Tue, 13 Apr 2021

Package yaps updated to version 1.2.5 with previous version 1.2.4 dated 2021-02-10

Title: Track Estimation using YAPS (Yet Another Positioning Solver)
Description: Estimate tracks of animals tagged with acoustic transmitters. 'yaps' was introduced in 2017 as a transparent open-source tool to estimate positions of fish (and other aquatic animals) tagged with acoustic transmitters. Based on registrations of acoustic transmitters on hydrophones positioned in a fixed array, 'yaps' enables users to synchronize the collected data (i.e. correcting for drift in the internal clocks of the hydrophones/receivers) and subsequently to estimate tracks of the tagged animals. The paper introducing 'yaps' is available in open access at Baktoft, Gjelland, Økland & Thygesen (2017) <doi:10.1038/s41598-017-14278-z>. Also check out our cookbook with a completely worked through example at Baktoft, Gjelland, Økland, Rehage, Rodemann, Corujo, Viadero & Thygesen (2019) <DOI:10.1101/2019.12.16.877688>. Additional tutorials will eventually make their way onto the project website at <https://baktoft.github.io/yaps/>.
Author: Henrik Baktoft [cre, aut] (<https://orcid.org/0000-0002-3644-4960>), Karl Gjelland [aut] (<https://orcid.org/0000-0003-4036-4207>), Uffe H. Thygesen [aut] (<https://orcid.org/0000-0002-4311-6324>), Finn Økland [aut] (<https://orcid.org/0000-0002-1938-5460>)
Maintainer: Henrik Baktoft <hba@aqua.dtu.dk>

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 R/zzz.R                                                                |   10 
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Package unifed updated to version 1.1.5 with previous version 1.1.3 dated 2020-06-19

Title: The Unifed Distribution
Description: Probability functions, family for glm() and Stan code for working with the unifed distribution (Quijano Xacur, 2019; <doi:10.1186/s40488-019-0102-6>).
Author: Oscar Alberto Quijano Xacur [aut,cre]
Maintainer: Oscar Alberto Quijano Xacur <oscar.quijano@use.startmail.com>

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Package tramME updated to version 0.1.1 with previous version 0.1.0 dated 2021-03-30

Title: Transformation Models with Mixed Effects
Description: Likelihood-based estimation of mixed-effects transformation models using the Template Model Builder (TMB, Kristensen et al., 2016, <doi:10.18637/jss.v070.i05>). The technical details of transformation models are given in Hothorn et al. (2018, <doi:10.1111/sjos.12291>). Likelihood contributions of exact, randomly censored (left, right, interval) and truncated observations are supported. The random effects are assumed to be normally distributed on the scale of the transformation function, the marginal likelihood is evaluated using the Laplace approximation, and the gradients are calculated with automatic differentiation (AD).
Author: Balint Tamasi [aut, cre] (<https://orcid.org/0000-0002-2629-7362>), Torsten Hothorn [ctb] (<https://orcid.org/0000-0001-8301-0471>)
Maintainer: Balint Tamasi <balint.tamasi@uzh.ch>

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Package stevetemplates updated to version 0.5.0 with previous version 0.4.0 dated 2021-03-05

Title: Steve's 'R Markdown' Templates
Description: These are my collection of 'R Markdown' templates, mostly for compilation to PDF. These are useful for all things academic and professional, if you are using 'R Markdown' for things like your CV or your articles and manuscripts.
Author: Steven V. Miller
Maintainer: Steven V. Miller <steven.v.miller@gmail.com>

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New package MVTests with initial version 2.0.3
Package: MVTests
Date: 2021-04-06
Title: Multivariate Hypothesis Tests
Version: 2.0.3
Author: Hasan BULUT [aut,cre]
Maintainer: Hasan BULUT <hasan.bulut@omu.edu.tr>
Description: Multivariate hypothesis tests and the confidence intervals. It can be used to test the hypothesizes about mean vector or vectors (one-sample, two independent samples, paired samples), covariance matrix (one or more matrices), and the correlation matrix. For this package, we have benefited from the studies Rencher (2003), Nel and Merwe (1986) <DOI: 10.1080/03610928608829342>, Tatlidil (1996), James (1994) <DOI: 10.2307/2333003>, Tsagris (2014), Villasenor Alva and Estrada (2009) <DOI: 10.1080/03610920802474465>.
License: GPL-2
Depends: stats
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2021-04-06 21:39:23 UTC; user
Repository: CRAN
Date/Publication: 2021-04-13 21:20:09 UTC

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Package mboost updated to version 2.9-5 with previous version 2.9-4 dated 2020-12-10

Title: Model-Based Boosting
Description: Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in \doi{10.1214/07-STS242}, a hands-on tutorial is available from \doi{10.1007/s00180-012-0382-5}. The package allows user-specified loss functions and base-learners.
Author: Torsten Hothorn [cre, aut] (<https://orcid.org/0000-0001-8301-0471>), Peter Buehlmann [aut] (<https://orcid.org/0000-0002-1782-6015>), Thomas Kneib [aut] (<https://orcid.org/0000-0003-3390-0972>), Matthias Schmid [aut] (<https://orcid.org/0000-0002-0788-0317>), Benjamin Hofner [aut] (<https://orcid.org/0000-0003-2810-3186>), Fabian Otto-Sobotka [ctb] (<https://orcid.org/0000-0002-9874-1311>), Fabian Scheipl [ctb] (<https://orcid.org/0000-0001-8172-3603>), Andreas Mayr [ctb] (<https://orcid.org/0000-0001-7106-9732>)
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

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Package Matching updated to version 4.9-9 with previous version 4.9-7 dated 2020-02-06

Title: Multivariate and Propensity Score Matching with Balance Optimization
Description: Provides functions for multivariate and propensity score matching and for finding optimal balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance has been obtained are also provided. For details, see the paper by Jasjeet Sekhon (2007, <doi:10.18637/jss.v042.i07>).
Author: Jasjeet Singh Sekhon <jas.sekhon@yale.edu>
Maintainer: Jasjeet Singh Sekhon <jas.sekhon@yale.edu>

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Package lokern updated to version 1.1-9 with previous version 1.1-8.1 dated 2020-07-15

Title: Kernel Regression Smoothing with Local or Global Plug-in Bandwidth
Description: Kernel regression smoothing with adaptive local or global plug-in bandwidth selection.
Author: Eva Herrmann <eherrmann@mathematik.tu-darmstadt.de> (F77 & S original); Packaged for R and enhanced by Martin Maechler
Maintainer: Martin Maechler <maechler@stat.math.ethz.ch>

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Package gtsummary updated to version 1.4.0 with previous version 1.3.7 dated 2021-02-26

Title: Presentation-Ready Data Summary and Analytic Result Tables
Description: Creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.
Author: Daniel D. Sjoberg [aut, cre] (<https://orcid.org/0000-0003-0862-2018>), Michael Curry [aut] (<https://orcid.org/0000-0002-0261-4044>), Margie Hannum [aut] (<https://orcid.org/0000-0002-2953-0449>), Joseph Larmarange [aut] (<https://orcid.org/0000-0001-7097-700X>), Karissa Whiting [aut] (<https://orcid.org/0000-0002-4683-1868>), Emily C. Zabor [aut] (<https://orcid.org/0000-0002-1402-4498>), Esther Drill [ctb] (<https://orcid.org/0000-0002-3315-4538>), Jessica Flynn [ctb] (<https://orcid.org/0000-0001-8310-6684>), Jessica Lavery [ctb] (<https://orcid.org/0000-0002-2746-5647>), Stephanie Lobaugh [ctb], Gustavo Zapata Wainberg [ctb] (<https://orcid.org/0000-0002-2524-3637>)
Maintainer: Daniel D. Sjoberg <danield.sjoberg@gmail.com>

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Package bigutilsr updated to version 0.3.4 with previous version 0.3.3 dated 2020-10-15

Title: Utility Functions for Large-scale Data
Description: Utility functions for large-scale data. For now, package 'bigutilsr' mainly includes functions for outlier detection and unbiased PCA projection.
Author: Florian Privé [aut, cre]
Maintainer: Florian Privé <florian.prive.21@gmail.com>

Diff between bigutilsr versions 0.3.3 dated 2020-10-15 and 0.3.4 dated 2021-04-13

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More information about bigutilsr at CRAN
Permanent link

Package bettermc updated to version 1.1.1 with previous version 1.0.1 dated 2021-03-25

Title: Enhanced Fork-Based Parallelization
Description: Drop-in replacement for 'parallel::mclapply()' adding e.g. tracebacks, crash dumps, retries, condition handling, improved seeding, progress bars and faster inter process communication. Some of the internal functions are also exported for other use: 'etry()' (extended try), 'copy2shm()/allocate_from_shm()' (copy to and allocate from POSIX shared memory), 'char_map/map2char()' (split a character vector into its unique elements and a mapping on these) and various semaphore related functions.
Author: Andreas Kersting [aut, cre, cph], GfK SE [cph], R Core team [ctb] ('etry()' and its print method borrow a lot from base R)
Maintainer: Andreas Kersting <andreas.kersting@gfk.com>

Diff between bettermc versions 1.0.1 dated 2021-03-25 and 1.1.1 dated 2021-04-13

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More information about bettermc at CRAN
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Package NSO1212 updated to version 1.2.0 with previous version 1.1.0 dated 2020-05-13

Title: National Statistical Office of Mongolia's Open Data API Handler
Description: National Statistical Office of Mongolia (NSO) is the national statistical service and an organization of Mongolian government. NSO provides open access to official data via its API <http://opendata.1212.mn/en/doc>. The package NSO1212 has functions for accessing the API service. The functions are compatible with the API v2.0 and get data sets and its detailed informations from the API.
Author: Makhgal Ganbold [aut, cre]
Maintainer: Makhgal Ganbold <makhgal@seas.num.edu.mn>

Diff between NSO1212 versions 1.1.0 dated 2020-05-13 and 1.2.0 dated 2021-04-13

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More information about NSO1212 at CRAN
Permanent link

Package bssm updated to version 1.1.4 with previous version 1.1.3-2 dated 2021-02-26

Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models
Description: Efficient methods for Bayesian inference of state space models via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>). Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported.
Author: Jouni Helske [aut, cre] (<https://orcid.org/0000-0001-7130-793X>), Matti Vihola [aut] (<https://orcid.org/0000-0002-8041-7222>)
Maintainer: Jouni Helske <jouni.helske@iki.fi>

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More information about bssm at CRAN
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Package statsExpressions updated to version 1.0.1 with previous version 1.0.0 dated 2021-03-11

Title: Tidy Dataframes and Expressions with Statistical Details
Description: Utilities for producing dataframes with rich details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for 'ggstatsplot'.
Author: Indrajeet Patil [cre, aut, cph] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets)
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

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Package PROsetta updated to version 0.3.0 with previous version 0.2.1 dated 2020-11-25

Title: Linking Patient-Reported Outcomes Measures
Description: Perform scale linking to establish relationships between instruments that measure similar constructs according to the PROsetta Stone methodology, as in Choi, Schalet, Cook, & Cella (2014) <doi:10.1037/a0035768>.
Author: Seung W. Choi [aut, cre] (<https://orcid.org/0000-0003-4777-5420>), Sangdon Lim [aut] (<https://orcid.org/0000-0002-2988-014X>), Benjamin D. Schalet [ctb], Aaron J. Kaat [ctb], David Cella [ctb]
Maintainer: Seung W. Choi <schoi@austin.utexas.edu>

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New package mlr3spatiotempcv with initial version 0.3.0
Package: mlr3spatiotempcv
Title: Spatiotemporal Resampling Methods for 'mlr3'
Version: 0.3.0
Date: 2021-04-08
Authors@R: c(person(given = "Patrick", family = "Schratz", role = c("aut", "cre"), email = "patrick.schratz@gmail.com", comment = c(ORCID = "0000-0003-0748-6624")), person(given = "Marc", family = "Becker", role = "aut", email = "marcbecker@posteo.de", comment = c(ORCID = "0000-0002-8115-0400")), person(given = "Jannes", family = "Muenchow", role = "ctb", email = "jannes.muenchow@uni-jena.de", comment = c(ORCID = "0000-0001-7834-4717")), person(given = "Michel", family = "Lang", role = "ctb", email = "michellang@gmail.com", comment = c(ORCID = "0000-0001-9754-0393")))
Description: Extends the mlr3 ML framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored.
License: LGPL-3
URL: https://mlr3spatiotempcv.mlr-org.com/, https://github.com/mlr-org/mlr3spatiotempcv, https://mlr3book.mlr-org.com
BugReports: https://github.com/mlr-org/mlr3spatiotempcv/issues
Depends: R (>= 3.5.0)
Imports: checkmate, data.table, ggplot2, mlr3 (>= 0.7.0), mlr3misc (>= 0.7.0), paradox, R6, utils
Suggests: bbotk, blockCV (>= 2.1.1), CAST, ggsci, ggtext, knitr, lgr, mlr3filters, mlr3pipelines, mlr3tuning, patchwork, plotly, raster, rmarkdown, rpart, sf, skmeans, testthat (>= 3.0.0), vdiffr, withr
VignetteBuilder: knitr
Config/testthat/parallel: true
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2021-04-09 13:11:52 UTC; pjs
Author: Patrick Schratz [aut, cre] (<https://orcid.org/0000-0003-0748-6624>), Marc Becker [aut] (<https://orcid.org/0000-0002-8115-0400>), Jannes Muenchow [ctb] (<https://orcid.org/0000-0001-7834-4717>), Michel Lang [ctb] (<https://orcid.org/0000-0001-9754-0393>)
Maintainer: Patrick Schratz <patrick.schratz@gmail.com>
Repository: CRAN
Date/Publication: 2021-04-13 15:10:06 UTC

More information about mlr3spatiotempcv at CRAN
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New package mlquantify with initial version 0.1.5
Package: mlquantify
Title: Algorithms for Class Distribution Estimation
Version: 0.1.5
Authors@R: c( person("Andre", "Maletzke", email = "andregustavom@gmail.com", role = c("aut","cre")), person("Everton", "Cherman", email = "evertoncherman@gmail.com", role = "ctb"), person("Denis", "dos Reis", email = "denismr@gmail.com", role = "ctb"), person("Gustavo", "Batista", email = "g.batista@unsw.edu.au", role = "ths"))
Maintainer: Andre Maletzke <andregustavom@gmail.com>
Description: Quantification is a prominent machine learning task that has received an increasing amount of attention in the last years. The objective is to predict the class distribution of a data sample. This package is a collection of machine learning algorithms for class distribution estimation. This package include algorithms from different paradigms of quantification. These methods are described in the paper: A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set size in quantification assessment. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020. <doi:10.24963/ijcai.2020/366>.
License: GPL (>= 2.0)
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Author: Andre Maletzke [aut, cre], Everton Cherman [ctb], Denis dos Reis [ctb], Gustavo Batista [ths]
BugReports: https://github.com/andregustavom/mlquantify/issues
URL: https://github.com/andregustavom/mlquantify
Imports: caret, randomForest, stats
Suggests: CORElearn
Packaged: 2021-04-10 22:40:11 UTC; andregustavom
Repository: CRAN
Date/Publication: 2021-04-13 15:20:02 UTC

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Package MCMCvis updated to version 0.15.1 with previous version 0.15.0 dated 2021-03-17

Title: Tools to Visualize, Manipulate, and Summarize MCMC Output
Description: Performs key functions for MCMC analysis using minimal code - visualizes, manipulates, and summarizes MCMC output. Functions support simple and straightforward subsetting of model parameters within the calls, and produce presentable and 'publication-ready' output. MCMC output may be derived from Bayesian model output fit with 'Stan', 'NIMBLE', 'JAGS', and other software.
Author: Casey Youngflesh [aut, cre] (<https://orcid.org/0000-0001-6343-3311>), Christian Che-Castaldo [aut] (<https://orcid.org/0000-0002-7670-2178>), Tyler Hardy [ctb]
Maintainer: Casey Youngflesh <caseyyoungflesh@gmail.com>

Diff between MCMCvis versions 0.15.0 dated 2021-03-17 and 0.15.1 dated 2021-04-13

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Package EGRETci updated to version 2.0.4 with previous version 2.0.3 dated 2019-03-15

Title: Exploration and Graphics for RivEr Trends Confidence Intervals
Description: Collection of functions to evaluate uncertainty of results from water quality analysis using the Weighted Regressions on Time Discharge and Season (WRTDS) method. This package is an add-on to the EGRET package that performs the WRTDS analysis. The WRTDS modeling method was initially introduced and discussed in Hirsch et al. (2010) <doi:10.1111/j.1752-1688.2010.00482.x>, and expanded in Hirsch and De Cicco (2015) <doi:10.3133/tm4A10>. The paper describing the uncertainty and confidence interval calculations is Hirsch et al. (2015) <doi:10.1016/j.envsoft.2015.07.017>.
Author: Laura DeCicco [aut, cre] (<https://orcid.org/0000-0002-3915-9487>), Robert Hirsch [aut] (<https://orcid.org/0000-0002-4534-075X>), Jennifer Murphy [ctb]
Maintainer: Laura DeCicco <ldecicco@usgs.gov>

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Package viridisLite updated to version 0.4.0 with previous version 0.3.0 dated 2018-02-01

Title: Colorblind-Friendly Color Maps (Lite Version)
Description: Color maps designed to improve graph readability for readers with common forms of color blindness and/or color vision deficiency. The color maps are also perceptually-uniform, both in regular form and also when converted to black-and-white for printing. This is the 'lite' version of the 'viridis' package that also contains 'ggplot2' bindings for discrete and continuous color and fill scales and can be found at <https://cran.r-project.org/package=viridis>.
Author: Simon Garnier [aut, cre], Noam Ross [ctb, cph], Bob Rudis [ctb, cph], Marco Sciaini [ctb, cph], Antônio Pedro Camargo [ctb, cph], Cédric Scherer [ctb, cph]
Maintainer: Simon Garnier <garnier@njit.edu>

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More information about viridisLite at CRAN
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New package SIBER with initial version 2.1.6
Package: SIBER
Title: Stable Isotope Bayesian Ellipses in R
Version: 2.1.6
Depends: R (>= 4.0.0), spatstat (>= 2.0-0),
SystemRequirements: JAGS (>= 4.1)
Imports: hdrcde, graphics, grDevices, mnormt, rjags, spatstat.geom, spatstat.utils, stats, tidyr, dplyr, ggplot2, magrittr, purrr
Suggests: coda, ellipse, knitr, rmarkdown, tidyverse, viridis
Author: Andrew Jackson and Andrew Parnell
Maintainer: Andrew Jackson <jacksoan@tcd.ie>
Description: Fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche.
License: GPL (>= 2)
Language: en-GB
LazyLoad: yes
LazyData: true
Encoding: UTF-8
Packaged: 2021-04-13 08:24:33 UTC; andrew
NeedsCompilation: yes
VignetteBuilder: knitr
Repository: CRAN
Date/Publication: 2021-04-13 14:50:08 UTC

More information about SIBER at CRAN
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Package RcppArmadillo updated to version 0.10.4.0.0 with previous version 0.10.2.2.0 dated 2021-03-09

Title: 'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra Library
Description: 'Armadillo' is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. The 'RcppArmadillo' package includes the header files from the templated 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Armadillo requires a C++11 compiler.
Author: Dirk Eddelbuettel, Romain Francois, Doug Bates and Binxiang Ni
Maintainer: Dirk Eddelbuettel <edd@debian.org>

Diff between RcppArmadillo versions 0.10.2.2.0 dated 2021-03-09 and 0.10.4.0.0 dated 2021-04-13

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 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_eigs_sym.hpp                         |    8 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_expmat.hpp                           |    7 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_hess.hpp                             |    4 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_inv.hpp                              |   42 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_log_det.hpp                          |   90 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_logmat.hpp                           |    6 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_lu.hpp                               |    4 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_mvnrnd.hpp                           |   12 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_norm.hpp                             |  193 --
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_orth_null.hpp                        |    6 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_pinv.hpp                             |   37 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_polyfit.hpp                          |    2 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_powmat.hpp                           |   59 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_princomp.hpp                         |    8 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_qr.hpp                               |    6 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_qz.hpp                               |    2 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_randg.hpp                            |   48 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_randn.hpp                            |   12 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_randu.hpp                            |   12 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_rank.hpp                             |   11 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_reshape.hpp                          |    2 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_roots.hpp                            |    6 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_schur.hpp                            |    4 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_solve.hpp                            |   30 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_spsolve.hpp                          |   22 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_sqrtmat.hpp                          |    6 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_svd.hpp                              |   34 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_svds.hpp                             |    8 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_sylvester.hpp                        |only
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_trimat_ind.hpp                       |    4 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_wishrnd.hpp                          |   32 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/glue_conv_meat.hpp                      |    2 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/glue_histc_meat.hpp                     |    2 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/glue_mvnrnd_meat.hpp                    |    8 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/glue_polyfit_meat.hpp                   |    2 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/glue_solve_meat.hpp                     |   41 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/glue_times_meat.hpp                     |   16 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/glue_toeplitz_meat.hpp                  |    2 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/gmm_diag_meat.hpp                       |   28 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/gmm_full_meat.hpp                       |   28 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/newarp_SparseGenRealShiftSolve_meat.hpp |    5 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_chol_meat.hpp                        |    8 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_cond_meat.hpp                        |    9 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_det_bones.hpp                        |only
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_det_meat.hpp                         |only
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_diagmat_meat.hpp                     |    2 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_diagvec_meat.hpp                     |    2 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_expmat_meat.hpp                      |    4 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_inv_bones.hpp                        |   32 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_inv_meat.hpp                         |  283 ++-
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_log_det_bones.hpp                    |only
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_log_det_meat.hpp                     |only
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_logmat_meat.hpp                      |   10 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_norm_bones.hpp                       |   18 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_norm_meat.hpp                        |  134 -
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_orth_null_meat.hpp                   |   28 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_pinv_bones.hpp                       |    4 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_pinv_meat.hpp                        |  165 -
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_powmat_bones.hpp                     |    8 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_powmat_meat.hpp                      |  102 -
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_roots_meat.hpp                       |    8 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_shift_meat.hpp                       |    4 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_sqrtmat_meat.hpp                     |   12 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_toeplitz_meat.hpp                    |    4 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_trimat_meat.hpp                      |    4 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/op_wishrnd_meat.hpp                     |    6 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/podarray_bones.hpp                      |    2 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/podarray_meat.hpp                       |   10 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/running_stat_meat.hpp                   |    4 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/running_stat_vec_meat.hpp               |    8 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/sp_auxlib_meat.hpp                      |   66 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/spdiagview_meat.hpp                     |    8 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/spop_diagmat_meat.hpp                   |    2 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/spop_misc_meat.hpp                      |    2 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/spop_norm_bones.hpp                     |only
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/spop_norm_meat.hpp                      |only
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/spop_trimat_meat.hpp                    |    4 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/subview_cube_each_meat.hpp              |   22 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/subview_cube_meat.hpp                   |    8 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/subview_cube_slices_meat.hpp            |   16 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/subview_each_meat.hpp                   |   44 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/subview_elem1_meat.hpp                  |   52 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/subview_elem2_meat.hpp                  |   42 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/subview_field_meat.hpp                  |    8 
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/subview_meat.hpp                        |  108 -
 RcppArmadillo-0.10.4.0.0/RcppArmadillo/inst/include/armadillo_bits/sympd_helper.hpp                        |   19 
 135 files changed, 3180 insertions(+), 2585 deletions(-)

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Package RadData updated to version 1.0.1 with previous version 1.0.0 dated 2019-04-05

Title: Nuclear Decay Data for Dosimetric Calculations - ICRP 107
Description: Nuclear Decay Data for Dosimetric Calculations from the International Commission on Radiological Protection from ICRP Publication 107. Ann. ICRP 38 (3). Eckerman, Keith and Endo, Akira 2008 <doi:10.1016/j.icrp.2008.10.004> <https://www.icrp.org/publication.asp?id=ICRP%20Publication%20107>. This is a database of the physical data needed in calculations of radionuclide-specific protection and operational quantities. The data is prescribed by the ICRP, the international authority on radiation dose standards, for estimating dose from the intake of or exposure to radionuclides in the workplace and the environment. The database contains information on the half-lives, decay chains, and yields and energies of radiations emitted in nuclear transformations of 1252 radionuclides of 97 elements.
Author: Mark Hogue [aut, cre], KF Eckerman [dtc, cph], A Endo [dtc, cph]
Maintainer: Mark Hogue <mark.hogue.chp@gmail.com>

Diff between RadData versions 1.0.0 dated 2019-04-05 and 1.0.1 dated 2021-04-13

 RadData-1.0.0/RadData/data/datalist        |only
 RadData-1.0.1/RadData/DESCRIPTION          |   13 +++++++------
 RadData-1.0.1/RadData/MD5                  |   19 ++++++++++---------
 RadData-1.0.1/RadData/NEWS.md              |only
 RadData-1.0.1/RadData/R/data.R             |    2 +-
 RadData-1.0.1/RadData/README.md            |    4 ++--
 RadData-1.0.1/RadData/data/ICRP_07.NDX.rda |binary
 RadData-1.0.1/RadData/man/ICRP_07.BET.Rd   |    6 ++++--
 RadData-1.0.1/RadData/man/ICRP_07.NDX.Rd   |    8 +++++---
 RadData-1.0.1/RadData/man/ICRP_07.RAD.Rd   |    6 ++++--
 RadData-1.0.1/RadData/man/figures          |only
 RadData-1.0.1/RadData/man/rad_codes.Rd     |    6 ++++--
 12 files changed, 37 insertions(+), 27 deletions(-)

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New package promotionImpact with initial version 0.1.5
Package: promotionImpact
Title: Analysis & Measurement of Promotion Effectiveness
Version: 0.1.5
Date: 2021-04-13
Authors@R: c( person("Nahyun", "Kim", email = "nhkim1302@ncsoft.com", role = c("cre", "aut")), person("Hyemin", "Um", email = "windy0126@ncsoft.com", role = "aut"), person("Eunjo", "Lee", email = "gimmesilver@ncsoft.com", role = "aut"), person(family = "NCSOFT Corporation", role = "cph") )
Description: Analysis and measurement of promotion effectiveness on a given target variable (e.g. daily sales). After converting promotion schedule into dummy or smoothed predictor variables, the package estimates the effects of these variables controlled for trend/periodicity/structural change using prophet by Taylor and Letham (2017) <doi:10.7287/peerj.preprints.3190v2> and some prespecified variables (e.g. start of a month).
Depends: R (>= 3.5.0), Rcpp (>= 0.12.17), dplyr (>= 0.7.6), ggplot2 (>= 3.0.0), scales (>= 1.0.0)
Imports: KernSmooth (>= 2.23.15), ggpubr (>= 0.1.8), reshape2 (>= 1.4.3), stringr (>= 1.3.1), strucchange (>= 1.5.1), lmtest (>= 0.9), crayon (>= 1.3.4), prophet (>= 0.6.1)
License: BSD_3_clause + file LICENSE
URL: https://github.com/ncsoft/promotionImpact
LazyData: true
Encoding: UTF-8
Author: Nahyun Kim [cre, aut], Hyemin Um [aut], Eunjo Lee [aut], NCSOFT Corporation [cph]
Maintainer: Nahyun Kim <nhkim1302@ncsoft.com>
NeedsCompilation: no
Packaged: 2021-04-13 08:07:44 UTC; nhkim1302
Repository: CRAN
Date/Publication: 2021-04-13 15:00:05 UTC

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New package mlr3db with initial version 0.4.1
Package: mlr3db
Title: Data Base Backend for 'mlr3'
Version: 0.4.1
Authors@R: person(given = "Michel", family = "Lang", role = c("cre", "aut"), email = "michellang@gmail.com", comment = c(ORCID = "0000-0001-9754-0393"))
Description: Extends the 'mlr3' package with a backend to transparently work with databases. Includes two extra backends: One relies on relies on the abstraction of package 'dbplyr' to interact with one of the many supported database management systems (DBMS). The other one is specialized for package 'duckdb'.
License: LGPL-3
URL: https:///mlr3db.mlr-org.com, https://github.com/mlr-org/mlr3db
BugReports: https://github.com/mlr-org/mlr3db/issues
Depends: mlr3 (>= 0.9.0), R (>= 3.1.0)
Imports: R6, backports, checkmate, data.table, digest, mlr3misc,
Suggests: DBI, RSQLite, dbplyr, dplyr, duckdb, future, future.apply, future.callr, lgr, testthat (>= 3.0.0), tibble
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2021-04-12 08:26:39 UTC; lang
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>)
Maintainer: Michel Lang <michellang@gmail.com>
Repository: CRAN
Date/Publication: 2021-04-13 14:50:02 UTC

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New package hlaR with initial version 0.1.0
Package: hlaR
Title: Tools for HLA Data
Version: 0.1.0
Description: A streamlined tool for eplet analysis of donor and recipient HLA (human leukocyte antigen) mismatch. Messy, low-resolution HLA typing data is cleaned, and imputed to high-resolution using the NMDP (National Marrow Donor Program) haplotype reference database <https://haplostats.org/haplostats>. High resolution data is analyzed for overall or single antigen eplet mismatch using a reference table (currently supporting 'HLAMatchMaker' <http://www.epitopes.net> versions 2 and 3). Data can enter or exit the workflow at different points depending on the user's aims and initial data quality.
Authors@R: c(person("Joan","Zhang",email = "joan.zhang@emory.edu", role = c("aut", "cre")), person("Aileen","Johnson",email = "aileen.johnson@emory.edu ", role = c("aut")), person("Christian P","Larsen",email = "clarsen@emory.edu", role = c("cph", "aut")))
Depends: R (>= 3.5.0)
License: MIT + file LICENSE
Encoding: UTF-8
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Imports: devtools, usethis, tidyverse, dplyr, reshape2, schoolmath, tibble, stringr, purrr, tidyr, utils, readr, here, readxlsb
NeedsCompilation: no
Packaged: 2021-04-12 13:15:13 UTC; xzhan50
Author: Joan Zhang [aut, cre], Aileen Johnson [aut], Christian P Larsen [cph, aut]
Maintainer: Joan Zhang <joan.zhang@emory.edu>
Repository: CRAN
Date/Publication: 2021-04-13 14:50:05 UTC

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Package datamods updated to version 1.1.3 with previous version 1.1.2 dated 2021-03-27

Title: Modules to Import and Manipulate Data in 'Shiny'
Description: 'Shiny' modules to import data into an application or 'addin' from various sources, and to manipulate them after that.
Author: Victor Perrier [aut, cre, cph], Fanny Meyer [aut], Zauad Shahreer Abeer [aut]
Maintainer: Victor Perrier <victor.perrier@dreamrs.fr>

Diff between datamods versions 1.1.2 dated 2021-03-27 and 1.1.3 dated 2021-04-13

 DESCRIPTION                        |    6 
 MD5                                |   62 -
 NAMESPACE                          |  218 +++---
 NEWS.md                            |   35 
 R/doc.R                            |   46 -
 R/filter-data.R                    | 1082 +++++++++++++++---------------
 R/import-copypaste.R               |  300 ++++----
 R/import-file.R                    |  518 +++++++-------
 R/import-googlesheets.R            |  398 +++++------
 R/import-modal.R                   |  788 +++++++++++-----------
 R/update-variables.R               | 1298 ++++++++++++++++++-------------------
 R/utils-shiny.R                    |  356 +++++-----
 R/utils.R                          |    6 
 R/validation.R                     |  610 ++++++++---------
 R/zzz.R                            |    4 
 inst/doc/i18n.Rmd                  |  194 ++---
 inst/extdata/mtcars.csv            |   66 -
 inst/i18n/fr.csv                   |  118 +--
 man/filter-data.Rd                 |  372 +++++-----
 man/get_data_packages.Rd           |   34 
 man/import-copypaste.Rd            |  164 ++--
 man/import-file.Rd                 |  166 ++--
 man/import-globalenv.Rd            |  220 +++---
 man/import-googlesheets.Rd         |  164 ++--
 man/import-modal.Rd                |  206 ++---
 man/list_pkg_data.Rd               |   42 -
 man/show_data.Rd                   |  144 ++--
 man/update-variables.Rd            |  178 ++---
 tests/testthat.R                   |    8 
 tests/testthat/test-filter-data.R  |  284 ++++----
 tests/testthat/test-import-modal.R |   32 
 vignettes/i18n.Rmd                 |  194 ++---
 32 files changed, 4161 insertions(+), 4152 deletions(-)

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New package cheatsheet with initial version 0.1.0
Package: cheatsheet
Title: Download R Cheat Sheets Locally
Version: 0.1.0
Authors@R: c(person(given = "Brad", family = "Lindblad", role = c("aut", "cre"), email = "me@bradlindblad.com"), person(given = "RStudio", role = "cph"))
Description: A simple package to grab cheat sheets and save them to your local computer.
License: MIT + file LICENSE
Suggests: testthat, covr
Encoding: UTF-8
LazyData: false
URL: https://bradlindblad.github.io/cheatsheet/
BugReports: https://github.com/bradlindblad/cheatsheet/issues
Imports: magrittr, git2r, fs, crayon, cli, purrr, rappdirs, stringr, rstudioapi
NeedsCompilation: no
Packaged: 2021-04-12 01:29:15 UTC; brad
Author: Brad Lindblad [aut, cre], RStudio [cph]
Maintainer: Brad Lindblad <me@bradlindblad.com>
Repository: CRAN
Date/Publication: 2021-04-13 14:10:02 UTC

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New package IPWpn with initial version 0.1.0
Package: IPWpn
Title: Inverse-Propensity-Weighting for Partially Nested Designs
Version: 0.1.0
Author: Xiao Liu
Maintainer: Xiao Liu <xliu19@nd.edu>
Description: Use inverse-propensity-weighted estimation approaches to estimating the treatment effect from a partially nested design where one study arm (the treatment arm) is nested and the other study arm (the control arm) is not. Two estimators are provided: IPW mean difference and IPW multilevel modeling. <https://github.com/xliu12/IPWpn>.
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: MplusAutomation, tidyverse, mvtnorm, stats, utils, dplyr, tibble, tidyr
Suggests: knitr, rmarkdown, testthat (>= 2.0.0)
VignetteBuilder: knitr
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2021-04-11 14:22:18 UTC; xliu19
Repository: CRAN
Date/Publication: 2021-04-13 13:30:05 UTC

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Package ipeadatar updated to version 0.1.2 with previous version 0.1.1 dated 2020-05-14

Title: API Wrapper for 'Ipeadata'
Description: Allows direct access to the macroeconomic, financial and regional database maintained by Brazilian Institute for Applied Economic Research ('Ipea'). This R package uses the 'Ipeadata' API. For more information, see <http://www.ipeadata.gov.br/>.
Author: Luiz Eduardo S. Gomes [aut, cre], Jessyka A. P. Goltara [ctb]
Maintainer: Luiz Eduardo S. Gomes <gomes.leduardo@gmail.com>

Diff between ipeadatar versions 0.1.1 dated 2020-05-14 and 0.1.2 dated 2021-04-13

 DESCRIPTION               |   16 ++++++++--------
 MD5                       |    6 +++---
 R/ipeadata_pkg.R          |    7 +++++--
 man/available_subjects.Rd |    3 +++
 4 files changed, 19 insertions(+), 13 deletions(-)

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New package AsthmaNHANES with initial version 1.1.0
Package: AsthmaNHANES
Title: Asthma Data Sets from NHANES
Version: 1.1.0
Authors@R: c( person("Tao", "Sun",email="sun.tao@ruc.edu.cn", role = c("aut", "cre")), person("Qiyao", "Qin",role = "aut"), person("Zihan", "Qian",role = "aut"), person("Yang", "Li",role = "aut") )
Maintainer: Tao Sun <sun.tao@ruc.edu.cn>
Description: Data sets and examples from National Health and Nutritional Examination Survey (NHANES).
License: GPL-3
Depends: R (>= 3.5.0)
Repository: CRAN
Date: 2021-04-11 05:00:09 UTC
NeedsCompilation: no
Packaged: 2021-04-11 14:59:16 UTC; Lenovo
Author: Tao Sun [aut, cre], Qiyao Qin [aut], Zihan Qian [aut], Yang Li [aut]
Date/Publication: 2021-04-13 14:00:02 UTC

More information about AsthmaNHANES at CRAN
Permanent link

Package gtfs2gps updated to version 1.4-1 with previous version 1.4-0 dated 2021-03-29

Title: Converting Transport Data from GTFS Format to GPS-Like Records
Description: Convert general transit feed specification (GTFS) data to global positioning system (GPS) records in 'data.table' format. It also has some functions to subset GTFS data in time and space and to convert both representations to simple feature format.
Author: Rafael H. M. Pereira [aut] (<https://orcid.org/0000-0003-2125-7465>), Pedro R. Andrade [aut, cre] (<https://orcid.org/0000-0001-8675-4046>), Joao Bazzo [aut] (<https://orcid.org/0000-0003-4536-5006>), Marcin Stepniak [ctb], Marcus Saraiva [ctb] (<https://orcid.org/0000-0001-6218-2338>), Ipea - Institue for Applied Economic Research [cph, fnd]
Maintainer: Pedro R. Andrade <pedro.andrade@inpe.br>

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Package nCov2019 updated to version 0.4.4 with previous version 0.4.3 dated 2021-04-08

Title: Exploring 'COVID'-19 Statistics
Description: Provides easy-to-use programming API to access real time and historical data of 'COVID'-19 cases, vaccine and therapeutics data, and a Shiny app to help users exploring the data. Fetching data using API provided by <https://disease.sh> .
Author: Guangchuang Yu [cre, aut, cph] (<https://orcid.org/0000-0002-6485-8781>), Tianzhi Wu [aut], Erqiang Hu [ctb], Patrick Tung [ctb], Xijin Ge [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>

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Package idiogramFISH updated to version 2.0.3 with previous version 2.0.2 dated 2021-03-04

Title: Shiny App. Idiograms with Marks and Karyotype Indices
Description: Plot idiograms of karyotypes, plasmids, circular chr. having a set of data.frames for chromosome data and optionally mark data. Two styles of chromosomes can be used: without or with visible chromatids (when not circular). Supports micrometers, cM and Mb or any unit. Three styles of centromeres are available: triangular, rounded and inProtein; and six styles of marks are available: square (squareLeft), dots, cM (cMLeft), cenStyle, upArrow (downArrow), exProtein (inProtein); its legend (label) can be drawn inline or to the right of karyotypes. Idiograms can also be plotted in concentric circles. It is possible to calculate chromosome indices by Levan et al. (1964) <doi:10.1111/j.1601-5223.1964.tb01953.x>, karyotype indices of Watanabe et al. (1999) <doi:10.1007/PL00013869> and Romero-Zarco (1986) <doi:10.2307/1221906> and classify chromosomes by morphology Guerra (1986) and Levan et al. (1964).
Author: Fernando Roa [aut, cre], Mariana PC Telles [ctb]
Maintainer: Fernando Roa <froao@unal.edu.co>

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Package AWR updated to version 1.11.189-1 with previous version 1.11.189 dated 2017-09-07

Title: 'AWS' Java 'SDK' for R
Description: Make the compiled Java modules of the Amazon Web Services ('AWS') 'SDK' available to be used in downstream R packages interacting with 'AWS'. See <https://aws.amazon.com/sdk-for-java> for more information on the 'AWS' 'SDK' for Java.
Author: Gergely Daroczi <daroczig@rapporter.net>
Maintainer: Gergely Daroczi <daroczig@rapporter.net>

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Package peopleanalyticsdata updated to version 0.2.1 with previous version 0.2.0 dated 2021-02-16

Title: Data Sets for Keith McNulty's Handbook of Regression Modeling in People Analytics
Description: Data sets for statistical inference modeling related to People Analytics. Contains various data sets from the book 'Handbook of Regression Modeling in People Analytics' by Keith McNulty (2020).
Author: Keith McNulty [aut, cre] (<https://orcid.org/0000-0002-2332-1654>)
Maintainer: Keith McNulty <keith.mcnulty@gmail.com>

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New package glmtoolbox with initial version 0.1.0
Package: glmtoolbox
Title: Set of Tools to Data Analysis using Generalized Linear Models
Version: 0.1.0
Authors@R: c(person("Luis Hernando","Vanegas",role=c("aut","cre"),email="lhvanegasp@unal.edu.co"), person("Luz Marina","Rondón",role="aut",email="lmrondonp@unal.edu.co"), person("Gilberto A.","Paula",role="aut",email="giapaula@ime.usp.br"))
Description: Set of tools to the statistical analysis of data using: (1) normal linear models; (2) generalized linear models; (3) negative binomial regression models as alternative to the Poisson regression models under the presence of overdispersion; (4) beta-binomial regression models as alternative to the binomial regression models under the presence of overdispersion; (5) generalized estimating equations for cluster correlated data.
License: GPL-2 | GPL-3
Imports: methods, stats, graphics
Suggests: aplore3, MASS, ISLR
Depends: numDeriv, Rfast
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2021-04-10 23:58:55 UTC; Luis
Author: Luis Hernando Vanegas [aut, cre], Luz Marina Rondón [aut], Gilberto A. Paula [aut]
Maintainer: Luis Hernando Vanegas <lhvanegasp@unal.edu.co>
Repository: CRAN
Date/Publication: 2021-04-13 08:10:02 UTC

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Package giscoR updated to version 0.2.4 with previous version 0.2.2 dated 2020-11-23

Title: Download Map Data from GISCO API - Eurostat
Description: Tools to download data from the GISCO (Geographic Information System of the Commission) Eurostat database <https://ec.europa.eu/eurostat/web/gisco>. Global and European map data available. This package is in no way officially related to or endorsed by Eurostat.
Author: Diego Hernangómez [aut, cre, cph] (<https://orcid.org/0000-0001-8457-4658>), EuroGeographics [cph] (for the administrative boundaries.), Vincent Arel-Bundock [cph] (<https://orcid.org/0000-0003-2042-7063>, for the gisco_countrycode dataset.)
Maintainer: Diego Hernangómez <diego.hernangomezherrero@gmail.com>

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Package ClimMobTools updated to version 0.3.9 with previous version 0.3.7 dated 2021-03-23

Title: API Client for the 'ClimMob' Platform
Description: API client for 'ClimMob', an open source software for experimental crowdsourcing citizen science under the 'tricot' approach <https://climmob.net/>. Developed by van Etten et al. (2019) <doi:10.1017/S0014479716000739>, it turns the research paradigm on its head; instead of a few researchers designing complicated trials to compare several technologies in search of the best solutions for the target environment, it enables many participants to carry out reasonably simple experiments that taken together can offer even more information. 'ClimMobTools' enables project managers to deep explore and analyse their 'ClimMob' data in R.
Author: Kauê de Sousa [aut, cre] (<https://orcid.org/0000-0002-7571-7845>), Jacob van Etten [aut] (<https://orcid.org/0000-0001-7554-2558>), Brandon Madriz [ctb] (API Client implementation)
Maintainer: Kauê de Sousa <kaue.desousa@inn.no>

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More information about ClimMobTools at CRAN
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Package RMariaDB updated to version 1.1.1 with previous version 1.1.0 dated 2021-01-07

Title: Database Interface and 'MariaDB' Driver
Description: Implements a 'DBI'-compliant interface to 'MariaDB' (<https://mariadb.org/>) and 'MySQL' (<https://www.mysql.com/>) databases.
Author: Kirill Müller [aut, cre] (<https://orcid.org/0000-0002-1416-3412>), Jeroen Ooms [aut] (<https://orcid.org/0000-0002-4035-0289>), David James [aut], Saikat DebRoy [aut], Hadley Wickham [aut], Jeffrey Horner [aut], R Consortium [fnd], RStudio [cph]
Maintainer: Kirill Müller <krlmlr+r@mailbox.org>

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Package pillar updated to version 1.6.0 with previous version 1.5.1 dated 2021-03-05

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

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Package hscovar updated to version 0.4.2 with previous version 0.4.0 dated 2020-08-06

Title: Calculation of Covariance Between Markers for Half-Sib Families
Description: The theoretical covariance between pairs of markers is calculated from either paternal haplotypes and maternal linkage disequilibrium (LD) or vise versa. A genetic map is required. Grouping of markers is based on the correlation matrix and a representative marker is suggested for each group. Employing the correlation matrix, optimal sample size can be derived for association studies based on a SNP-BLUP approach. The implementation relies on paternal half-sib families and biallelic markers. If maternal half-sib families are used, the roles of sire/dam are swapped. Multiple families can be considered. Wittenburg, Bonk, Doschoris, Reyer (2020) "Design of Experiments for Fine-Mapping Quantitative Trait Loci in Livestock Populations" <doi:10.1186/s12863-020-00871-1>. Carlson, Eberle, Rieder, Yi, Kruglyak, Nickerson (2004) "Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium" <doi:10.1086/381000>.
Author: Dörte Wittenburg [aut, cre], Michael Doschoris [aut], Jan Klosa [ctb]
Maintainer: Dörte Wittenburg <wittenburg@fbn-dummerstorf.de>

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New package ggmix with initial version 0.0.2
Package: ggmix
Title: Variable Selection in Linear Mixed Models for SNP Data
Version: 0.0.2
Authors@R: c( person("Sahir", "Bhatnagar", email = "sahir.bhatnagar@gmail.com", role = c("aut", "cre"), comment = "https://sahirbhatnagar.com/"), person("Karim", "Oualkacha", email = "oualkacha.karim@uqam.ca", role = c("aut"), comment = "http://karimoualkacha.uqam.ca/"), person("Yi", "Yang", email = "yi.yang6@mcgill.ca", role = c("aut"), comment = "http://www.math.mcgill.ca/yyang/"), person("Celia", "Greenwood", email = "celia.greenwood@mcgill.ca", role = c("aut"), comment = "http://www.mcgill.ca/statisticalgenetics/") )
Description: Fit penalized multivariable linear mixed models with a single random effect to control for population structure in genetic association studies. The goal is to simultaneously fit many genetic variants at the same time, in order to select markers that are independently associated with the response. Can also handle prior annotation information, for example, rare variants, in the form of variable weights. For more information, see the website below and the accompanying paper: Bhatnagar et al., "Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models", 2020, <DOI:10.1371/journal.pgen.1008766>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.4.0)
Imports: glmnet, methods, stats, MASS, Matrix
Suggests: RSpectra, popkin, bnpsd, testthat, covr, knitr, rmarkdown
BugReports: https://github.com/sahirbhatnagar/ggmix/issues
URL: https://github.com/sahirbhatnagar/ggmix
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-04-10 04:23:08 UTC; sahir
Author: Sahir Bhatnagar [aut, cre] (https://sahirbhatnagar.com/), Karim Oualkacha [aut] (http://karimoualkacha.uqam.ca/), Yi Yang [aut] (http://www.math.mcgill.ca/yyang/), Celia Greenwood [aut] (http://www.mcgill.ca/statisticalgenetics/)
Maintainer: Sahir Bhatnagar <sahir.bhatnagar@gmail.com>
Repository: CRAN
Date/Publication: 2021-04-13 07:00:02 UTC

More information about ggmix at CRAN
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Package nanny (with last version 0.1.8) was removed from CRAN

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

2020-06-13 0.1.8

Permanent link
Package lightgbm updated to version 3.2.1 with previous version 3.2.0 dated 2021-03-22

Title: Light Gradient Boosting Machine
Description: Tree based algorithms can be improved by introducing boosting frameworks. 'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>. This package offers an R interface to work with it. It is designed to be distributed and efficient with the following advantages: 1. Faster training speed and higher efficiency. 2. Lower memory usage. 3. Better accuracy. 4. Parallel learning supported. 5. Capable of handling large-scale data. In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions. Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines.
Author: Guolin Ke [aut, cre], Damien Soukhavong [aut], James Lamb [aut], Qi Meng [aut], Thomas Finley [aut], Taifeng Wang [aut], Wei Chen [aut], Weidong Ma [aut], Qiwei Ye [aut], Tie-Yan Liu [aut], Yachen Yan [ctb], Microsoft Corporation [cph], Dropbox, Inc. [cph], Jay Loden [cph], Dave Daeschler [cph], Giampaolo Rodola [cph], Alberto Ferreira [ctb], Daniel Lemire [ctb], Victor Zverovich [cph], IBM Corporation [ctb]
Maintainer: Guolin Ke <guolin.ke@microsoft.com>

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Package ffscrapr updated to version 1.4.0 with previous version 1.3.0 dated 2021-03-07

Title: API Client for Fantasy Football League Platforms
Description: Helps access various Fantasy Football APIs by handling authentication and rate-limiting, forming appropriate calls, and returning tidy dataframes which can be easily connected to other data sources.
Author: Tan Ho [aut, cre], Tony ElHabr [ctb], Joe Sydlowski [ctb]
Maintainer: Tan Ho <tan@tanho.ca>

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Package akmedoids updated to version 1.3.0 with previous version 0.1.5 dated 2020-01-09

Title: Anchored Kmedoids for Longitudinal Data Clustering
Description: Advances a novel adaptation of longitudinal k-means clustering technique (Genolini et al. (2015) <doi:10.18637/jss.v065.i04>) for grouping trajectories based on the similarities of their long-term trends and determines the optimal solution based on either the average silhouette width (Rousseeuw P. J. 1987) or the Calinski-Harabatz criterion (Calinski and Harabatz (1974) <doi:10.1080/03610927408827101>). Includes functions to extract descriptive statistics and generate a visualisation of the resulting groups, drawing methods from the 'ggplot2' library (Wickham H. (2016) <doi:10.1007/978-3-319-24277-4>). The package also includes a number of other useful functions for exploring and manipulating longitudinal data prior to the clustering process.
Author: Monsuru Adepeju [cre, aut], Samuel Langton [aut], Jon Bannister [aut]
Maintainer: Monsuru Adepeju <monsuur2010@yahoo.com>

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