Fri, 28 Feb 2020

Package sp updated to version 1.4-1 with previous version 1.4-0 dated 2020-02-21

Title: Classes and Methods for Spatial Data
Description: Classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc.
Author: Edzer Pebesma [aut, cre], Roger Bivand [aut], Barry Rowlingson [ctb], Virgilio Gomez-Rubio [ctb], Robert Hijmans [ctb], Michael Sumner [ctb], Don MacQueen [ctb], Jim Lemon [ctb], Josh O'Brien [ctb], Joseph O'Rourke [ctb]
Maintainer: Edzer Pebesma <edzer.pebesma@uni-muenster.de>

Diff between sp versions 1.4-0 dated 2020-02-21 and 1.4-1 dated 2020-02-28

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New package link2GI with initial version 0.4.3
Package: link2GI
Type: Package
Title: Linking Geographic Information Systems, Remote Sensing and Other Command Line Tools
Version: 0.4.3
Date: 2020-02-22
Authors@R: c(person("Chris", "Reudenbach", email = "reudenbach@uni-marburg.de", role = c("cre", "aut")), person("Tim", "Appelhans", email = "tim.appelhans@gmail.com>", role = c("ctb")) )
Encoding: UTF-8
Maintainer: Chris Reudenbach <reudenbach@uni-marburg.de>
Description: Functions to simplify the linking of open source GIS and remote sensing related command line interfaces.
URL: https://github.com/r-spatial/link2GI/, https://r-spatial.github.io/link2GI/
BugReports: https://github.com/r-spatial/link2GI/issues/
License: GPL (>= 3) | file LICENSE
Depends: R (>= 3.5.0)
Imports: devtools, R.utils,roxygen2,sf,stringr,raster, methods
SystemRequirements: GNU make
RoxygenNote: 7.0.2
Suggests: knitr, rmarkdown, rgdal, sp, RSAGA, rgrass7, curl, listviewer
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-02-24 09:45:32 UTC; creu
Author: Chris Reudenbach [cre, aut], Tim Appelhans [ctb]
Repository: CRAN
Date/Publication: 2020-02-28 23:50:02 UTC

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New package DCEM with initial version 2.0.1
Package: DCEM
Type: Package
Title: Clustering for Multivariate and Univariate Data Using Expectation Maximization Algorithm
Version: 2.0.1
Authors@R: c( person("Sharma", "Parichit", email = "parishar@iu.edu", role = c("aut","cre", "ctb")), person("Kurban", "Hasan", role = c("aut","ctb")), person("Jenne", "Mark", role = c("aut","ctb")), person("Dalkilic", "Mehmet", role = "aut"))
Maintainer: Sharma Parichit <parishar@iu.edu>
Description: Implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering finite gaussian mixture models for both multivariate and univariate datasets. The initialization is done by randomly selecting the samples from the dataset as the mean (meu) of the Gaussian(s). This version implements the faster alternative EM* that avoids revisiting data by leveraging the heap structure. The algorithm returns a set of Gaussian parameters-posterior probabilities, mean (meu), co-variance matrices (multivariate)/standard-deviation (univariate) and priors. Reference: Hasan Kurban, Mark Jenne, Mehmet M. Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>. This work is partially supported by NCI Grant 1R01CA213466-01.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: mvtnorm (>= 1.0.7), matrixcalc (>= 1.0.3), MASS (>= 7.3.49), Rcpp (>= 1.0.2)
LinkingTo: Rcpp
RoxygenNote: 6.1.1
Depends: R(>= 3.5.0)
URL: https://github.iu.edu/parishar/DCEM
BugReports: https://github.iu.edu/parishar/DCEM/issues
NeedsCompilation: yes
Packaged: 2020-02-27 02:37:51 UTC; schmuck
Author: Sharma Parichit [aut, cre, ctb], Kurban Hasan [aut, ctb], Jenne Mark [aut, ctb], Dalkilic Mehmet [aut]
Repository: CRAN
Date/Publication: 2020-02-28 23:40:02 UTC

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New package ursa with initial version 3.8.16
Package: ursa
Type: Package
Title: Non-Interactive Spatial Tools for Raster Processing and Visualization
Version: 3.8.16
Authors@R: person(given="Nikita",family="Platonov",role=c("aut","cre"),email="platonov@sevin.ru",comment=c(ORCID="0000-0001-7196-7882"))
Author: Nikita Platonov [aut, cre] (<https://orcid.org/0000-0001-7196-7882>)
Maintainer: Nikita Platonov <platonov@sevin.ru>
Description: S3 classes and methods for manipulation with georeferenced raster data: reading/writing, processing, multi-panel visualization.
License: GPL (>= 2)
URL: https://github.com/nplatonov/ursa
BugReports: https://github.com/nplatonov/ursa/issues
Depends: R (>= 3.0.0)
Imports: utils, graphics, grDevices, stats, rgdal, png, jpeg
Suggests: proj4, sf (>= 0.6-1), raster, ncdf4, locfit, knitr, tcltk, sp, methods, fasterize, IRdisplay, caTools, ggmap, shiny, rgeos, tools, webp, htmlwidgets, htmltools, leaflet, leafem, leafpop, RColorBrewer
NeedsCompilation: yes
ByteCompile: no
Packaged: 2020-02-27 13:31:18 UTC; pl
Repository: CRAN
Date/Publication: 2020-02-28 22:40:02 UTC

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Package sra updated to version 0.1.3 with previous version 0.1.1 dated 2015-01-08

Title: Selection Response Analysis
Description: Artificial selection through selective breeding is an efficient way to induce changes in traits of interest in experimental populations. This package (sra) provides a set of tools to analyse artificial-selection response datasets. The data typically feature for several generations the average value of a trait in a population, the variance of the trait, the population size and the average value of the parents that were chosen to breed. Sra implements two families of models aiming at describing the dynamics of the genetic architecture of the trait during the selection response. The first family relies on purely descriptive (phenomenological) models, based on an autoregressive framework. The second family provides different mechanistic models, accounting e.g. for inbreeding, mutations, genetic and environmental canalization, or epistasis. The parameters underlying the dynamics of the time series are estimated by maximum likelihood. The sra package thus provides (i) a wrapper for the R functions mle() and optim() aiming at fitting in a convenient way a predetermined set of models, and (ii) some functions to plot and analyze the output of the models.
Author: Arnaud Le Rouzic
Maintainer: Arnaud Le Rouzic <lerouzic@egce.cnrs-gif.fr>

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Package plm updated to version 2.2-3 with previous version 2.2-2 dated 2020-02-21

Title: Linear Models for Panel Data
Description: A set of estimators and tests for panel data econometrics, as described in Baltagi (2013) Econometric Analysis of Panel Data, ISBN-13:978-1-118-67232-7, Hsiao (2014) Analysis of Panel Data <doi:10.1017/CBO9781139839327> and Croissant and Millo (2018), Panel Data Econometrics with R, ISBN:978-1-118-94918-4.
Author: Yves Croissant [aut, cre], Giovanni Millo [aut], Kevin Tappe [aut], Ott Toomet [ctb], Christian Kleiber [ctb], Achim Zeileis [ctb], Arne Henningsen [ctb], Liviu Andronic [ctb], Nina Schoenfelder [ctb]
Maintainer: Yves Croissant <yves.croissant@univ-reunion.fr>

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New package pcadapt with initial version 4.3.1
Package: pcadapt
Type: Package
Title: Fast Principal Component Analysis for Outlier Detection
Version: 4.3.1
Date: 2020-01-31
Authors@R: c( person("Keurcien", "Luu", role = c("aut")), person("Michael", "Blum", role = c("aut")), person("Florian", "Privé", role = c("aut", "cre"), email = "florian.prive.21@gmail.com"), person("Eric", "Bazin", role = c("ctb")), person("Nicolas", "Duforet-Frebourg", role = c("ctb")))
Description: Methods to detect genetic markers involved in biological adaptation. 'pcadapt' provides statistical tools for outlier detection based on Principal Component Analysis. Implements the method described in (Luu, 2016) <DOI:10.1111/1755-0998.12592>.
License: GPL (>= 2)
Imports: bigutilsr (>= 0.3), data.table, ggplot2, magrittr, mmapcharr (>= 0.3), Rcpp (>= 0.12.8), RSpectra
LinkingTo: Rcpp, rmio, mmapcharr
LazyData: TRUE
Suggests: covr, knitr, plotly, rmarkdown, shiny, testthat, vcfR
RoxygenNote: 7.0.2
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2020-02-28 17:51:47 UTC; au639593
Author: Keurcien Luu [aut], Michael Blum [aut], Florian Privé [aut, cre], Eric Bazin [ctb], Nicolas Duforet-Frebourg [ctb]
Maintainer: Florian Privé <florian.prive.21@gmail.com>
Repository: CRAN
Date/Publication: 2020-02-28 22:50:09 UTC

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Package forecastML updated to version 0.8.0 with previous version 0.7.0 dated 2020-01-07

Title: Time Series Forecasting with Machine Learning Methods
Description: The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.
Author: Nickalus Redell
Maintainer: Nickalus Redell <nickalusredell@gmail.com>

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New package fdaPDE with initial version 1.0-6
Package: fdaPDE
Version: 1.0-6
Date: 2020-02-26
Title: Functional Data Analysis and Partial Differential Equations (PDE); Statistical Analysis of Functional and Spatial Data, Based on Regression with PDE Regularization
Authors@R: c(person("Eardi", "Lila", role = c("aut"), email = "elila@uw.edu"), person("Laura M.", "Sangalli", role = c("aut"), email = "laura.sangalli@polimi.it"), person("Eleonora", "Arnone", role = c("aut", "cre"), email = "eleonora.arnone@polimi.it"), person("Jim", "Ramsay", role = "aut", email = " ramsay@psych.mcgill.ca"), person("Luca", "Formaggia", role = "aut", email = " luca.formaggia@polimi.it"), person("Alessandra", "Colli", role = "ctb"), person("Luca", "Colombo", role = "ctb"), person("Carlo", "de Falco", role = "ctb"))
Maintainer: Eleonora Arnone <eleonora.arnone@polimi.it>
Depends: R (>= 3.5.0), stats, grDevices, graphics, geometry, rgl, Matrix, plot3D, plot3Drgl
LinkingTo: RcppEigen
Suggests: MASS, testthat
Description: An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization.
License: CC BY-NC-SA 4.0
Copyright: See the individual source files for copyrights information
NeedsCompilation: yes
SystemRequirements: C++11
RoxygenNote: 7.0.2
Encoding: UTF-8
Packaged: 2020-02-27 09:02:23 UTC; eleon_000
Author: Eardi Lila [aut], Laura M. Sangalli [aut], Eleonora Arnone [aut, cre], Jim Ramsay [aut], Luca Formaggia [aut], Alessandra Colli [ctb], Luca Colombo [ctb], Carlo de Falco [ctb]
Repository: CRAN
Date/Publication: 2020-02-28 22:40:17 UTC

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New package dti with initial version 1.5.1
Package: dti
Version: 1.5.1
Date: 2020-02-25
Title: Analysis of Diffusion Weighted Imaging (DWI) Data
Authors@R: c(person("Karsten", "Tabelow", role = c("aut", "cre"), email = "karsten.tabelow@wias-berlin.de"), person("Joerg", "Polzehl", role = c("aut"), email = "joerg.polzehl@wias-berlin.de"), person("Felix", "Anker", role = c("ctb")))
Author: Karsten Tabelow [aut, cre], Joerg Polzehl [aut], Felix Anker [ctb]
Maintainer: Karsten Tabelow <karsten.tabelow@wias-berlin.de>
Depends: R (>= 3.5.0), awsMethods (>= 1.1-1)
SystemRequirements: gsl
Imports: methods, parallel, adimpro (>= 0.9), aws (>= 2.4.1), rgl, oro.nifti (>= 0.3.9), oro.dicom, gsl, quadprog
LazyData: TRUE
Description: Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging modality, that measures diffusion of water in tissues like the human brain. The package contains R-functions to process diffusion-weighted data. The functionality includes diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), modeling for high angular resolution diffusion weighted imaging (HARDI) using Q-ball-reconstruction and tensor mixture models, several methods for structural adaptive smoothing including POAS and msPOAS, and a streamline fiber tracking for tensor and tensor mixture models. The package provides functionality to manipulate and visualize results in 2D and 3D.
License: GPL (>= 2)
Copyright: This package is Copyright (C) 2005-2020 Weierstrass Institute for Applied Analysis and Stochastics.
URL: http://www.wias-berlin.de/research/ats/imaging/
Suggests: covr
RoxygenNote: 6.1.0
NeedsCompilation: yes
Packaged: 2020-02-27 08:24:57 UTC; tabelow
Repository: CRAN
Date/Publication: 2020-02-28 22:40:20 UTC

More information about dti at CRAN
Permanent link

Package dendextend updated to version 1.13.4 with previous version 1.13.3 dated 2020-02-08

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/), David Bradley [ctb] (https://github.com/DBradley27)
Maintainer: Tal Galili <tal.galili@gmail.com>

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More information about dendextend at CRAN
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Package mlrMBO updated to version 1.1.4 with previous version 1.1.3 dated 2019-12-02

Title: Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions
Description: Flexible and comprehensive R toolbox for model-based optimization ('MBO'), also known as Bayesian optimization. It implements the Efficient Global Optimization Algorithm and is designed for both single- and multi- objective optimization with mixed continuous, categorical and conditional parameters. The machine learning toolbox 'mlr' provide dozens of regression learners to model the performance of the target algorithm with respect to the parameter settings. It provides many different infill criteria to guide the search process. Additional features include multi-point batch proposal, parallel execution as well as visualization and sophisticated logging mechanisms, which is especially useful for teaching and understanding of algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that single components can be easily replaced or adapted by the user for specific use cases.
Author: Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Jakob Richter [aut, cre] (<https://orcid.org/0000-0003-4481-5554>), Jakob Bossek [aut] (<https://orcid.org/0000-0002-4121-4668>), Daniel Horn [aut], Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Janek Thomas [aut] (<https://orcid.org/0000-0003-4511-6245>)
Maintainer: Jakob Richter <code@jakob-r.de>

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More information about mlrMBO at CRAN
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Package MatTransMix updated to version 0.1.7 with previous version 0.1.6 dated 2019-11-08

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 <xzhu20@cba.ua.edu>

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Package jackalope updated to version 1.1.0 with previous version 1.0.0 dated 2019-11-22

Title: A Swift, Versatile Phylogenomic and High-Throughput Sequencing Simulator
Description: Simply and efficiently simulates (i) variants from reference genomes and (ii) reads from both Illumina <https://www.illumina.com/> and Pacific Biosciences (PacBio) <https://www.pacb.com/> platforms. It can either read reference genomes from FASTA files or simulate new ones. Genomic variants can be simulated using summary statistics, phylogenies, Variant Call Format (VCF) files, and coalescent simulations—the latter of which can include selection, recombination, and demographic fluctuations. 'jackalope' can simulate single, paired-end, or mate-pair Illumina reads, as well as PacBio reads. These simulations include sequencing errors, mapping qualities, multiplexing, and optical/polymerase chain reaction (PCR) duplicates. Simulating Illumina sequencing is based on ART by Huang et al. (2012) <doi:10.1093/bioinformatics/btr708>. PacBio sequencing simulation is based on SimLoRD by Stöcker et al. (2016) <doi:10.1093/bioinformatics/btw286>. All outputs can be written to standard file formats.
Author: Lucas A. Nell [cph, aut, cre] (<https://orcid.org/0000-0003-3209-0517>)
Maintainer: Lucas A. Nell <lucas@lucasnell.com>

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Package implicitMeasures updated to version 0.1.1 with previous version 0.1.0 dated 2019-08-20

Title: Computes the Scores for Different Implicit Measures
Description: A tool for computing the scores for the Implicit Association Test (IAT; Greenwald, McGhee & Schwartz (1998) <doi:10.1037/0022-3514.74.6.1464>) and the Single Category-IAT (SC-IAT: Karpinski & Steinman (2006) <doi:10.1037/0022-3514.91.1.16>). Functions for preparing the data (both for the IAT and the SC-IAT), plotting the results, and obtaining a table with the scores of implicit measures descriptive statistics are provided.
Author: Ottavia M. Epifania [aut, cre], Pasquale Anselmi [ctb], Egidio Robusto [ctb]
Maintainer: Ottavia M. Epifania <otta.epifania@gmail.com>

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Package WGCNA updated to version 1.69 with previous version 1.68 dated 2019-05-23

Title: Weighted Correlation Network Analysis
Description: Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559>. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.
Author: Peter Langfelder <Peter.Langfelder@gmail.com> and Steve Horvath <SHorvath@mednet.ucla.edu> with contributions by Chaochao Cai, Jun Dong, Jeremy Miller, Lin Song, Andy Yip, and Bin Zhang
Maintainer: Peter Langfelder <Peter.Langfelder@gmail.com>

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Package fptdApprox updated to version 2.2 with previous version 2.1 dated 2015-08-25

Title: Approximation of First-Passage-Time Densities for Diffusion Processes
Description: Efficient approximation of first-passage-time densities for diffusion processes based on the First-Passage-Time Location (FPTL) function.
Author: Patricia Rom<e1>n-Rom<e1>n, Juan J. Serrano-P<e9>rez and Francisco Torres-Ruiz.
Maintainer: Juan J. Serrano-P<e9>rez <jjserra@ugr.es>

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Package xlsx updated to version 0.6.3 with previous version 0.6.2 dated 2020-02-12

Title: Read, Write, Format Excel 2007 and Excel 97/2000/XP/2003 Files
Description: Provide R functions to read/write/format Excel 2007 and Excel 97/2000/XP/2003 file formats.
Author: Adrian Dragulescu [aut], Cole Arendt [aut, cre]
Maintainer: Cole Arendt <cole.arendt@outlook.com>

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Package tsmp updated to version 0.4.8 with previous version 0.3.5 dated 2019-06-05

Title: Time Series with Matrix Profile
Description: A toolkit implementing the Matrix Profile concept that was created by CS-UCR <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.
Author: Francisco Bischoff [aut, cre] (<https://orcid.org/0000-0002-5301-8672>), Michael Yeh [res, ccp, ctb] (<https://orcid.org/0000-0002-9807-2963>), Diego Silva [res, ccp, ctb] (<https://orcid.org/0000-0002-5184-9413>), Yan Zhu [res, ccp, ctb] (<https://orcid.org/0000-0002-5952-2108>), Hoang Dau [res, ccp, ctb] (<https://orcid.org/0000-0003-2439-5185>), Michele Linardi [res, ccp, ctb] (<https://orcid.org/0000-0002-3249-2068>)
Maintainer: Francisco Bischoff <fbischoff@med.up.pt>

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Package TRAMPR updated to version 1.0-9 with previous version 1.0-8 dated 2016-06-13

Title: 'TRFLP' Analysis and Matching Package for R
Description: Matching terminal restriction fragment length polymorphism ('TRFLP') profiles between unknown samples and a database of known samples. 'TRAMPR' facilitates analysis of many unknown profiles at once, and provides tools for working directly with electrophoresis output through to generating summaries suitable for community analyses with R's rich set of statistical functions. 'TRAMPR' also resolves the issues of multiple 'TRFLP' profiles within a species, and shared 'TRFLP' profiles across species.
Author: Rich FitzJohn & Ian Dickie
Maintainer: Rich FitzJohn <rich.fitzjohn@gmail.com>

Diff between TRAMPR versions 1.0-8 dated 2016-06-13 and 1.0-9 dated 2020-02-28

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Package mefa4 updated to version 0.3-7 with previous version 0.3-6 dated 2019-06-21

Title: Multivariate Data Handling with S4 Classes and Sparse Matrices
Description: An S4 update of the 'mefa' package using sparse matrices for enhanced efficiency. Sparse array-like objects are supported via lists of sparse matrices.
Author: Peter Solymos
Maintainer: Peter Solymos <solymos@ualberta.ca>

Diff between mefa4 versions 0.3-6 dated 2019-06-21 and 0.3-7 dated 2020-02-28

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Package healthcareai updated to version 2.4.0 with previous version 2.3.0 dated 2018-12-12

Title: Tools for Healthcare Machine Learning
Description: A machine learning toolbox tailored to healthcare data.
Author: Levi Thatcher [aut], Michael Levy [aut], Mike Mastanduno [aut, cre], Taylor Larsen [aut], Taylor Miller [aut], Rex Sumsion [aut]
Maintainer: Mike Mastanduno <michael.mastanduno@healthcatalyst.com>

Diff between healthcareai versions 2.3.0 dated 2018-12-12 and 2.4.0 dated 2020-02-28

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Package gpclib updated to version 1.5-6 with previous version 1.5-5 dated 2013-04-01

Title: General Polygon Clipping Library for R
Description: General polygon clipping routines for R based on Alan Murta's C library.
Author: Roger D. Peng <rpeng@jhsph.edu> with contributions from Duncan Murdoch and Barry Rowlingson; GPC library by Alan Murta
Maintainer: Roger D. Peng <rpeng@jhsph.edu>

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Package esvis updated to version 0.3.0 with previous version 0.2.0 dated 2018-04-09

Title: Visualization and Estimation of Effect Sizes
Description: A variety of methods are provided to estimate and visualize distributional differences in terms of effect sizes. Particular emphasis is upon evaluating differences between two or more distributions across the entire scale, rather than at a single point (e.g., differences in means). For example, Probability-Probability (PP) plots display the difference between two or more distributions, matched by their empirical CDFs (see Ho and Reardon, 2012; <doi:10.3102/1076998611411918>), allowing for examinations of where on the scale distributional differences are largest or smallest. The area under the PP curve (AUC) is an effect-size metric, corresponding to the probability that a randomly selected observation from the x-axis distribution will have a higher value than a randomly selected observation from the y-axis distribution. Binned effect size plots are also available, in which the distributions are split into bins (set by the user) and separate effect sizes (Cohen's d) are produced for each bin - again providing a means to evaluate the consistency (or lack thereof) of the difference between two or more distributions at different points on the scale. Evaluation of empirical CDFs is also provided, with built-in arguments for providing annotations to help evaluate distributional differences at specific points (e.g., semi-transparent shading). All function take a consistent argument structure. Calculation of specific effect sizes is also possible. The following effect sizes are estimable: (a) Cohen's d, (b) Hedges' g, (c) percentage above a cut, (d) transformed (normalized) percentage above a cut, (e) area under the PP curve, and (f) the V statistic (see Ho, 2009; <doi:10.3102/1076998609332755>), which essentially transforms the area under the curve to standard deviation units. By default, effect sizes are calculated for all possible pairwise comparisons, but a reference group (distribution) can be specified.
Author: Daniel Anderson [aut, cre]
Maintainer: Daniel Anderson <daniela@uoregon.edu>

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Package anipaths updated to version 0.9.8 with previous version 0.9.7 dated 2019-02-26

Title: Animation of Observed Trajectories Using Spline-Based Interpolation
Description: Animation of observed trajectories using spline-based interpolation (see for example, Buderman, F. E., Hooten, M. B., Ivan, J. S. and Shenk, T. M. (2016), <doi:10.1111/2041-210X.12465> "A functional model for characterizing long-distance movement behaviour". Methods Ecol Evol). Intended to be used exploratory data analysis, and perhaps for preparation of presentations.
Author: Henry Scharf
Maintainer: Henry Scharf <hscharf@sdsu.edu>

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New package zoon with initial version 0.6.5
Package: zoon
Type: Package
Title: Reproducible, Accessible & Shareable Species Distribution Modelling
Version: 0.6.5
Date: 2020-02-27
Authors@R: c(person("Tom", "August", role = c("aut", "cre"), email = "tomaug@ceh.ac.uk"), person("Tim", "Lucas", role = "aut", comment = c(ORCID = "0000-0003-4694-8107")), person("Nick", "Golding", role = "aut"), person("Emiel", "van Loon", role = "ctb"), person("Greg", "McInerny", role = "aut"))
Maintainer: Tom August <tomaug@ceh.ac.uk>
Description: Reproducible and remixable species distribution modelling. The package reads user submitted modules from an online repository, runs full species distribution modelling workflows and returns output that is fully reproducible. For examples and detailed discussion refer to: N.Golding et al. (2017) 'The zoon r package for reproducible and shareable species distribution modelling'. Methods in Ecology and Evolution. <doi:10.1111/2041-210X.12858>. The package 'SDMTools' is used for testing and, though this package is archived, you can access it here if needed, <https://cran.r-project.org/src/contrib/Archive/SDMTools/>.
License: BSD_3_clause + file LICENSE
Imports: dismo, methods, plyr, randomForest, RCurl, rfigshare, rgdal, roxygen2, rworldmap, sp, testthat
Depends: raster (>= 2.4-20), R (>= 3.2.0)
Suggests: knitr, maxlike, SDMTools
VignetteBuilder: knitr
LazyData: TRUE
URL: https://github.com/zoonproject/zoon
BugReports: https://github.com/zoonproject/zoon/issues
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-02-28 13:09:05 UTC; tomaug
Author: Tom August [aut, cre], Tim Lucas [aut] (<https://orcid.org/0000-0003-4694-8107>), Nick Golding [aut], Emiel van Loon [ctb], Greg McInerny [aut]
Repository: CRAN
Date/Publication: 2020-02-28 16:30:02 UTC

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Package transplantr updated to version 0.2.0 with previous version 0.1.0 dated 2020-01-07

Title: Audit and Research Functions for Transplantation
Description: A set of vectorised functions to calculate medical equations used in transplantation, focused mainly on transplantation of abdominal organs. These functions include donor and recipient risk indices as used by NHS Blood & Transplant, OPTN/UNOS and Eurotransplant, tools for quantifying HLA mismatches, functions for calculating estimated glomerular filtration rate (eGFR), a function to calculate the APRI (AST to platelet ratio) score used in initial screening of suitability to receive a transplant from a hepatitis C seropositive donor and some biochemical unit converter functions. All functions are designed to work with either US or international units. References for the equations are provided in the vignettes and function documentation.
Author: John Asher [aut, cre] (<https://orcid.org/0000-0001-8735-6453>)
Maintainer: John Asher <john.asher@outlook.com>

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Package tidyfst updated to version 0.6.9 with previous version 0.6.6 dated 2020-02-24

Title: Tidy Verbs for Fast Data Manipulation
Description: A toolkit of tidy data manipulation verbs with 'data.table' as the backend. Combining the merits of syntax elegance from 'dplyr' and computing performance from 'data.table', 'tidyfst' intends to provide users with state-of-the-art data manipulation tools with least pain. This package is inspired by 'maditr', but follows a different philosophy of design, such as prohibiting in place replacement and used a "_dt" suffix API. Also, 'tidyfst' would introduce more tidy data verbs from (and for) other packages, including but not limited to 'fst','tidyverse' and 'data.table'.
Author: Tian-Yuan Huang [aut, cre] (<https://orcid.org/0000-0002-3591-4203>)
Maintainer: Tian-Yuan Huang <huang.tian-yuan@qq.com>

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Package spork updated to version 0.1.6 with previous version 0.1.1 dated 2020-02-27

Title: Generalized Label Formatting
Description: The 'spork' syntax describes label formatting concisely, supporting mixed nesting of subscripts and superscripts to arbitrary depth. It intends to be easy to read and write in plain text, and easy to convert to equivalent presentations in 'plotmath' and 'latex'. Greek symbols and a multiplication symbol are explicitly supported. See ?as_spork and ?as_previews.
Author: Tim Bergsma
Maintainer: Tim Bergsma <bergsmat@gmail.com>

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New package SDALGCP with initial version 0.3.0
Package: SDALGCP
Title: Spatially Discrete Approximation to Log-Gaussian Cox Processes for Aggregated Disease Count Data
Version: 0.3.0
Authors@R: c( person("Olatunji", "Johnson", email = "olatunjijohnson21111@gmail.com", role = c("aut", "cre")), person("Emanuele", "Giorgi", email = "e.giorgi@lancaster.ac.uk", role = "aut"), person("Peter", "Diggle", email = "p.diggle@lancaster.ac.uk", role = "aut"))
Description: Provides a computationally efficient discrete approximation to log-Gaussian Cox process model for spatially aggregated disease count data. It uses Monte Carlo Maximum Likelihood for model parameter estimation as proposed by Christensen (2004) <doi: 10.1198/106186004X2525> and delivers prediction of spatially discrete and continuous relative risk. It performs inference for static spatial and spatio-temporal dataset. The details of the methods are provided in Johnson et al (2019) <doi:10.1002/sim.8339>.
Depends: R (>= 3.4.0)
License: GPL-2 | GPL-3
Encoding: UTF-8
LazyData: true
Imports: pdist(>= 1.2), Matrix(>= 1.2.14), PrevMap(>= 1.4.1), raster(>= 2.6.7), sp(>= 1.2.7), spatstat(>= 1.55.1), splancs(>= 2.1.40), maptools(>= 0.9.2), progress(>= 1.1.2), methods, spacetime(>= 1.2.2), mapview(>= 2.6.0), geoR(>= 1.7-5.2.1)
RoxygenNote: 7.0.0
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-02-27 14:53:30 UTC; olatunji
Author: Olatunji Johnson [aut, cre], Emanuele Giorgi [aut], Peter Diggle [aut]
Maintainer: Olatunji Johnson <olatunjijohnson21111@gmail.com>
Repository: CRAN
Date/Publication: 2020-02-28 16:40:02 UTC

More information about SDALGCP at CRAN
Permanent link

Package politeness updated to version 0.5.1 with previous version 0.5.0 dated 2020-02-26

Title: Detecting Politeness Features in Text
Description: Detecting markers of politeness in English natural language. This package allows researchers to easily visualize and quantify politeness between groups of documents. This package combines prior research on the linguistic markers of politeness (Brown & Levinson, 1987 <http://psycnet.apa.org/record/1987-97641-000>; Danescu-Niculescu-Mizil et al., 2013 <arXiv:1306.6078>; Voigt et al., 2017 <doi:10.1073/pnas.1702413114>). We thank the Spencer Foundation, the Hewlett Foundation, and Harvard's Institute for Quantitative Social Science for support.
Author: Mike Yeomans, Alejandro Kantor, Dustin Tingley
Maintainer: Mike Yeomans <mk.yeomans@gmail.com>

Diff between politeness versions 0.5.0 dated 2020-02-26 and 0.5.1 dated 2020-02-28

 DESCRIPTION                         |   10 +++++-----
 MD5                                 |    7 ++++---
 data/receptTrain.rdata              |binary
 inst/doc/politeness.html            |    6 +++---
 tests/testthat/test-receptiveness.R |only
 5 files changed, 12 insertions(+), 11 deletions(-)

More information about politeness at CRAN
Permanent link

Package PKNCA updated to version 0.9.2 with previous version 0.9.1 dated 2019-07-29

Title: Perform Pharmacokinetic Non-Compartmental Analysis
Description: Compute standard Non-Compartmental Analysis (NCA) parameters for typical pharmacokinetic analyses and summarize them.
Author: Bill Denney [aut, cre] (<https://orcid.org/0000-0002-5759-428X>), Clare Buckeridge [aut], Sridhar Duvvuri [ctb]
Maintainer: Bill Denney <wdenney@humanpredictions.com>

Diff between PKNCA versions 0.9.1 dated 2019-07-29 and 0.9.2 dated 2020-02-28

 PKNCA-0.9.1/PKNCA/R/actual_time_calc.R                                                 |only
 PKNCA-0.9.2/PKNCA/DESCRIPTION                                                          |   18 
 PKNCA-0.9.2/PKNCA/MD5                                                                  |  340 +-
 PKNCA-0.9.2/PKNCA/NAMESPACE                                                            |    9 
 PKNCA-0.9.2/PKNCA/NEWS.md                                                              |   27 
 PKNCA-0.9.2/PKNCA/R/001-add.interval.col.R                                             |   19 
 PKNCA-0.9.2/PKNCA/R/002-pk.business.rules.R                                            |  108 
 PKNCA-0.9.2/PKNCA/R/AIC.list.R                                                         |    2 
 PKNCA-0.9.2/PKNCA/R/PKNCA.R                                                            |   14 
 PKNCA-0.9.2/PKNCA/R/PKNCA.options.R                                                    |    2 
 PKNCA-0.9.2/PKNCA/R/auc.R                                                              |  140 
 PKNCA-0.9.2/PKNCA/R/aucint.R                                                           |   19 
 PKNCA-0.9.2/PKNCA/R/check.intervals.R                                                  |  206 -
 PKNCA-0.9.2/PKNCA/R/choose.intervals.R                                                 |  100 
 PKNCA-0.9.2/PKNCA/R/class-PKNCAconc.R                                                  |   23 
 PKNCA-0.9.2/PKNCA/R/class-PKNCAdose.R                                                  |    4 
 PKNCA-0.9.2/PKNCA/R/class-PKNCAresults.R                                               |  123 
 PKNCA-0.9.2/PKNCA/R/cleaners.R                                                         |    2 
 PKNCA-0.9.2/PKNCA/R/general.functions.R                                                |   38 
 PKNCA-0.9.2/PKNCA/R/half.life.R                                                        |   68 
 PKNCA-0.9.2/PKNCA/R/interpolate.conc.R                                                 |  227 -
 PKNCA-0.9.2/PKNCA/R/parseFormula.R                                                     |    3 
 PKNCA-0.9.2/PKNCA/R/pk.calc.all.R                                                      |   67 
 PKNCA-0.9.2/PKNCA/R/pk.calc.simple.R                                                   |    9 
 PKNCA-0.9.2/PKNCA/R/provenance.R                                                       |    2 
 PKNCA-0.9.2/PKNCA/R/superposition.R                                                    |    1 
 PKNCA-0.9.2/PKNCA/R/time_calc.R                                                        |only
 PKNCA-0.9.2/PKNCA/R/tss.R                                                              |   14 
 PKNCA-0.9.2/PKNCA/R/tss.monoexponential.R                                              |  114 
 PKNCA-0.9.2/PKNCA/R/tss.stepwise.linear.R                                              |    7 
 PKNCA-0.9.2/PKNCA/README.md                                                            |    4 
 PKNCA-0.9.2/PKNCA/build/vignette.rds                                                   |binary
 PKNCA-0.9.2/PKNCA/inst/doc/AUC-Calculation-with-PKNCA.R                                |   24 
 PKNCA-0.9.2/PKNCA/inst/doc/AUC-Calculation-with-PKNCA.html                             |   11 
 PKNCA-0.9.2/PKNCA/inst/doc/Data-Cleaning-in-PKNCA.html                                 |    7 
 PKNCA-0.9.2/PKNCA/inst/doc/Example-theophylline.R                                      |   30 
 PKNCA-0.9.2/PKNCA/inst/doc/Example-theophylline.html                                   |  175 -
 PKNCA-0.9.2/PKNCA/inst/doc/Introduction-and-Usage.R                                    |   28 
 PKNCA-0.9.2/PKNCA/inst/doc/Introduction-and-Usage.html                                 |  101 
 PKNCA-0.9.2/PKNCA/inst/doc/Methods-for-Dose-Aware-Interpolation-and-Extrapolation.R    |    8 
 PKNCA-0.9.2/PKNCA/inst/doc/Methods-for-Dose-Aware-Interpolation-and-Extrapolation.html |   11 
 PKNCA-0.9.2/PKNCA/inst/doc/Options-for-Controlling-PKNCA.R                             |    4 
 PKNCA-0.9.2/PKNCA/inst/doc/Options-for-Controlling-PKNCA.html                          |   13 
 PKNCA-0.9.2/PKNCA/inst/doc/PKNCA-validation.R                                          |   14 
 PKNCA-0.9.2/PKNCA/inst/doc/PKNCA-validation.Rmd                                        |   10 
 PKNCA-0.9.2/PKNCA/inst/doc/PKNCA-validation.html                                       |   44 
 PKNCA-0.9.2/PKNCA/inst/doc/Selection-of-Calculation-Intervals.R                        |   28 
 PKNCA-0.9.2/PKNCA/inst/doc/Selection-of-Calculation-Intervals.html                     |   11 
 PKNCA-0.9.2/PKNCA/inst/doc/Superposition.R                                             |   26 
 PKNCA-0.9.2/PKNCA/inst/doc/Superposition.html                                          |   21 
 PKNCA-0.9.2/PKNCA/inst/doc/Writing-Parameter-Functions.R                               |    2 
 PKNCA-0.9.2/PKNCA/inst/doc/Writing-Parameter-Functions.Rmd                             |    4 
 PKNCA-0.9.2/PKNCA/inst/doc/Writing-Parameter-Functions.html                            |   14 
 PKNCA-0.9.2/PKNCA/man/AIC.list.Rd                                                      |   58 
 PKNCA-0.9.2/PKNCA/man/PKNCA.Rd                                                         |   72 
 PKNCA-0.9.2/PKNCA/man/PKNCA.choose.option.Rd                                           |   61 
 PKNCA-0.9.2/PKNCA/man/PKNCA.options.Rd                                                 |  125 
 PKNCA-0.9.2/PKNCA/man/PKNCA.options.describe.Rd                                        |   38 
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 PKNCA-0.9.2/PKNCA/man/PKNCAdata.Rd                                                     |  141 
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 PKNCA-0.9.2/PKNCA/man/PKNCAresults.Rd                                                  |   66 
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 PKNCA-0.9.2/PKNCA/man/addProvenance.Rd                                                 |   52 
 PKNCA-0.9.2/PKNCA/man/adj.r.squared.Rd                                                 |   36 
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 PKNCA-0.9.2/PKNCA/man/business.mean.Rd                                                 |  138 
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 PKNCA-0.9.2/PKNCA/man/checkProvenance.Rd                                               |   44 
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 PKNCA-0.9.2/PKNCA/man/clean.conc.blq.Rd                                                |  142 
 PKNCA-0.9.2/PKNCA/man/clean.conc.na.Rd                                                 |   74 
 PKNCA-0.9.2/PKNCA/man/exclude.Rd                                                       |  123 
 PKNCA-0.9.2/PKNCA/man/exclude_nca.Rd                                                   |  117 
 PKNCA-0.9.2/PKNCA/man/find.tau.Rd                                                      |   84 
 PKNCA-0.9.2/PKNCA/man/findOperator.Rd                                                  |   65 
 PKNCA-0.9.2/PKNCA/man/fit_half_life.Rd                                                 |   60 
 PKNCA-0.9.2/PKNCA/man/formula.PKNCAconc.Rd                                             |   44 
 PKNCA-0.9.2/PKNCA/man/formula.parseFormula.Rd                                          |   62 
 PKNCA-0.9.2/PKNCA/man/geomean.Rd                                                       |   82 
 PKNCA-0.9.2/PKNCA/man/get.best.model.Rd                                                |   46 
 PKNCA-0.9.2/PKNCA/man/get.first.model.Rd                                               |   38 
 PKNCA-0.9.2/PKNCA/man/get.interval.cols.Rd                                             |   59 
 PKNCA-0.9.2/PKNCA/man/get.parameter.deps.Rd                                            |   55 
 PKNCA-0.9.2/PKNCA/man/getAttributeColumn.Rd                                            |   48 
 PKNCA-0.9.2/PKNCA/man/getColumnValueOrNot.Rd                                           |   62 
 PKNCA-0.9.2/PKNCA/man/getData.PKNCAconc.Rd                                             |   32 
 PKNCA-0.9.2/PKNCA/man/getData.PKNCAdata.Rd                                             |   26 
 PKNCA-0.9.2/PKNCA/man/getData.PKNCAresults.Rd                                          |   26 
 PKNCA-0.9.2/PKNCA/man/getDataName.Rd                                                   |   93 
 PKNCA-0.9.2/PKNCA/man/getDepVar.Rd                                                     |   53 
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 PKNCA-0.9.2/PKNCA/man/getIndepVar.Rd                                                   |   53 
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 PKNCA-0.9.2/PKNCA/man/merge.splitlist.Rd                                               |   58 
 PKNCA-0.9.2/PKNCA/man/model.frame.PKNCAconc.Rd                                         |   52 
 PKNCA-0.9.2/PKNCA/man/normalize_exclude.Rd                                             |   34 
 PKNCA-0.9.2/PKNCA/man/parseFormula.Rd                                                  |  101 
 PKNCA-0.9.2/PKNCA/man/pk.business.Rd                                                   |   67 
 PKNCA-0.9.2/PKNCA/man/pk.calc.ae.Rd                                                    |   60 
 PKNCA-0.9.2/PKNCA/man/pk.calc.aucint.Rd                                                |  253 +
 PKNCA-0.9.2/PKNCA/man/pk.calc.aucpext.Rd                                               |   52 
 PKNCA-0.9.2/PKNCA/man/pk.calc.auxc.Rd                                                  |  342 +-
 PKNCA-0.9.2/PKNCA/man/pk.calc.c0.Rd                                                    |  147 
 PKNCA-0.9.2/PKNCA/man/pk.calc.cav.Rd                                                   |   48 
 PKNCA-0.9.2/PKNCA/man/pk.calc.ceoi.Rd                                                  |   50 
 PKNCA-0.9.2/PKNCA/man/pk.calc.cl.Rd                                                    |   68 
 PKNCA-0.9.2/PKNCA/man/pk.calc.clast.obs.Rd                                             |   46 
 PKNCA-0.9.2/PKNCA/man/pk.calc.clr.Rd                                                   |   58 
 PKNCA-0.9.2/PKNCA/man/pk.calc.cmax.Rd                                                  |   58 
 PKNCA-0.9.2/PKNCA/man/pk.calc.ctrough.Rd                                               |   44 
 PKNCA-0.9.2/PKNCA/man/pk.calc.deg.fluc.Rd                                              |   48 
 PKNCA-0.9.2/PKNCA/man/pk.calc.dn.Rd                                                    |   42 
 PKNCA-0.9.2/PKNCA/man/pk.calc.f.Rd                                                     |   50 
 PKNCA-0.9.2/PKNCA/man/pk.calc.fe.Rd                                                    |   56 
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 PKNCA-0.9.2/PKNCA/man/pk.calc.kel.Rd                                                   |   36 
 PKNCA-0.9.2/PKNCA/man/pk.calc.mrt.Rd                                                   |   78 
 PKNCA-0.9.2/PKNCA/man/pk.calc.mrt.md.Rd                                                |   72 
 PKNCA-0.9.2/PKNCA/man/pk.calc.ptr.Rd                                                   |   42 
 PKNCA-0.9.2/PKNCA/man/pk.calc.swing.Rd                                                 |   46 
 PKNCA-0.9.2/PKNCA/man/pk.calc.thalf.eff.Rd                                             |   38 
 PKNCA-0.9.2/PKNCA/man/pk.calc.tlag.Rd                                                  |   46 
 PKNCA-0.9.2/PKNCA/man/pk.calc.tlast.Rd                                                 |   62 
 PKNCA-0.9.2/PKNCA/man/pk.calc.tmax.Rd                                                  |   75 
 PKNCA-0.9.2/PKNCA/man/pk.calc.vd.Rd                                                    |   70 
 PKNCA-0.9.2/PKNCA/man/pk.calc.vss.Rd                                                   |   42 
 PKNCA-0.9.2/PKNCA/man/pk.calc.vz.Rd                                                    |   36 
 PKNCA-0.9.2/PKNCA/man/pk.nca.Rd                                                        |   67 
 PKNCA-0.9.2/PKNCA/man/pk.nca.interval.Rd                                               |  158 -
 PKNCA-0.9.2/PKNCA/man/pk.tss.Rd                                                        |   60 
 PKNCA-0.9.2/PKNCA/man/pk.tss.data.prep.Rd                                              |  102 
 PKNCA-0.9.2/PKNCA/man/pk.tss.monoexponential.Rd                                        |  109 
 PKNCA-0.9.2/PKNCA/man/pk.tss.monoexponential.individual.Rd                             |   75 
 PKNCA-0.9.2/PKNCA/man/pk.tss.monoexponential.population.Rd                             |   79 
 PKNCA-0.9.2/PKNCA/man/pk.tss.stepwise.linear.Rd                                        |  106 
 PKNCA-0.9.2/PKNCA/man/print.PKNCAconc.Rd                                               |   66 
 PKNCA-0.9.2/PKNCA/man/print.PKNCAdata.Rd                                               |   32 
 PKNCA-0.9.2/PKNCA/man/print.provenance.Rd                                              |   36 
 PKNCA-0.9.2/PKNCA/man/print.summary_PKNCAresults.Rd                                    |   46 
 PKNCA-0.9.2/PKNCA/man/reexports.Rd                                                     |   30 
 PKNCA-0.9.2/PKNCA/man/roundString.Rd                                                   |   74 
 PKNCA-0.9.2/PKNCA/man/roundingSummarize.Rd                                             |   44 
 PKNCA-0.9.2/PKNCA/man/setAttributeColumn.Rd                                            |   92 
 PKNCA-0.9.2/PKNCA/man/setDuration.Rd                                                   |   58 
 PKNCA-0.9.2/PKNCA/man/setExcludeColumn.Rd                                              |   46 
 PKNCA-0.9.2/PKNCA/man/setRoute.Rd                                                      |   56 
 PKNCA-0.9.2/PKNCA/man/signifString.Rd                                                  |   95 
 PKNCA-0.9.2/PKNCA/man/sort.interval.cols.Rd                                            |   20 
 PKNCA-0.9.2/PKNCA/man/split.PKNCAconc.Rd                                               |   76 
 PKNCA-0.9.2/PKNCA/man/summary.PKNCAdata.Rd                                             |   36 
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 PKNCA-0.9.2/PKNCA/man/superposition.Rd                                                 |  190 -
 PKNCA-0.9.2/PKNCA/man/time_calc.Rd                                                     |only
 PKNCA-0.9.2/PKNCA/man/tss.monoexponential.generate.formula.Rd                          |   38 
 PKNCA-0.9.2/PKNCA/tests/testthat/generate.data.R                                       |   41 
 PKNCA-0.9.2/PKNCA/tests/testthat/test-auc.R                                            |   24 
 PKNCA-0.9.2/PKNCA/tests/testthat/test-check.intervals.R                                |   41 
 PKNCA-0.9.2/PKNCA/tests/testthat/test-class-PKNCAresults.R                             |   60 
 PKNCA-0.9.2/PKNCA/tests/testthat/test-cleaners.R                                       |   44 
 PKNCA-0.9.2/PKNCA/tests/testthat/test-general.functions.R                              |   25 
 PKNCA-0.9.2/PKNCA/tests/testthat/test-half.life.R                                      |   63 
 PKNCA-0.9.2/PKNCA/tests/testthat/test-interpolate.conc.R                               | 1475 ++++++----
 PKNCA-0.9.2/PKNCA/tests/testthat/test-pk.calc.simple.R                                 |    9 
 PKNCA-0.9.2/PKNCA/tests/testthat/test-superpostion.R                                   |   56 
 PKNCA-0.9.2/PKNCA/tests/testthat/test-time.to.steady.state.R                           |  123 
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 PKNCA-0.9.2/PKNCA/vignettes/PKNCA-validation.Rmd                                       |   10 
 PKNCA-0.9.2/PKNCA/vignettes/Writing-Parameter-Functions.Rmd                            |    4 
 173 files changed, 6779 insertions(+), 5762 deletions(-)

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Package multigraph updated to version 0.93 with previous version 0.92 dated 2019-12-02

Title: Plot and Manipulate Multigraphs
Description: Functions to plot and manipulate multigraphs, signed and valued graphs, bipartite graphs, multilevel graphs, and Cayley graphs with different layout options. Please note that this package still under a devel version.
Author: Antonio Rivero Ostoic [aut, cre]
Maintainer: Antonio Rivero Ostoic <multiplex@post.com>

Diff between multigraph versions 0.92 dated 2019-12-02 and 0.93 dated 2020-02-28

 DESCRIPTION               |   10 +++---
 MD5                       |   23 +++++++-------
 R/multigraph.R            |   73 ++++++++++++++++++++++++++++++----------------
 inst                      |only
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 man/multigraph.Rd         |   58 ++++++++++++++++++++++--------------
 man/stsm.Rd               |    8 ++---
 13 files changed, 141 insertions(+), 97 deletions(-)

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New package Mqrcm with initial version 1.1
Package: Mqrcm
Type: Package
Title: M-Quantile Regression Coefficients Modeling
Version: 1.1
Date: 2020-02-26
Author: Paolo Frumento <paolo.frumento@unipi.it>
Maintainer: Paolo Frumento <paolo.frumento@unipi.it>
Description: Parametric modeling of M-quantile regression coefficient functions.
Imports: stats, utils, graphics, Hmisc
Depends: pch (>= 1.4)
License: GPL-2
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-02-26 18:29:07 UTC; Paolo Frumento
Repository: CRAN
Date/Publication: 2020-02-28 16:10:15 UTC

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Package MatchThem updated to version 0.9.2 with previous version 0.9.1 dated 2019-12-13

Title: Matching and Weighting Multiply Imputed Datasets
Description: Provides the necessary tools for the pre-processing techniques of matching and weighting multiply imputed datasets to control for effects of confounders and to reduce the degree of dependence on certain modeling assumptions in studying the causal associations between an exposure and an outcome. This package includes functions to perform matching within and across the multiply imputed datasets using several matching methods, to estimate weights of units in the imputed datasets using several weighting methods, to calculate the causal effect estimate in each matched or weighted dataset using parametric or non-parametric statistical models, and to pool the obtained estimates from these models according to Rubin's rules (please see <https://github.com/FarhadPishgar/MatchThem> for details).
Author: Farhad Pishgar [aut, cre], Noah Greifer [aut], Clémence Leyrat [ctb], Elizabeth Stuart [ctb]
Maintainer: Farhad Pishgar <Farhad.Pishgar@Gmail.com>

Diff between MatchThem versions 0.9.1 dated 2019-12-13 and 0.9.2 dated 2020-02-28

 DESCRIPTION           |    8 +-
 MD5                   |   48 ++++++++--------
 NAMESPACE             |    3 -
 NEWS.md               |   10 ++-
 R/class.functions.R   |   30 +++++++---
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 R/with.R              |    8 +-
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 man/osteoarthritis.Rd |   52 +++++++++---------
 man/pool.Rd           |  116 ++++++++++++++++++++--------------------
 man/weightthem.Rd     |  120 +++++++++++++++++++++--------------------
 man/wimids.Rd         |   50 ++++++++---------
 man/with.Rd           |  126 +++++++++++++++++++++----------------------
 25 files changed, 738 insertions(+), 729 deletions(-)

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Package GCD updated to version 4.0.6 with previous version 4.0.5 dated 2019-12-11

Title: Global Charcoal Database
Description: Contains the Global Charcoal database data. Data include charcoal series (age, depth, charcoal quantity, associated units and methods) and information on sedimentary sites (localisation, depositional environment, biome, etc.) as well as publications informations. Since 4.0.0 the GCD mirrors the online SQL database at <http://paleofire.org>.
Author: Global Paleofire Working Group <paleofire@univ-fcomte.fr>
Maintainer: Olivier Blarquez <blarquez@gmail.com>

Diff between GCD versions 4.0.5 dated 2019-12-11 and 4.0.6 dated 2020-02-28

 DESCRIPTION |    9 +++++----
 MD5         |    4 ++--
 NEWS        |    6 ++++++
 3 files changed, 13 insertions(+), 6 deletions(-)

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New package dynsbm with initial version 0.7
Package: dynsbm
Type: Package
Title: Dynamic Stochastic Block Models
Version: 0.7
Date: 2020-02-27
Author: Catherine Matias, Vincent Miele
Maintainer: Vincent Miele <vincent.miele@univ-lyon1.fr>
Description: Dynamic stochastic block model that combines a stochastic block model (SBM) for its static part with independent Markov chains for the evolution of the nodes groups through time, developed in Matias and Miele (2016) <doi:10.1111/rssb.12200>.
Imports: Rcpp, parallel, riverplot, RColorBrewer, grDevices, graphics, stats
LinkingTo: Rcpp
License: GPL-3
URL: http://lbbe.univ-lyon1.fr/dynsbm
NeedsCompilation: yes
Packaged: 2020-02-27 16:03:47 UTC; vmiele
Repository: CRAN
Date/Publication: 2020-02-28 16:30:09 UTC

More information about dynsbm at CRAN
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Package diffman updated to version 0.1.1 with previous version 0.1.0 dated 2019-08-08

Title: Detect Differentiation Problems
Description: An algorithm based on graph theory tools to detect differentiation problems. A differentiation problem occurs when aggregated data are disseminated according to two different nomenclatures. By making the difference for an additive variable X between an aggregate composed of categories of the first nomenclature and an other aggregate, included in that first aggregate, composed of categories of the second nomenclature, it is sometimes possible to derive X on a small aggregate of records which could then lead to a break of confidentiality. The purpose of this package is to detect the set of aggregates composed of categories of the first nomenclature which lead to a differentiation problem, when given a confidentiality threshold. Reference: Vianney Costemalle (2019) <doi: 10.3233/SJI-190564>.
Author: Vianney Costemalle [aut, cre], Arlindo Dos Santos [aut], Francois Semecurbe [aut]
Maintainer: Vianney Costemalle <vianney.costemalle@insee.fr>

Diff between diffman versions 0.1.0 dated 2019-08-08 and 0.1.1 dated 2020-02-28

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Package covatest updated to version 1.1.2 with previous version 1.1.1 dated 2018-12-02

Title: Tests on Properties of Space-Time Covariance Functions
Description: Tests on properties of space-time covariance functions. Tests on symmetry, separability and for assessing different forms of non-separability are available. Moreover tests on some classes of covariance functions, such that the classes of product-sum models, Gneiting models and integrated product models have been provided. It is the companion R package to the paper of Cappello C., De Iaco S. and Posa D. <doi:10.1007/s00477-017-1472-2>.
Author: Sandra De Iaco [aut, cre], Claudia Cappello [aut], Donato Posa [aut], Sabrina Maggio [ctb]
Maintainer: Sandra De Iaco <sandra.deiaco@unisalento.it>

Diff between covatest versions 1.1.1 dated 2018-12-02 and 1.1.2 dated 2020-02-28

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Package airGR updated to version 1.4.3.65 with previous version 1.4.3.60 dated 2020-01-29

Title: Suite of GR Hydrological Models for Precipitation-Runoff Modelling
Description: Hydrological modelling tools developed at INRAE-Antony (HYCAR Research Unit, France). The package includes several conceptual rainfall-runoff models (GR4H, GR5H, GR4J, GR5J, GR6J, GR2M, GR1A), a snow accumulation and melt model (CemaNeige) and the associated functions for their calibration and evaluation. Use help(airGR) for package description and references.
Author: Laurent Coron [aut, trl] (<https://orcid.org/0000-0002-1503-6204>), Olivier Delaigue [aut, cre] (<https://orcid.org/0000-0002-7668-8468>), Guillaume Thirel [aut] (<https://orcid.org/0000-0002-1444-1830>), Charles Perrin [aut, ths] (<https://orcid.org/0000-0001-8552-1881>), Claude Michel [aut, ths], Vazken Andréassian [ctb, ths] (<https://orcid.org/0000-0001-7124-9303>), François Bourgin [ctb] (<https://orcid.org/0000-0002-2820-7260>, 'Parameter estimation' vignettes), Pierre Brigode [ctb] (<https://orcid.org/0000-0001-8257-0741>), Nicolas Le Moine [ctb], Thibaut Mathevet [ctb] (<https://orcid.org/0000-0002-4142-4454>), Safouane Mouelhi [ctb], Ludovic Oudin [ctb] (<https://orcid.org/0000-0002-3712-0933>), Raji Pushpalatha [ctb], Audrey Valéry [ctb]
Maintainer: Olivier Delaigue <airGR@inrae.fr>

Diff between airGR versions 1.4.3.60 dated 2020-01-29 and 1.4.3.65 dated 2020-02-28

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Package SSBtools updated to version 0.6.0 with previous version 0.5.0 dated 2019-11-26

Title: Statistics Norway's Miscellaneous Tools
Description: Functions used by other packages from Statistics Norway are gathered. General data manipulation functions, and functions for hierarchical computations are included. The hierarchy specification functions are useful within statistical disclosure control.
Author: Øyvind Langsrud [aut, cre], Bjørn-Helge Mevik [cph]
Maintainer: Øyvind Langsrud <oyl@ssb.no>

Diff between SSBtools versions 0.5.0 dated 2019-11-26 and 0.6.0 dated 2020-02-28

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 38 files changed, 1070 insertions(+), 557 deletions(-)

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Package sciplot updated to version 1.2-0 with previous version 1.1-1 dated 2017-06-19

Title: Scientific Graphing Functions for Factorial Designs
Description: A collection of functions that creates graphs with error bars for data collected from one-way or higher factorial designs.
Author: Manuel Morales <mmorales@williams.edu>, with code developed by the R Development Core Team and with general advice from the R-help listserv community, especially Duncan Murdoch.
Maintainer: Manuel Morales <Manuel.A.Morales@williams.edu>

Diff between sciplot versions 1.1-1 dated 2017-06-19 and 1.2-0 dated 2020-02-28

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Package Rssa updated to version 1.0.2 with previous version 1.0 dated 2017-10-04

Title: A Collection of Methods for Singular Spectrum Analysis
Description: Methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series.
Author: Anton Korobeynikov, Alex Shlemov, Konstantin Usevich, Nina Golyandina
Maintainer: Anton Korobeynikov <anton@korobeynikov.info>

Diff between Rssa versions 1.0 dated 2017-10-04 and 1.0.2 dated 2020-02-28

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New package RestRserve with initial version 0.2.0
Package: RestRserve
Type: Package
Title: A Framework for Building HTTP API
Description: Allows to easily create high-performance full featured HTTP APIs from R functions. Provides high-level classes such as 'Request', 'Response', 'Application', 'Middleware' in order to streamline server side application development. Out of the box allows to serve requests using 'Rserve' package, but flexible enough to integrate with other HTTP servers such as 'httpuv'.
Version: 0.2.0
Authors@R: c( person(given = "Dmitriy", family = "Selivanov", role = c("aut", "cre"), email = "ds@rexy.ai", comment = c(ORCID = "0000-0001-5413-1506")), person(given = "Artem", family = "Klevtsov", role = "aut", email = "a.a.klevtsov@gmail.com", comment = c(ORCID = "0000-0003-0492-6647")), person(given = "rexy.ai", role = c("cph", "fnd")))
URL: http://restrserve.org, https://github.com/rexyai/RestRserve
BugReports: https://github.com/rexyai/RestRserve/issues
License: GPL (>= 2)
Depends: R (>= 3.6.0)
Imports: methods, Rserve (>= 1.7.3), Rcpp (>= 1.0.3), R6 (>= 2.4.0), uuid (>= 0.1-2), checkmate (>= 1.9.4), mime (>= 0.7), jsonlite (>= 1.6)
Suggests: tinytest (>= 1.0.0), lgr (>= 0.3.2), lintr, knitr, rmarkdown, curl, sys
LinkingTo: Rcpp
SystemRequirements: C++11
ByteCompile: true
KeepSource: true
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2020-02-21 06:03:53 UTC; dselivanov
Author: Dmitriy Selivanov [aut, cre] (<https://orcid.org/0000-0001-5413-1506>), Artem Klevtsov [aut] (<https://orcid.org/0000-0003-0492-6647>), rexy.ai [cph, fnd]
Maintainer: Dmitriy Selivanov <ds@rexy.ai>
Repository: CRAN
Date/Publication: 2020-02-28 14:20:07 UTC

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Package compareDF updated to version 2.0.1 with previous version 2.0.0 dated 2020-01-08

Title: Do a Git Style Diff of the Rows Between Two Dataframes with Similar Structure
Description: Compares two dataframes which have the same column structure to show the rows that have changed. Also gives a git style diff format to quickly see what has changed in addition to summary statistics.
Author: Alex Joseph [aut, cre]
Maintainer: Alex Joseph <alexsanjoseph@gmail.com>

Diff between compareDF versions 2.0.0 dated 2020-01-08 and 2.0.1 dated 2020-02-28

 DESCRIPTION    |    8 ++++----
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Package shinymanager updated to version 1.0.200 with previous version 1.0.100 dated 2019-12-11

Title: Authentication Management for 'Shiny' Applications
Description: Simple and secure authentification mechanism for single 'Shiny' applications. Credentials are stored in an encrypted 'SQLite' database. Source code of main application is protected until authentication is successful.
Author: Benoit Thieurmel [aut, cre], Victor Perrier [aut]
Maintainer: Benoit Thieurmel <benoit.thieurmel@datastorm.fr>

Diff between shinymanager versions 1.0.100 dated 2019-12-11 and 1.0.200 dated 2020-02-28

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Package RWiener updated to version 1.3-2 with previous version 1.3-1 dated 2017-02-22

Title: Wiener Process Distribution Functions
Description: Provides Wiener process distribution functions, namely the Wiener first passage time density, CDF, quantile and random functions. Additionally supplies a modelling function (wdm) and further methods for the resulting object.
Author: Dominik Wabersich [aut, cre]
Maintainer: Dominik Wabersich <dominik.wabersich@gmail.com>

Diff between RWiener versions 1.3-1 dated 2017-02-22 and 1.3-2 dated 2020-02-28

 DESCRIPTION  |    8 ++++----
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 4 files changed, 11 insertions(+), 11 deletions(-)

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Package neatStats updated to version 1.0.4 with previous version 1.0.3 dated 2020-02-20

Title: Neat and Painless Statistical Reporting
Description: User-friendly, clear and simple statistics, primarily for publication in psychological science. The main functions are wrappers for other packages, but there are various additions as well. Every relevant step from data aggregation to reportable printed statistics is covered for basic experimental designs.
Author: Gáspár Lukács [aut, cre] (<https://orcid.org/0000-0001-9401-4830>), Bennett Kleinberg [ctb] (<https://orcid.org/0000-0003-1658-9086>)
Maintainer: Gáspár Lukács <lkcsgaspar@gmail.com>

Diff between neatStats versions 1.0.3 dated 2020-02-20 and 1.0.4 dated 2020-02-28

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Package haplotypes updated to version 1.1.2 with previous version 1.1.1 dated 2020-02-20

Title: Manipulating DNA Sequences and Estimating Unambiguous Haplotype Network with Statistical Parsimony
Description: Provides S4 classes and methods for reading and manipulating aligned DNA sequences, supporting an indel coding methods (only simple indel coding method is available in the current version), showing base substitutions and indels, calculating absolute pairwise distances between DNA sequences, and collapses identical DNA sequences into haplotypes or inferring haplotypes using user provided absolute pairwise character difference matrix. This package also includes S4 classes and methods for estimating genealogical relationships among haplotypes using statistical parsimony and plotting parsimony networks.
Author: Caner Aktas
Maintainer: Caner Aktas <caktas.aca@gmail.com>

Diff between haplotypes versions 1.1.1 dated 2020-02-20 and 1.1.2 dated 2020-02-28

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Package SSDM updated to version 0.2.8 with previous version 0.2.7 dated 2020-02-13

Title: Stacked Species Distribution Modelling
Description: Allows to map species richness and endemism based on stacked species distribution models (SSDM). Individuals SDMs can be created using a single or multiple algorithms (ensemble SDMs). For each species, an SDM can yield a habitat suitability map, a binary map, a between-algorithm variance map, and can assess variable importance, algorithm accuracy, and between- algorithm correlation. Methods to stack individual SDMs include summing individual probabilities and thresholding then summing. Thresholding can be based on a specific evaluation metric or by drawing repeatedly from a Bernoulli distribution. The SSDM package also provides a user-friendly interface.
Author: Sylvain Schmitt, Robin Pouteau, Dimitri Justeau, Florian de Boissieu, Lukas Baumbach, Philippe Birnbaum
Maintainer: Sylvain Schmitt <sylvain.m.schmitt@gmail.com>

Diff between SSDM versions 0.2.7 dated 2020-02-13 and 0.2.8 dated 2020-02-28

 DESCRIPTION        |    6 ++--
 MD5                |   10 +++----
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 6 files changed, 51 insertions(+), 46 deletions(-)

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New package spacey with initial version 0.1.0
Package: spacey
Type: Package
Title: Easily Obtain Spatial Data and Make Better Maps
Version: 0.1.0
Authors@R: person(given = "Michael", family = "Mahoney", role = c("aut", "cre"), email = "mike.mahoney.218@gmail.com", comment = c(ORCID = "0000-0003-2402-304X"))
Description: One of the remaining pain points in making beautiful maps via packages like 'rayshader' is both obtaining and processing spatial data to build from. 'spacey' aims to make it easier to obtain and use this data for locations within the United States, providing utilities to download 'USGS' and 'ESRI' geospatial data and quickly turn it into maps.
License: MIT + file LICENSE
URL: https://github.com/mikemahoney218/spacey, https://mikemahoney218.github.io/spacey/
BugReports: https://github.com/mikemahoney218/spacey/issues
Encoding: UTF-8
LazyData: true
Imports: httr, raster, rayshader, magrittr, png, jsonlite, rgdal
RoxygenNote: 7.0.2
Suggests: testthat (>= 2.1.0), covr, knitr, rmarkdown, future, pkgload
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-02-21 01:21:54 UTC; mikemahoney218
Author: Michael Mahoney [aut, cre] (<https://orcid.org/0000-0003-2402-304X>)
Maintainer: Michael Mahoney <mike.mahoney.218@gmail.com>
Repository: CRAN
Date/Publication: 2020-02-28 12:30:03 UTC

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Package pbdSLAP updated to version 0.3-0 with previous version 0.2-9 dated 2020-02-01

Title: Programming with Big Data -- Scalable Linear Algebra Packages
Description: Utilizing scalable linear algebra packages mainly including 'BLACS', 'PBLAS', and 'ScaLAPACK' in double precision via 'pbdMPI' based on 'ScaLAPACK' version 2.0.2.
Author: Wei-Chen Chen [aut, cre], Drew Schmidt [aut], George Ostrouchov [aut], Pragneshkumar Patel [aut], Brian Ripley [ctb] (Solaris & Mac)
Maintainer: Wei-Chen Chen <wccsnow@gmail.com>

Diff between pbdSLAP versions 0.2-9 dated 2020-02-01 and 0.3-0 dated 2020-02-28

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New package nnGarrote with initial version 1.0.0
Package: nnGarrote
Type: Package
Title: Non-Negative Garrote Estimation with Penalized Initial Estimators
Version: 1.0.0
Date: 2020-02-06
Author: Anthony Christidis <anthony.christidis@stat.ubc.ca>, Stefan Van Aelst <stefan.vanaelst@kuleuven.be>, Ruben Zamar <ruben@stat.ubc.ca>
Maintainer: Anthony Christidis <anthony.christidis@stat.ubc.ca>
Description: Functions to compute the non-negative garrote estimator as proposed by Breiman (1995) <https://www.jstor.org/stable/1269730> with the penalized initial estimators extension as proposed by Yuan and Lin (2007) <https://www.jstor.org/stable/4623260>.
License: GPL (>= 2)
Biarch: true
Imports: multicool, glmnet, caret, mvnfast
RoxygenNote: 7.0.2
Suggests: testthat
NeedsCompilation: no
Packaged: 2020-02-21 01:25:47 UTC; antho
Repository: CRAN
Date/Publication: 2020-02-28 12:10:06 UTC

More information about nnGarrote at CRAN
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Package multiplex updated to version 2.9.4 with previous version 2.9.2 dated 2019-12-02

Title: Algebraic Tools for the Analysis of Multiple Social Networks
Description: Algebraic procedures for the analysis of multiple social networks are delivered with this package as described in Ostoic (2020) <DOI:10.18637/jss.v092.i11>. Among other things, it makes it possible to create and manipulate multiplex, multimode, and multilevel network data with different formats. There are effective ways available to treat multiple networks with routines that combine algebraic systems like the partially ordered semigroup or the semiring structure with the relational bundles occurring in different types of multivariate network data sets. It also provides an algebraic approach for affiliation networks through Galois derivations between families of the pairs of subsets in the two domains.
Author: Antonio Rivero Ostoic [aut, cre]
Maintainer: Antonio Rivero Ostoic <multiplex@post.com>

Diff between multiplex versions 2.9.2 dated 2019-12-02 and 2.9.4 dated 2020-02-28

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More information about multiplex at CRAN
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Package groupedstats updated to version 0.2.0 with previous version 0.1.1 dated 2020-01-14

Title: Grouped Statistical Analyses in a Tidy Way
Description: Collection of functions to run statistical tests across all combinations of multiple grouping variables.
Author: Indrajeet Patil [aut, cre] (<https://orcid.org/0000-0003-1995-6531>)
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

Diff between groupedstats versions 0.1.1 dated 2020-01-14 and 0.2.0 dated 2020-02-28

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More information about groupedstats at CRAN
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Package cli updated to version 2.0.2 with previous version 2.0.1 dated 2020-01-08

Title: Helpers for Developing Command Line Interfaces
Description: A suite of tools to build attractive command line interfaces ('CLIs'), from semantic elements: headings, lists, alerts, paragraphs, etc. Supports custom themes via a 'CSS'-like language. It also contains a number of lower level 'CLI' elements: rules, boxes, trees, and 'Unicode' symbols with 'ASCII' alternatives. It integrates with the 'crayon' package to support 'ANSI' terminal colors.
Author: Gábor Csárdi [aut, cre], Hadley Wickham [ctb], Kirill Müller [ctb]
Maintainer: Gábor Csárdi <csardi.gabor@gmail.com>

Diff between cli versions 2.0.1 dated 2020-01-08 and 2.0.2 dated 2020-02-28

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New package xrnet with initial version 0.1.6
Package: xrnet
Type: Package
Title: Hierarchical Regularized Regression
Version: 0.1.6
Authors@R: c( person("Garrett", "Weaver", email = "gmweaver.usc@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9918-8386")), person("Juan Pablo", "Lewinger", email = "lewinger@usc.edu", role = c("ctb", "ths")) )
URL: https://github.com/USCbiostats/xrnet
Description: Fits hierarchical regularized regression models to incorporate potentially informative external data, Weaver and Lewinger (2019) <doi:10.21105/joss.01761>. Utilizes coordinate descent to efficiently fit regularized regression models both with and without external information with the most common penalties used in practice (i.e. ridge, lasso, elastic net). Support for standard R matrices, sparse matrices and big.matrix objects.
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Suggests: knitr, rmarkdown, testthat, Matrix, glmnet, doParallel
LinkingTo: Rcpp, RcppEigen, BH, bigmemory
Imports: Rcpp (>= 0.12.19), foreach, bigmemory, methods
Depends: R (>= 3.6)
SystemRequirements: C++11
NeedsCompilation: yes
Packaged: 2020-02-20 19:37:38 UTC; gmweaver
Author: Garrett Weaver [aut, cre] (<https://orcid.org/0000-0002-9918-8386>), Juan Pablo Lewinger [ctb, ths]
Maintainer: Garrett Weaver <gmweaver.usc@gmail.com>
Repository: CRAN
Date/Publication: 2020-02-28 11:20:03 UTC

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Permanent link

Package vamc updated to version 0.2.1 with previous version 0.2.0 dated 2020-02-03

Title: A Monte Carlo Valuation Framework for Variable Annuities
Description: Implementation of a Monte Carlo simulation engine for valuing synthetic portfolios of variable annuities, which reflect realistic features of common annuity contracts in practice. It aims to facilitate the development and dissemination of research related to the efficient valuation of a portfolio of large variable annuities. The main valuation methodology was proposed by Gan (2017) <doi:10.1515/demo-2017-0021>.
Author: Hengxin Li [aut, cph], Ben Feng [aut, cph], Mingyi Jiang [aut, cph, cre], GuoJun Gan [ctb]
Maintainer: Mingyi Jiang <m64jiang@uwaterloo.ca>

Diff between vamc versions 0.2.0 dated 2020-02-03 and 0.2.1 dated 2020-02-28

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Package radsafer updated to version 2.2.0 with previous version 2.1.0 dated 2019-12-18

Title: Radiation Safety
Description: Provides functions for radiation safety, also known as "radiation protection" and "radiological control". The science of radiation protection is called "health physics" and its engineering functions are called "radiological engineering". Functions in this package cover many of the computations needed by radiation safety professionals. Examples include: obtaining updated calibration and source check values for radiation monitors to account for radioactive decay in a reference source, simulating instrument readings to better understand measurement uncertainty, correcting instrument readings for geometry and ambient atmospheric conditions. Many of these functions are described in Johnson and Kirby (2011, ISBN-13: 978-1609134198). Utilities are also included for developing inputs and processing outputs with radiation transport codes, such as MCNP, a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron, or coupled neutron/photon/electron transport (Werner et. al. (2018) <doi:10.2172/1419730>).
Author: Mark Hogue <mark.hogue.chp@gmail.com>
Maintainer: Mark Hogue <mark.hogue.chp@gmail.com>

Diff between radsafer versions 2.1.0 dated 2019-12-18 and 2.2.0 dated 2020-02-28

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New package PanelMatch with initial version 1.0.0
Package: PanelMatch
Type: Package
Title: Matching Methods for Causal Inference with Time-Series Cross-Sectional Data
Version: 1.0.0
Date: 2020-02-10
Authors@R: c( person("In Song", "Kim", email = "insong@mit.edu", role = c("aut", "cre")), person("Adam", "Rauh", email = "adamrauh@mit.edu", role = c("aut")), person("Erik", "Wang", email = "haixiaow@Princeton.edu", role = c("aut")), person("Kosuke", "Imai", email = "imai@harvard.edu", role = c("aut")) )
Description: Implements a set of methodological tools that enable researchers to apply matching methods to time-series cross-sectional data. Imai, Kim, and Wang (2018) <http://web.mit.edu/insong/www/pdf/tscs.pdf> proposes a nonparametric generalization of the difference-in-differences estimator, which does not rely on the linearity assumption as often done in practice. Researchers first select a method of matching each treated observation for a given unit in a particular time period with control observations from other units in the same time period that have a similar treatment and covariate history. These methods include standard matching methods based on propensity score and Mahalanobis distance, as well as weighting methods. Once matching is done, both short-term and long-term average treatment effects for the treated can be estimated with standard errors. The package also offers a visualization technique that allows researchers to assess the quality of matches by examining the resulting covariate balance.
License: GPL (>= 3)
Imports: Rcpp (>= 0.12.5), data.table, ggplot2, CBPS, stats, graphics, grDevices, MASS, Matrix, methods
Depends: R (>= 2.14.0)
SystemRequirements: C++11
LinkingTo: RcppArmadillo, Rcpp, RcppEigen
Encoding: UTF-8
LazyData: true
BugReports: https://github.com/insongkim/PanelMatch/issues
RoxygenNote: 7.0.2
Suggests: knitr, rmarkdown, testthat (>= 2.1.0)
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2020-02-20 18:49:55 UTC; insong
Author: In Song Kim [aut, cre], Adam Rauh [aut], Erik Wang [aut], Kosuke Imai [aut]
Maintainer: In Song Kim <insong@mit.edu>
Repository: CRAN
Date/Publication: 2020-02-28 11:20:13 UTC

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Package neatRanges updated to version 0.1.2 with previous version 0.1.1 dated 2020-02-26

Title: Tidy Up Date/Time Ranges
Description: Collapse, partition, combine, fill gaps in and expand date/time ranges.
Author: Aljaz Jelenko [aut, cre]
Maintainer: Aljaz Jelenko <aljaz.jelenko@amis.net>

Diff between neatRanges versions 0.1.1 dated 2020-02-26 and 0.1.2 dated 2020-02-28

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New package lchemix with initial version 0.1.0
Package: lchemix
Version: 0.1.0
Title: A Bayesian Multi-Dimensional Couple-Based Latent Risk Model
Authors@R: c(person(given = "Beom Seuk", family = "Hwang", role = "aut"), person(given = "Zhen", family = "Chen", role = "aut", email = "chenzhe@mail.nih.gov"), person(given = "Germaine M. Buck", family = "Louis", role ="aut"), person(given = "Paul S.", family = "Albert", role = "aut"), person(given = "Weimin", family = "Zhang", email = "zhangwm@hotmail.com", role = "cre" ))
Description: A joint latent class model where a hierarchical structure exists, with an interaction between female and male partners of a couple. A Bayesian perspective to inference and Markov chain Monte Carlo algorithms to obtain posterior estimates of model parameters. The reference paper is: Beom Seuk Hwang, Zhen Chen, Germaine M.Buck Louis, Paul S. Albert, (2018) "A Bayesian multi-dimensional couple-based latent risk model with an application to infertility". Biometrics, 75, 315-325. <doi:10.1111/biom.12972>.
License: GPL-3
Imports: MCMCpack, mvtnorm
Encoding: UTF-8
LazyData: TRUE
RoxygenNote: 7.0.2
URL: http://github.com/wzhang17/lchemix.git, https://doi.org/10.1111/biom.12972
BugReports: http://github.com/wzhang17/lchemix/issues
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-02-20 20:40:34 UTC; zhangw16
Author: Beom Seuk Hwang [aut], Zhen Chen [aut], Germaine M. Buck Louis [aut], Paul S. Albert [aut], Weimin Zhang [cre]
Maintainer: Weimin Zhang <zhangwm@hotmail.com>
Repository: CRAN
Date/Publication: 2020-02-28 11:50:02 UTC

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Package ggroups updated to version 2.0.2 with previous version 2.0.1 dated 2020-02-18

Title: Pedigree and Genetic Groups
Description: Calculates additive and dominance genetic relationship matrices and their inverses, in matrix and tabular-sparse formats. It includes functions for checking and processing pedigree, as well as functions to calculate the matrix of genetic group contributions (Q), and adding those contributions to the genetic merit of animals (Quaas (1988) <doi:10.3168/jds.S0022-0302(88)79691-5>). Calculation of Q is computationally extensive. There are computationally optimized functions to calculate Q.
Author: Mohammad Ali Nilforooshan [aut, cre] (<https://orcid.org/0000-0003-0339-5442>)
Maintainer: Mohammad Ali Nilforooshan <m.a.nilforooshan@gmail.com>

Diff between ggroups versions 2.0.1 dated 2020-02-18 and 2.0.2 dated 2020-02-28

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New package delayed with initial version 0.3.0
Package: delayed
Title: A Framework for Parallelizing Dependent Tasks
Version: 0.3.0
Authors@R: c( person("Jeremy", "Coyle", email = "jeremyrcoyle@gmail.com", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-9874-6649")), person("Nima", "Hejazi", email = "nh@nimahejazi.org", role = "ctb", comment = c(ORCID = "0000-0002-7127-2789")))
Description: Mechanisms to parallelize dependent tasks in a manner that optimizes the compute resources available. It provides access to "delayed" computations, which may be parallelized using futures. It is, to an extent, a facsimile of the 'Dask' library (<https://dask.org/>), for the 'Python' language.
Depends: R (>= 3.2.0)
Imports: R6, igraph, future, rstackdeque, rlang, data.table, assertthat, visNetwork, uuid, BBmisc, progress
Suggests: testthat, knitr, rmarkdown, shiny
License: GPL-3
URL: https://tlverse.org/delayed
BugReports: https://github.com/tlverse/delayed/issues
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-02-20 19:55:01 UTC; nsh
Author: Jeremy Coyle [aut, cre, cph] (<https://orcid.org/0000-0002-9874-6649>), Nima Hejazi [ctb] (<https://orcid.org/0000-0002-7127-2789>)
Maintainer: Jeremy Coyle <jeremyrcoyle@gmail.com>
Repository: CRAN
Date/Publication: 2020-02-28 11:40:02 UTC

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Package baggr updated to version 0.4.0 with previous version 0.3.0 dated 2020-01-08

Title: Bayesian Aggregate Treatment Effects
Description: Running and comparing meta-analyses of data with hierarchical Bayesian models in Stan, including convenience functions for formatting data, plotting and pooling measures specific to meta-analysis.
Author: Witold Wiecek [cre, aut], Rachael Meager [aut], Brice Green [ctb] (predict(), loo_compare, many visuals), Trustees of Columbia University [cph] (tools/make_cc.R)
Maintainer: Witold Wiecek <witold.wiecek@gmail.com>

Diff between baggr versions 0.3.0 dated 2020-01-08 and 0.4.0 dated 2020-02-28

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New package climatrends with initial version 0.1.0
Package: climatrends
Type: Package
Title: Precipitation and Temperature Indices for Climate Variability Analysis
Version: 0.1.0
Authors@R: c(person("Kaue", "de Sousa", email = "kaue.desousa@inn.no", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7571-7845")), person("Jacob", "van Etten", role = c("aut", "ths"), comment = c(ORCID = "0000-0001-7554-2558")), person("Svein", "Solberg", role = c("aut","ths"), comment = c(ORCID = "0000-0002-4491-4483")))
URL: https://agrobioinfoservices.github.io/climatrends
BugReports: https://github.com/agrobioinfoservices/climatrends/issues
Description: Toolkit for climate variability indices for ecological modelling. The indices produced here can be used as explanatory variables for crop modelling, trends in climate change and to assess the interactions of plants and animals with the environment.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0)
Imports: nasapower, stats, tibble
Suggests: ClimMobTools, knitr, raster, rmarkdown, testthat
Language: en-GB
RoxygenNote: 7.0.2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-02-20 20:39:25 UTC; kauefeitosadiasdesousa
Author: Kaue de Sousa [aut, cre] (<https://orcid.org/0000-0002-7571-7845>), Jacob van Etten [aut, ths] (<https://orcid.org/0000-0001-7554-2558>), Svein Solberg [aut, ths] (<https://orcid.org/0000-0002-4491-4483>)
Maintainer: Kaue de Sousa <kaue.desousa@inn.no>
Repository: CRAN
Date/Publication: 2020-02-28 11:40:05 UTC

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New package selectSNPs with initial version 1.0.1
Package: selectSNPs
Type: Package
Title: Optimal Selection of SNPs Using a Unified Local Function
Version: 1.0.1
Date: 2020-02-20
Author: Xiao-Lin Nick Wu
Maintainer: Nick Wu <xwu8@wisc.edu>
Description: Applies a unified local function to select SNPs for low-density SNPs. The method is described in Wu et al. (2020) <doi:10.1111/age.12916>. Briefly, the local function gives different weights to the uniformity (U score) and average Shannon entropy (E score), depending on the inputs of the weights, thus facilitating SNP designs under varied scenarios for low-density SNP chips, including selecting uniformly-distributed SNPs or SNPs with the highest minor allele frequencies. This is an educational version. For more information about this package, please contact Dr. Nick Wu.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 2.10)
Imports: Rcpp (>= 1.0.3), methods, graphics, stats, utils
LinkingTo: Rcpp
LazyData: true
RoxygenNote: 7.0.2
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2020-02-20 16:39:35 UTC; NickWu
Repository: CRAN
Date/Publication: 2020-02-28 10:30:02 UTC

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Package pbdBASE updated to version 0.5-3 with previous version 0.5-2 dated 2020-02-02

Title: Programming with Big Data -- Base Wrappers for Distributed Matrices
Description: An interface to and extensions for the 'PBLAS' and 'ScaLAPACK' numerical libraries. This enables R to utilize distributed linear algebra for codes written in the 'SPMD' fashion. This interface is deliberately low-level and mimics the style of the native libraries it wraps. For a much higher level way of managing distributed matrices, see the 'pbdDMAT' package.
Author: Drew Schmidt [aut], Wei-Chen Chen [aut, cre], Sebastien Lamy de la Chapelle [aut], George Ostrouchov [aut], Pragneshkumar Patel [aut], Ewan Higgs [ctb]
Maintainer: Wei-Chen Chen <wccsnow@gmail.com>

Diff between pbdBASE versions 0.5-2 dated 2020-02-02 and 0.5-3 dated 2020-02-28

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 DESCRIPTION                  |    6 +++---
 MD5                          |   16 ++++++++--------
 README.md                    |    1 -
 inst/doc/pbdBASE-guide.Rnw   |    2 +-
 inst/doc/pbdBASE-guide.pdf   |binary
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 src/base/linalg/pinverse.f90 |   13 ++++++++++---
 vignettes/pbdBASE-guide.Rnw  |    2 +-
 9 files changed, 37 insertions(+), 20 deletions(-)

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New package MVQuickGraphs with initial version 0.1.2
Package: MVQuickGraphs
Type: Package
Title: Quick Multivariate Graphs
Version: 0.1.2
Author: Douglas Whitaker
Maintainer: Douglas Whitaker <douglas.whitaker@msvu.ca>
Description: Functions used for graphing in multivariate contexts. These functions are designed to support produce reasonable graphs with minimal input of graphing parameters. The motivation for these functions was to support students learning multivariate concepts and R - there may be other functions and packages better-suited to practical data analysis. For details about the ellipse methods see Johnson and Wichern (2007, ISBN:9780131877153).
License: GPL-2 | GPL-3
Encoding: UTF-8
LazyData: true
Imports: plotrix
NeedsCompilation: no
Packaged: 2020-02-20 12:57:52 UTC; Douglas Whitaker
Repository: CRAN
Date/Publication: 2020-02-28 11:00:02 UTC

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New package IOHexperimenter with initial version 0.1.0
Package: IOHexperimenter
Type: Package
Title: Benchmarking Part of the 'IOHprofiler'
Version: 0.1.0
Authors@R: c( person("Furong", "Ye", email = "f.ye@liacs.leidenuniv.nl", role = "aut"), person("Hao", "Wang", email = "h.wang@liacs.leidenuniv.nl", role = "aut"), person("Diederick", "Vermetten", email="d.l.vermetten@liacs.leidenuniv.nl", role = c("aut","cre")), person("Carola", "Doerr", email = "Carola.Doerr@mpi-inf.mpg.de", role = "aut"), person("Thomas", "Bäck", email="t.h.w.baeck@liacs.leidenuniv.nl", role = "aut"))
Maintainer: Diederick Vermetten <d.l.vermetten@liacs.leidenuniv.nl>
Description: The benchmarking module for the Iterative Optimization Heuristics Profiler ('IOHprofiler'). This module provides benchmarking in the 'IOHprofiler' format. It enables performance tracking and analysis for any iterative optimization heuristic, which can be visualized and analyzed using the 'IOHanalyzer' package. It also provides access to predefined benchmarking functions, both for pseudo-boolean and continuous domains, as well as some sample algorithms.
License: BSD_3_clause + file LICENSE
Encoding: UTF-8
LazyData: true
URL: https://github.com/IOHprofiler/IOHexperimenter
BugReports: https://github.com/IOHprofiler/IOHexperimenter/issues
Imports: Rcpp (>= 1.0.1), assertthat, magrittr
LinkingTo: Rcpp, BH
RoxygenNote: 6.1.1
SystemRequirements: GNU make
Suggests: testthat
NeedsCompilation: yes
Packaged: 2020-02-20 16:11:40 UTC; diedi
Author: Furong Ye [aut], Hao Wang [aut], Diederick Vermetten [aut, cre], Carola Doerr [aut], Thomas Bäck [aut]
Repository: CRAN
Date/Publication: 2020-02-28 10:30:09 UTC

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New package gsbm with initial version 0.1.0
Package: gsbm
Type: Package
Title: Estimate Parameters in the Generalized SBM
Version: 0.1.0
Authors@R: c(person("Genevieve", "Robin", role = c("aut"), email = "genevieve.robin@inria.fr"), person("Solenne", "Gaucher", role = c("aut", "cre"), email = "solenne.gaucher@math.u-psud.fr"))
Maintainer: Solenne Gaucher <solenne.gaucher@math.u-psud.fr>
Description: Given an adjacency matrix drawn from a Generalized Stochatsic Block Model with missing observations, this package robustly estimates the probabilities of connection between nodes and detects outliers nodes, as describes in Gaucher, Klopp and Robin (2019) <arXiv:1911.13122>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Depends: R (>= 3.5.0)
Imports: softImpute
Suggests: knitr, rmarkdown, igraph, missSBM, RColorBrewer, Matrix
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-02-20 17:08:21 UTC; Mathilde
Author: Genevieve Robin [aut], Solenne Gaucher [aut, cre]
Repository: CRAN
Date/Publication: 2020-02-28 10:50:03 UTC

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New package FinePop2 with initial version 0.4
Package: FinePop2
Type: Package
Title: Fine-Scale Population Analysis (Rewrite for Gene-Trait-Environment Interaction Analysis)
Version: 0.4
Date: 2020-02-20
Author: Reiichiro Nakamichi, Shuichi Kitada, Hirohisa Kishino
Maintainer: Reiichiro Nakamichi <nakamichi@affrc.go.jp>
Description: Statistical tool set for population genetics. The package provides following functions: 1) estimators of genetic differentiation (FST), 2) regression analysis of environmental effects on genetic differentiation using generalized least squares (GLS) method, 3) interfaces to read and manipulate 'GENEPOP' format data files). For more information, see Kitada, Nakamichi and Kishino (2020) <doi:10.1101/2020.01.30.927186>.
License: GPL (>= 2.0)
Depends: R (>= 3.4.0)
LazyLoad: yes
NeedsCompilation: no
Encoding: UTF-8
Packaged: 2020-02-20 14:55:40 UTC; nakamichi
Repository: CRAN
Date/Publication: 2020-02-28 10:20:02 UTC

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New package beats with initial version 0.1.1
Type: Package
Package: beats
Title: Heartbeat Detection in Electrocardiogram Data
Version: 0.1.1
Authors@R: person(given = "Max", family = "Czapanskiy", role = c("aut", "cre"), email = "maxczapanskiy@gmail.com", comment = c("ORCID-ID" = "0000-0002-6302-905X"))
Maintainer: Max Czapanskiy <maxczapanskiy@gmail.com>
Description: Import and process electrocardiogram (ECG) data. Reads binary data files from UFI devices (.ube files) and provides a Shiny app for finding and exporting heart beats.
License: MIT + file LICENSE
URL: https://github.com/FlukeAndFeather/beats
BugReports: https://github.com/FlukeAndFeather/beats/issues
Depends: R (>= 2.10)
Imports: dplyr, ggplot2, lubridate, purrr, readr, shiny, zoo
Suggests: gganimate, gifski, plotly, png, testthat (>= 2.1.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-02-20 16:45:33 UTC; frank
Author: Max Czapanskiy [aut, cre] (0000-0002-6302-905X)
Repository: CRAN
Date/Publication: 2020-02-28 10:40:02 UTC

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Package anomaly updated to version 2.0.4 with previous version 2.0.2 dated 2019-09-19

Title: Detecting Anomalies in Data
Description: Implements Collective And Point Anomaly (CAPA) <arXiv:1806.01947>, Multi-Variate Collective And Point Anomaly (MVCAPA) <arXiv:1909.01691>, and Proportion Adaptive Segment Selection (PASS) <doi:10.1093/biomet/ass059> methods for the detection of anomalies in time series data.
Author: Alex Fisch [aut], Daniel Grose [aut, cre], Lawrence Bardwell [ctb], Idris Eckley [ths], Paul Fearnhead [ths]
Maintainer: Daniel Grose <dan.grose@lancaster.ac.uk>

Diff between anomaly versions 2.0.2 dated 2019-09-19 and 2.0.4 dated 2020-02-28

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Package hypr updated to version 0.1.6 with previous version 0.1.5 dated 2019-12-04

Title: Hypothesis Matrix Translation
Description: Translation between experimental null hypotheses, hypothesis matrices, and contrast matrices as used in linear regression models. The package is based on the method described in Schad, Vasishth, Hohenstein, and Kliegl (2019) <doi:10.1016/j.jml.2019.104038>.
Author: Maximilian M. Rabe [aut, cre], Shravan Vasishth [aut], Sven Hohenstein [aut], Reinhold Kliegl [aut], Daniel J. Schad [aut]
Maintainer: Maximilian M. Rabe <maximilian.rabe@uni-potsdam.de>

Diff between hypr versions 0.1.5 dated 2019-12-04 and 0.1.6 dated 2020-02-28

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New package CompoundEvents with initial version 0.1.0
Package: CompoundEvents
Type: Package
Title: Statistical Modeling of Compound Events
Version: 0.1.0
Author: Zengchao Hao
Maintainer: Zengchao Hao <z.hao4univ@gmail.com>
Description: Tools for extracting occurrences, assessing potential driving factors, predicting occurrences, and quantifying impacts of compound events in hydrology and climatology. Please see Hao Zengchao et al. (2019) <doi:10.1088/1748-9326/ab4df5>.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
Repository: CRAN
LazyData: true
RoxygenNote: 7.0.1
Date: 2020-02-02
NeedsCompilation: no
Packaged: 2020-02-20 11:24:44 UTC; HAO
Date/Publication: 2020-02-28 09:20:02 UTC

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Package TDPanalysis updated to version 1.0 with previous version 0.99 dated 2016-10-18

Title: Granier's Sap Flow Sensors (TDP) Analysis
Description: Set of functions designed to help in the analysis of TDP sensors. Features includes dates and time conversion, weather data interpolation, daily maximum of tension analysis and calculations required to convert sap flow density data to sap flow rates at the tree and plot scale (For more information see : Granier (1985) <DOI:10.1051/forest:19850204> & Granier (1987) <DOI:10.1093/treephys/3.4.309>).
Author: Maxime Durand
Maintainer: Maxime Durand <duran1211@gmail.com>

Diff between TDPanalysis versions 0.99 dated 2016-10-18 and 1.0 dated 2020-02-28

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 NAMESPACE              |    2 -
 R/Tmax.R               |   12 ++++-----
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 man/Wat.transp.Rd      |   61 ++++++++++++++++++++++++-------------------------
 man/date.to.DOY.Rd     |   47 ++++++++++++++++++-------------------
 man/datetime.Rd        |   55 +++++++++++++++++++++-----------------------
 man/remove.fun.Rd      |   61 ++++++++++++++++++++++++-------------------------
 man/tens.to.sapflow.Rd |   59 +++++++++++++++++++++++------------------------
 man/timecont.Rd        |   53 ++++++++++++++++++++----------------------
 15 files changed, 316 insertions(+), 327 deletions(-)

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Package strataG updated to version 2.4.905 with previous version 2.0.2 dated 2017-04-11

Title: Summaries and Population Structure Analyses of Genetic Data
Description: A toolkit for analyzing stratified population genetic data. Functions are provided for summarizing and checking loci (haploid, diploid, and polyploid), single stranded DNA sequences, calculating most population subdivision metrics, and running external programs such as structure and fastsimcoal. The package is further described in Archer et al (2016) <doi:10.1111/1755-0998.12559>.
Author: Eric Archer [aut, cre], Paula Adams [aut], Brita Schneiders [aut], Sarina Fernandez [aut], Warren Asfazadour [aut]
Maintainer: Eric Archer <eric.archer@noaa.gov>

Diff between strataG versions 2.0.2 dated 2017-04-11 and 2.4.905 dated 2020-02-28

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Package qrcm updated to version 2.2 with previous version 2.1 dated 2017-03-27

Title: Quantile Regression Coefficients Modeling
Description: Parametric modeling of quantile regression coefficient functions. Can be used with censored and truncated data.
Author: Paolo Frumento <paolo.frumento@unipi.it>
Maintainer: Paolo Frumento <paolo.frumento@unipi.it>

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Package optiscale updated to version 1.2 with previous version 1.1 dated 2014-08-01

Title: Optimal Scaling
Description: Optimal scaling of a data vector is obtained through a least-squares transformation subject to appropriate measurement constraints. Further information about this methodology is provided by Young (1981)<doi:10.1007/BF02293796> and Jacoby (1999)< doi:10.2307/2991794>.
Author: William G. Jacoby
Maintainer: William G. Jacoby <wm.g.jacoby@gmail.com>

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Package naniar updated to version 0.5.0 with previous version 0.4.2 dated 2019-02-15

Title: Data Structures, Summaries, and Visualisations for Missing Data
Description: Missing values are ubiquitous in data and need to be explored and handled in the initial stages of analysis. 'naniar' provides data structures and functions that facilitate the plotting of missing values and examination of imputations. This allows missing data dependencies to be explored with minimal deviation from the common work patterns of 'ggplot2' and tidy data. The work is fully discussed at Tierney & Cook (2018) <arXiv:1809.02264>.
Author: Nicholas Tierney [aut, cre] (<https://orcid.org/0000-0003-1460-8722>), Di Cook [aut] (<https://orcid.org/0000-0002-3813-7155>), Miles McBain [aut] (<https://orcid.org/0000-0003-2865-2548>), Colin Fay [aut] (<https://orcid.org/0000-0001-7343-1846>), Mitchell O'Hara-Wild [ctb], Jim Hester [ctb], Luke Smith [ctb]
Maintainer: Nicholas Tierney <nicholas.tierney@gmail.com>

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Package hypercube updated to version 0.2.1 with previous version 0.2.0 dated 2019-09-18

Title: Organizing Data in Hypercubes
Description: Provides functions and methods for organizing data in hypercubes (i.e., a multi-dimensional cube). Cubes are generated from molten data frames. Each cube can be manipulated with five operations: rotation (change.dimensionOrder()), dicing and slicing (add.selection(), remove.selection()), drilling down (add.aggregation()), and rolling up (remove.aggregation()).
Author: Michael Scholz
Maintainer: Michael Scholz <michael.scholz@th-deg.de>

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Package hardhat updated to version 0.1.2 with previous version 0.1.1 dated 2020-01-08

Title: Construct Modeling Packages
Description: Building modeling packages is hard. A large amount of effort generally goes into providing an implementation for a new method that is efficient, fast, and correct, but often less emphasis is put on the user interface. A good interface requires specialized knowledge about S3 methods and formulas, which the average package developer might not have. The goal of 'hardhat' is to reduce the burden around building new modeling packages by providing functionality for preprocessing, predicting, and validating input.
Author: Davis Vaughan [aut, cre], Max Kuhn [aut], RStudio [cph]
Maintainer: Davis Vaughan <davis@rstudio.com>

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Package GENLIB updated to version 1.0.7 with previous version 1.0.6 dated 2018-05-30

Title: Genealogical Data Analysis
Description: Genealogical data analysis including descriptive statistics (e.g., kinship and inbreeding coefficients) and gene-dropping simulations.
Author: Louis Houde [aut], Jean-Francois Lefebvre [aut], Valery Roy-Lagace [aut], Sebastien Lemieux [aut], Michael J. Fromberger [ctb], Marie-Helene Roy-Gagnon [cre]
Maintainer: Marie-Helene Roy-Gagnon <mroygagn@uottawa.ca>

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Package ddpcr updated to version 1.13 with previous version 1.12 dated 2020-01-13

Title: Analysis and Visualization of Droplet Digital PCR in R and on the Web
Description: An interface to explore, analyze, and visualize droplet digital PCR (ddPCR) data in R. This is the first non-proprietary software for analyzing two-channel ddPCR data. An interactive tool was also created and is available online to facilitate this analysis for anyone who is not comfortable with using R.
Author: Dean Attali [aut, cre]
Maintainer: Dean Attali <daattali@gmail.com>

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Package symmetry updated to version 0.2.1 with previous version 0.1.1 dated 2019-08-29

Title: Testing for Symmetry of Data and Model Residuals
Description: Implementations of a large number of tests for symmetry and their bootstrap variants, which can be used for testing the symmetry of random samples around a known or unknown mean. Functions are also there for testing the symmetry of model residuals around zero. Currently, the supported models are linear models and generalized autoregressive conditional heteroskedasticity (GARCH) models (fitted with the 'fGarch' package). All tests are implemented using the 'Rcpp' package which ensures great performance of the code.
Author: Blagoje Ivanović [aut, cre] Bojana Milošević [aut] Marko Obradović [aut]
Maintainer: Blagoje Ivanović <blagoje_ivanovic@matf.bg.ac.rs>

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Package df2json (with last version 0.0.2) was removed from CRAN

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2013-04-08 0.0.2
2013-04-01 0.0.1

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