Fri, 07 Aug 2020

Package SetMethods updated to version 2.6 with previous version 2.5 dated 2020-04-14

Title: Functions for Set-Theoretic Multi-Method Research and Advanced QCA
Description: Functions for performing set-theoretic multi-method research, QCA for clustered data, theory evaluation, Enhanced Standard Analysis, indirect calibration, radar visualisations. Additionally it includes data to replicate the examples in the book by Oana, I.E, C. Q. Schneider, and E. Thomann. Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge University Press and C. Q. Schneider and C. Wagemann "Set Theoretic Methods for the Social Sciences", Cambridge University Press.
Author: Ioana-Elena Oana [aut, cre], Juraj Medzihorsky [aut], Mario Quaranta [aut], Carsten Q. Schneider [aut]
Maintainer: Ioana-Elena Oana <ioana.oana@eui.eu>

Diff between SetMethods versions 2.5 dated 2020-04-14 and 2.6 dated 2020-08-07

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 SetMethods-2.6/SetMethods/MD5                       |   78 ++---
 SetMethods-2.6/SetMethods/NAMESPACE                 |    2 
 SetMethods-2.6/SetMethods/NEWS                      |    8 
 SetMethods-2.6/SetMethods/R/QCAfit.R                |   10 
 SetMethods-2.6/SetMethods/R/cluster.plot.R          |    4 
 SetMethods-2.6/SetMethods/R/esa.R                   |   36 +-
 SetMethods-2.6/SetMethods/R/helper_case_smmr.R      |    4 
 SetMethods-2.6/SetMethods/R/helper_match_smmr.R     |   28 -
 SetMethods-2.6/SetMethods/R/helper_rob.R            |   14 
 SetMethods-2.6/SetMethods/R/helper_theory_eval.R    |    4 
 SetMethods-2.6/SetMethods/R/pimplot.R               |    9 
 SetMethods-2.6/SetMethods/R/property.cube.R         |    6 
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 SetMethods-2.6/SetMethods/R/skew.check.R            |   19 +
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 SetMethods-2.6/SetMethods/R/theory.evaluation.R     |   16 -
 SetMethods-2.6/SetMethods/R/xy.plot.R               |    6 
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 SetMethods-2.6/SetMethods/man/SetMethods-package.Rd |   11 
 SetMethods-2.6/SetMethods/man/pimplot.Rd            |  304 ++++++++++----------
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 54 files changed, 338 insertions(+), 271 deletions(-)

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New package RapidoPGS with initial version 1.0.2
Package: RapidoPGS
Title: A Fast and Light Package to Compute Polygenic Risk Scores
Version: 1.0.2
Authors@R: c( person("Guillermo","Reales", role = c("aut", "cre"), email = "gr440@cam.ac.uk", comment = c(ORCID = "0000-0001-9993-3916")), person("Chris", "Wallace", role = "aut", email = "cew54@cam.ac.uk", comment = c(ORCID = "0000-0001-9755-1703")), person("Olly", "Burren", role = "ctb", email = "ob219@cam.ac.uk", comment = c(ORCID = "0000-0002-3388-5760")))
Description: Quickly computes polygenic scores from GWAS summary statistics of either case-control or quantitative traits, without LD matrix computation or parameter tuning. Reales,G., Kelemen,M., Wallace,C. (2020) <doi:10.1101/2020.07.24.220392> "RápidoPGS: A rapid polygenic score calculator for summary GWAS data without validation dataset".
License: GPL-3
Depends: R (>= 3.6.0), data.table, RCurl, curl
Imports: GenomicRanges, IRanges, bigsnpr
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-08-03 17:58:48 UTC; guille
Author: Guillermo Reales [aut, cre] (<https://orcid.org/0000-0001-9993-3916>), Chris Wallace [aut] (<https://orcid.org/0000-0001-9755-1703>), Olly Burren [ctb] (<https://orcid.org/0000-0002-3388-5760>)
Maintainer: Guillermo Reales <gr440@cam.ac.uk>
Repository: CRAN
Date/Publication: 2020-08-07 14:20:02 UTC

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New package starschemar with initial version 1.0.0
Package: starschemar
Title: Obtaining Star Schemas from Flat Tables
Version: 1.0.0
Authors@R: person(given = "Jose", family = "Samos", role = c("aut", "cre", "cph"), email = "jsamos@ugr.es", comment = c(ORCID = "0000-0002-4457-3439"))
Description: Data in multidimensional systems is obtained from operational systems and is transformed to adapt it to the new structure. Frequently, the operations to be performed aim to transform a flat table into a star schema. Transformations can be carried out using professional ETL (extract, transform and load) tools or tools intended for data transformation for end users. With the tools mentioned, this transformation can be carried out, but it requires a lot of work. The main objective this package is to define transformations that allow obtaining star schemas from flat tables easily. In addition, it includes basic data cleaning operations and incremental data refresh operations, adapted to this context.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-GB
LazyData: true
RoxygenNote: 7.1.1
Imports: dplyr, readr, tibble, tidyr, tidyselect, snakecase, purrr, rlang, stats, pander
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2020-08-03 16:12:43 UTC; jsamos
Author: Jose Samos [aut, cre, cph] (<https://orcid.org/0000-0002-4457-3439>)
Maintainer: Jose Samos <jsamos@ugr.es>
Repository: CRAN
Date/Publication: 2020-08-07 13:50:02 UTC

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New package mosqcontrol with initial version 0.1.0
Package: mosqcontrol
Type: Package
Title: Mosquito Control Resource Optimization
Version: 0.1.0
Authors@R: c( person("Jeff", "Demers", email = "jdemeripi@gmail.com", role = c("aut")), person("Anshuman", "Swain", email = "answain@terpmail.umd.edu", role = c("aut")), person("Travis", "Byrum", email = "tbyrum@terpmail.umd.edu", role = c("aut", "cre")), person("Sharon", "Bewick", email = "sharon_bewick@hotmail.com", role = c("aut")), person("William", "Fagan", email = "bfagan@umd.edu", role = c("aut")) )
Description: This project aims to make an accessible model for mosquito control resource optimization. The model uses data provided by users to estimate the mosquito populations in the sampling area for the sampling time period, and the optimal time to apply a treatment or multiple treatments.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
RoxygenNote: 7.0.2
Imports: magrittr, assertthat, pracma, NlcOptim, nloptr, sfsmisc
NeedsCompilation: no
Packaged: 2020-08-03 14:40:15 UTC; travisbyrum
Author: Jeff Demers [aut], Anshuman Swain [aut], Travis Byrum [aut, cre], Sharon Bewick [aut], William Fagan [aut]
Maintainer: Travis Byrum <tbyrum@terpmail.umd.edu>
Repository: CRAN
Date/Publication: 2020-08-07 13:50:07 UTC

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New package mlr3oml with initial version 0.1.0
Package: mlr3oml
Title: Connector Between 'mlr3' and 'OpenML'
Version: 0.1.0
Authors@R: person(given = "Michel", family = "Lang", role = c("cre", "aut"), email = "michellang@gmail.com", comment = c(ORCID = "0000-0001-9754-0393"))
Description: Provides an interface to 'OpenML.org' to list and download machine learning data and tasks. Data and tasks can be automatically converted to 'mlr3' tasks. For a more sophisticated interface which also allows uploading experiments, see the 'OpenML' package.
License: LGPL-3
URL: https://mlr3oml.mlr-org.com, https://github.com/mlr-org/mlr3oml
BugReports: https://github.com/mlr-org/mlr3oml
Depends: R (>= 3.1.0)
Imports: backports (>= 1.1.6), checkmate, curl, data.table, jsonlite, lgr, mlr3, mlr3misc, R6, stringi
Suggests: bibtex, foreign, qs, testthat
RdMacros: mlr3misc
Encoding: UTF-8
NeedsCompilation: yes
RoxygenNote: 7.1.1
Packaged: 2020-08-03 12:58:34 UTC; lang
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>)
Maintainer: Michel Lang <michellang@gmail.com>
Repository: CRAN
Date/Publication: 2020-08-07 13:40:03 UTC

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New package lcra with initial version 1.1.2
Package: lcra
Version: 1.1.2
Title: Bayesian Joint Latent Class and Regression Models
Type: Package
Description: For fitting Bayesian joint latent class and regression models using Gibbs sampling. See the documentation for the model. The technical details of the model implemented here are described in Elliott, Michael R., Zhao, Zhangchen, Mukherjee, Bhramar, Kanaya, Alka, Needham, Belinda L., "Methods to account for uncertainty in latent class assignments when using latent classes as predictors in regression models, with application to acculturation strategy measures" (2020) In press at Epidemiology <doi:10.1097/EDE.0000000000001139>.
Authors@R: c( person(given = "Michael", family = "Elliot", role = c("aut"), email = "mrelliot@umich.edu"), person(given = "Zhangchen", family = "Zhao", role = c("aut"), email = "zczhao@umich.edu"), person(given = "Michael", family = "Kleinsasser", role = c("aut", "cre"), email = "mkleinsa@umich.edu"))
License: GPL-2
Encoding: UTF-8
LazyData: true
Biarch: true
Depends: R (>= 3.4.0)
Imports: rlang, coda, rjags
Suggests: R2WinBUGS, gtools
SystemRequirements: JAGS 4.x.y or WinBUGS 1.4
URL: https://github.com/umich-biostatistics/lcra
BugReports: https://github.com/umich-biostatistics/lcra/issues
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-08-03 15:21:45 UTC; mkleinsa
Author: Michael Elliot [aut], Zhangchen Zhao [aut], Michael Kleinsasser [aut, cre]
Maintainer: Michael Kleinsasser <mkleinsa@umich.edu>
Repository: CRAN
Date/Publication: 2020-08-07 13:50:11 UTC

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New package fmtr with initial version 1.0.1
Package: fmtr
Type: Package
Title: Easily Apply Formats to Data
Version: 1.0.1
Author: David Bosak
Maintainer: David Bosak <dbosak01@gmail.com>
Description: Contains a set of functions that can be used to apply formats to data frames or vectors. The package aims to provide to R functionality similar to that of SAS® formats. Formats are assigned to the format attribute on data frame columns. Then when the fdata() function is called, a new data frame is created with the column data formatted as specified. The package also contains a value() function to create a user-defined format, similar to a SAS® user-defined format.
License: CC0
Encoding: UTF-8
LazyData: true
URL: https://github.com/dbosak01/fmtr
BugReports: https://github.com/dbosak01/fmtr/issues
Suggests: testthat, knitr, rmarkdown
Imports: tibble
RoxygenNote: 7.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-08-03 11:52:54 UTC; dbosak
Repository: CRAN
Date/Publication: 2020-08-07 13:30:02 UTC

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New package eyeRead with initial version 0.0.4
Package: eyeRead
Title: Prepare/Analyse Eye Tracking Data for Reading
Version: 0.0.4
Authors@R: c( person( given = "San", family = "Verhavert", role = c("aut", "cre"), email = "verhavertsan@gmail.com" ), person( given = "Tine", family = "van Daal", role = c( "aut" ), email = "tine.vandaal@uantwerpen.be" ), person( given = "Leen", family = "Catrysse", role = c( "aut" ), email = "Leen.Catrysse@uantwerpen.be" ) )
Description: Functions to prepare and analyse eye tracking data of reading exercises. The functions allow some basic data preparations and code fixations as first and second pass. First passes can be further devided into forward and reading. The package further allows for aggregating fixation times per AOI or per AOI and per type of pass (first forward, first rereading, second). These methods are based on Hyönä, Lorch, and Rinck (2003) <doi:10.1016/B978-044451020-4/50018-9> and Hyönä, and Lorch (2004) <doi:10.1016/j.learninstruc.2004.01.001>. It is also possible to convert between metric length and visual degrees.
Depends: R (>= 3.6)
URL: https://github.com/SanVerhavert/eyeRead
BugReports: https://github.com/SanVerhavert/eyeRead/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
Suggests: testthat, dplyr
RoxygenNote: 7.1.1
Imports: tibble (>= 2.1.3), data.table (>= 1.12.8), tidyr (>= 1.0.0)
NeedsCompilation: no
Packaged: 2020-08-03 10:27:25 UTC; SVerhavert
Author: San Verhavert [aut, cre], Tine van Daal [aut], Leen Catrysse [aut]
Maintainer: San Verhavert <verhavertsan@gmail.com>
Repository: CRAN
Date/Publication: 2020-08-07 13:00:02 UTC

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Package tidyHeatmap updated to version 1.1.4 with previous version 1.0.1 dated 2020-06-23

Title: A Tidy Implementation of Heatmap
Description: This is a tidy implementation for heatmap. At the moment it is based on the (great) package 'ComplexHeatmap'. The goal of this package is to interface a tidy data frame with this powerful tool. Some of the advantages are: Row and/or columns colour annotations are easy to integrate just specifying one parameter (column names). Custom grouping of rows is easy to specify providing a grouped tbl. For example: df %>% group_by(...). Labels size adjusted by row and column total number. Default use of Brewer and Viridis palettes.
Author: Stefano Mangiola [aut, cre], Anthony Papenfuss [ctb]
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>

Diff between tidyHeatmap versions 1.0.1 dated 2020-06-23 and 1.1.4 dated 2020-08-07

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 47 files changed, 1619 insertions(+), 668 deletions(-)

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Package scgwr updated to version 0.1.2-1 with previous version 0.1.2 dated 2020-08-03

Title: Scalable Geographically Weighted Regression
Description: Fast and regularized version of GWR for large dataset, detailed in Murakami, Tsutsumida, Yoshida, Nakaya, and Lu (2019) <arXiv:1905.00266>.
Author: Daisuke Murakami[cre,aut], Narumasa Tsutsumida[ctb], Takahiro Yoshida[ctb], Tomoki Nakaya[ctb], Lu Binbin[ctb]
Maintainer: Daisuke Murakami <dmuraka@ism.ac.jp>

Diff between scgwr versions 0.1.2 dated 2020-08-03 and 0.1.2-1 dated 2020-08-07

 DESCRIPTION |    8 +-
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 4 files changed, 46 insertions(+), 132 deletions(-)

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Package predictoR updated to version 1.1.3 with previous version 1.1.2 dated 2020-06-26

Title: Predictive Data Analysis System
Description: Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.
Author: Oldemar Rodriguez R. with contributions from Diego Jimenez A. and Andres Navarro D.
Maintainer: Oldemar Rodriguez <oldemar.rodriguez@ucr.ac.cr>

Diff between predictoR versions 1.1.2 dated 2020-06-26 and 1.1.3 dated 2020-08-07

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 4 files changed, 15 insertions(+), 15 deletions(-)

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Package pls updated to version 2.7-3 with previous version 2.7-2 dated 2019-10-01

Title: Partial Least Squares and Principal Component Regression
Description: Multivariate regression methods Partial Least Squares Regression (PLSR), Principal Component Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).
Author: Bjørn-Helge Mevik [aut, cre], Ron Wehrens [aut], Kristian Hovde Liland [aut], Paul Hiemstra [ctb]
Maintainer: Bjørn-Helge Mevik <b-h@mevik.net>

Diff between pls versions 2.7-2 dated 2019-10-01 and 2.7-3 dated 2020-08-07

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 27 files changed, 285 insertions(+), 274 deletions(-)

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Package mlr3viz updated to version 0.2.0 with previous version 0.1.1 dated 2020-02-19

Title: Visualizations for 'mlr3'
Description: Provides visualizations for 'mlr3' objects such as tasks, predictions, resample results or benchmark results via the autoplot() generic of 'ggplot2'. The returned 'ggplot' objects are intended to provide sensible defaults, yet can easily be customized to create camera-ready figures. Visualizations include barplots, boxplots, histograms, ROC curves, and Precision-Recall curves.
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Patrick Schratz [aut] (<https://orcid.org/0000-0003-0748-6624>), Raphael Sonabend [aut] (<https://orcid.org/0000-0001-9225-4654>)
Maintainer: Michel Lang <michellang@gmail.com>

Diff between mlr3viz versions 0.1.1 dated 2020-02-19 and 0.2.0 dated 2020-08-07

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Package HDLSSkST updated to version 1.0.1 with previous version 1.0 dated 2020-08-04

Title: Distribution-Free Exact High Dimensional Low Sample Size k-Sample Tests
Description: We construct four new exact level (size) alpha tests for testing the equality of k distributions, which can be conveniently used in high dimensional low sample size setup based on clustering. These tests are easy to implement and distribution-free. Under mild conditions, we have proved the consistency of these tests as the dimension d of each observation grows to infinity, whereas the sample size remains fixed. We also apply step-down-procedure (1979) for multiple testing. Details are in Biplab Paul, Shyamal K De and Anil K Ghosh (2020); Soham Sarkar and Anil K Ghosh (2019) <doi:10.1109/TPAMI.2019.2912599>; William M Rand (1971) <doi:10.1080/01621459.1971.10482356>; Cyrus R Mehta and Nitin R Patel (1983) <doi:10.2307/2288652>; Joseph C Dunn (1973) <doi:10.1080/01969727308546046>; Sture Holm (1979) <doi:10.2307/4615733>.
Author: Biplab Paul [aut, cre], Shyamal K. De [aut], Anil K. Ghosh [aut]
Maintainer: Biplab Paul <biplab.paul@niser.ac.in>

Diff between HDLSSkST versions 1.0 dated 2020-08-04 and 1.0.1 dated 2020-08-07

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Package flexpolyline updated to version 0.1.1 with previous version 0.1.0 dated 2020-06-25

Title: Flexible Polyline Encoding
Description: Binding to the C++ implementation of the flexible polyline encoding by HERE <https://github.com/heremaps/flexible-polyline>. The flexible polyline encoding is a lossy compressed representation of a list of coordinate pairs or coordinate triples. The encoding is achieved by: (1) Reducing the decimal digits of each value; (2) encoding only the offset from the previous point; (3) using variable length for each coordinate delta; and (4) using 64 URL-safe characters to display the result.
Author: Merlin Unterfinger [aut, cre] (<https://orcid.org/0000-0003-2020-2366>), HERE Europe B.V. [aut, cph] (Flexible polyline encoding C++ implementation)
Maintainer: Merlin Unterfinger <info@munterfinger.ch>

Diff between flexpolyline versions 0.1.0 dated 2020-06-25 and 0.1.1 dated 2020-08-07

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Package Benchmarking updated to version 0.29 with previous version 0.28 dated 2019-12-18

Title: Benchmark and Frontier Analysis Using DEA and SFA
Description: Methods for frontier analysis, Data Envelopment Analysis (DEA), under different technology assumptions (fdh, vrs, drs, crs, irs, add/frh, and fdh+), and using different efficiency measures (input based, output based, hyperbolic graph, additive, super, and directional efficiency). Peers and slacks are available, partial price information can be included, and optimal cost, revenue and profit can be calculated. Evaluation of mergers is also supported. Methods for graphing the technology sets are also included. There is also support for comparative methods based on Stochastic Frontier Analyses (SFA) and for convex nonparametric least squares for convex functions (StoNED). In general, the methods can be used to solve not only standard models, but also many other model variants. It complements the book, Bogetoft and Otto, Benchmarking with DEA, SFA, and R, Springer-Verlag, 2011, but can of course also be used as a stand-alone package.
Author: Peter Bogetoft and Lars Otto
Maintainer: Lars Otto <larsot23@gmail.com>

Diff between Benchmarking versions 0.28 dated 2019-12-18 and 0.29 dated 2020-08-07

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New package changepoint.mv with initial version 1.0.2
Package: changepoint.mv
Type: Package
Title: Changepoint Analysis for Multivariate Time Series
Version: 1.0.2
Date: 2020-08-05
Author: Lawrence Bardwell <l.bardwell@lancaster.ac.uk> [aut], Idris Eckley <i.eckley@lancaster.ac.uk> [ths,ctb], Paul Fearnhead <p.fearnhead@lancaster.ac.uk> [ths,ctb], Daniel Grose <dan.grose@lancaster.ac.uk> [aut,cre]
Maintainer: Daniel Grose <dan.grose@lancaster.ac.uk>
Description: Detects the Most Recent Changepoints (mrc) for panel data consisting of many related univariate timeseries using the method developed by Bardwell, Fearnhead, Eckley, Smith and Spott (2018) <doi:10.1080/00401706.2018.1438926>.
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.12),methods,zoo,tbart,Rdpack,ggplot2,reshape2,assertive
LinkingTo: Rcpp
Suggests: gridExtra
RdMacros: Rdpack
RoxygenNote: 7.1.0
NeedsCompilation: yes
Packaged: 2020-08-05 15:44:26 UTC; grosedj
Repository: CRAN
Date/Publication: 2020-08-07 10:00:03 UTC

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New package beastier with initial version 2.1.3
Package: beastier
Type: Package
Title: Call 'BEAST2'
Version: 2.1.3
Authors@R: c( person("Richèl J.C.", "Bilderbeek", email = "richel@richelbilderbeek.nl", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1107-7049")), person("Joëlle", "Barido-Sottani", role = "rev", comment = "Joëlle reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/209"), person("David", "Winter", role = "rev", comment = "David reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/209"), person("Thijs", "Janzen", role = "ctb"))
Maintainer: Richèl J.C. Bilderbeek <richel@richelbilderbeek.nl>
Description: 'BEAST2' (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAST2' is a command-line tool. This package provides a way to call 'BEAST2' from an 'R' function call.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: ape, assertive, beautier (>= 2.3.5), phangorn, pryr, rappdirs, remotes, rJava, stringr, xml2
Suggests: hunspell, knitr, rmarkdown, spelling, testit, testthat (>= 2.1.0), tracerer
URL: https://docs.ropensci.org/beastier (website) https://github.com/ropensci/beastier
BugReports: https://github.com/ropensci/beastier
Language: en-US
VignetteBuilder: knitr
SystemRequirements: BEAST2 (https://www.beast2.org/)
NeedsCompilation: no
Packaged: 2020-08-06 06:22:36 UTC; richel
Author: Richèl J.C. Bilderbeek [aut, cre] (<https://orcid.org/0000-0003-1107-7049>), Joëlle Barido-Sottani [rev] (Joëlle reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/209), David Winter [rev] (David reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/209), Thijs Janzen [ctb]
Repository: CRAN
Date/Publication: 2020-08-07 09:40:06 UTC

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New package anomaly with initial version 3.0.2
Package: anomaly
Type: Package
Title: Detecting Anomalies in Data
Version: 3.0.2
Date: 2020-08-05
Authors@R: c(person("Alex","Fisch",email="a.t.fisch@lancaster.ac.uk",role=c("aut")), person("Daniel","Grose",email="dan.grose@lancaster.ac.uk",role=c("aut","cre")), person("Lawrence","Bardwell",email="l.bardwell@lancaster.ac.uk",role=c("ctb")), person("Idris","Eckley",email="i.eckley@lancaster.ac.uk",role=c("ths")), person("Paul","Fearnhead",email="p.fearnhead@lancaster.ac.uk",role=c("ths")))
Description: Implements Collective And Point Anomaly (CAPA) <arXiv:1806.01947>, Multi-Variate Collective And Point Anomaly (MVCAPA) <arXiv:1909.01691>, Proportion Adaptive Segment Selection (PASS) <doi:10.1093/biomet/ass059>, and Bayesian Abnormal Region Detector (BARD) <doi:10.1214/16-BA998> methods for the detection of anomalies in time series data.
License: GPL
Imports: dplyr,rlang,methods,assertive,Rdpack,ggplot2,reshape2,Rcpp (>= 0.12.18),robustbase,cowplot
LinkingTo: Rcpp,BH
Suggests: magrittr
Depends: R (>= 3.5.0)
NeedsCompilation: yes
RoxygenNote: 7.1.0
RdMacros: Rdpack
Packaged: 2020-08-05 15:50:30 UTC; grosedj
Author: Alex Fisch [aut], Daniel Grose [aut, cre], Lawrence Bardwell [ctb], Idris Eckley [ths], Paul Fearnhead [ths]
Maintainer: Daniel Grose <dan.grose@lancaster.ac.uk>
Repository: CRAN
Date/Publication: 2020-08-07 10:00:06 UTC

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Package betadiv (with last version 1.0.1) was removed from CRAN

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

2020-07-24 1.0.1
2020-06-05 1.0.0

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Package rpubchem (with last version 1.5.10) was removed from CRAN

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

2016-12-27 1.5.10
2014-04-24 1.5.0.2
2014-03-11 1.5.0.1
2009-11-20 1.4.3
2009-02-08 1.4.2
2006-11-22 1.4

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Package icesSAG (with last version 1.3-6) was removed from CRAN

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

2019-03-12 1.3-6

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Package J4R (with last version 1.0.8) was removed from CRAN

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

2020-07-23 1.0.8
2020-04-14 1.0.7
2020-04-07 1.0.6

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Package icesVocab (with last version 1.1-4) was removed from CRAN

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

2019-03-12 1.1-4

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Package thurstonianIRT updated to version 0.11.1 with previous version 0.11.0 dated 2020-07-19

Title: Thurstonian IRT Models
Description: Fit Thurstonian Item Response Theory (IRT) models in R. This package supports fitting Thurstonian IRT models and its extensions using 'Stan', 'lavaan', or 'Mplus' for the model estimation. Functionality for extracting results, making predictions, and simulating data is provided as well. References: Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>; Bürkner et al. (2019) <doi:10.1177/0013164419832063>.
Author: Paul-Christian Bürkner [aut, cre], Trustees of Columbia University [cph]
Maintainer: Paul-Christian Bürkner <paul.buerkner@gmail.com>

Diff between thurstonianIRT versions 0.11.0 dated 2020-07-19 and 0.11.1 dated 2020-08-07

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Package RVowpalWabbit updated to version 0.0.15 with previous version 0.0.14 dated 2020-06-14

Title: R Interface to the Vowpal Wabbit
Description: The 'Vowpal Wabbit' project is a fast out-of-core learning system sponsored by Microsoft Research (having started at Yahoo! Research) and written by John Langford along with a number of contributors. This R package does not include the distributed computing implementation of the cluster/ directory of the upstream sources. Use of the software as a network service is also not directly supported as the aim is a simpler direct call from R for validation and comparison. Note that this package contains an embedded older version of 'Vowpal Wabbit'. The package 'rvw' at the GitHub repo <https://github.com/eddelbuettel/rvw> can provide an alternative using an external 'Vowpal Wabbit' library installation.
Author: Dirk Eddelbuettel
Maintainer: Dirk Eddelbuettel <edd@debian.org>

Diff between RVowpalWabbit versions 0.0.14 dated 2020-06-14 and 0.0.15 dated 2020-08-07

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Package rddtools updated to version 1.4.0 with previous version 1.2.0 dated 2020-07-22

Title: Toolbox for Regression Discontinuity Design ('RDD')
Description: Set of functions for Regression Discontinuity Design ('RDD'), for data visualisation, estimation and testing.
Author: Matthieu Stigler [aut], Bastiaan Quast [aut, cre]
Maintainer: Bastiaan Quast <bquast@gmail.com>

Diff between rddtools versions 1.2.0 dated 2020-07-22 and 1.4.0 dated 2020-08-07

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New package pulsar with initial version 0.3.7
Package: pulsar
Title: Parallel Utilities for Lambda Selection along a Regularization Path
Version: 0.3.7
Encoding: UTF-8
Authors@R: c(person("Zachary", "Kurtz", role = c("aut", "cre"), email="zdkurtz@gmail.com"), person("Christian", "M\u00FCller", role = c("aut", "ctb"), email="cmueller@simonsfoundation.org"))
Description: Model selection for penalized graphical models using the Stability Approach to Regularization Selection ('StARS'), with options for speed-ups including Bounded StARS (B-StARS), batch computing, and other stability metrics (e.g., graphlet stability G-StARS). Christian L. Müller, Richard Bonneau, Zachary Kurtz (2016) <arXiv:1605.07072>.
URL: http://github.com/zdk123/pulsar, http://arxiv.org/abs/1605.07072
BugReports: http://github.com/zdk123/pulsar/issues
Depends: R (>= 3.2.0)
License: GPL (>= 2)
Suggests: batchtools (>= 0.9.10), fs (>= 1.2.2), checkmate (>= 1.8.5), orca, huge, MASS, BigQuic, glmnet, network, cluster, testthat, knitr, rmarkdown
Imports: methods, parallel, graphics, stats, utils, tools, Matrix
RoxygenNote: 7.1.0
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-08-06 19:12:25 UTC; zachary
Author: Zachary Kurtz [aut, cre], Christian Müller [aut, ctb]
Maintainer: Zachary Kurtz <zdkurtz@gmail.com>
Repository: CRAN
Date/Publication: 2020-08-07 08:40:07 UTC

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Package glmglrt updated to version 0.2.2 with previous version 0.1.0 dated 2020-03-19

Title: GLRT P-Values in Generalized Linear Models
Description: Provides functions to compute Generalized Likelihood Ratio Tests (GLRT) also known as Likelihood Ratio Tests (LRT) and Rao's score tests of simple and complex contrasts of Generalized Linear Models (GLMs). It provides the same interface as summary.glm(), adding GLRT P-values, less biased than Wald's P-values and consistent with profile-likelihood confidence interval generated by confint(). See Wilks (1938) <doi:10.1214/aoms/1177732360> for the LRT chi-square approximation. See Rao (1948) <doi:10.1017/S0305004100023987> for Rao's score test. See Wald (1943) <doi:10.2307/1990256> for Wald's test.
Author: André GILLIBERT [aut, cre]
Maintainer: André GILLIBERT <andre.gillibert@chu-rouen.fr>

Diff between glmglrt versions 0.1.0 dated 2020-03-19 and 0.2.2 dated 2020-08-07

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Package BayesMallows updated to version 0.4.4 with previous version 0.4.3 dated 2020-06-20

Title: Bayesian Preference Learning with the Mallows Rank Model
Description: An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <http://jmlr.org/papers/v18/15-481.html>; Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>). Both Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).
Author: Oystein Sorensen, Valeria Vitelli, Marta Crispino, Qinghua Liu
Maintainer: Oystein Sorensen <oystein.sorensen.1985@gmail.com>

Diff between BayesMallows versions 0.4.3 dated 2020-06-20 and 0.4.4 dated 2020-08-07

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Package RoBMA updated to version 1.0.4 with previous version 1.0.3 dated 2020-08-06

Title: Robust Bayesian Meta-Analyses
Description: A framework for estimating ensembles of meta-analytic models (assuming either presence or absence of the effect, heterogeneity, and publication bias) and using Bayesian model averaging to combine them. The ensembles use Bayes Factors to test for the presence or absence of the individual components (e.g., effect vs. no effect) and model-averages parameter estimates based on posterior model probabilities (Maier, Bartoš & Wagenmakers, 2020, <doi:10.31234/osf.io/u4cns>). The user can define a wide range of non-informative or informative priors for the effect size, heterogeneity, and weight functions. The package provides convenient functions for summary, visualizations, and fit diagnostics.
Author: František Bartoš [aut, cre] (<https://orcid.org/0000-0002-0018-5573>), Maximilian Maier [aut] (<https://orcid.org/0000-0002-9873-6096>), Eric-Jan Wagenmakers [ths] (<https://orcid.org/0000-0003-1596-1034>), Joris Goosen [ctb]
Maintainer: František Bartoš <f.bartos96@gmail.com>

Diff between RoBMA versions 1.0.3 dated 2020-08-06 and 1.0.4 dated 2020-08-07

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Package PSSMCOOL updated to version 0.2.0 with previous version 0.1.0 dated 2020-05-08

Title: Features Extracted from Position Specific Scoring Matrix (PSSM)
Description: Returns almost all features that has been extracted from Position Specific Scoring Matrix (PSSM) so far, which is a matrix of L rows (L is protein length) and 20 columns produced by 'PSI-BLAST' which is a program to produce PSSM Matrix from multiple sequence alignment of proteins see <https://www.ncbi.nlm.nih.gov/books/NBK2590/> for mor details. some of these features are described in Zahiri, J., et al.(2013) <DOI:10.1016/j.ygeno.2013.05.006>, Saini, H., et al.(2016) <DOI:10.17706/jsw.11.8.756-767>, Ding, S., et al.(2014) <DOI:10.1016/j.biochi.2013.09.013>, Cheng, C.W., et al.(2008) <DOI:10.1186/1471-2105-9-S12-S6>, Juan, E.Y., et al.(2009) <DOI:10.1109/CISIS.2009.194>.
Author: Alireza Mohammadi <alireza691111@gmail.com>
Maintainer: Alireza mohammadi <alireza691111@gmail.com>

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Package modeLLtest updated to version 1.0.3 with previous version 1.0.2 dated 2020-06-11

Title: Compare Models with Cross-Validated Log-Likelihood
Description: An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) <doi:10.1007/s11135-013-9884-7> (see also Harden and Desmarais, 2011 <doi:10.1177/1532440011408929>) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012) <doi:10.1093/pan/mpr042>. These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) <doi:10.1177/0010414009341714> and Joshi & Mason (2008) <doi:10.1177/0022343308096155> that are included to facilitate examples from real-world analysis.
Author: Shana Scogin <shanarscogin@gmail.com>, Sarah Petersen <sarahllpetersen@gmail.com>, Jeff Harden <jeff.harden@nd.edu>, Bruce A. Desmarais <bdesmarais@psu.edu>
Maintainer: Shana Scogin <shanarscogin@gmail.com>

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Package BANOVA updated to version 1.1.8 with previous version 1.1.7 dated 2020-05-04

Title: Hierarchical Bayesian ANOVA Models
Description: It covers several Bayesian Analysis of Variance (BANOVA) models used in analysis of experimental designs in which both within- and between- subjects factors are manipulated. They can be applied to data that are common in the behavioral and social sciences. The package includes: Hierarchical Bayes ANOVA models with normal response, t response, Binomial (Bernoulli) response, Poisson response, ordered multinomial response and multinomial response variables. All models accommodate unobserved heterogeneity by including a normal distribution of the parameters across individuals. Outputs of the package include tables of sums of squares, effect sizes and p-values, and tables of predictions, which are easily interpretable for behavioral and social researchers. The floodlight analysis and mediation analysis based on these models are also provided. BANOVA uses 'Stan' and 'JAGS' as the computational platform.
Author: Chen Dong, Michel Wedel, Anna Kopyakova
Maintainer: Chen Dong <cdong@math.umd.edu>

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Package sensitivity updated to version 1.22.1 with previous version 1.22.0 dated 2020-07-18

Title: Global Sensitivity Analysis of Model Outputs
Description: A collection of functions for factor screening, global sensitivity analysis and robustness analysis. Most of the functions have to be applied on model with scalar output, but several functions support multi-dimensional outputs.
Author: Bertrand Iooss, Sebastien Da Veiga, Alexandre Janon and Gilles Pujol, with contributions from Baptiste Broto, Khalid Boumhaout, Thibault Delage, Reda El Amri, Jana Fruth, Laurent Gilquin, Joseph Guillaume, Loic Le Gratiet, Paul Lemaitre, Amandine Marrel, Anouar Meynaoui, Barry L. Nelson, Filippo Monari, Roelof Oomen, Oldrich Rakovec, Bernardo Ramos, Olivier Roustant, Eunhye Song, Jeremy Staum, Roman Sueur, Taieb Touati, Frank Weber
Maintainer: Bertrand Iooss <biooss@yahoo.fr>

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Package rgdal updated to version 1.5-16 with previous version 1.5-15 dated 2020-08-04

Title: Bindings for the 'Geospatial' Data Abstraction Library
Description: Provides bindings to the 'Geospatial' Data Abstraction Library ('GDAL') (>= 1.11.4) and access to projection/transformation operations from the 'PROJ' library. Use is made of classes defined in the 'sp' package. Raster and vector map data can be imported into R, and raster and vector 'sp' objects exported. The 'GDAL' and 'PROJ' libraries are external to the package, and, when installing the package from source, must be correctly installed first; it is important that 'GDAL' < 3 be matched with 'PROJ' < 6. From 'rgdal' 1.5-8, installed with to 'GDAL' >=3, 'PROJ' >=6 and 'sp' >= 1.4, coordinate reference systems use 'WKT2_2019' strings, not 'PROJ' strings. 'Windows' and 'macOS' binaries (including 'GDAL', 'PROJ' and their dependencies) are provided on 'CRAN'.
Author: Roger Bivand [cre, aut] (<https://orcid.org/0000-0003-2392-6140>), Tim Keitt [aut], Barry Rowlingson [aut, ctb], Edzer Pebesma [ctb], Michael Sumner [ctb], Robert Hijmans [ctb], Even Rouault [cph, ctb], Frank Warmerdam [cph, ctb], Jeroen Ooms [ctb], Colin Rundel [ctb]
Maintainer: Roger Bivand <Roger.Bivand@nhh.no>

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Package radiant.data updated to version 1.3.10 with previous version 1.3.9 dated 2020-06-16

Title: Data Menu for Radiant: Business Analytics using R and Shiny
Description: The Radiant Data menu includes interfaces for loading, saving, viewing, visualizing, summarizing, transforming, and combining data. It also contains functionality to generate reproducible reports of the analyses conducted in the application.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

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Package mlr3 updated to version 0.5.0 with previous version 0.4.0 dated 2020-07-22

Title: Machine Learning in R - Next Generation
Description: Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality.
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Jakob Richter [aut] (<https://orcid.org/0000-0003-4481-5554>), Patrick Schratz [aut] (<https://orcid.org/0000-0003-0748-6624>), Giuseppe Casalicchio [ctb] (<https://orcid.org/0000-0001-5324-5966>), Stefan Coors [ctb] (<https://orcid.org/0000-0002-7465-2146>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [aut]
Maintainer: Michel Lang <michellang@gmail.com>

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Package Epi updated to version 2.41 with previous version 2.40 dated 2019-11-25

Title: A Package for Statistical Analysis in Epidemiology
Description: Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data, in particular representation, manipulation and simulation of multistate data - the Lexis suite of functions, which includes interfaces to 'mstate', 'etm' and 'cmprsk' packages. Also contains functions for Age-Period-Cohort and Lee-Carter modeling and a function for interval censored data and some useful functions for tabulation and plotting, as well as a number of epidemiological data sets.
Author: Bendix Carstensen [aut, cre], Martyn Plummer [aut], Esa Laara [ctb], Michael Hills [ctb]
Maintainer: Bendix Carstensen <b@bxc.dk>

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More information about Epi at CRAN
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Package AzureStor updated to version 3.2.3 with previous version 3.2.2 dated 2020-07-01

Title: Storage Management in 'Azure'
Description: Manage storage in Microsoft's 'Azure' cloud: <https://azure.microsoft.com/services/storage>. On the admin side, 'AzureStor' includes features to create, modify and delete storage accounts. On the client side, it includes an interface to blob storage, file storage, and 'Azure Data Lake Storage Gen2': upload and download files and blobs; list containers and files/blobs; create containers; and so on. Authenticated access to storage is supported, via either a shared access key or a shared access signature (SAS). Part of the 'AzureR' family of packages.
Author: Hong Ooi [aut, cre], Microsoft [cph]
Maintainer: Hong Ooi <hongooi@microsoft.com>

Diff between AzureStor versions 3.2.2 dated 2020-07-01 and 3.2.3 dated 2020-08-07

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More information about AzureStor at CRAN
Permanent link


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