Thu, 04 Jul 2019

Package exams updated to version 2.3-3 with previous version 2.3-2 dated 2018-12-08

Title: Automatic Generation of Exams in R
Description: Automatic generation of exams based on exercises in Markdown or LaTeX format, possibly including R code for dynamic generation of exercise elements. Exercise types include single-choice and multiple-choice questions, arithmetic problems, string questions, and combinations thereof (cloze). Output formats include standalone files (PDF, HTML, Docx, ODT, ...), Moodle XML, QTI 1.2, QTI 2.1, Blackboard, Canvas, OpenOLAT, ARSnova, and TCExam. In addition to fully customizable PDF exams, a standardized PDF format (NOPS) is provided that can be printed, scanned, and automatically evaluated.
Author: Achim Zeileis [aut, cre] (<https://orcid.org/0000-0003-0918-3766>), Bettina Gruen [aut] (<https://orcid.org/0000-0001-7265-4773>), Friedrich Leisch [aut] (<https://orcid.org/0000-0001-7278-1983>), Nikolaus Umlauf [aut], Mirko Birbaumer [ctb], Dominik Ernst [ctb], Patrik Keller [ctb], Niels Smits [ctb], Reto Stauffer [ctb]
Maintainer: Achim Zeileis <Achim.Zeileis@R-project.org>

Diff between exams versions 2.3-2 dated 2018-12-08 and 2.3-3 dated 2019-07-04

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Package rvinecopulib updated to version 0.3.2.1.0 with previous version 0.3.1.1.0 dated 2019-04-19

Title: High Performance Algorithms for Vine Copula Modeling
Description: Provides an interface to 'vinecopulib', a C++ library for vine copula modeling based on 'Boost' and 'Eigen'. The 'rvinecopulib' package implements the core features of the popular 'VineCopula' package, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker and more modern API, improved performances, especially in high dimensions, nonparametric and multi-parameter families. The 'rvinecopulib' package includes 'vinecopulib' as header-only C++ library (currently version 0.3.1). Thus users do not need to install 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' is licensed under the GNU GPL version 3.
Author: Thomas Nagler [aut, cre], Thibault Vatter [aut]
Maintainer: Thomas Nagler <info@vinecopulib.org>

Diff between rvinecopulib versions 0.3.1.1.0 dated 2019-04-19 and 0.3.2.1.0 dated 2019-07-04

 DESCRIPTION                                                          |    6 
 MD5                                                                  |  194 +--
 NAMESPACE                                                            |    7 
 NEWS.md                                                              |   19 
 R/as_rvine_structure.R                                               |    6 
 R/bicop.R                                                            |  189 +--
 R/bicop_methods.R                                                    |  295 ++---
 R/bicop_plot.R                                                       |  354 +++---
 R/family_sets.R                                                      |   26 
 R/getters.R                                                          |  300 ++---
 R/pairs_copula_data.R                                                |only
 R/rosenblatt.R                                                       |  131 +-
 R/rvine_structure.R                                                  |    2 
 R/rvinecopulib.R                                                     |   26 
 R/tools.R                                                            |  392 +++----
 R/truncate_model.R                                                   |  163 +-
 R/vine.R                                                             |  336 +++---
 R/vine_methods.R                                                     |  342 +++---
 R/vinecop.R                                                          |  306 ++---
 R/vinecop_methods.R                                                  |  286 ++---
 R/vinecop_plot.R                                                     |  555 +++++-----
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 man/mBICV.Rd                                                         |    2 
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 man/rvinecopulib.Rd                                                  |    6 
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 man/vine.Rd                                                          |   11 
 man/vine_methods.Rd                                                  |   10 
 man/vinecop.Rd                                                       |   11 
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Package bomrang updated to version 0.6.0 with previous version 0.5.0 dated 2019-03-21

Title: Australian Government Bureau of Meteorology ('BOM') Data Client
Description: Provides functions to interface with Australian Government Bureau of Meteorology ('BOM') data, fetching data and returning a tidy data frame of precis forecasts, historical and current weather data from stations, agriculture bulletin data, 'BOM' 0900 or 1500 weather bulletins and downloading and importing radar and satellite imagery files. Data (c) Australian Government Bureau of Meteorology Creative Commons (CC) Attribution 3.0 licence or Public Access Licence (PAL) as appropriate. See <http://www.bom.gov.au/other/copyright.shtml> for further details.
Author: Adam Sparks [aut, cre] (<https://orcid.org/0000-0002-0061-8359>), Jonathan Carroll [aut] (<https://orcid.org/0000-0002-1404-5264>), James Goldie [aut] (<https://orcid.org/0000-0002-5024-6207>), Dean Marchiori [aut], Mark Padgham [aut, rev] (<https://orcid.org/0000-0003-2172-5265>), Hugh Parsonage [aut] (<https://orcid.org/0000-0003-4055-0835>), Keith Pembleton [aut] (<https://orcid.org/0000-0002-1896-4516>), James Balamuta [ctb] (<https://orcid.org/0000-0003-2826-8458>), Brooke Anderson [rev] (<https://orcid.org/0000-0002-5012-9035>)
Maintainer: Adam Sparks <adamhsparks@gmail.com>

Diff between bomrang versions 0.5.0 dated 2019-03-21 and 0.6.0 dated 2019-07-04

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Package not updated to version 1.2 with previous version 1.0 dated 2016-08-24

Title: Narrowest-Over-Threshold Change-Point Detection
Description: Provides efficient implementation of the Narrowest-Over-Threshold methodology for detecting an unknown number of change-points occurring at unknown locations in one-dimensional data following deterministic signal + noise model, see R. Baranowski, Y. Chen and P. Fryzlewicz (2019) <doi:10.1111/rssb.12322>. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise.
Author: Rafal Baranowski, Yining Chen, Piotr Fryzlewicz
Maintainer: Rafal Baranowski <package_maintenance@rbaranowski.com>

Diff between not versions 1.0 dated 2016-08-24 and 1.2 dated 2019-07-04

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Package mds updated to version 0.3.0 with previous version 0.2.1 dated 2018-12-10

Title: Medical Devices Surveillance
Description: A set of core functions for handling medical device event data in the context of post-market surveillance, pharmacovigilance, signal detection and trending, and regulatory reporting. Primary inputs are data on events by device and data on exposures by device. Outputs include: standardized device-event and exposure datasets, defined analyses, and time series.
Author: Gary Chung [aut, cre]
Maintainer: Gary Chung <gchung05@gmail.com>

Diff between mds versions 0.2.1 dated 2018-12-10 and 0.3.0 dated 2019-07-04

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Package rquery updated to version 1.3.6 with previous version 1.3.5 dated 2019-06-26

Title: Relational Query Generator for Data Manipulation at Scale
Description: A piped query generator based on Edgar F. Codd's relational algebra, and on production experience using 'SQL' and 'dplyr' at big data scale. The design represents an attempt to make 'SQL' more teachable by denoting composition by a sequential pipeline notation instead of nested queries or functions. The implementation delivers reliable high performance data processing on large data systems such as 'Spark', databases, and 'data.table'. Package features include: data processing trees or pipelines as observable objects (able to report both columns produced and columns used), optimized 'SQL' generation as an explicit user visible table modeling step, plus explicit query reasoning and checking.
Author: John Mount [aut, cre], Win-Vector LLC [cph]
Maintainer: John Mount <jmount@win-vector.com>

Diff between rquery versions 1.3.5 dated 2019-06-26 and 1.3.6 dated 2019-07-04

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Package rqdatatable updated to version 1.1.9 with previous version 1.1.8 dated 2019-06-01

Title: 'rquery' for 'data.table'
Description: Implements the 'rquery' piped Codd-style query algebra using 'data.table'. This allows for a high-speed in memory implementation of Codd-style data manipulation tools.
Author: John Mount [aut, cre], Win-Vector LLC [cph]
Maintainer: John Mount <jmount@win-vector.com>

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Package Qtools updated to version 1.4 with previous version 1.3 dated 2018-04-13

Title: Utilities for Quantiles
Description: Functions for unconditional and conditional quantiles. These include methods for transformation-based quantile regression, quantile-based measures of location, scale and shape, methods for quantiles of discrete variables, quantile-based multiple imputation, and restricted quantile regression. A vignette is given in Geraci (2016, The R Journal) <doi:10.32614/RJ-2016-037> and included in the package.
Author: Marco Geraci [aut, cph, cre] (<https://orcid.org/0000-0002-6311-8685>), Alessio Farcomeni [ctb] (Contributions to midrq code)
Maintainer: Marco Geraci <geraci@mailbox.sc.edu>

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Package ips updated to version 0.0.11 with previous version 0.0-7 dated 2014-11-09

Title: Interfaces to Phylogenetic Software in R
Description: Functions that wrap popular phylogenetic software for sequence alignment, masking of sequence alignments, and estimation of phylogenies and ancestral character states.
Author: Christoph Heibl [aut, cre], Natalie Cusimano [aut], Franz-Sebastian Krah [aut]
Maintainer: Christoph Heibl <christoph.heibl@gmx.net>

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Package graphlayouts updated to version 0.2.0 with previous version 0.1.0 dated 2019-04-05

Title: Additional Layout Algorithms for Network Visualizations
Description: Several new layout algorithms to visualize networks are provided which are not part of 'igraph'. Most are based on the concept of stress majorization by Gansner et al. (2004) <doi:10.1007/978-3-540-31843-9_25>. Some more specific algorithms allow to emphasize hidden group structures in networks or focus on specific nodes.
Author: David Schoch [aut, cre]
Maintainer: David Schoch <david.schoch@manchester.ac.uk>

Diff between graphlayouts versions 0.1.0 dated 2019-04-05 and 0.2.0 dated 2019-07-04

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Package dabestr updated to version 0.2.2 with previous version 0.2.1 dated 2019-06-26

Title: Data Analysis using Bootstrap-Coupled Estimation
Description: Data Analysis using Bootstrap-Coupled ESTimation. Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values. An estimation plot has two key features: 1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution. 2. It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes. Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105. <doi:10.1038/s41592-019-0470-3>. The free-to-view PDF is located at <https://rdcu.be/bHhJ4>.
Author: Joses W. Ho [cre, aut], Tayfun Tumkaya [aut]
Maintainer: Joses W. Ho <joseshowh@gmail.com>

Diff between dabestr versions 0.2.1 dated 2019-06-26 and 0.2.2 dated 2019-07-04

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Package checkmate updated to version 1.9.4 with previous version 1.9.3 dated 2019-05-03

Title: Fast and Versatile Argument Checks
Description: Tests and assertions to perform frequent argument checks. A substantial part of the package was written in C to minimize any worries about execution time overhead.
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Bernd Bischl [ctb]
Maintainer: Michel Lang <michellang@gmail.com>

Diff between checkmate versions 1.9.3 dated 2019-05-03 and 1.9.4 dated 2019-07-04

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Package spacyr updated to version 1.2 with previous version 1.0 dated 2018-12-15

Title: Wrapper to the 'spaCy' 'NLP' Library
Description: An R wrapper to the 'Python' 'spaCy' 'NLP' library, from <http://spacy.io>.
Author: Kenneth Benoit [cre, aut, cph] (<https://orcid.org/0000-0002-0797-564X>), Akitaka Matsuo [aut] (<https://orcid.org/0000-0002-3323-6330>), European Research Council [fnd] (ERC-2011-StG 283794-QUANTESS)
Maintainer: Kenneth Benoit <kbenoit@lse.ac.uk>

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Package scorecard updated to version 0.2.5 with previous version 0.2.4 dated 2019-03-26

Title: Credit Risk Scorecard
Description: The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring.
Author: Shichen Xie [aut, cre]
Maintainer: Shichen Xie <xie@shichen.name>

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Package replyr updated to version 1.0.1 with previous version 1.0.0 dated 2019-03-31

Title: Patches to Use 'dplyr' on Remote Data Sources
Description: Patches to use 'dplyr' on remote data sources ('SQL' databases, 'Spark' 2.0.0 and above) in a reliable "generic" fashion (generic meaning user code works similarly on all such sources, without needing per-source adaption). Due to the fluctuating nature of 'dplyr'/'dbplyr'/'rlang' 'APIs' this package is going into maintenance mode. Most of the 'replyr' functions are already done better by one of the non-monolithic replacement packages: 'wrapr', 'seplyr', 'rquery', or 'cdata'.
Author: John Mount [aut, cre], Nina Zumel [aut], Win-Vector LLC [cph]
Maintainer: John Mount <jmount@win-vector.com>

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Package gmvarkit updated to version 1.1.0 with previous version 1.0.3 dated 2018-09-22

Title: Estimate Gaussian Mixture Vector Autoregressive Model
Description: Unconstrained and constrained maximum likelihood estimation of Gaussian Mixture Vector Autoregressive (GMVAR) model, quantile residual tests, graphical diagnostics, simulations, and forecasting. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>.
Author: Savi Virolainen [aut, cre]
Maintainer: Savi Virolainen <savi.virolainen@helsinki.fi>

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Package DSAIDE updated to version 0.8.2 with previous version 0.8.0 dated 2019-03-30

Title: Dynamical Systems Approach to Infectious Disease Epidemiology
Description: A collection of 'shiny' apps that allow for the simulation and exploration of various infectious disease transmission dynamics scenarios. The purpose of the package is to help individuals learn about infectious disease epidemiology from a dynamical systems perspective. All apps include explanations of the underlying models and instructions on what to do with the models.
Author: Andreas Handel [aut, cre] (<https://orcid.org/0000-0002-4622-1146>), Christine Casey [ctb] (Parasite Model App), Isaac Chun-Hai Fung [ctb], Ronald Galiwango [ctb] (Surveillance App), Chase Golden [ctb] (Maternal Immunity App), Spencer Hall [ctb], Ben Listyg [ctb], Brian McKay [ctb], John Rossow [ctb], Sina Solaimanpour [ctb], Kevin Spiegel [ctb] (Global Warming App), Henok Woldu [ctb]
Maintainer: Andreas Handel <ahandel@uga.edu>

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Package dplyr updated to version 0.8.3 with previous version 0.8.2 dated 2019-06-29

Title: A Grammar of Data Manipulation
Description: A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
Author: Hadley Wickham [aut, cre] (<https://orcid.org/0000-0003-4757-117X>), Romain François [aut] (<https://orcid.org/0000-0002-2444-4226>), Lionel Henry [aut], Kirill Müller [aut] (<https://orcid.org/0000-0002-1416-3412>), RStudio [cph, fnd]
Maintainer: Hadley Wickham <hadley@rstudio.com>

Diff between dplyr versions 0.8.2 dated 2019-06-29 and 0.8.3 dated 2019-07-04

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Package bigreadr updated to version 0.1.6 with previous version 0.1.5 dated 2019-06-17

Title: Read Large Text Files
Description: Read large text files by splitting them in smaller files.
Author: Florian Privé [aut, cre]
Maintainer: Florian Privé <florian.prive.21@gmail.com>

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Package RZigZag updated to version 0.2.0 with previous version 0.1.6 dated 2018-04-30

Title: Zig-Zag Sampler
Description: Implements the Zig-Zag algorithm (Bierkens, Fearnhead, Roberts, 2016) <arXiv:1607.03188> applied and Bouncy Particle Sampler <arXiv:1510.02451> for a Gaussian target and Student distribution.
Author: Joris Bierkens
Maintainer: Joris Bierkens <j.bierkens@vu.nl>

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Package haven updated to version 2.1.1 with previous version 2.1.0 dated 2019-02-19

Title: Import and Export 'SPSS', 'Stata' and 'SAS' Files
Description: Import foreign statistical formats into R via the embedded 'ReadStat' C library, <https://github.com/WizardMac/ReadStat>.
Author: Hadley Wickham [aut, cre], Evan Miller [aut, cph] (Author of included ReadStat code), RStudio [cph, fnd]
Maintainer: Hadley Wickham <hadley@rstudio.com>

Diff between haven versions 2.1.0 dated 2019-02-19 and 2.1.1 dated 2019-07-04

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More information about haven at CRAN
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Package digest updated to version 0.6.20 with previous version 0.6.19 dated 2019-05-20

Title: Create Compact Hash Digests of R Objects
Description: Implementation of a function 'digest()' for the creation of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32', 'xxhash', 'murmurhash' and 'spookyhash' algorithms) permitting easy comparison of R language objects, as well as functions such as'hmac()' to create hash-based message authentication code. Please note that this package is not meant to be deployed for cryptographic purposes for which more comprehensive (and widely tested) libraries such as 'OpenSSL' should be used.
Author: Dirk Eddelbuettel <edd@debian.org> with contributions by Antoine Lucas, Jarek Tuszynski, Henrik Bengtsson, Simon Urbanek, Mario Frasca, Bryan Lewis, Murray Stokely, Hannes Muehleisen, Duncan Murdoch, Jim Hester, Wush Wu, Qiang Kou, Thierry Onkelinx, Michel Lang, Viliam Simko, Kurt Hornik, Radford Neal, and Kendon Bell.
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package parsetools (with last version 0.1.0) was removed from CRAN

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

2019-01-04 0.1.0

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Package testextra (with last version 0.1.0) was removed from CRAN

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

2019-01-18 0.1.0

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Package purrrogress (with last version 0.1.0) was removed from CRAN

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

2019-06-04 0.1.0

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Package TAM updated to version 3.2-24 with previous version 3.1-45 dated 2019-03-18

Title: Test Analysis Modules
Description: Includes marginal maximum likelihood estimation and joint maximum likelihood estimation for unidimensional and multidimensional item response models. The package functionality covers the Rasch model, 2PL model, 3PL model, generalized partial credit model, multi-faceted Rasch model, nominal item response model, structured latent class model, mixture distribution IRT models, and located latent class models. Latent regression models and plausible value imputation are also supported. For details see Adams, Wilson and Wang, 1997 <doi:10.1177/0146621697211001>, Adams, Wilson and Wu, 1997 <doi:10.3102/10769986022001047>, Formann, 1982 <doi:10.1002/bimj.4710240209>, Formann, 1992 <doi:10.1080/01621459.1992.10475229>.
Author: Alexander Robitzsch [aut, cre], Thomas Kiefer [aut], Margaret Wu [aut]
Maintainer: Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>

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Package quanteda updated to version 1.5.0 with previous version 1.4.3 dated 2019-04-01

Title: Quantitative Analysis of Textual Data
Description: A fast, flexible, and comprehensive framework for quantitative text analysis in R. Provides functionality for corpus management, creating and manipulating tokens and ngrams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more.
Author: Kenneth Benoit [cre, aut, cph] (<https://orcid.org/0000-0002-0797-564X>), Kohei Watanabe [aut] (<https://orcid.org/0000-0001-6519-5265>), Haiyan Wang [aut] (<https://orcid.org/0000-0003-4992-4311>), Paul Nulty [aut] (<https://orcid.org/0000-0002-7214-4666>), Adam Obeng [aut] (<https://orcid.org/0000-0002-2906-4775>), Stefan Müller [aut] (<https://orcid.org/0000-0002-6315-4125>), Akitaka Matsuo [aut] (<https://orcid.org/0000-0002-3323-6330>), Jiong Wei Lua [aut], Patrick O. Perry [aut] (<https://orcid.org/0000-0001-7460-127X>), Jouni Kuha [aut] (<https://orcid.org/0000-0002-1156-8465>), Benjamin Lauderdale [aut] (<https://orcid.org/0000-0003-3090-0969>), William Lowe [aut] (<https://orcid.org/0000-0002-1549-6163>), Christian Müller [ctb], Lori Young [dtc] (Lexicoder Sentiment Dictionary 2015), Stuart Soroka [dtc] (Lexicoder Sentiment Dictionary 2015), Ian Fellows [cph] (authored wordcloud C source code (modified)), European Research Council [fnd] (ERC-2011-StG 283794-QUANTESS)
Maintainer: Kenneth Benoit <kbenoit@lse.ac.uk>

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Package usethis updated to version 1.5.1 with previous version 1.5.0 dated 2019-04-07

Title: Automate Package and Project Setup
Description: Automate package and project setup tasks that are otherwise performed manually. This includes setting up unit testing, test coverage, continuous integration, Git, 'GitHub', licenses, 'Rcpp', 'RStudio' projects, and more.
Author: Hadley Wickham [aut] (<https://orcid.org/0000-0003-4757-117X>), Jennifer Bryan [aut, cre] (<https://orcid.org/0000-0002-6983-2759>), RStudio [cph, fnd]
Maintainer: Jennifer Bryan <jenny@rstudio.com>

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Package mkin updated to version 0.9.49.5 with previous version 0.9.48.1 dated 2019-03-04

Title: Kinetic Evaluation of Chemical Degradation Data
Description: Calculation routines based on the FOCUS Kinetics Report (2006, 2014). Includes a function for conveniently defining differential equation models, model solution based on eigenvalues if possible or using numerical solvers and a choice of the optimisation methods made available by the 'FME' package. If a C compiler (on windows: 'Rtools') is installed, differential equation models are solved using compiled C functions. Please note that no warranty is implied for correctness of results or fitness for a particular purpose.
Author: Johannes Ranke [aut, cre, cph] (<https://orcid.org/0000-0003-4371-6538>), Katrin Lindenberger [ctb], René Lehmann [ctb], Eurofins Regulatory AG [cph]
Maintainer: Johannes Ranke <jranke@uni-bremen.de>

Diff between mkin versions 0.9.48.1 dated 2019-03-04 and 0.9.49.5 dated 2019-07-04

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Package manymodelr updated to version 0.2.1 with previous version 0.1.0 dated 2019-03-02

Title: Build and Tune Several Models
Description: Frequently one needs a convenient way to build and tune several models in one go.The goal is to provide a number of convenience functions useful in machine learning applications. It provides the ability to build, tune and obtain predictions of several models in one function. The models are built using 'caret' functions with easier to read syntax. Kuhn(2014)<arXiv:1405.6974v14>. Kuhn(2008)<doi10.18637/jss.v028.i05>. Chambers,J.M.(1992)<doi:10.1371/journal.pone.0053143>. Wilkinson,G.N. and Rogers, C. E. (1973)<doi:10.2307/2346786>.
Author: Nelson Gonzabato [aut, cre]
Maintainer: Nelson Gonzabato <gonzabato@hotmail.com>

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New package iCiteR with initial version 0.1.0
Package: iCiteR
Title: Returns Data from the NIH's 'iCite' API
Version: 0.1.0
Authors@R: person("Travis", "Riddle", email = "travis.riddle@nih.gov", role = c("aut", "cre"))
Description: A minimal wrapper around the NIH's 'iCite' API (<https://icite.od.nih.gov/api>). Given a vector of pubmed IDs, this package returns a dataframe of the information yielded by the 'iCite' API. The primary metric yielded by 'iCite' is the paper's relative citation ratio, but the API also returns other meta-data from the paper, including author names, publication journal, publication year, paper title, doi, and a number of other citation metrics.
Depends: R (>= 3.5)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: httr (>= 1.4), jsonlite (>= 1.6)
Suggests: testthat, knitr, rmarkdown
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-07-03 16:22:29 UTC; riddleta
Author: Travis Riddle [aut, cre]
Maintainer: Travis Riddle <travis.riddle@nih.gov>
Repository: CRAN
Date/Publication: 2019-07-04 09:20:03 UTC

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New package HaDeX with initial version 0.1
Package: HaDeX
Title: Analysis and Visualisation of Hydrogen/Deuterium Exchange Mass Spectrometry Data
Version: 0.1
Authors@R: c(person("Weronika", "Puchala", email = "puchala.weronika@gmail.com", role = c("cre", "aut")), person("Michal", "Burdukiewicz", email = "michalburdukiewicz@gmail.com", comment = c(ORCID = "0000-0001-8926-582X"), role = c("aut")), person("Dominik", "Rafacz", email = "d.rafacz@student.mini.pw.edu.pl", role = c("ctb")))
Description: Functions for processing, analysis and visualization of Hydrogen Deuterium eXchange monitored by Mass Spectrometry experiments (HDX-MS). 'HaDeX' introduces a new standardized and reproducible workflow for the analysis of the HDX-MS data, including novel uncertainty intervals. Additionally, it covers data exploration, quality control and generation of publication-quality figures. All functionalities are also available in the in-built 'Shiny' app.
Depends: R (>= 3.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: data.table, dplyr, DT, ggplot2, gsubfn, latex2exp, reshape2, readr, readxl, shiny, stringr, tidyr
Suggests: covr, digest, knitr, pander, rmarkdown, testthat, vdiffr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-07-03 14:41:45 UTC; pucha
Author: Weronika Puchala [cre, aut], Michal Burdukiewicz [aut] (<https://orcid.org/0000-0001-8926-582X>), Dominik Rafacz [ctb]
Maintainer: Weronika Puchala <puchala.weronika@gmail.com>
Repository: CRAN
Date/Publication: 2019-07-04 09:10:03 UTC

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Package gld updated to version 2.5 with previous version 2.4.2 dated 2019-04-30

Title: Estimation and Use of the Generalised (Tukey) Lambda Distribution
Description: The generalised lambda distribution, or Tukey lambda distribution, provides a wide variety of shapes with one functional form. This package provides random numbers, quantiles, probabilities, densities and density quantiles for four different types of the distribution; see documentation for details. It provides the density function, distribution function, and Quantile-Quantile plots. It implements a variety of estimation methods for the distribution, including diagnostic plots. Estimation methods include the starship (all 4 types), method of L-Moments for the GPD and FKML types, and a number of methods for only the FKML parameterisation. These include maximum likelihood, maximum product of spacings, Titterington's method, Moments, Trimmed L-Moments and Distributional Least Absolutes.
Author: Robert King <Robert.King@newcastle.edu.au>, Benjamin Dean <Benjamin.Dean@uon.edu.au>, Sigbert Klinke, Paul van Staden
Maintainer: Robert King <Robert.King@newcastle.edu.au>

Diff between gld versions 2.4.2 dated 2019-04-30 and 2.5 dated 2019-07-04

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 gld-2.5/gld/R/CheckParameters.R |    4 +-
 gld-2.5/gld/R/fit.gpd.R         |   71 ++++++++++++++++++++++++++++++----------
 gld-2.5/gld/R/gld.gpd.se.R      |   10 ++---
 gld-2.5/gld/R/gldgpdNus.R       |   14 +++----
 gld-2.5/gld/R/methods.R         |    6 +--
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New package ggpval with initial version 0.2.2
Package: ggpval
Type: Package
Title: Annotate Statistical Tests for 'ggplot2'
Version: 0.2.2
Authors@R: person("Jun", "Cheng", email = "s6juncheng@gmail.com", role = c("aut", "cre"))
Description: Automatically performs desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, and adds the resulting p-values to the plot with an annotation bar. Visualizing group differences are frequently performed by boxplots, bar plots, etc. Statistical test results are often needed to be annotated on these plots. This package provides a convenient function that works on 'ggplot2' objects, performs the desired statistical test between groups of interest and annotates the test results on the plot.
URL: https://github.com/s6juncheng/ggpval
License: GPL-3
Encoding: UTF-8
Imports: ggplot2, data.table
Suggests: knitr, rmarkdown, testthat
LazyData: true
VignetteBuilder: knitr
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-07-03 19:54:38 UTC; chengju
Author: Jun Cheng [aut, cre]
Maintainer: Jun Cheng <s6juncheng@gmail.com>
Repository: CRAN
Date/Publication: 2019-07-04 09:40:24 UTC

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Package ctrlGene updated to version 1.0.1 with previous version 1.0.0 dated 2018-09-30

Title: Assess the Stability of Candidate Housekeeping Genes
Description: A simple way to assess the stability of candidate housekeeping genes is implemented in this package.
Author: Shanliang Zhong [aut, cre]
Maintainer: Shanliang Zhong <slzhong@foxmail.com>

Diff between ctrlGene versions 1.0.0 dated 2018-09-30 and 1.0.1 dated 2019-07-04

 DESCRIPTION    |   14 +++++++-------
 MD5            |    4 ++--
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 3 files changed, 10 insertions(+), 15 deletions(-)

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New package distantia with initial version 1.0.0
Package: distantia
Type: Package
Title: Assessing Dissimilarity Between Multivariate Time Series
Version: 1.0.0
Author: Blas M. Benito
URL: https://blasbenito.github.io/distantia/
Maintainer: Blas M. Benito <blasbenito@gmail.com>
Description: Provides tools to assess the dissimilarity between multivariate time-series. It is based on the psi measure described in "Numerical methods in Quaternary pollen analysis" (Birks and Gordon, 1985 <doi:10.1002/jqs.3390020110>), which computes dissimilarity constrained by sample order, but the original idea has been extended to work with any kind of multivariate time-series, no matter whether they are regular, irregular, aligned or unaligned. Furthermore, the package allows to assess the significance of dissimilarity values by applying a restricted permutation test, allows to measure the contribution of individual variables to dissimilarity, and offers tools to transfer attributes (generally time or age, but other are possible) between sequences based on the similarity of their samples.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: R (>= 3.5.0)
Imports: plyr, grDevices, foreach, parallel, doParallel, fields, viridis, viridisLite, RColorBrewer
Suggests: devtools, formatR, kableExtra, magrittr, knitr, rmarkdown
NeedsCompilation: no
Packaged: 2019-07-03 16:54:30 UTC; blas
Repository: CRAN
Date/Publication: 2019-07-04 09:20:10 UTC

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Package callr updated to version 3.3.0 with previous version 3.2.0 dated 2019-03-15

Title: Call R from R
Description: It is sometimes useful to perform a computation in a separate R process, without affecting the current R process at all. This packages does exactly that.
Author: Gábor Csárdi [aut, cre, cph] (<https://orcid.org/0000-0001-7098-9676>), Winston Chang [aut], RStudio [cph, fnd], Mango Solutions [cph, fnd]
Maintainer: Gábor Csárdi <csardi.gabor@gmail.com>

Diff between callr versions 3.2.0 dated 2019-03-15 and 3.3.0 dated 2019-07-04

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Package abn updated to version 2.1 with previous version 2.0 dated 2019-07-01

Title: Modelling Multivariate Data with Additive Bayesian Networks
Description: Bayesian network analysis is a form of probabilistic graphical models which derives from empirical data a directed acyclic graph, DAG, describing the dependency structure between random variables. An additive Bayesian network model consists of a form of a DAG where each node comprises a generalized linear model, GLM. Additive Bayesian network models are equivalent to Bayesian multivariate regression using graphical modelling, they generalises the usual multivariable regression, GLM, to multiple dependent variables. 'abn' provides routines to help determine optimal Bayesian network models for a given data set, where these models are used to identify statistical dependencies in messy, complex data. The additive formulation of these models is equivalent to multivariate generalised linear modelling (including mixed models with iid random effects). The usual term to describe this model selection process is structure discovery. The core functionality is concerned with model selection - determining the most robust empirical model of data from interdependent variables. Laplace approximations are used to estimate goodness of fit metrics and model parameters, and wrappers are also included to the INLA package which can be obtained from <http://www.r-inla.org>. A comprehensive set of documented case studies, numerical accuracy/quality assurance exercises, and additional documentation are available from the 'abn' website <http://r-bayesian-networks.org>.
Author: Gilles Kratzer [aut, cre] (<https://orcid.org/0000-0002-5929-8935>), Fraser Ian Lewis [aut], Reinhard Furrer [ctb] (<https://orcid.org/0000-0002-6319-2332>), Marta Pittavino [ctb] (<https://orcid.org/0000-0002-1232-1034>)
Maintainer: Gilles Kratzer <gilles.kratzer@math.uzh.ch>

Diff between abn versions 2.0 dated 2019-07-01 and 2.1 dated 2019-07-04

 DESCRIPTION     |    6 +++---
 MD5             |    4 ++--
 src/Makevars.in |    4 ++--
 3 files changed, 7 insertions(+), 7 deletions(-)

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Package gprofiler2 updated to version 0.1.5 with previous version 0.1.4 dated 2019-05-24

Title: Interface to the 'g:Profiler' Toolset
Description: A toolset for functional enrichment analysis and visualization, gene/protein/SNP identifier conversion and mapping orthologous genes across species via 'g:Profiler' (<https://biit.cs.ut.ee/gprofiler>). The main tools are: (1) 'g:GOSt' - functional enrichment analysis and visualization of gene lists; (2) 'g:Convert' - gene/protein/transcript identifier conversion across various namespaces; (3) 'g:Orth' - orthology search across species; (4) 'g:SNPense' - mapping SNP rs identifiers to chromosome positions, genes and variant effects This package is an R interface corresponding to the 2019 update of 'g:Profiler' and provides access to 'g:Profiler' for versions 'e94_eg41_p11' and higher. See the package 'gProfileR' for accessing older versions from the 'g:Profiler' toolset.
Author: Liis Kolberg <liis.kolberg@ut.ee>, Uku Raudvere <uku.raudvere@ut.ee>
Maintainer: Liis Kolberg <liis.kolberg@ut.ee>

Diff between gprofiler2 versions 0.1.4 dated 2019-05-24 and 0.1.5 dated 2019-07-04

 DESCRIPTION    |    6 +++---
 MD5            |    6 +++---
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 4 files changed, 20 insertions(+), 15 deletions(-)

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New package emax.glm with initial version 0.1.2
Package: emax.glm
Title: General Tools for Building GLM Expectation-Maximization Models
Version: 0.1.2
Authors@R: person("Robert M.", "Cook", email="robert.cook@bcu.ac.uk", role=c("aut", "cre"), comment = c(ORCID = "0000-0003-3343-8271"))
Maintainer: Robert M. Cook <robert.cook@bcu.ac.uk>
Description: Implementation of Expectation Maximization (EM) regression of general linear models. The package currently supports Poisson and Logistic regression with variable weights, with underlying theory included in the vignettes. New users are recommended to look at the em.glm() and small.em() functions - the outputs of which are supported by AIC(), BIC(), and logLik() calls. Several plot functions have been included for useful diagnostics and model exploration. Methods are based on the theory of Dempster et al (1977, ISBN:00359246), and follow the methods of Hastie et al. (2009) <doi:10.1007/978-0-387-21606-5_7> and A. Zeileis et al (2017) <doi:10.18637/jss.v027.i08>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: R (>= 2.10)
Imports: MASS, pracma, stats, pander, pROC, AER
Suggests: spelling, knitr, rmarkdown
VignetteBuilder: knitr
Language: en-US
NeedsCompilation: no
Packaged: 2019-07-01 13:24:49 UTC; id125490
Author: Robert M. Cook [aut, cre] (<https://orcid.org/0000-0003-3343-8271>)
Repository: CRAN
Date/Publication: 2019-07-04 08:30:03 UTC

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New package EloChoice with initial version 0.29.4
Package: EloChoice
Type: Package
Title: Preference Rating for Visual Stimuli Based on Elo Ratings
Version: 0.29.4
Date: 2019-07-03
Author: Christof Neumann
Maintainer: Christof Neumann <christofneumann1@gmail.com>
Description: Allows calculating global scores for characteristics of visual stimuli as assessed by human raters. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses preference for one stimulus over the other (forced choice). The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants' scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest. Clark et al (2018) <doi:10.1371/journal.pone.0190393> provide details.
License: GPL (>= 3)
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp, psychotools, Rdpack
Suggests: xtable, knitr, rmarkdown
VignetteBuilder: knitr
Encoding: UTF-8
RoxygenNote: 6.1.1
RdMacros: Rdpack
URL: https://github.com/gobbios/EloChoice
BugReports: https://github.com/gobbios/EloChoice/issues
NeedsCompilation: yes
Packaged: 2019-07-03 12:31:56 UTC; cn
Repository: CRAN
Date/Publication: 2019-07-04 08:40:02 UTC

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New package TSMSN with initial version 0.0.1
Package: TSMSN
Title: Truncated Scale Mixtures of Skew-Normal Distributions
Version: 0.0.1
Authors@R: c(person(given = "Eraldo", family = "B. dos Anjos Filho", role = c("aut", "cre"), email = "ebdaf1@de.ufpe.br", comment = c(ORCID = "0000-0002-1272-2794")), person(given = "Aldo", family = "M. Garay", role = c("aut"), email = "agaray@de.ufpe.br", comment = c(ORCID = "0000-0002-4510-639X")))
Description: Return the first four moments, estimation of parameters and sample of the TSMSN distributions (Skew Normal, Skew t, Skew Slash or Skew Contaminated Normal).
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: sn, mvtnorm, numDeriv, progress
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-07-02 23:16:05 UTC; eraldofilho
Author: Eraldo B. dos Anjos Filho [aut, cre] (<https://orcid.org/0000-0002-1272-2794>), Aldo M. Garay [aut] (<https://orcid.org/0000-0002-4510-639X>)
Maintainer: Eraldo B. dos Anjos Filho <ebdaf1@de.ufpe.br>
Repository: CRAN
Date/Publication: 2019-07-04 07:40:03 UTC

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Package sparklyr updated to version 1.0.2 with previous version 1.0.1 dated 2019-05-17

Title: R Interface to Apache Spark
Description: R interface to Apache Spark, a fast and general engine for big data processing, see <http://spark.apache.org>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.
Author: Javier Luraschi [aut, cre], Kevin Kuo [aut] (<https://orcid.org/0000-0001-7803-7901>), Kevin Ushey [aut], JJ Allaire [aut], Samuel Macedo [ctb], RStudio [cph], The Apache Software Foundation [aut, cph]
Maintainer: Javier Luraschi <javier@rstudio.com>

Diff between sparklyr versions 1.0.1 dated 2019-05-17 and 1.0.2 dated 2019-07-04

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 42 files changed, 289 insertions(+), 144 deletions(-)

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Package coxme updated to version 2.2-14 with previous version 2.2-10 dated 2018-05-13

Title: Mixed Effects Cox Models
Description: This package fits Cox proportional hazards models containing both fixed and random effects. The random effects can have a general form, of which familial interactions (a "kinship" matrix) is a particular special case. Note that the simplest case of a mixed effects Cox model, i.e. a single random per-group intercept, is also called a "frailty" model. The approach is based on Rippatti and Palgren, Biometrics 2002.
Author: Terry M. Therneau [aut, cre]
Maintainer: Terry M. Therneau <therneau@mayo.edu>

Diff between coxme versions 2.2-10 dated 2018-05-13 and 2.2-14 dated 2019-07-04

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 20 files changed, 72 insertions(+), 49 deletions(-)

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Package compound.Cox updated to version 3.18 with previous version 3.17 dated 2019-06-01

Title: Univariate Feature Selection and Compound Covariate for Predicting Survival
Description: Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions). Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>, statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627>, Emura & Chen (2016 Stat Methods Med Res) <DOI:10.1177/0962280214533378>, and Emura et al. (2019)<DOI:10.1016/j.cmpb.2018.10.020>. Algorithms for generating correlated gene expressions are also available.
Author: Takeshi Emura, Hsuan-Yu Chen, Shigeyuki Matsui, Yi-Hau Chen
Maintainer: Takeshi Emura <takeshiemura@gmail.com>

Diff between compound.Cox versions 3.17 dated 2019-06-01 and 3.18 dated 2019-07-04

 DESCRIPTION                 |    8 ++++----
 MD5                         |   18 +++++++++---------
 R/CG.Clayton.R              |    3 +++
 man/CG.Clayton.Rd           |   15 ++++++++++-----
 man/CG.Gumbel.Rd            |   11 ++++++++---
 man/Lung.Rd                 |    8 +++++++-
 man/compound.Cox-package.Rd |    4 ++--
 man/uni.Wald.Rd             |    6 +++---
 man/uni.score.Rd            |    2 +-
 man/uni.selection.Rd        |   10 +++++-----
 10 files changed, 52 insertions(+), 33 deletions(-)

More information about compound.Cox at CRAN
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