grosjean [ctb], Frdric Ibanez [ctb], Caterina Savi [ctb], Charles Geyer [ctb], Jens Oehlschlgel [ctb]
Maintainer: Martin Maechler <maechler@stat.math.ethz.ch>

Diff between sfsmisc versions 1.1-3 dated 2018-11-27 and 1.1-4 dated 2019-05-11

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Package seg updated to version 0.5-6 with previous version 0.5-5 dated 2018-09-25

Title: Measuring Spatial Segregation
Description: Measuring spatial segregation. The methods implemented in this package include White's P index (1983) <doi:10.1086/227768>, Morrill's D(adj) (1991), Wong's D(w) and D(s) (1993) <doi:10.1080/00420989320080551>, and Reardon and O'Sullivan's set of spatial segregation measures (2004) <doi:10.1111/j.0081-1750.2004.00150.x>.
Author: Seong-Yun Hong <syhong@khu.ac.kr> and David O'Sullivan <david.osullivan@vuw.ac.nz>, with code contributions by Benjamin Jarvis and Changlock Choi
Maintainer: Seong-Yun Hong <syhong@khu.ac.kr>

Diff between seg versions 0.5-5 dated 2018-09-25 and 0.5-6 dated 2019-05-11

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Package riingo updated to version 0.2.0 with previous version 0.1.0 dated 2018-04-16

Title: An R Interface to the 'Tiingo' Stock Price API
Description: Functionality to download stock prices, cryptocurrency data, and more from the 'Tiingo' API <https://api.tiingo.com/>.
Author: Davis Vaughan [aut, cre], Matt Dancho [aut]
Maintainer: Davis Vaughan <davis@rstudio.com>

Diff between riingo versions 0.1.0 dated 2018-04-16 and 0.2.0 dated 2019-05-11

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Package jsmodule updated to version 0.8.2 with previous version 0.8.1 dated 2019-04-19

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 DoE.wrapper updated to version 0.10 with previous version 0.9 dated 2017-09-15

Title: Wrapper Package for Design of Experiments Functionality
Description: Various kinds of designs for (industrial) experiments can be created. The package uses, and sometimes enhances, design generation routines from other packages. So far, response surface designs from package rsm, latin hypercube samples from packages lhs and DiceDesign, and D-optimal designs from package AlgDesign have been implemented.
Author: Ulrike Groemping [aut, cre], Lenth Russ [ctb]
Maintainer: Ulrike Groemping <groemping@beuth-hochschule.de>

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