Mon, 31 Aug 2020

Package HydroCode updated to version 1.0.2 with previous version 1.0.1 dated 2020-01-24

Title: Hydrological Codes
Description: Pfafstetter Hydrological Codes as cited in Verdin and Verdin (1999) <doi: 10.1016/S0022-1694(99)00011-6> are decoded for upstream or downstream queries.
Author: Thorsten Pohlert [aut, cre] (<https://orcid.org/0000-0003-3855-3025>)
Maintainer: Thorsten Pohlert <thorsten.pohlert@gmx.de>

Diff between HydroCode versions 1.0.1 dated 2020-01-24 and 1.0.2 dated 2020-08-31

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Package ADAPTS updated to version 1.0.5 with previous version 1.0.3 dated 2020-08-05

Title: Automated Deconvolution Augmentation of Profiles for Tissue Specific Cells
Description: Tools to construct (or add to) cell-type signature matrices using flow sorted or single cell samples and deconvolve bulk gene expression data. Useful for assessing the quality of single cell RNAseq experiments, estimating the accuracy of signature matrices, and determining cell-type spillover. Please cite: Danziger SA et al. (2019) ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells <doi:10.1371/journal.pone.0224693>.
Author: Samuel A Danziger
Maintainer: Samuel A Danziger <sam.danziger@gmail.com>

Diff between ADAPTS versions 1.0.3 dated 2020-08-05 and 1.0.5 dated 2020-08-31

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Package shinymaterial updated to version 1.2.0 with previous version 1.1.0 dated 2020-05-16

Title: Implement Material Design in Shiny Applications
Description: Allows shiny developers to incorporate UI elements based on Google's Material design. See <https://material.io/guidelines/> for more information.
Author: Eric Anderson [aut, cre], Alvin Wang [ctb, cph] (Materialize CSS library), Alan Chang [ctb, cph] (Materialize CSS library), Alex Mark [ctb, cph] (Materialize CSS library), Kevin Louie [ctb, cph] (Materialize CSS library)
Maintainer: Eric Anderson <eric.ray.anderson@gmail.com>

Diff between shinymaterial versions 1.1.0 dated 2020-05-16 and 1.2.0 dated 2020-08-31

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Package NSM3 updated to version 1.15 with previous version 1.14 dated 2020-04-19

Title: Functions and Datasets to Accompany Hollander, Wolfe, and Chicken - Nonparametric Statistical Methods, Third Edition
Description: Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods. Exact procedures are performed when computationally possible. Monte Carlo and Asymptotic procedures are performed otherwise. For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.
Author: Grant Schneider, Eric Chicken, Rachel Becvarik
Maintainer: Grant Schneider <gschneidz@gmail.com>

Diff between NSM3 versions 1.14 dated 2020-04-19 and 1.15 dated 2020-08-31

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Package mlt.docreg updated to version 1.0-7 with previous version 1.0-6 dated 2020-08-27

Title: Most Likely Transformations: Documentation and Regression Tests
Description: Additional documentation, a package vignette and regression tests for package mlt.
Author: Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>)
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between mlt.docreg versions 1.0-6 dated 2020-08-27 and 1.0-7 dated 2020-08-31

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Package tram updated to version 0.5-0 with previous version 0.4-0 dated 2020-05-13

Title: Transformation Models
Description: Formula-based user-interfaces to specific transformation models implemented in package 'mlt'. Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to transformation models for clustered data is provided (Hothorn, 2019, <arxiv:1910.09219>). Multivariate conditional transformation models (<arxiv:1906.03151>) can be fitted as well.
Author: Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>), Luisa Barbanti [aut], Brian Ripley [ctb], Bill Venables [ctb], Douglas M. Bates [ctb]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

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Package ROI.plugin.quadprog updated to version 1.0-0 with previous version 0.2-5 dated 2017-05-17

Title: 'quadprog' Plug-in for the 'R' Optimization Infrastructure
Description: Enhances the R Optimization Infrastructure ('ROI') package by registering the 'quadprog' solver. It allows for solving quadratic programming (QP) problems.
Author: Stefan Theussl [aut, cre]
Maintainer: Stefan Theussl <Stefan.Theussl@R-Project.org>

Diff between ROI.plugin.quadprog versions 0.2-5 dated 2017-05-17 and 1.0-0 dated 2020-08-31

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Package dragon updated to version 1.0.2 with previous version 1.0.1 dated 2020-07-18

Title: Deep Time Redox Analysis of the Geobiology Ontology Network
Description: Create, visualize, manipulate, and analyze bipartite mineral-chemistry networks for set of focal element(s) across deep-time on Earth. The method is described in Spielman and Moore (2020) <doi:10.31223/osf.io/z7k9q>.
Author: Stephanie J. Spielman [aut, cre] (<https://orcid.org/0000-0002-9090-4788>)
Maintainer: Stephanie J. Spielman <spielman@rowan.edu>

Diff between dragon versions 1.0.1 dated 2020-07-18 and 1.0.2 dated 2020-08-31

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Package carat updated to version 1.2 with previous version 1.1 dated 2020-05-16

Title: Covariate-Adaptive Randomization for Clinical Trials
Description: Provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. The package currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria.
Author: Fuyi Tu [aut], Xiaoqing Ye [aut, cre], Wei Ma [aut, ths], Feifang Hu [aut, ths]
Maintainer: Xiaoqing Ye <ye_xiaoq@163.com>

Diff between carat versions 1.1 dated 2020-05-16 and 1.2 dated 2020-08-31

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Package BASS (with last version 1.2.0) was removed from CRAN

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

2020-05-06 1.2.0
2017-03-17 0.2.2
2017-01-10 0.2.0
2016-12-16 0.1.0

Permanent link
Package slm updated to version 1.2.0 with previous version 1.1.0 dated 2019-10-24

Title: Stationary Linear Models
Description: Provides statistical procedures for linear regression in the general context where the errors are assumed to be correlated. Different ways to estimate the asymptotic covariance matrix of the least squares estimators are available. Starting from this estimation of the covariance matrix, the confidence intervals and the usual tests on the parameters are modified. The functions of this package are very similar to those of 'lm': it contains methods such as summary(), plot(), confint() and predict(). The 'slm' package is described in the paper by E. Caron, J. Dedecker and B. Michel (2019), "Linear regression with stationary errors: the R package slm", arXiv preprint <arXiv:1906.06583>.
Author: Emmanuel Caron, Jérôme Dedecker, Bertrand Michel
Maintainer: Emmanuel Caron <emmanuelcaron3@gmail.com>

Diff between slm versions 1.1.0 dated 2019-10-24 and 1.2.0 dated 2020-08-31

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Package ROI.plugin.symphony updated to version 1.0-0 with previous version 0.2-5 dated 2017-05-17

Title: 'SYMPHONY' Plug-in for the 'R' Optimization Interface
Description: Enhances the R Optimization Infrastructure ('ROI') package by registering the 'SYMPHONY' open-source solver from the COIN-OR suite. It allows for solving mixed integer linear programming (MILP) problems as well as all variants/combinations of LP, IP.
Author: Stefan Theussl [aut, cre]
Maintainer: Stefan Theussl <Stefan.Theussl@R-Project.org>

Diff between ROI.plugin.symphony versions 0.2-5 dated 2017-05-17 and 1.0-0 dated 2020-08-31

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Package PROJ updated to version 0.3.1 with previous version 0.1.6 dated 2020-04-15

Title: Generic Coordinate System Transformations Using 'PROJ'
Description: A wrapper around the generic coordinate transformation software 'PROJ' that transforms geospatial coordinates from one coordinate reference system ('CRS') to another. This includes cartographic projections as well as geodetic transformations. Version 7.1.0-1 or higher of the R package 'libproj' is required. The intention is for this package to be used by user-packages such as 'reproj', and that the older 'PROJ.4' and version 5 pathways be provided by the 'proj4' package. Separating this disruptive version change (from 4.0 and 5.0, to 6.0 and above) allows the use of existing and stable code in 'proj4' alongside the new idioms and requirements of modern 'PROJ'. For control of metadata files for the 'PROJ' library, see tools in the 'libproj' package.
Author: Michael D. Sumner [aut, cre] (<https://orcid.org/0000-0002-2471-7511>), Jeroen Ooms [ctb] (provided PROJ library support on Windows, and assistance with Windows configuration), Simon Urbanek [cph, ctb] (wrote original code versions for PROJ version 6), Dewey Dunnington [ctb] (key code contributions, and provided libproj to improve things a lot)
Maintainer: Michael D. Sumner <mdsumner@gmail.com>

Diff between PROJ versions 0.1.6 dated 2020-04-15 and 0.3.1 dated 2020-08-31

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Package brinton updated to version 0.2.1 with previous version 0.2.0 dated 2020-06-30

Title: A Graphical EDA Tool
Description: An automated graphical exploratory data analysis (EDA) tool that introduces: a.) wideplot() graphics for exploring the structure of a dataset through a grid of variables and graphic types. b.) longplot() graphics, which present the entire catalog of available graphics for representing a particular variable using a grid of graphic types and variations on these types. c.) plotup() function, which presents a particular graphic for a specific variable of a dataset. The plotup() function also makes it possible to obtain the code used to generate the graphic, meaning that the user can adjust its properties as needed.
Author: Pere Millán-Martínez [aut, cre], Ramon Oller Piqué [ctb]
Maintainer: Pere Millán-Martínez <info@sciencegraph.org>

Diff between brinton versions 0.2.0 dated 2020-06-30 and 0.2.1 dated 2020-08-31

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New package unga with initial version 0.1.0
Package: unga
Type: Package
Title: Makes Multiple Contingency Tables of a Data Frame
Version: 0.1.0
Author: Peter Thuresson <jpeter.thuresson@gmail.com>
Maintainer: Peter Thuresson <jpeter.thuresson@gmail.com>
Description: Makes it easy to extract data from a survey. You can use an explanatory variable and make multiple contingency tables for all variables in a data frame. You can also summarize response alternatives. URL: <https://en.wikipedia.org/wiki/Contingency_table>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-08-26 11:24:11 UTC; prota
Repository: CRAN
Date/Publication: 2020-08-31 09:40:03 UTC

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New package SRS with initial version 0.1.0
Package: SRS
Type: Package
Title: Scaling with Ranked Subsampling
Version: 0.1.0
Authors@R: c(person("Lukas", "Beule", role = c("aut", "cre"), email = "lukas.beule@agr.uni-goettingen.de"), person("Petr", "Karlovsky", role = "aut") )
Description: Analysis of species count data in ecology often requires normalization to an identical sample size. Rarefying (random subsampling without replacement), which is a popular method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. An alternative to rarefying is scaling with ranked subsampling (SRS). SRS consists of two steps. In the first step, the total counts for all OTUs (operational taxonomic units) or species in each sample are divided by a scaling factor chosen in such a way that the sum of the scaled counts Cscaled equals Cmin. In the second step, the non-integer Cscaled values are converted into integers by an algorithm that we dub ranked subsampling. The Cscaled value for each OTU or species is split into the integer part Cint (Cint = floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cints). Since the sum of Cint is smaller or equal to Cmin, additional delta C = Cmin - the sum of Cint counts have to be added to the library to reach the total count of Cmin. This is achieved as follows. OTUs are ranked in the descending order of their Cfrac values. Beginning with the OTU of the highest rank, single count per OTU is added to the normalized library until the total number of added counts reaches delta C and the sum of all counts in the normalized library equals Cmin. When the lowest Cfrag involved in picking delta C counts is shared by several OTUs, the OTUs used for adding a single count to the library are selected in the order of their Cint values. This selection minimizes the effect of normalization on the relative frequencies of OTUs. OTUs with identical Cfrag as well as Cint are sampled randomly without replacement. See Beule & Karlovsky (2020) <doi:10.7717/peerj.9593> for details.
License: CC BY-SA 4.0
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-08-26 11:56:09 UTC; lukasbeule
Author: Lukas Beule [aut, cre], Petr Karlovsky [aut]
Maintainer: Lukas Beule <lukas.beule@agr.uni-goettingen.de>
Repository: CRAN
Date/Publication: 2020-08-31 10:00:15 UTC

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New package shinyChakraSlider with initial version 0.1.0
Package: shinyChakraSlider
Title: Combined Slider and Numeric Input for 'Shiny'
Version: 0.1.0
Authors@R: c( person(given = "Stéphane", family = "Laurent", role = c("aut", "cre"), email = "laurent_step@outlook.fr"), person(given = "Segun", family = "Adebayo", role = c("ctb", "cph"), email = "sage@adebayosegun.com", comment = "'chakra-ui' library (https://github.com/chakra-ui/chakra-ui)"), person(given = "Goran", family = "Gajic", role = c("ctb", "cph"), comment = "'react-icons' library (https://github.com/react-icons/react-icons)"), person(given = "Peter", family = "Newnham", role = c("ctb", "cph"), comment = "'react-html-parser' library (https://github.com/wrakky/react-html-parser)") )
Description: Provides a combined slider and numeric input for usage in a 'Shiny' app. The slider and the numeric input are linked together: each one is updated when the other one changes. Many styling properties are customizable (e.g. colors and size).
URL: https://github.com/stla/shinyChakraSlider
BugReports: https://github.com/stla/shinyChakraSlider/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: htmltools, shiny, reactR, grDevices, utils
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-08-26 12:30:33 UTC; stla
Author: Stéphane Laurent [aut, cre], Segun Adebayo [ctb, cph] ('chakra-ui' library (https://github.com/chakra-ui/chakra-ui)), Goran Gajic [ctb, cph] ('react-icons' library (https://github.com/react-icons/react-icons)), Peter Newnham [ctb, cph] ('react-html-parser' library (https://github.com/wrakky/react-html-parser))
Maintainer: Stéphane Laurent <laurent_step@outlook.fr>
Repository: CRAN
Date/Publication: 2020-08-31 10:00:02 UTC

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New package sdetorus with initial version 0.1.6
Package: sdetorus
Type: Package
Title: Statistical Tools for Toroidal Diffusions
Version: 0.1.6
Date: 2020-08-26
Authors@R: person("Eduardo", "García-Portugués", role = c("aut", "cre"), email = "edgarcia@est-econ.uc3m.es", comment = c(ORCID = "0000-0002-9224-4111"))
Description: Implementation of statistical methods for the estimation of toroidal diffusions. Several diffusive models are provided, most of them belonging to the Langevin family of diffusions on the torus. Specifically, the wrapped normal and von Mises processes are included, which can be seen as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of methods for approximate maximum likelihood estimation, organized in four blocks, is given: (i) based on the exact transition probability density, obtained as the numerical solution to the Fokker-Plank equation; (ii) based on wrapped pseudo-likelihoods; (iii) based on specific analytic approximations by wrapped processes; (iv) based on maximum likelihood of the stationary densities. The package allows the reproducibility of the results in García-Portugués et al. (2019) <doi:10.1007/s11222-017-9790-2>.
License: GPL-3
Depends: R (>= 3.6.0), Rcpp, mvtnorm, colorRamps
Suggests: rgl, Bessel, manipulate
LinkingTo: Rcpp, RcppArmadillo
URL: https://github.com/egarpor/sdetorus
BugReports: https://github.com/egarpor/sdetorus
Encoding: UTF-8
RoxygenNote: 7.1.0
NeedsCompilation: yes
Packaged: 2020-08-26 12:13:56 UTC; Eduardo
Author: Eduardo García-Portugués [aut, cre] (<https://orcid.org/0000-0002-9224-4111>)
Maintainer: Eduardo García-Portugués <edgarcia@est-econ.uc3m.es>
Repository: CRAN
Date/Publication: 2020-08-31 09:50:02 UTC

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Package RolWinMulCor updated to version 1.0.0 with previous version 0.4.0 dated 2020-05-22

Title: Subroutines to Estimate Rolling Window Multiple Correlation
Description: Rolling Window Multiple Correlation ('RolWinMulCor') estimates the rolling (running) window correlation for the bi- and multi-variate cases between regular (sampled on identical time points) time series, with especial emphasis to ecological data (although this can be applied to other kinds of data sets). 'RolWinMulCor' is based on the concept of rolling, running or sliding window and is useful to evaluate the evolution of correlation through time and time-scales. 'RolWinMulCor' contains six functions. The first two focus on the bi-variate case: (1) rolwincor_1win() and (2) rolwincor_heatmap(), estimate the correlation coefficients and the their respective p-values for only one window-length (time-scale) and considering all possible window-lengths or a band of window-lengths, respectively. The second two functions: (3) rolwinmulcor_1win() and (4) rolwinmulcor_heatmap() are designed to analyze the multi-variate case, following the bi-variate case to visually display the results, but these two approaches are methodologically different: the multi-variate case estimates the adjusted coefficients of determination instead of the correlation coefficients. The last two functions: (5) plot_1win() and (6) plot_heatmap(), are used to represent graphically the outputs of the four aforementioned functions as simple plots or as heat maps. The functions contained in 'RolWinMulCor' are highly flexible since this contains several parameters to control the estimation of correlation and the features of the plot output, e.g. to remove the (linear) trend contained in the time series under analysis, to choose different p-value correction methods (which are used to address the multiple comparison problem) or to personalise the plot outputs. The 'RolWinMulCor' package also provides examples with synthetic and real-life ecological time series to exemplify its use. Methods derived from H. Abdi. (2007) <https://personal.utdallas.edu/~herve/Abdi-MCC2007-pretty.pdf>, J. M. Polanco-Martinez (2019) <doi:10.1007/s11071-019-04974-y>, and R. Telford (2013) <https://quantpalaeo.wordpress.com/2013/01/04/running-correlations-running-into-problems/>.
Author: Josue M. Polanco-Martinez [aut, cph, cre]
Maintainer: Josue M. Polanco-Martinez <josue.m.polanco@gmail.com>

Diff between RolWinMulCor versions 0.4.0 dated 2020-05-22 and 1.0.0 dated 2020-08-31

 RolWinMulCor-0.4.0/RolWinMulCor/data/synthetic_data.rda     |only
 RolWinMulCor-0.4.0/RolWinMulCor/man/synthetic_data.Rd       |only
 RolWinMulCor-1.0.0/RolWinMulCor/DESCRIPTION                 |   10 
 RolWinMulCor-1.0.0/RolWinMulCor/MD5                         |   32 
 RolWinMulCor-1.0.0/RolWinMulCor/NAMESPACE                   |    1 
 RolWinMulCor-1.0.0/RolWinMulCor/R/plot_1win.R               |only
 RolWinMulCor-1.0.0/RolWinMulCor/R/plot_heatmap.R            |only
 RolWinMulCor-1.0.0/RolWinMulCor/R/rolwincor_1win.R          |  253 ++-----
 RolWinMulCor-1.0.0/RolWinMulCor/R/rolwincor_heatmap.R       |  370 ++++-------
 RolWinMulCor-1.0.0/RolWinMulCor/R/rolwinmulcor_1win.R       |  261 ++-----
 RolWinMulCor-1.0.0/RolWinMulCor/R/rolwinmulcor_heatmap.R    |  402 ++++--------
 RolWinMulCor-1.0.0/RolWinMulCor/data/syntDATA.rda           |only
 RolWinMulCor-1.0.0/RolWinMulCor/man/RolWinMulCor-package.Rd |   64 -
 RolWinMulCor-1.0.0/RolWinMulCor/man/YX_ecological_data.Rd   |    4 
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 RolWinMulCor-1.0.0/RolWinMulCor/man/plot_heatmap.Rd         |only
 RolWinMulCor-1.0.0/RolWinMulCor/man/rolwincor_1win.Rd       |  100 --
 RolWinMulCor-1.0.0/RolWinMulCor/man/rolwincor_heatmap.Rd    |  103 ---
 RolWinMulCor-1.0.0/RolWinMulCor/man/rolwinmulcor_1win.Rd    |   98 --
 RolWinMulCor-1.0.0/RolWinMulCor/man/rolwinmulcor_heatmap.Rd |  103 ---
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 21 files changed, 609 insertions(+), 1192 deletions(-)

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New package RankAggregator with initial version 0.0.1
Package: RankAggregator
Title: Aggregation of (Partial) Ordinal Rankings
Version: 0.0.1
Authors@R: c( person(given = "Jay", family = "Burns", role = c("aut", "cre"), email = "jay.burns@sruc.ac.uk"), person(given = "Adam", family = "Butler", role = c("aut"), email = "adam.butler@bioss.ac.uk") )
Description: Easily compute an aggregate ranking (also called a median ranking or a consensus ranking) according to the axiomatic approach presented by Cook et al. (2007). This approach minimises the number of violations between all candidate consensus rankings and all input (partial) rankings, and draws on a branch and bound algorithm and a heuristic algorithm to drastically improve speed. The package also provides an option to bootstrap a consensus ranking based on resampling input rankings (with replacement). Input rankings can be either incomplete (partial) or complete. Reference: Cook, W.D., Golany, B., Penn, M. and Raviv, T. (2007) <doi:10.1016/j.cor.2005.05.030>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Depends: R (>= 2.10)
Suggests: knitr
NeedsCompilation: no
Packaged: 2020-08-26 10:38:22 UTC; jburns
Author: Jay Burns [aut, cre], Adam Butler [aut]
Maintainer: Jay Burns <jay.burns@sruc.ac.uk>
Repository: CRAN
Date/Publication: 2020-08-31 09:20:10 UTC

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New package pcSteiner with initial version 1.0.0
Package: pcSteiner
Type: Package
Version: 1.0.0
Date: 2020-08-21
Title: Convenient Tool for Solving the Prize-Collecting Steiner Tree Problem
Description: The Prize-Collecting Steiner Tree problem asks to find a subgraph connecting a given set of vertices with the most expensive nodes and least expensive edges. Since it is proven to be NP-hard, exact and efficient algorithm does not exist. This package provides convenient functionality for obtaining an approximate solution to this problem using loopy belief propagation algorithm.
Author: Aleksei Krasikov <krasikov.as@phystech.edu>
Maintainer: Aleksei Krasikov <krasikov.as@phystech.edu>
URL: https://github.com/krashkov/pcSteiner
BugReports: https://github.com/krashkov/pcSteiner/issues
Depends: R (>= 3.1.0), igraph (>= 0.6.0)
Imports: stats
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
License: GPL-3
Repository: CRAN
Encoding: UTF-8
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-08-26 12:36:50 UTC; krashkov
Date/Publication: 2020-08-31 10:00:06 UTC

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Package MaOEA updated to version 0.6.2 with previous version 0.5.2 dated 2019-10-28

Title: Many Objective Evolutionary Algorithm
Description: A set of evolutionary algorithms to solve many-objective optimization. Hybridization between the algorithms are also facilitated. Available algorithms are: 'SMS-EMOA' <doi:10.1016/j.ejor.2006.08.008> 'NSGA-III' <doi:10.1109/TEVC.2013.2281535> 'MO-CMA-ES' <doi:10.1145/1830483.1830573> The following many-objective benchmark problems are also provided: 'DTLZ1'-'DTLZ4' from Deb, et al. (2001) <doi:10.1007/1-84628-137-7_6> and 'WFG4'-'WFG9' from Huband, et al. (2005) <doi:10.1109/TEVC.2005.861417>.
Author: Dani Irawan [aut, cre] (<https://orcid.org/0000-0002-4213-941X>)
Maintainer: Dani Irawan <irawan_dani@yahoo.com>

Diff between MaOEA versions 0.5.2 dated 2019-10-28 and 0.6.2 dated 2020-08-31

 DESCRIPTION                     |   12 
 MD5                             |   76 +--
 R/CMAES_main.R                  |   14 
 R/CMAES_steady.R                |    6 
 R/NSGA3.r                       |    2 
 R/R2mtch.R                      |    5 
 R/SMSEMOA.r                     |   87 ++-
 R/WFG.R                         |  954 ++++++++++++++++++++--------------------
 R/WFG_base.R                    |   54 +-
 R/cmaes_gen.R                   |    4 
 R/functions.R                   |  259 ++++++----
 R/maoea.R                       |   70 ++
 R/pygmo.R                       |  176 ++++++-
 README.md                       |    2 
 man/DTLZ1.Rd                    |    4 
 man/DTLZ2.Rd                    |    4 
 man/DTLZ3.Rd                    |    4 
 man/DTLZ4.Rd                    |    4 
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 man/InitializePopulationLHS.Rd  |   13 
 man/MOCMAES.Rd                  |    4 
 man/NSGA3.Rd                    |    2 
 man/SMOCMAES.Rd                 |    4 
 man/SMSEMOA.Rd                  |   14 
 man/WFG1.Rd                     |    2 
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 man/WFG7.Rd                     |    2 
 man/WFG8.Rd                     |    2 
 man/WFG9.Rd                     |    2 
 man/cmaes_gen.Rd                |   12 
 man/compute_R2HVC.Rd            |   10 
 man/optimMaOEA.Rd               |   21 
 tests/testthat/test.benchmark.R |   88 ++-
 39 files changed, 1219 insertions(+), 765 deletions(-)

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New package LongituRF with initial version 0.9
Package: LongituRF
Title: Random Forests for Longitudinal Data
Version: 0.9
Authors@R: person(given = "Louis", family = "Capitaine", role = c("aut", "cre"), email = "Louis.capitaine@u-bordeaux.fr",comment = c(ORCID = "0000-0001-6800-2342"))
Description: Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data. In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020) <doi:10.1177/0962280220946080> Random forests for high-dimensional longitudinal data.
License: GPL-2
Imports: stats, randomForest, rpart, mvtnorm, latex2exp
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Suggests: testthat
NeedsCompilation: no
Packaged: 2020-08-25 22:24:05 UTC; capitainelouis
Author: Louis Capitaine [aut, cre] (<https://orcid.org/0000-0001-6800-2342>)
Maintainer: Louis Capitaine <Louis.capitaine@u-bordeaux.fr>
Repository: CRAN
Date/Publication: 2020-08-31 09:10:07 UTC

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New package jsTreeR with initial version 0.1.0
Package: jsTreeR
Type: Package
Title: A Wrapper of the JavaScript Library 'jsTree'
Version: 0.1.0
Authors@R: c( person("Stéphane", "Laurent", email = "laurent_step@outlook.fr", role = c("aut", "cre")), person("jQuery contributors", role = c("ctb", "cph"), comment = "jQuery"), person("Ivan", "Bozhanov", role = c("ctb", "cph"), comment = "jsTree"), person("Vedran", "Opacic", role = c("ctb", "cph"), comment = "jsTree bootstrap theme"), person("Avi", "Deitcher", role = c("ctb", "cph"), comment = "jsTreeGrid"), person("Terence", "Eden", role = c("ctb", "cph"), comment = "SuperTinyIcons") )
Description: Creates interactive trees that can be included in 'Shiny' apps and R markdown documents. A tree allows to represent hierarchical data (e.g. the contents of a directory). Similar to the 'shinyTree' package but offers more features and options, such as the grid extension, restricting the drag-and-drop behavior, and settings for the search functionality. It is possible to attach some data to the nodes of a tree and then to get these data in 'Shiny' when a node is selected.
License: GPL-3
URL: https://github.com/stla/jsTreeR
BugReports: https://github.com/stla/jsTreeR/issues
Encoding: UTF-8
LazyData: true
Imports: htmlwidgets, jsonlite, shiny, htmltools
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-08-26 12:20:05 UTC; stla
Author: Stéphane Laurent [aut, cre], jQuery contributors [ctb, cph] (jQuery), Ivan Bozhanov [ctb, cph] (jsTree), Vedran Opacic [ctb, cph] (jsTree bootstrap theme), Avi Deitcher [ctb, cph] (jsTreeGrid), Terence Eden [ctb, cph] (SuperTinyIcons)
Maintainer: Stéphane Laurent <laurent_step@outlook.fr>
Repository: CRAN
Date/Publication: 2020-08-31 09:50:09 UTC

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New package ftrCOOL with initial version 0.1.0
Package: ftrCOOL
Type: Package
Title: Feature Extraction from Biological Sequences
Version: 0.1.0
Author: Sare Amerifar
Maintainer: Sare Amerifar <sare.ameri.01@gmail.com>
Description: Extracts features from biological sequences. It contains most features which are presented in related work and also includes features which have never been introduced before. It extracts numerous features from Nucleotide and Peptide sequences. Each feature converts the input sequences to discrete numbers in order to use them as predictors in machine learning models. There are many features and information which are hidden inside a sequence. Utilizing the package, users can convert biological sequences to discrete models based on chosen properties. References: 'iLearn' 'Z. Chen et al.' (2019) <DOI:10.1093/bib/bbz041>. 'iFeature' 'Z. Chen et al.' (2018) <DOI:10.1093/bioinformatics/bty140>. <https://CRAN.R-project.org/package=rDNAse>. 'PseKRAAC' 'Y. Zuo et al.' 'PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition' (2017) <DOI:10.1093/bioinformatics/btw564>. 'iDNA6mA-PseKNC' 'P. Feng et al.' 'iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC' (2019) <DOI:10.1016/j.ygeno.2018.01.005>. 'I. Dubchak et al.' 'Prediction of protein folding class using global description of amino acid sequence' (1995) <DOI:10.1073/pnas.92.19.8700>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Imports: stats, utils
Suggests: testthat
NeedsCompilation: no
Packaged: 2020-08-26 08:27:41 UTC; sareameri
Repository: CRAN
Date/Publication: 2020-08-31 09:10:02 UTC

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Package dreamerr updated to version 1.2.1 with previous version 1.2.0 dated 2020-06-08

Title: Error Handling Made Easy
Description: Set of tools to facilitate package development and make R a more user-friendly place. Mostly for developers (or anyone who writes/shares functions). Provides a simple, powerful and flexible way to check the arguments passed to functions. The developer can easily describe the type of argument needed. If the user provides a wrong argument, then an informative error message is prompted with the requested type and the problem clearly stated--saving the user a lot of time in debugging.
Author: Laurent Berge [aut, cre]
Maintainer: Laurent Berge <laurent.berge@uni.lu>

Diff between dreamerr versions 1.2.0 dated 2020-06-08 and 1.2.1 dated 2020-08-31

 DESCRIPTION                         |   10 +++++-----
 MD5                                 |   17 +++++++++--------
 NEWS.md                             |   15 ++++++++++++++-
 R/check_arg.R                       |   26 ++++++++++++++------------
 R/misc_funs.R                       |    7 ++-----
 build/vignette.rds                  |binary
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 10 files changed, 51 insertions(+), 51 deletions(-)

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Package CoordinateCleaner updated to version 2.0-17 with previous version 2.0-15 dated 2020-05-11

Title: Automated Cleaning of Occurrence Records from Biological Collections
Description: Automated flagging of common spatial and temporal errors in biological and paleontological collection data, for the use in conservation, ecology and paleontology. Includes automated tests to easily flag (and exclude) records assigned to country or province centroid, the open ocean, the headquarters of the Global Biodiversity Information Facility, urban areas or the location of biodiversity institutions (museums, zoos, botanical gardens, universities). Furthermore identifies per species outlier coordinates, zero coordinates, identical latitude/longitude and invalid coordinates. Also implements an algorithm to identify data sets with a significant proportion of rounded coordinates. Especially suited for large data sets. The reference for the methodology is: Zizka et al. (2019) doi:10.1111/2041-210X.13152.
Author: Alexander Zizka [aut, cre], Daniele Silvestro [ctb], Tobias Andermann [ctb], Josue Azevedo [ctb], Camila Duarte Ritter [ctb], Daniel Edler [ctb], Harith Farooq [ctb], Andrei Herdean [ctb], Maria Ariza [ctb], Ruud Scharn [ctb], Sten Svanteson [ctb], Niklas Wengstrom [ctb], Vera Zizka [ctb], Alexandre Antonelli [ctb], Irene Steves [rev] (Irene reviewed the package for ropensci, see <https://github.com/ropensci/onboarding/issues/210>), Francisco Rodriguez-Sanchez [rev] (Francisco reviewed the package for ropensci, see <https://github.com/ropensci/onboarding/issues/210>)
Maintainer: Alexander Zizka <alexander.zizka@idiv.de>

Diff between CoordinateCleaner versions 2.0-15 dated 2020-05-11 and 2.0-17 dated 2020-08-31

 DESCRIPTION                   |   17 ++++++----
 MD5                           |   67 +++++++++++++++++++++++++-----------------
 NAMESPACE                     |    1 
 NEWS.md                       |   17 ++++++++++
 R/CoordinateCleaner-package.R |   13 +++++---
 R/cc_coun.R                   |    2 -
 R/cc_dupl.R                   |    3 -
 R/cc_equ.R                    |    3 -
 R/cc_gbif.R                   |    3 -
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 man/plot.spatialvalid.Rd      |    5 +++
 vignettes                     |only
 30 files changed, 117 insertions(+), 90 deletions(-)

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Package cobalt updated to version 4.2.3 with previous version 4.2.2 dated 2020-06-26

Title: Covariate Balance Tables and Plots
Description: Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'WeightIt', 'cem', 'sbw', and 'designmatch' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with longitudinal treatments.
Author: Noah Greifer [aut, cre] (<https://orcid.org/0000-0003-3067-7154>)
Maintainer: Noah Greifer <noah.greifer@gmail.com>

Diff between cobalt versions 4.2.2 dated 2020-06-26 and 4.2.3 dated 2020-08-31

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New package calendR with initial version 1.0
Package: calendR
Title: Ready to Print Monthly and Yearly Calendars Made with 'ggplot2'
Version: 1.0
Authors@R: c( person("José Carlos", "Soage González", email = "jsoage@uvigo.es", role = c("aut", "cre")), person("Natalia", "Pérez Veiga", email = "naperez@uvigo.es", role = "aut") )
Description: Contains the function calendR() for creating fully customizable monthly and yearly calendars (colors, fonts, formats, ...) and even heatmap calendars. In addition, it allows saving the calendars in ready to print A4 format PDF files.
Imports: ggplot2, dplyr, forcats
License: GPL-2
Encoding: UTF-8
URL: https://r-coder.com/, https://r-coder.com/calendar-plot-r/
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-08-26 10:53:53 UTC; Matlab
Author: José Carlos Soage González [aut, cre], Natalia Pérez Veiga [aut]
Maintainer: José Carlos Soage González <jsoage@uvigo.es>
Repository: CRAN
Date/Publication: 2020-08-31 09:30:03 UTC

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Permanent link

Package xmlparsedata updated to version 1.0.4 with previous version 1.0.3 dated 2019-09-27

Title: Parse Data of 'R' Code as an 'XML' Tree
Description: Convert the output of 'utils::getParseData()' to an 'XML' tree, that one can search via 'XPath', and easier to manipulate in general.
Author: Gábor Csárdi
Maintainer: Gábor Csárdi <csardi.gabor@gmail.com>

Diff between xmlparsedata versions 1.0.3 dated 2019-09-27 and 1.0.4 dated 2020-08-31

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Package Rfast updated to version 2.0.0 with previous version 1.9.9 dated 2020-03-08

Title: A Collection of Efficient and Extremely Fast R Functions
Description: A collection of fast (utility) functions for data analysis. Column- and row- wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. <doi:10.6339/JDS.201810_16(4).00006>.
Author: Manos Papadakis, Michail Tsagris, Marios Dimitriadis, Stefanos Fafalios, Ioannis Tsamardinos, Matteo Fasiolo, Giorgos Borboudakis, John Burkardt, Changliang Zou, Kleanthi Lakiotaki and Christina Chatzipantsiou.
Maintainer: Manos Papadakis <rfastofficial@gmail.com>

Diff between Rfast versions 1.9.9 dated 2020-03-08 and 2.0.0 dated 2020-08-31

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Package RcmdrPlugin.EZR updated to version 1.52 with previous version 1.51 dated 2020-07-21

Title: R Commander Plug-in for the EZR (Easy R) Package
Description: EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10901-8 or Nankodo, ISBN: 978-4-524-26158-1), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below. This report has been cited in more than 3,000 scientific articles.
Author: Yoshinobu Kanda
Maintainer: Yoshinobu Kanda <ycanda-tky@umin.ac.jp>

Diff between RcmdrPlugin.EZR versions 1.51 dated 2020-07-21 and 1.52 dated 2020-08-31

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Package questionr updated to version 0.7.2 with previous version 0.7.1 dated 2020-05-26

Title: Functions to Make Surveys Processing Easier
Description: Set of functions to make the processing and analysis of surveys easier : interactive shiny apps and addins for data recoding, contingency tables, dataset metadata handling, and several convenience functions.
Author: Julien Barnier [aut, cre], François Briatte [aut], Joseph Larmarange [aut]
Maintainer: Julien Barnier <julien.barnier@cnrs.fr>

Diff between questionr versions 0.7.1 dated 2020-05-26 and 0.7.2 dated 2020-08-31

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New package glow with initial version 0.10.1
Package: glow
Title: Make Plots that Glow
Version: 0.10.1
Date: 2020-8-25
Authors@R: c( person("Travers", "Ching", email = "traversc@gmail.com", role = c("aut", "cre", "cph")) )
Maintainer: Travers Ching <traversc@gmail.com>
Description: Provides a framework for creating plots with glowing points.
License: GPL-3
Biarch: true
Encoding: UTF-8
SystemRequirements: C++11
LinkingTo: Rcpp, RcppEigen, RcppParallel
Imports: Rcpp, RcppParallel, scales, R6
Depends: ggplot2
Suggests: knitr, rmarkdown, viridisLite, magick, qs
VignetteBuilder: knitr
RoxygenNote: 7.1.0
URL: https://github.com/traversc/glow
BugReports: https://github.com/traversc/glow/issues
NeedsCompilation: yes
Packaged: 2020-08-26 04:16:23 UTC; tching
Author: Travers Ching [aut, cre, cph]
Repository: CRAN
Date/Publication: 2020-08-31 08:40:02 UTC

More information about glow at CRAN
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New package getable with initial version 1.0.2
Package: getable
Type: Package
Title: Fetching Tabular Data "Onload" in Compiled R Markdown HTML Documents
Version: 1.0.2
Authors@R: person(given = "Yongfu", family = "Liao", email = "liao961120@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-1814-2993"))
Description: Dynamically retrieve data from the web to render HTML tables on inspection in R Markdown HTML documents.
URL: https://yongfu.name/getable/, https://github.com/liao961120/getable/
BugReports: https://github.com/liao961120/getable/issues/
Depends: R (>= 3.4.0)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Suggests: jsonlite, rmarkdown, knitr
RoxygenNote: 7.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-08-25 23:49:36 UTC; liao
Author: Yongfu Liao [aut, cre] (<https://orcid.org/0000-0002-1814-2993>)
Maintainer: Yongfu Liao <liao961120@gmail.com>
Repository: CRAN
Date/Publication: 2020-08-31 08:30:07 UTC

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Package convergEU updated to version 0.5.0 with previous version 0.4.7 dated 2020-06-11

Title: Monitoring Convergence of EU Countries
Description: Indicators and measures by country and time describe what happens at economic and social levels. This package provides functions to calculate several measures of convergence after imputing missing values. The automated downloading of Eurostat data, followed by the production of country fiches and indicator fiches, makes possible to produce automated reports. The Eurofound report (<doi:10.2806/68012>) "Upward convergence in the EU: Concepts, measurements and indicators", 2018, is a detailed presentation of convergence.
Author: Federico M. Stefanini [arc, aut, cre], Massimiliano Mascherini [arc], Eleonora Peruffo [ctb], Nedka Nikiforova [ctb], Chiara Litardi [ctb]
Maintainer: Federico M. Stefanini <federico.stefanini@unifi.it>

Diff between convergEU versions 0.4.7 dated 2020-06-11 and 0.5.0 dated 2020-08-31

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Package coalitions updated to version 0.6.15 with previous version 0.6.12 dated 2020-02-06

Title: Bayesian "Now-Cast" Estimation of Event Probabilities in Multi-Party Democracies
Description: An implementation of a Bayesian framework for the opinion poll based estimation of event probabilities in multi-party electoral systems (Bender and Bauer (2018) <doi:10.21105/joss.00606>).
Author: Andreas Bender [aut, cre] (<https://orcid.org/0000-0001-5628-8611>), Alexander Bauer [aut] (<https://orcid.org/0000-0003-3495-5131>)
Maintainer: Andreas Bender <bender.at.R@gmail.com>

Diff between coalitions versions 0.6.12 dated 2020-02-06 and 0.6.15 dated 2020-08-31

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Package starsExtra updated to version 0.1.2 with previous version 0.1.0 dated 2020-06-25

Title: Miscellaneous Functions for Working with 'stars' Rasters
Description: Miscellaneous functions for working with 'stars' objects, mainly single-band rasters. Currently includes functions for: (1) focal filtering, (2) detrending of Digital Elevation Models, (3) calculating flow length, (4) calculating the Convergence Index, (5) calculating topographic aspect and topographic slope.
Author: Michael Dorman [aut, cre]
Maintainer: Michael Dorman <dorman@post.bgu.ac.il>

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Package ROI updated to version 1.0-0 with previous version 0.3-3 dated 2020-03-01

Title: R Optimization Infrastructure
Description: The R Optimization Infrastructure ('ROI') <doi:10.18637/jss.v094.i15> is a sophisticated framework for handling optimization problems in R. Additional information can be found on the 'ROI' homepage <http://roi.r-forge.r-project.org/>.
Author: Kurt Hornik [aut], David Meyer [aut], Florian Schwendinger [aut], Stefan Theussl [aut, cre], Diethelm Wuertz [ctb]
Maintainer: Stefan Theussl <Stefan.Theussl@R-Project.org>

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Package revdbayes updated to version 1.3.8 with previous version 1.3.7 dated 2020-06-26

Title: Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis
Description: Provides functions for the Bayesian analysis of extreme value models. The 'rust' package <https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution. The functionality of 'revdbayes' is similar to the 'evdbayes' package <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain Monte Carlo ('MCMC') methods for posterior simulation. Also provided are functions for making inferences about the extremal index, using the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292>. Also provided are d,p,q,r functions for the Generalised Extreme Value ('GEV') and Generalised Pareto ('GP') distributions that deal appropriately with cases where the shape parameter is very close to zero.
Author: Paul J. Northrop [aut, cre, cph], Scott D. Grimshaw [ctb]
Maintainer: Paul J. Northrop <p.northrop@ucl.ac.uk>

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Package contentid updated to version 0.0.6 with previous version 0.0.5 dated 2020-08-27

Title: An Interface for Content-Based Identifiers
Description: An interface for creating, registering, and resolving content-based identifiers for data management. Content-based identifiers rely on the 'cryptographic' hashes to refer to the files they identify, thus, anyone possessing the file can compute the identifier using a well-known standard algorithm, such as 'SHA256'. By registering a URL at which the content is accessible to a public archive, such as <https://hash-archive.org>, the content identifier can serve many functions typically associated with A Digital Object Identifier ('DOI'). Unlike location-based identifiers like 'DOIs', content-based identifiers permit the same content to be registered in many locations.
Author: Carl Boettiger [aut, cre] (<https://orcid.org/0000-0002-1642-628X>), Jorrit Poelen [aut] (<https://orcid.org/0000-0003-3138-4118>), NSF OAC 1839201 [fnd] (https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839201)
Maintainer: Carl Boettiger <cboettig@gmail.com>

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Package brio updated to version 1.1.0 with previous version 1.0.0 dated 2020-03-26

Title: Basic R Input Output
Description: Functions to handle basic input output, these functions always read and write UTF-8 (8-bit Unicode Transformation Format) files and provide more explicit control over line endings.
Author: Jim Hester [aut, cre] (<https://orcid.org/0000-0002-2739-7082>), RStudio [cph, fnd]
Maintainer: Jim Hester <jim.hester@rstudio.com>

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Package agrmt updated to version 1.42.4 with previous version 1.40.4 dated 2016-04-02

Title: Calculate Concentration and Dispersion in Ordered Rating Scales
Description: Calculates concentration and dispersion in ordered rating scales. It implements various measures of concentration and dispersion to describe what researchers variably call agreement, concentration, consensus, dispersion, or polarization among respondents in ordered data. It also implements other related measures to classify distributions. In addition to a generic city-block based concentration measure and a generic dispersion measure, the package implements various measures, including van der Eijk's (2001) <DOI: 10.1023/A:1010374114305> measure of agreement A, measures of concentration by Leik, Tatsle and Wierman, Blair and Lacy, Kvalseth, Berry and Mielke, Reardon, and Garcia-Montalvo and Reynal-Querol. Furthermore, the package provides an implementation of Galtungs AJUS-system to classify distributions, as well as a function to identify the position of multiple modes.
Author: Didier Ruedin [aut, cre] Clem Aeppli [ctb]
Maintainer: Didier Ruedin <didier.ruedin@unine.ch>

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Package whSample updated to version 0.9.0 with previous version 0.8.9 dated 2020-08-28

Title: Utilities for Sampling
Description: Interactive tools for generating random samples. Users select an .xlsx or .csv file with population data and are walked through selecting the sample type (Simple Random Sample or Stratified), the number of backups desired, and a "stratify_on" value (if desired). An .xlsx file is created with the sample and key metadata for reference. It is menu-driven and lets users pick an output directory. See vignettes for a detailed walk-through.
Author: Paul West [aut, cre]
Maintainer: Paul West <fp8049@gmail.com>

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New package torch with initial version 0.0.2
Package: torch
Type: Package
Title: Tensors and Neural Networks with 'GPU' Acceleration
Version: 0.0.2
Authors@R: c( person("Daniel", "Falbel", email = "daniel@rstudio.com", role = c("aut", "cre", "cph")), person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut", "cph")), person("Dmitriy", "Selivanov", role = c("ctb")), person("Athos", "Damiani", role = c("ctb")), person(family = "RStudio", role = c("cph")) )
Description: Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) <arXiv:1912.01703> but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.
License: MIT + file LICENSE
URL: https://torch.mlverse.org/docs, https://github.com/mlverse/torch
BugReports: https://github.com/mlverse/torch/issues
Encoding: UTF-8
LazyData: true
SystemRequirements: C++11, LibTorch (https://pytorch.org/)
LinkingTo: Rcpp
Imports: Rcpp, R6, withr, rlang, methods, utils, stats, bit64
RoxygenNote: 7.1.1
Suggests: testthat (>= 2.1.0), covr, knitr, rmarkdown, magrittr, glue
VignetteBuilder: knitr
Collate: 'R7.R' 'RcppExports.R' 'tensor.R' 'autograd.R' 'backends.R' 'codegen-utils.R' 'conditions.R' 'creation-ops.R' 'cuda.R' 'device.R' 'dimname_list.R' 'dtype.R' 'gen-method.R' 'gen-namespace-docs.R' 'gen-namespace-examples.R' 'gen-namespace.R' 'generator.R' 'help.R' 'indexing.R' 'install.R' 'lantern_load.R' 'lantern_sync.R' 'layout.R' 'memory_format.R' 'utils-data.R' 'nn.R' 'nn-activation.R' 'nn-batchnorm.R' 'nn-conv.R' 'nn-dropout.R' 'nn-init.R' 'nn-linear.R' 'nn-loss.R' 'nn-pooling.R' 'nn-rnn.R' 'nn-sparse.R' 'nn-utils-rnn.R' 'nn-utils.R' 'nn_adaptive.R' 'nnf-activation.R' 'nnf-batchnorm.R' 'nnf-conv.R' 'nnf-distance.R' 'nnf-dropout.R' 'nnf-embedding.R' 'nnf-fold.R' 'nnf-instancenorm.R' 'nnf-linear.R' 'nnf-loss.R' 'nnf-normalization.R' 'nnf-padding.R' 'nnf-pixelshuffle.R' 'nnf-pooling.R' 'nnf-upsampling.R' 'nnf-vision.R' 'operators.R' 'optim.R' 'optim-adam.R' 'optim-sgd.R' 'package.R' 'qscheme.R' 'quantization.R' 'reduction.R' 'save.R' 'scalar.R' 'storage.R' 'tensor_list.R' 'tensor_options.R' 'type-info.R' 'utils-data-collate.R' 'utils-data-dataloader.R' 'utils-data-enum.R' 'utils-data-fetcher.R' 'utils-data-sampler.R' 'utils.R' 'variable_list.R' 'with-indices.R' 'wrapers.R'
NeedsCompilation: yes
Packaged: 2020-08-29 15:28:15 UTC; dfalbel
Author: Daniel Falbel [aut, cre, cph], Javier Luraschi [aut, cph], Dmitriy Selivanov [ctb], Athos Damiani [ctb], RStudio [cph]
Maintainer: Daniel Falbel <daniel@rstudio.com>
Repository: CRAN
Date/Publication: 2020-08-31 07:00:02 UTC

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New package sourceR with initial version 1.1.0
Package: sourceR
Type: Package
Title: Fits a Non-Parametric Bayesian Source Attribution Model
Version: 1.1.0
Date: 2020-08-31
Authors@R: c(person("Poppy", "Miller", email = "p.miller@lancaster.ac.uk", role = c("aut", "cre", "cph")), person("Chris", "Jewell", role = "aut"), person("Jonathan", "Marshall", role = "ctb"), person("Nigel", "French", role = "ctb"))
Description: Implements a non-parametric source attribution model to attribute cases of disease to sources in Bayesian framework with source and type effects. Type effects are clustered using a Dirichlet Process. Multiple times and locations are supported.
License: GPL-3
Depends: R (>= 3.4.0), dplyr, tensorA, assertthat
Imports: methods, Rcpp (>= 1.0.4), gtools, R6, cluster, stats, gplots, SPIn, grDevices, reshape2
LinkingTo: Rcpp
RoxygenNote: 7.1.0
KeepSource: TRUE
LazyData: TRUE
Suggests: testthat, R.rsp
VignetteBuilder: R.rsp
NeedsCompilation: yes
Packaged: 2020-08-31 06:14:24 UTC; Poppy Miller
Author: Poppy Miller [aut, cre, cph], Chris Jewell [aut], Jonathan Marshall [ctb], Nigel French [ctb]
Maintainer: Poppy Miller <p.miller@lancaster.ac.uk>
Repository: CRAN
Date/Publication: 2020-08-31 06:40:03 UTC

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Package rtypeform updated to version 2.1.0 with previous version 2.0.1 dated 2020-03-23

Title: Interface to 'typeform' Results
Description: An R interface to the 'typeform' <https://typeform.com> application program interface. Also provides functions for downloading your results.
Author: Colin Gillespie [aut, cre], Jamie Owen [aut]
Maintainer: Colin Gillespie <csgillespie@gmail.com>

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Package plm updated to version 2.2-4 with previous version 2.2-3 dated 2020-02-28

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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Package JWileymisc updated to version 1.2.0 with previous version 1.1.1 dated 2020-04-28

Title: Miscellaneous Utilities and Functions
Description: Miscellaneous tools and functions, including: generate descriptive statistics tables, format output, visualize relations among variables or check distributions, and generic functions for residual and model diagnostics.
Author: Joshua F. Wiley [aut, cre] (<https://orcid.org/0000-0002-0271-6702>)
Maintainer: Joshua F. Wiley <jwiley.psych@gmail.com>

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Package JointAI updated to version 1.0.0 with previous version 0.6.1 dated 2020-02-12

Title: Joint Analysis and Imputation of Incomplete Data
Description: Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, survival models, or joint models for longitudinal and survival data. Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' <http://mcmc-jags.sourceforge.net/> with the help of the package 'rjags'.
Author: Nicole S. Erler [aut, cre] (<https://orcid.org/0000-0002-9370-6832>)
Maintainer: Nicole S. Erler <n.erler@erasmusmc.nl>

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Package garma updated to version 0.9.3 with previous version 0.9.2 dated 2020-08-06

Title: Fitting and Forecasting Gegenbauer ARMA Time Series Models
Description: Methods for estimating long memory-seasonal/cyclical Gegenbauer univariate time series processes. See for example (2018) <doi:10.1214/18-STS649>. Refer to the vignette for details of fitting these processes.
Author: Richard Hunt [aut, cre]
Maintainer: Richard Hunt <maint@huntemail.id.au>

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Package belg updated to version 1.4.1 with previous version 1.4.0 dated 2020-07-19

Title: Boltzmann Entropy of a Landscape Gradient
Description: Calculates the Boltzmann entropy of a landscape gradient. This package uses the analytical method created by Gao, P., Zhang, H. and Li, Z., 2018 (<doi:10.1111/tgis.12315>) and by Gao, P. and Li, Z., 2019 (<doi:10.1007/s10980-019-00854-3>). It also extend the original ideas by allowing calculations on data with missing values.
Author: Jakub Nowosad [aut, cre] (<https://orcid.org/0000-0002-1057-3721>), Space Informatics Lab [cph]
Maintainer: Jakub Nowosad <nowosad.jakub@gmail.com>

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Package spatialwidget updated to version 0.2.3 with previous version 0.2.2 dated 2020-03-21

Title: Formats Spatial Data for Use in Htmlwidgets
Description: Many packages use 'htmlwidgets' <https://CRAN.R-project.org/package=htmlwidgets> for interactive plotting of spatial data. This package provides functions for converting R objects, such as simple features, into structures suitable for use in 'htmlwidgets' mapping libraries.
Author: David Cooley [aut, cre]
Maintainer: David Cooley <dcooley@symbolix.com.au>

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Package MQMF updated to version 0.1.1 with previous version 0.1.0 dated 2020-03-19

Title: Modelling and Quantitative Methods in Fisheries
Description: Complements the book "Using R for Modelling and Quantitative Methods in Fisheries" ISBN: 9780367469894, being published in September 2020 by Chapman & Hall in their "Using R series". There are numerous functions and data-sets that are used in the book's many practical examples.
Author: Malcolm Haddon [aut, cre]
Maintainer: Malcolm Haddon <malcolm.haddon@gmail.com>

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Package maotai updated to version 0.1.7 with previous version 0.1.6 dated 2020-05-17

Title: Tools for Matrix Algebra, Optimization and Inference
Description: Matrix is an universal and sometimes primary object/unit in applied mathematics and statistics. We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388).
Author: Kisung You [aut, cre] (<https://orcid.org/0000-0002-8584-459X>)
Maintainer: Kisung You <kyoustat@gmail.com>

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Package ChemoSpec2D updated to version 0.4.176 with previous version 0.4.147 dated 2020-02-19

Title: Exploratory Chemometrics for 2D Spectroscopy
Description: A collection of functions for exploratory chemometrics of 2D spectroscopic data sets such as COSY (correlated spectroscopy) and HSQC (heteronuclear single quantum coherence) 2D NMR (nuclear magnetic resonance) spectra. 'ChemoSpec2D' deploys methods aimed primarily at classification of samples and the identification of spectral features which are important in distinguishing samples from each other. Each 2D spectrum (a matrix) is treated as the unit of observation, and thus the physical sample in the spectrometer corresponds to the sample from a statistical perspective. In addition to chemometric tools, a few tools are provided for plotting 2D spectra, but these are not intended to replace the functionality typically available on the spectrometer. 'ChemoSpec2D' takes many of its cues from 'ChemoSpec' and tries to create consistent graphical output and to be very user friendly.
Author: Bryan A. Hanson [aut, cre] (<https://orcid.org/0000-0003-3536-8246>)
Maintainer: Bryan A. Hanson <hanson@depauw.edu>

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Package catchr updated to version 0.2.3 with previous version 0.2.2 dated 2020-01-09

Title: Taking the Pain Out of Catching and Handling Conditions
Description: R has a unique way of dealing with warnings, errors, messages, and other conditions, but it can often be troublesome to users coming from different programming backgrounds. The purpose of this package is to provide flexible and useful tools for handling R conditions with less hassle. In order to lower the barrier of entry, keep code clean and readable, and reduce the amount of typing required, `catchr` uses a very simple domain-specific language that simplifies things on the front-end. This package aims to maintain a continuous learning curve that lets new users jump straight in to condition-handling, while simultaneously offering depth and complexity for more advanced users.
Author: Zachary Burchill [aut, cre, cph]
Maintainer: Zachary Burchill <zach.burchill.code@gmail.com>

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