Sun, 06 Oct 2019

Package tabr updated to version 0.3.5 with previous version 0.3.0 dated 2019-06-23

Title: Music Notation Syntax, Manipulation, Analysis and Transcription in R
Description: Provides a music notation syntax and a collection of music programming functions for generating, manipulating, organizing and analyzing musical information in R. The music notation framework facilitates creating and analyzing music data in notation form. The package also provides API wrapper functions for transcribing musical representations in R into guitar tablature ("tabs") using the 'LilyPond' backend (<http://lilypond.org>). 'LilyPond' is open source music engraving software for generating high quality sheet music based on markup syntax. 'tabr' generates 'LilyPond' files from R code and can pass them to 'LilyPond' to be rendered into sheet music pdf files from R. While 'LilyPond' caters to sheet music in general and 'tabr' can be used to create basic sheet music, the transcription functions focus on leveraging 'LilyPond' specifically for creating quality guitar tablature. The package offers nominal MIDI file output support in conjunction with rendering tablature output. See the 'tuneR' package for more general use of MIDI files in R.
Author: Matthew Leonawicz [aut, cre]
Maintainer: Matthew Leonawicz <matt_leonawicz@esource.com>

Diff between tabr versions 0.3.0 dated 2019-06-23 and 0.3.5 dated 2019-10-06

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More information about tabr at CRAN
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Package rSEA updated to version 2.1.0 with previous version 2.0.0 dated 2019-10-03

Title: Simultaneous Enrichment Analysis
Description: SEA performs simultaneous feature-set testing for (gen)omics data. It tests the unified null hypothesis and controls the family-wise error rate for all possible pathways. The unified null hypothesis is defined as: "The proportion of true features in the set is less than or equal to a threshold." Family-wise error rate control is provided through use of closed testing with Simes test. There are some practical functions to play around with the pathways of interest.
Author: Mitra Ebrahimpoor
Maintainer: Mitra Ebrahimpoor<m.ebrahimpoor@lumc.nl>

Diff between rSEA versions 2.0.0 dated 2019-10-03 and 2.1.0 dated 2019-10-06

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More information about rSEA at CRAN
Permanent link

Package valueEQ5D updated to version 0.4.5 with previous version 0.4.4 dated 2019-09-13

Title: Scoring the EQ-5D Descriptive System
Description: EQ-5D is a standard instrument (<https://euroqol.org/eq-5d-instruments/>) that measures the quality of life often used in clinical and economic evaluations of health care technologies. Both adult versions of EQ-5D (EQ-5D-3L and EQ-5D-5L) contain a descriptive system and visual analog scale. The descriptive system measures the patient's health in 5 dimensions: the 5L versions has 5 levels and 3L version has 3 levels. The descriptive system scores are usually converted to index values using country specific values sets (that incorporates the country preferences). This package allows the calculation of both descriptive system scores to the index value scores. The value sets for EQ-5D-3L are from the references mentioned in the website <https://euroqol.org/eq-5d-instruments/eq-5d-3l-about/valuation/> The value sets for EQ-5D-3L for a total of 31 countries are used for the valuation (see the user guide for a complete list of references). The value sets for EQ-5D-5L are obtained from references mentioned in the <https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/valuation-standard-value-sets/> and other sources. The value sets for EQ-5D-5L for a total of 17 countries are used for the valuation (see the user guide for a complete list of references). The package can also be used to map 5L scores to 3L index values for 10 countries: Denmark, France, Germany, Japan, Netherlands, Spain, Thailand, UK, USA, and Zimbabwe. The value set and method for mapping are obtained from Van Hout et al (2012) <doi: 10.1016/j.jval.2012.02.008>.
Author: Sheeja Manchira Krishnan [aut, cre]
Maintainer: Sheeja Manchira Krishnan <sheejamk@gmail.com>

Diff between valueEQ5D versions 0.4.4 dated 2019-09-13 and 0.4.5 dated 2019-10-06

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More information about valueEQ5D at CRAN
Permanent link

Package corporaexplorer updated to version 0.6.3 with previous version 0.6.2 dated 2019-09-09

Title: A 'Shiny' App for Exploration of Text Collections
Description: Facilitates dynamic exploration of text collections through an intuitive graphical user interface. The package contains 1) a helper function to convert a data frame to a 'corporaexplorerobject', 2) a 'Shiny' app for fast and flexible exploration of a 'corporaexplorerobject', and 3) a 'Shiny' app for simple retrieval/extraction of documents from a 'corporaexplorerobject' in a reading-friendly format.
Author: Kristian Lundby Gjerde [aut, cre]
Maintainer: Kristian Lundby Gjerde <kristian.gjerde@gmail.com>

Diff between corporaexplorer versions 0.6.2 dated 2019-09-09 and 0.6.3 dated 2019-10-06

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Package cobalt updated to version 3.9.0 with previous version 3.8.0 dated 2019-09-12

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

Diff between cobalt versions 3.8.0 dated 2019-09-12 and 3.9.0 dated 2019-10-06

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Package sunburstR updated to version 2.1.2 with previous version 2.1.1 dated 2019-03-04

Title: 'Htmlwidget' for 'Kerry Rodden' 'd3.js' Sequence and 'd2b' Sunburst
Description: Make interactive 'd3.js' sequence sunburst diagrams in R with the convenience and infrastructure of an 'htmlwidget'.
Author: Mike Bostock [aut, cph] (d3.js library, http://d3js.org), Kerry Rodden [aut, cph] (sequences library in htmlwidgets/lib, https://gist.github.com/kerryrodden/7090426), Kevin Warne [aut, cph] (d2b sunburst library in htmlwidgets/lib, https://github.com/d2bjs/d2b), Kent Russell [aut, cre] (R interface), Florian Breitwieser [ctb] (R interface), CJ Yetman [ctb] (R interface, <https://orcid.org/0000-0001-5099-9500>)
Maintainer: Kent Russell <kent.russell@timelyportfolio.com>

Diff between sunburstR versions 2.1.1 dated 2019-03-04 and 2.1.2 dated 2019-10-06

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Package mpath updated to version 0.3-18 with previous version 0.3-17 dated 2019-08-01

Title: Regularized Linear Models
Description: Algorithms for fitting model-based penalized coefficient paths. Currently the models include penalized Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial regression models. The penalties include least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP), and each possibly combining with L_2 penalty. See Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016) <doi:10.1177/0962280214530608>.
Author: Zhu Wang, with contributions from Achim Zeileis, Simon Jackman, Brian Ripley, Trevor Hastie, Rob Tibshirani, Balasubramanian Narasimhan, Gil Chu and Patrick Breheny
Maintainer: Zhu Wang <wangz1@uthscsa.edu>

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Package shrinkTVP updated to version 1.1.1 with previous version 1.1.0 dated 2019-10-06

Title: Efficient Bayesian Inference for Time-Varying Parameter Models with Shrinkage
Description: Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006>.
Author: Peter Knaus [aut, cre] (<https://orcid.org/0000-0001-6498-7084>), Angela Bitto-Nemling [aut], Annalisa Cadonna [aut] (<https://orcid.org/0000-0003-0360-7628>), Sylvia Frühwirth-Schnatter [aut] (<https://orcid.org/0000-0003-0516-5552>), Daniel Winkler [ctb], Kemal Dingic [ctb]
Maintainer: Peter Knaus <peter.knaus@wu.ac.at>

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Package hdf5r updated to version 1.3.0 with previous version 1.2.0 dated 2019-04-20

Title: Interface to the 'HDF5' Binary Data Format
Description: 'HDF5' is a data model, library and file format for storing and managing large amounts of data. This package provides a nearly feature complete, object oriented wrapper for the 'HDF5' API <https://support.hdfgroup.org/HDF5/doc/RM/RM_H5Front.html> using R6 classes. Additionally, functionality is added so that 'HDF5' objects behave very similar to their corresponding R counterparts.
Author: Holger Hoefling [aut, cre], Mario Annau [aut], Novartis Institute for BioMedical Research (NIBR) [cph]
Maintainer: Holger Hoefling <hhoeflin@gmail.com>

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Package factorstochvol updated to version 0.9.3 with previous version 0.9.2 dated 2019-06-27

Title: Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models
Description: Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving <doi:10.1080/10618600.2017.1322091>. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix <doi:10.1016/j.jeconom.2018.11.007>.
Author: Gregor Kastner [aut, cre] (<https://orcid.org/0000-0002-8237-8271>), Darjus Hosszejni [ctb] (<https://orcid.org/0000-0002-3803-691X>)
Maintainer: Gregor Kastner <gregor.kastner@wu.ac.at>

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Package shrinkTVP updated to version 1.1.0 with previous version 1.0.2 dated 2019-08-07

Title: Efficient Bayesian Inference for Time-Varying Parameter Models with Shrinkage
Description: Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006>.
Author: Peter Knaus [aut, cre] (<https://orcid.org/0000-0001-6498-7084>), Angela Bitto-Nemling [aut], Annalisa Cadonna [aut] (<https://orcid.org/0000-0003-0360-7628>), Sylvia Frühwirth-Schnatter [aut] (<https://orcid.org/0000-0003-0516-5552>), Daniel Winkler [ctb], Kemal Dingic [ctb]
Maintainer: Peter Knaus <peter.knaus@wu.ac.at>

Diff between shrinkTVP versions 1.0.2 dated 2019-08-07 and 1.1.0 dated 2019-10-06

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Package RobinHood updated to version 1.2.1 with previous version 1.2 dated 2019-09-30

Title: Interface for the RobinHood.com No Commission Investing Platform
Description: Execute API calls to the RobinHood <https://robinhood.com> investing platform. Functionality includes accessing account data and current holdings, retrieving investment statistics and quotes, placing and canceling orders, getting market trading hours, searching investments by popular tag, and interacting with watch lists.
Author: Joseph Blubaugh
Maintainer: Joseph Blubaugh <jestonblu@gmail.com>

Diff between RobinHood versions 1.2 dated 2019-09-30 and 1.2.1 dated 2019-10-06

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Package mlt updated to version 1.0-7 with previous version 1.0-6 dated 2019-10-04

Title: Most Likely Transformations
Description: Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>.
Author: Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>)
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between mlt versions 1.0-6 dated 2019-10-04 and 1.0-7 dated 2019-10-06

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Package GGIR updated to version 1.10-7 with previous version 1.10-4 dated 2019-09-09

Title: Raw Accelerometer Data Analysis
Description: A tool to process and analyse data collected with wearable raw acceleration sensors as described in van Hees and colleagues (2014) <doi: 10.1152/japplphysiol.00421.2014> and (2015) <doi: 10.1371/journal.pone.0142533>. The package has been developed and tested for binary data from 'GENEActiv' <https://www.activinsights.com/> and GENEA devices (not for sale), .csv-export data from 'Actigraph' <http://actigraphcorp.com> devices, and .cwa and .wav-format data from 'Axivity' <https://axivity.com/product/ax3>. These devices are currently widely used in research on human daily physical activity. Further, the package can handle accelerometer data file from any other sensor brand providing that the data is stored in csv format and has either no header or a two column header.
Author: Vincent T van Hees [aut, cre], Zhou Fang [ctb], Jing Hua Zhao [ctb], Joe Heywood [ctb], Evgeny Mirkes [ctb], Severine Sabia [ctb], Joan Capdevila Pujol [ctb], Jairo H Migueles [ctb]
Maintainer: Vincent T van Hees <vincentvanhees@gmail.com>

Diff between GGIR versions 1.10-4 dated 2019-09-09 and 1.10-7 dated 2019-10-06

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Package DriftBurstHypothesis updated to version 0.2.0.1 with previous version 0.2.0 dated 2019-10-02

Title: Calculates the Test-Statistic for the Drift Burst Hypothesis
Description: Calculates the T-Statistic for the drift burst hypothesis from the working paper Christensen, Oomen and Reno (2018) <DOI:10.2139/ssrn.2842535>. The authors' MATLAB code is available upon request, see: <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2842535>.
Author: Emil Sjoerup
Maintainer: Emil Sjoerup <emilsjoerup@live.dk>

Diff between DriftBurstHypothesis versions 0.2.0 dated 2019-10-02 and 0.2.0.1 dated 2019-10-06

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New package WR with initial version 0.1.0
Package: WR
Type: Package
Title: Win Ratio Analysis
Version: 0.1.0
Author: Lu Mao and Tuo Wang
Maintainer: Lu Mao <lmao@biostat.wisc.edu>
Description: Contains win-ratio analysis routines for prioritized composite time-to-event outcomes, e.g., death and non-fatal events. These routines include functions to fit the proportional win-fractions (PW) model and to compute and plot the standardized score process to assess the proportionality assumption.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Depends: R (>= 2.10), survival
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-09-21 01:22:23 UTC; tuowang
Repository: CRAN
Date/Publication: 2019-10-06 13:00:05 UTC

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New package rscontract with initial version 0.1.0
Package: rscontract
Title: Generic implementation of the 'RStudio' connections contract
Version: 0.1.0
Authors@R: person("Edgar", "Ruiz", email = "edgar@rstudio.com", role = c("aut", "cre"))
Description: Provides a generic implementation of the 'RStudio' connection contract to make it easier for database connections, and other type of connections, opened via R packages integrate with the connections pane inside the 'RStudio' interactive development environment (IDE).
License: GPL-3
Suggests: testthat (>= 2.1.0), covr
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
URL: https://github.com/edgararuiz/rscontract
BugReports: https://github.com/edgararuiz/rscontract/issues
Language: en-US
NeedsCompilation: no
Packaged: 2019-10-02 12:59:19 UTC; edgar
Author: Edgar Ruiz [aut, cre]
Maintainer: Edgar Ruiz <edgar@rstudio.com>
Repository: CRAN
Date/Publication: 2019-10-06 12:40:03 UTC

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New package PCMBaseCpp with initial version 0.1.5
Package: PCMBaseCpp
Type: Package
Title: Fast Likelihood Calculation for Phylogenetic Comparative Models
Version: 0.1.5
Authors@R: c(person("Venelin", "Mitov", email = "vmitov@gmail.com", role = c("aut", "cre", "cph")))
Maintainer: Venelin Mitov <vmitov@gmail.com>
Description: Provides a C++ backend for multivariate phylogenetic comparative models implemented in the R-package 'PCMBase'. Can be used in combination with 'PCMBase' to enable fast and parallel likelihood calculation. Implements the pruning likelihood calculation algorithm described in Mitov et al. (2018) <arXiv:1809.09014>. Uses the 'SPLITT' C++ library for parallel tree traversal described in Mitov and Stadler (2018) <doi:10.1111/2041-210X.13136>.
Encoding: UTF-8
License: GPL (>= 3.0)
LazyData: true
Depends: R (>= 3.1.0), Rcpp, methods
Imports: PCMBase, data.table, abind
Suggests: testthat, knitr, rmarkdown, covr
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 6.1.1
ByteCompile: yes
NeedsCompilation: yes
URL: https://github.com/venelin/PCMBaseCpp, https://venelin.github.io/PCMBase/
BugReports: https://github.com/venelin/PCMBaseCpp/issues
Repository: CRAN
Packaged: 2019-10-02 09:42:59 UTC; vmitov
Author: Venelin Mitov [aut, cre, cph]
Date/Publication: 2019-10-06 12:30:02 UTC

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New package ohenery with initial version 0.1.0
Package: ohenery
Maintainer: Steven E. Pav <shabbychef@gmail.com>
Authors@R: c(person(c("Steven", "E."), "Pav", role=c("aut","cre"), email="shabbychef@gmail.com", comment = c(ORCID = "0000-0002-4197-6195")))
Version: 0.1.0
Date: 2019-10-01
License: LGPL-3
Title: Modeling of Ordinal Random Variables via Softmax Regression
BugReports: https://github.com/shabbychef/ohenery/issues
Description: Supports the modeling of ordinal random variables, like the outcomes of races, via Softmax regression, under the Harville <doi:10.1080/01621459.1973.10482425> and Henery <doi:10.1111/j.2517-6161.1981.tb01153.x> models.
Depends: R (>= 3.0.2)
Imports: Rcpp (>= 0.12.3), maxLik, magrittr, methods, dplyr
LinkingTo: Rcpp
Suggests: rlang, tidyr, forcats, microbenchmark, testthat, numDeriv, ggplot2, scales, knitr
Encoding: UTF-8
URL: https://github.com/shabbychef/ohenery
Collate: 'data.r' 'harsm.r' 'hensm.r' 'linodds.r' 'ohenery.r' 'RcppExports.R' 'rsm.r' 'utils.r'
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-10-02 04:43:35 UTC; spav
Author: Steven E. Pav [aut, cre] (<https://orcid.org/0000-0002-4197-6195>)
Repository: CRAN
Date/Publication: 2019-10-06 12:10:02 UTC

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New package NMVANOVA with initial version 1.1.0
Package: NMVANOVA
Type: Package
Title: Novice Model Variation ANOVA
Version: 1.1.0
Author: Joseph V. Lipoff, Will Pauls, Kaylin C. Dobbs, Jordan L. Jensen, Kevin Woods, Evan T. Johnson, Benjamin F. Timson, Scott D. Zimmerman,and Paul Plummer
Maintainer: Joseph Lipoff <josephlipoff@msn.com>
Description: Due to 'Rstudio's' status as open source software, we believe it will be utilized frequently for future data analysis by users whom lack formal training or experience with 'R'. The 'NMVANOVA' (Novice Model Variation ANOVA) a streamlined variation of experimental design functions that allows novice 'Rstudio' users to perform different model variations one-way analysis of variance without downloading multiple libraries or packages. Users can easily manipulate the data block, and needed inputs so that users only have to plugin the four designed variables/values.
License: GPL-2 | GPL-3
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2019-10-01 14:59:38 UTC; wrpauls21
Repository: CRAN
Date/Publication: 2019-10-06 12:10:06 UTC

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New package MPBoost with initial version 0.1-3
Package: MPBoost
Type: Package
Title: Treatment Allocation in Clinical Trials by the Maximal Procedure
Version: 0.1-3
Date: 2019-10-02
Author: Ignacio López-de-Ullibarri [aut, cre]
Maintainer: Ignacio López-de-Ullibarri <ignacio.lopezdeullibarri@udc.es>
Description: Performs treatment allocation in two-arm clinical trials by the maximal procedure described by Berger et al. (2003) <doi:10.1002/sim.1538>. To that end, the algorithm provided by Salama et al. (2008) <doi:10.1002/sim.3014> is implemented.
License: GPL (>= 2)
Imports: Rcpp (>= 1.0.1)
LinkingTo: Rcpp, BH
Depends: R (>= 3.6.0)
Suggests: knitr, pinp, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Encoding: UTF-8
Packaged: 2019-10-02 10:54:22 UTC; ilu
Repository: CRAN
Date/Publication: 2019-10-06 12:50:08 UTC

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Package HelpersMG updated to version 3.8 with previous version 3.7 dated 2019-08-29

Title: Tools for Environmental Analyses, Ecotoxicology and Various R Functions
Description: Contains many functions useful for managing 'NetCDF' files (see <http://en.wikipedia.org/wiki/NetCDF>), get tide levels on any point of the globe, get moon phase and time for sun rise and fall, analyse and reconstruct periodic time series of temperature with irregular sinusoidal pattern, show scales and wind rose in plot with change of color of text, Metropolis-Hastings algorithm for Bayesian MCMC analysis, plot graphs or boxplot with error bars, search files in disk by there names or their content, read the contents of all files from a folder at one time.
Author: Marc Girondot
Maintainer: Marc Girondot <marc.girondot@u-psud.fr>

Diff between HelpersMG versions 3.7 dated 2019-08-29 and 3.8 dated 2019-10-06

 HelpersMG-3.7/HelpersMG/R/as.quantile.R              |only
 HelpersMG-3.7/HelpersMG/man/as.quantile.Rd           |only
 HelpersMG-3.8/HelpersMG/DESCRIPTION                  |    8 +-
 HelpersMG-3.8/HelpersMG/MD5                          |   52 +++++++++----------
 HelpersMG-3.8/HelpersMG/NAMESPACE                    |    2 
 HelpersMG-3.8/HelpersMG/NEWS                         |   10 +++
 HelpersMG-3.8/HelpersMG/R/HelpersMG-package.R        |    4 -
 HelpersMG-3.8/HelpersMG/R/MHalgoGen.R                |    7 ++
 HelpersMG-3.8/HelpersMG/R/as.quantiles.R             |only
 HelpersMG-3.8/HelpersMG/R/dSnbinom.R                 |   32 +++++------
 HelpersMG-3.8/HelpersMG/R/modifyVector.R             |   10 ++-
 HelpersMG-3.8/HelpersMG/R/pSnbinom.R                 |    6 +-
 HelpersMG-3.8/HelpersMG/R/plot.mcmcComposite.R       |   18 +++---
 HelpersMG-3.8/HelpersMG/R/qSnbinom.R                 |    8 +-
 HelpersMG-3.8/HelpersMG/R/rSnbinom.R                 |    2 
 HelpersMG-3.8/HelpersMG/R/summary.mcmcComposite.R    |   11 +++-
 HelpersMG-3.8/HelpersMG/man/HelpersMG-package.Rd     |    4 -
 HelpersMG-3.8/HelpersMG/man/MHalgoGen.Rd             |    2 
 HelpersMG-3.8/HelpersMG/man/as.mcmc.mcmcComposite.Rd |    2 
 HelpersMG-3.8/HelpersMG/man/as.parameters.Rd         |    2 
 HelpersMG-3.8/HelpersMG/man/as.quantiles.Rd          |only
 HelpersMG-3.8/HelpersMG/man/dSnbinom.Rd              |   26 +++------
 HelpersMG-3.8/HelpersMG/man/merge.mcmcComposite.Rd   |    2 
 HelpersMG-3.8/HelpersMG/man/modifyVector.Rd          |    5 +
 HelpersMG-3.8/HelpersMG/man/pSnbinom.Rd              |    4 -
 HelpersMG-3.8/HelpersMG/man/plot.mcmcComposite.Rd    |    2 
 HelpersMG-3.8/HelpersMG/man/qSnbinom.Rd              |    4 -
 HelpersMG-3.8/HelpersMG/man/rSnbinom.Rd              |    2 
 HelpersMG-3.8/HelpersMG/man/summary.mcmcComposite.Rd |    2 
 29 files changed, 128 insertions(+), 99 deletions(-)

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New package glmtree with initial version 0.1
Package: glmtree
Type: Package
Title: Logistic Regression Trees
Version: 0.1
Date: 2019-09-26
Authors@R: c(person("Adrien", "Ehrhardt", email = "adrien.ehrhardt@centraliens-lille.org", role = c("aut", "cre")))
Maintainer: Adrien Ehrhardt <adrien.ehrhardt@centraliens-lille.org>
Description: A logistic regression tree is a decision tree with logistic regressions at its leaves. A particular stochastic expectation maximization algorithm is used to draw a few good trees, that are then assessed via the user's criterion of choice among BIC / AIC / test set Gini. The formal development is given in a PhD chapter, see Ehrhardt (2019) <https://github.com/adimajo/manuscrit_these/releases/>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Imports: partykit, magrittr, methods, dplyr, caret
Suggests: FactoMineR, knitr, testthat, covr, rmarkdown
URL: https://adimajo.github.io
BugReports: https://github.com/adimajo/glmtree/issues
VignetteBuilder: knitr
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-10-02 15:53:37 UTC; adrien
Author: Adrien Ehrhardt [aut, cre]
Repository: CRAN
Date/Publication: 2019-10-06 12:50:02 UTC

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New package eqn2svg with initial version 0.1.0
Package: eqn2svg
Title: Create an SVG-Based Mathematical Formula
Version: 0.1.0
Authors@R: person("Richard", "Iannone", , "riannone@me.com", c("aut", "cre"), comment = c(ORCID = "0000-0003-3925-190X"))
Description: If you have a mathematical formula and the need to have that formula in the form of scalable vector graphics (SVG), you'll be delighted by what 'eqn2svg' will let you do. The incoming LaTeX math formula will be nicely converted to SVG tags. And you can use that code wherever SVGs are accepted.
License: MIT + file LICENSE
URL: https://github.com/rich-iannone/eqn2svg
BugReports: https://github.com/rich-iannone/eqn2svg/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: R (>= 3.2.0)
Imports: htmltools (>= 0.3.6), magrittr
Suggests: testthat (>= 2.1.0)
NeedsCompilation: no
Packaged: 2019-10-02 15:43:42 UTC; rich
Author: Richard Iannone [aut, cre] (<https://orcid.org/0000-0003-3925-190X>)
Maintainer: Richard Iannone <riannone@me.com>
Repository: CRAN
Date/Publication: 2019-10-06 12:50:05 UTC

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New package donut with initial version 1.0.0
Package: donut
Title: Nearest Neighbour Search with Variables on a Torus
Version: 1.0.0
Date: 2019-10-03
Authors@R: person(c("Paul", "J."), "Northrop", email = "p.northrop@ucl.ac.uk", role = c("aut", "cre", "cph"))
Description: Finds the k nearest neighbours in a dataset of specified points, adding the option to wrap certain variables on a torus. The user chooses the algorithm to use to find the nearest neighbours. Three such algorithms, provided by the packages 'RANN' <https://cran.r-project.org/package=RANN>, 'RANN.L1' <https://cran.r-project.org/package=RANN.L1>, and 'nabor' <https://cran.r-project.org/package=nabor>, are suggested.
Imports: graphics, methods
License: GPL (>= 2)
LazyData: TRUE
Encoding: UTF-8
Depends: R (>= 3.3.0)
RoxygenNote: 6.1.1
Suggests: knitr, nabor, RANN, RANN.L1, rmarkdown, testthat (>= 2.1.0)
VignetteBuilder: knitr
URL: http://github.com/paulnorthrop/donut
BugReports: http://github.com/paulnorthrop/donut/issues
NeedsCompilation: no
Packaged: 2019-10-02 23:20:27 UTC; paul
Author: Paul J. Northrop [aut, cre, cph]
Maintainer: Paul J. Northrop <p.northrop@ucl.ac.uk>
Repository: CRAN
Date/Publication: 2019-10-06 13:00:02 UTC

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New package diyar with initial version 0.0.1
Package: diyar
Type: Package
Title: Multistage Record Linkage and Case Definition for Epidemiological Analysis
Date: 2019-10-01
Version: 0.0.1
URL: https://github.com/OlisaNsonwu/diyar
BugReports: https://github.com/OlisaNsonwu/diyar/issues
Author: Olisa Nsonwu
Maintainer: Olisa Nsonwu <olisa.nsonwu@gmail.com>
Description: Perform multistage deterministic linkages, apply case definitions to datasets, and deduplicate records. Records (rows) from datasets are linked by different matching criteria and sub-criteria (columns) in a specified order of certainty. The linkage process handles missing data and conflicting matches based on this same order of certainty. For episode grouping, rows of dated events (e.g. sample collection) or interval of events (e.g. hospital admission) are grouped into chronological episodes beginning with a "case". The process permits several options such as episode lengths and recurrence periods which are used to build custom preferences for case assignment (definition). The record linkage and episode grouping processes assign unique group IDs to matching records or those grouped into episodes. This then allows for record deduplication or sub-analysis within these groups.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: methods, utils, rlang, dplyr (>= 0.7.5), lubridate (>= 1.7.4), stringr, tidyr (>= 0.8.2)
RoxygenNote: 6.1.1
Suggests: ggplot2, janitor, knitr, rmarkdown, testthat, covr
VignetteBuilder: knitr
Language: en-GB
NeedsCompilation: no
Packaged: 2019-10-02 06:14:35 UTC; St. Loki
Repository: CRAN
Date/Publication: 2019-10-06 12:20:02 UTC

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New package aliases2entrez with initial version 0.0.6
Package: aliases2entrez
Title: Converts Human gene symbols to entrez IDs
Version: 0.0.6
Authors@R: c(person(given = "Raphael", family = "Bonnet", role = c("aut", "cre"), email = "raphael.bonnet@univ-cotedazur.fr", comment="Université Côte d’Azur"), person(given = "Jean-François", family = "Peyron", role = c("aut"), comment="Inserm") )
Description: Queries multiple resources authors HGNC (2019) <https://www.genenames.org>, authors limma (2015) <doi:10.1093/nar/gkv007> to find the correspondence between evolving nomenclature of human gene symbols, aliases, previous symbols or synonyms with stable, curated gene entrezID from NCBI database. This allows fast, accurate and up-to-date correspondence between human gene expression datasets from various date and platform (e.g: gene symbol: BRCA1 - ID: 672).
BugReports: https://github.com/peyronlab/aliases2entrez/issues
Imports: doParallel, limma, utils, org.Hs.eg.db, AnnotationDbi, parallel, foreach, readr, RCurl
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: spelling
Language: en-US
NeedsCompilation: no
Packaged: 2019-10-02 07:49:39 UTC; raphael
Author: Raphael Bonnet [aut, cre] (Université Côte d’Azur), Jean-François Peyron [aut] (Inserm)
Maintainer: Raphael Bonnet <raphael.bonnet@univ-cotedazur.fr>
Repository: CRAN
Date/Publication: 2019-10-06 12:20:06 UTC

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New package prismatic with initial version 0.1.0
Package: prismatic
Title: Color Manipulation Tools
Version: 0.1.0
Authors@R: person(given = "Emil", family = "Hvitfeldt", role = c("aut", "cre"), email = "emilhhvitfeldt@gmail.com", comment = c(ORCID = "0000-0002-0679-1945"))
Description: Manipulate and visualize colors in a intuitive, low-dependency and functional way.
License: MIT + file LICENSE
URL: https://github.com/EmilHvitfeldt/prismatic
BugReports: https://github.com/EmilHvitfeldt/prismatic/issues
Depends: R (>= 3.2)
Imports: farver (>= 1.1.0)
Suggests: covr, crayon, testthat (>= 2.1.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-10-01 20:43:06 UTC; emilhvitfeldthansen
Author: Emil Hvitfeldt [aut, cre] (<https://orcid.org/0000-0002-0679-1945>)
Maintainer: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Repository: CRAN
Date/Publication: 2019-10-06 11:30:02 UTC

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New package interpret with initial version 0.1.18
Package: interpret
Title: Fit Interpretable Models and Explain Blackbox Machine Learning
Version: 0.1.18
Date: 2019-10-01
Description: Machine Learning package for training interpretable models and explaining blackbox systems. Historically, the most intelligible models were not very accurate, and the most accurate models were not intelligible. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and intelligibility. EBM uses modern machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, <doi:10.1145/2783258.2788613>).
URL: https://github.com/microsoft/interpret
BugReports: https://github.com/microsoft/interpret/issues
License: MIT + file LICENSE
Authors@R: c( person("Samuel", "Jenkins", role = c("aut")), person("Harsha", "Nori", role = c("aut")), person("Paul", "Koch", role = c("aut")), person("Rich", "Caruana", role = c("aut", "cre"), email = "interpretml@outlook.com"), person("Microsoft Corporation", role="cph") )
Depends: R (>= 3.0.0)
NeedsCompilation: yes
SystemRequirements: C++11
Packaged: 2019-10-01 17:28:16 UTC; admins
Author: Samuel Jenkins [aut], Harsha Nori [aut], Paul Koch [aut], Rich Caruana [aut, cre], Microsoft Corporation [cph]
Maintainer: Rich Caruana <interpretml@outlook.com>
Repository: CRAN
Date/Publication: 2019-10-06 11:30:05 UTC

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New package foodingraph with initial version 0.1.0
Package: foodingraph
Type: Package
Title: Food Network Inference and Visualization
Version: 0.1.0
Authors@R: c( person("Victor", "Gasque", , "victor.gasque@protonmail.com", c("cre", "aut")), person("Boris", "Hejblum", , "boris.hejblum@u-bordeaux.fr", "aut"), person("Cecilia", "Samieri", , "cecilia.samieri@u-bordeaux.fr", "aut"))
Maintainer: Victor Gasque <victor.gasque@protonmail.com>
Description: Displays a weighted undirected food graph from an adjacency matrix. Can perform confidence-interval bootstrap inference with mutual information or maximal information coefficient. Based on my Master 1 internship at the Bordeaux Population Health center. References : Reshef et al. (2011) <doi:10.1126/science.1205438>, Meyer et al. (2008) <doi:10.1186/1471-2105-9-461>, Liu et al. (2016) <doi:10.1371/journal.pone.0158247>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: dplyr, ggplot2, cowplot, magrittr, stringr, tibble, tidyr, viridis, igraph, ggraph, minerva, rlang, labeling, grid
Suggests: knitr, infotheo, minet,
VignetteBuilder: knitr
URL: https://github.com/vgasque/foodingraph/
BugReports: https://github.com/vgasque/foodingraph/issues
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-10-01 19:08:18 UTC; Victor
Author: Victor Gasque [cre, aut], Boris Hejblum [aut], Cecilia Samieri [aut]
Repository: CRAN
Date/Publication: 2019-10-06 11:30:08 UTC

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New package mlr3pipelines with initial version 0.1.0
Package: mlr3pipelines
Title: Preprocessing Operators and Pipelines for 'mlr3'
Version: 0.1.0
Authors@R: c(person(given = "Martin", family = "Binder", role = c("aut", "cre"), email = "mlr.developer@mb706.com"), person(given = "Florian", family = "Pfisterer", role = "aut", email = "pfistererf@googlemail.com", comment = c(ORCID = "0000-0001-8867-762X")), person(given = "Bernd", family = "Bischl", role = "aut", email = "bernd_bischl@gmx.net", comment = c(ORCID = "0000-0001-6002-6980")), person(given = "Michel", family = "Lang", role = "aut", email = "michellang@gmail.com", comment = c(ORCID = "0000-0001-9754-0393")), person(given = "Susanne", family = "Dandl", role = "aut", email = "dandl.susanne@googlemail.com"))
Description: Dataflow programming toolkit that enriches 'mlr3' with a diverse set of pipelining operators ('PipeOps') that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can therefore be resampled, benchmarked, and tuned.
License: LGPL-3
URL: https://mlr3pipelines.mlr-org.com, https://github.com/mlr-org/mlr3pipelines
BugReports: https://github.com/mlr-org/mlr3pipelines/issues
Depends: R (>= 3.1.0)
Imports: backports, checkmate, data.table, digest, mlr3 (>= 0.1.3), mlr3misc (>= 0.1.4), paradox, R6, withr
Suggests: ggplot2, glmnet, igraph, knitr, lgr, lme4, mlbench, mlr3filters, mlr3learners, nloptr, rmarkdown, rpart, testthat, visNetwork, bestNormalize, fastICA, kernlab, smotefamily
VignetteBuilder: knitr
ByteCompile: true
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
RoxygenNote: 6.1.1
Collate: 'Graph.R' 'GraphLearner.R' 'mlr_pipeops.R' 'utils.R' 'PipeOp.R' 'PipeOpEnsemble.R' 'LearnerAvg.R' 'NO_OP.R' 'PipeOpTaskPreproc.R' 'PipeOpBoxCox.R' 'PipeOpBranch.R' 'PipeOpChunk.R' 'PipeOpClassBalancing.R' 'PipeOpClassifAvg.R' 'PipeOpColApply.R' 'PipeOpCollapseFactors.R' 'PipeOpCopy.R' 'PipeOpEncode.R' 'PipeOpEncodeLmer.R' 'PipeOpFeatureUnion.R' 'PipeOpFilter.R' 'PipeOpFixFactors.R' 'PipeOpHistBin.R' 'PipeOpICA.R' 'PipeOpImpute.R' 'PipeOpImputeHist.R' 'PipeOpImputeMean.R' 'PipeOpImputeMedian.R' 'PipeOpImputeNewlvl.R' 'PipeOpImputeSample.R' 'PipeOpKernelPCA.R' 'PipeOpLearner.R' 'PipeOpLearnerCV.R' 'PipeOpMissingIndicators.R' 'PipeOpModelMatrix.R' 'PipeOpMutate.R' 'PipeOpNOP.R' 'PipeOpPCA.R' 'PipeOpQuantileBin.R' 'PipeOpRegrAvg.R' 'PipeOpRemoveConstants.R' 'PipeOpScale.R' 'PipeOpScaleMaxAbs.R' 'PipeOpScaleRange.R' 'PipeOpSelect.R' 'PipeOpSmote.R' 'PipeOpSpatialSign.R' 'PipeOpSubsample.R' 'PipeOpUnbranch.R' 'PipeOpYeoJohnson.R' 'Selector.R' 'assert_graph.R' 'greplicate.R' 'gunion.R' 'operators.R' 'po.R' 'reexports.R' 'typecheck.R' 'zzz.R'
Packaged: 2019-10-01 16:14:45 UTC; user
Author: Martin Binder [aut, cre], Florian Pfisterer [aut] (<https://orcid.org/0000-0001-8867-762X>), Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Susanne Dandl [aut]
Maintainer: Martin Binder <mlr.developer@mb706.com>
Repository: CRAN
Date/Publication: 2019-10-06 10:40:02 UTC

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New package HTLR with initial version 0.4
Package: HTLR
Version: 0.4
Title: Bayesian Logistic Regression with Heavy-Tailed Priors
Authors@R: c(person(given = "Longhai", family = "Li", role = c("aut", "cre"), email = "longhai@math.usask.ca", comment=c(ORCID="0000-0002-3074-8584")), person(given = "Steven", family = "Liu", role = c("aut"), email = "xil865@usask.ca"))
Description: Efficient Bayesian multinomial logistic regression based on heavy-tailed (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed description of the method: Li and Yao (2018), JSCS, 88:14, 2827-2851, <arXiv:1405.3319>.
License: GPL-2
URL: https://github.com/longhaiSK/HTLR
BugReports: https://github.com/longhaiSK/HTLR/issues
Depends: R (>= 3.1.0)
Suggests: rda, ggplot2, corrplot, testthat (>= 2.0.0), bayesplot, knitr, rmarkdown
Imports: Rcpp (>= 0.12.0), BCBCSF, glmnet, magrittr
LinkingTo: Rcpp (>= 0.12.0), RcppArmadillo
NeedsCompilation: yes
SystemRequirements: C++11
LazyData: true
Encoding: UTF-8
RoxygenNote: 6.1.1
VignetteBuilder: knitr
Packaged: 2019-09-30 22:45:06 UTC; xil
Author: Longhai Li [aut, cre] (<https://orcid.org/0000-0002-3074-8584>), Steven Liu [aut]
Maintainer: Longhai Li <longhai@math.usask.ca>
Repository: CRAN
Date/Publication: 2019-10-06 10:10:03 UTC

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New package BHAI with initial version 0.99.2
Package: BHAI
Title: Estimate the Burden of Healthcare-Associated Infections
Version: 0.99.2
Authors@R: person("Benedikt", "Zacher", email = "ZacherB@rki.de", role = c("aut", "cre"))
Author: Benedikt Zacher [aut, cre]
Maintainer: Benedikt Zacher <ZacherB@rki.de>
Description: Provides an approach which is based on the methodology of the Burden of Communicable Diseases in Europe (BCoDE) and can be used for large and small samples such as individual countries. The Burden of Healthcare-Associated Infections (BHAI) is estimated in disability-adjusted life years, number of infections as well as number of deaths per year. Results can be visualized with various plotting functions and exported into tables.
Depends: R (>= 3.6.0)
License: GPL-3
LazyData: true
RoxygenNote: 6.1.1
Imports: prevtoinc, MCMCpack, plotrix, graphics, grDevices, stats, methods
NeedsCompilation: no
Packaged: 2019-10-01 13:58:13 UTC; zacherb
Repository: CRAN
Date/Publication: 2019-10-06 10:20:02 UTC

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Package gamlss updated to version 5.1-5 with previous version 5.1-4 dated 2019-05-18

Title: Generalised Additive Models for Location Scale and Shape
Description: Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.
Author: Mikis Stasinopoulos [aut, cre, cph], Bob Rigby [aut], Vlasios Voudouris [ctb], Calliope Akantziliotou [ctb], Marco Enea [ctb], Daniil Kiose [ctb]
Maintainer: Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>

Diff between gamlss versions 5.1-4 dated 2019-05-18 and 5.1-5 dated 2019-10-06

 gamlss-5.1-4/gamlss/R/centilesPLOT.R          |only
 gamlss-5.1-4/gamlss/R/random.R                |only
 gamlss-5.1-4/gamlss/R/re_gamlss_lme.R         |only
 gamlss-5.1-4/gamlss/README.md                 |only
 gamlss-5.1-5/gamlss/DESCRIPTION               |    8 -
 gamlss-5.1-5/gamlss/MD5                       |  179 ++++++++++++--------------
 gamlss-5.1-5/gamlss/R/centilesFan.R           |   22 ++-
 gamlss-5.1-5/gamlss/R/centilesPlot.R          |only
 gamlss-5.1-5/gamlss/R/centilescom.R           |   21 ++-
 gamlss-5.1-5/gamlss/R/cubicSplines-10-08-12.R |    3 
 gamlss-5.1-5/gamlss/R/gamlss.R                |   10 -
 gamlss-5.1-5/gamlss/R/gamlssVGD.R             |    2 
 gamlss-5.1-5/gamlss/R/lms.R                   |   20 ++
 gamlss-5.1-5/gamlss/R/pb.R                    |    2 
 gamlss-5.1-5/gamlss/R/pb_goingtozero.R        |    2 
 gamlss-5.1-5/gamlss/R/pbc.R                   |   10 -
 gamlss-5.1-5/gamlss/R/pbm.R                   |    2 
 gamlss-5.1-5/gamlss/R/random.r                |only
 gamlss-5.1-5/gamlss/R/re_gamlss_lme_test.R    |only
 gamlss-5.1-5/gamlss/R/ri.R                    |    7 -
 gamlss-5.1-5/gamlss/R/wp.R                    |    2 
 gamlss-5.1-5/gamlss/build/partial.rdb         |binary
 gamlss-5.1-5/gamlss/inst/doc/NEWS.txt         |   46 +++++-
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Package wrapr updated to version 1.9.1 with previous version 1.9.0 dated 2019-09-28

Title: Wrap R Tools for Debugging and Parametric Programming
Description: Tools for writing and debugging R code. Provides: '%.>%' dot-pipe (an 'S3' configurable pipe), 'let()' (converts non-standard evaluation interfaces to parametric standard evaluation interfaces, inspired by 'gtools:strmacro()' and 'base::bquote()'), 'build_frame()'/'draw_frame()' ('data.frame' example tools), 'qc()' (quoting concatenate), ':=' (named map builder), and more.
Author: John Mount [aut, cre], Nina Zumel [aut], Win-Vector LLC [cph]
Maintainer: John Mount <jmount@win-vector.com>

Diff between wrapr versions 1.9.0 dated 2019-09-28 and 1.9.1 dated 2019-10-06

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Package tidyUSDA updated to version 0.2.1 with previous version 0.2.0 dated 2019-09-29

Title: A Minimal Tool Set for Gathering USDA Quick Stat Data for Analysis and Visualization
Description: Provides a consistent API to pull United States Department of Agriculture census and survey data from the National Agricultural Statistics Service (NASS) QuickStats service <https://quickstats.nass.usda.gov>.
Author: Brad Lindblad [aut, cre], Michael Thomas [ctb], Alex Mindeman [ctb]
Maintainer: Brad Lindblad <bradley.lindblad@gmail.com>

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Package moveVis updated to version 0.10.3 with previous version 0.10.2 dated 2019-04-30

Title: Movement Data Visualization
Description: Tools to visualize movement data (e.g. from GPS tracking) and temporal changes of environmental data (e.g. from remote sensing) by creating video animations.
Author: Jakob Schwalb-Willmann [aut, cre]
Maintainer: Jakob Schwalb-Willmann <movevis@schwalb-willmann.de>

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Package ggnewscale updated to version 0.4.0 with previous version 0.3.0 dated 2019-07-05

Title: Multiple Fill and Color Scales in 'ggplot2'
Description: Use multiple fill and color scales in 'ggplot2'.
Author: Elio Campitelli [cre, aut] (<https://orcid.org/0000-0002-7742-9230>)
Maintainer: Elio Campitelli <elio.campitelli@cima.fcen.uba.ar>

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Package BayesPostEst updated to version 0.1.0 with previous version 0.0.1 dated 2019-08-05

Title: Generate Postestimation Quantities for Bayesian MCMC Estimation
Description: An implementation of functions to generate and plot postestimation quantities after estimating Bayesian regression models using Markov chain Monte Carlo (MCMC). Functionality includes the estimation of the Precision-Recall curves (see Beger, 2016 <doi:10.2139/ssrn.2765419>), the implementation of the observed values method of calculating predicted probabilities by Hanmer and Kalkan (2013) <doi:10.1111/j.1540-5907.2012.00602.x>, the implementation of the average value method of calculating predicted probabilities (see King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>), and the generation and plotting of first differences to summarize typical effects across covariates (see Long 1997, ISBN:9780803973749; King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>). This package can be used with MCMC output generated by any Bayesian estimation tool including 'JAGS', 'BUGS', 'MCMCpack', and 'Stan'.
Author: Johannes Karreth [aut] (<https://orcid.org/0000-0003-4586-7153>), Shana Scogin [aut, cre] (<https://orcid.org/0000-0002-7801-853X>), Rob Williams [aut] (<https://orcid.org/0000-0001-9259-3883>), Andreas Beger [aut] (<https://orcid.org/0000-0003-1883-3169>), Myunghee Lee [ctb], Neil Williams [ctb]
Maintainer: Shana Scogin <shanarscogin@gmail.com>

Diff between BayesPostEst versions 0.0.1 dated 2019-08-05 and 0.1.0 dated 2019-10-06

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