Tue, 19 Jan 2021

Package shotGroups updated to version 0.8 with previous version 0.7.5.2 dated 2020-11-27

Title: Analyze Shot Group Data
Description: Analyzes shooting data with respect to group shape, precision, and accuracy. This includes graphical methods, descriptive statistics, and inference tests using standard, but also non-parametric and robust statistical methods. Implements distributions for radial error in bivariate normal variables. Works with files exported by 'OnTarget PC/TDS', 'Silver Mountain' e-target, 'ShotMarker' e-target, or 'Taran', as well as with custom data files in text format. Supports inference from range statistics like extreme spread. Includes a set of web-based graphical user interfaces.
Author: Daniel Wollschlaeger
Maintainer: Daniel Wollschlaeger <dwoll@kuci.org>

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Package ggvoronoi updated to version 0.8.4 with previous version 0.8.3 dated 2019-02-19

Title: Voronoi Diagrams and Heatmaps with 'ggplot2'
Description: Easy creation and manipulation of Voronoi diagrams using 'deldir' with visualization in 'ggplot2'.
Author: Robert C. Garrett [aut, cre], Austin Nar [aut], Thomas J. Fisher [aut], Karsten Maurer [ctb]
Maintainer: Robert C. Garrett <garretrc@miamioh.edu>

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Package autostsm updated to version 1.1 with previous version 1.0 dated 2021-01-15

Title: Automatic Structural Time Series Models
Description: Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components using the Kalman filter. See Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models <doi:10.1093/oxfordhb/9780195398649.013.0006>.
Author: Alex Hubbard
Maintainer: Alex Hubbard <hubbard.alex@gmail.com>

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Package lazytrade updated to version 0.4.4 with previous version 0.4.3 dated 2020-12-16

Title: Learn Computer and Data Science using Algorithmic Trading
Description: Provide sets of functions and methods to learn and practice data science using idea of algorithmic trading. Main goal is to process information within "Decision Support System" to come up with analysis or predictions. There are several utilities such as dynamic and adaptive risk management using reinforcement learning and even functions to generate predictions of price changes using pattern recognition deep regression learning. Summary of Methods used: Awesome H2O tutorials: <https://github.com/h2oai/awesome-h2o>, Market Type research of Van Tharp Institute: <https://www.vantharp.com/>, Reinforcement Learning R package: <https://CRAN.R-project.org/package=ReinforcementLearning>.
Author: Vladimir Zhbanko
Maintainer: Vladimir Zhbanko <vladimir.zhbanko@gmail.com>

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Package parsnip updated to version 0.1.5 with previous version 0.1.4 dated 2020-10-27

Title: A Common API to Modeling and Analysis Functions
Description: A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', etc).
Author: Max Kuhn [aut, cre], Davis Vaughan [aut], RStudio [cph]
Maintainer: Max Kuhn <max@rstudio.com>

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Package sendplot (with last version 4.0.0) was removed from CRAN

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

2013-04-25 4.0.0
2013-03-01 3.8.10.1
2012-04-06 3.8.10
2012-03-15 3.8.9
2012-01-13 3.8.8
2011-02-08 3.8.7
2010-03-15 3.8.6
2009-08-04 3.8.0
2009-04-04 3.7.0
2009-03-05 3.0.2
2009-03-03 3.0.0
2009-01-26 2.1.2
2008-03-26 1.1.0
2008-02-14 1.0.0
2008-01-03 0.2.1
2007-11-21 0.2.0
2007-11-09 0.1.4
2007-11-05 0.1.3

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Package tmaptools updated to version 3.1-1 with previous version 3.1 dated 2020-07-01

Title: Thematic Map Tools
Description: Set of tools for reading and processing spatial data. The aim is to supply the workflow to create thematic maps. This package also facilitates 'tmap', the package for visualizing thematic maps.
Author: Martijn Tennekes [aut, cre]
Maintainer: Martijn Tennekes <mtennekes@gmail.com>

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Package bcmaps updated to version 1.0.1 with previous version 1.0.0 dated 2021-01-10

Title: Map Layers and Spatial Utilities for British Columbia
Description: Provides access to various spatial layers for B.C., such as administrative boundaries, natural resource management boundaries, etc. Most layers are imported from the 'bcdata' package as 'sf' or 'Spatial' objects through function calls in this package.
Author: Andy Teucher [aut, cre], Stephanie Hazlitt [aut], Sam Albers [aut], Province of British Columbia [cph]
Maintainer: Andy Teucher <andy.teucher@gov.bc.ca>

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Package MachineShop updated to version 2.6.0 with previous version 2.5.0 dated 2020-08-05

Title: Machine Learning Models and Tools
Description: Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.
Author: Brian J Smith [aut, cre]
Maintainer: Brian J Smith <brian-j-smith@uiowa.edu>

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Permanent link

Package tmap updated to version 3.3 with previous version 3.2 dated 2020-09-15

Title: Thematic Maps
Description: Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.
Author: Martijn Tennekes [aut, cre], Jakub Nowosad [ctb], Joel Gombin [ctb], Sebastian Jeworutzki [ctb], Kent Russell [ctb], Richard Zijdeman [ctb], John Clouse [ctb], Robin Lovelace [ctb], Jannes Muenchow [ctb]
Maintainer: Martijn Tennekes <mtennekes@gmail.com>

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Package R.devices updated to version 2.17.0 with previous version 2.16.1 dated 2019-10-24

Title: Unified Handling of Graphics Devices
Description: Functions for creating plots and image files in a unified way regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.). Default device options as well as scales and aspect ratios are controlled in a uniform way across all device types. Switching output format requires minimal changes in code. This package is ideal for large-scale batch processing, because it will never leave open graphics devices or incomplete image files behind, even on errors or user interrupts.
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

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Package LNIRT updated to version 0.5.1 with previous version 0.5.0 dated 2020-11-16

Title: LogNormal Response Time Item Response Theory Models
Description: Allows the simultaneous analysis of responses and response times in an Item Response Theory (IRT) modelling framework. Supports variable person speed functions (intercept, trend, quadratic), and covariates for item and person (random) parameters. Data missing-by-design can be specified. Parameter estimation is done with a MCMC algorithm. LNIRT replaces the package CIRT, which was written by Rinke Klein Entink. For reference, see the paper by Fox, Klein Entink and Van der Linden (2007), "Modeling of Responses and Response Times with the Package cirt", Journal of Statistical Software, <doi:10.18637/jss.v020.i07>.
Author: Jean-Paul Fox, Konrad Klotzke, Rinke Klein Entink
Maintainer: Konrad Klotzke <omd.bms.utwente.stats@gmail.com>

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Package blogdown updated to version 1.1 with previous version 1.0 dated 2021-01-09

Title: Create Blogs and Websites with R Markdown
Description: Write blog posts and web pages in R Markdown. This package supports the static site generator 'Hugo' (<https://gohugo.io>) best, and it also supports 'Jekyll' (<https://jekyllrb.com>) and 'Hexo' (<https://hexo.io>).
Author: Yihui Xie [aut, cre] (<https://orcid.org/0000-0003-0645-5666>), Christophe Dervieux [aut] (<https://orcid.org/0000-0003-4474-2498>), Alison Presmanes Hill [aut] (<https://orcid.org/0000-0002-8082-1890>), Amber Thomas [ctb], Beilei Bian [ctb], Brandon Greenwell [ctb], Brian Barkley [ctb], Deependra Dhakal [ctb], Eric Nantz [ctb], Forest Fang [ctb], Garrick Aden-Buie [ctb], Hiroaki Yutani [ctb], Ian Lyttle [ctb], Jake Barlow [ctb], James Balamuta [ctb], JJ Allaire [ctb], Jon Calder [ctb], Jozef Hajnala [ctb], Juan Manuel Vazquez [ctb], Kevin Ushey [ctb], Leonardo Collado-Torres [ctb], Maëlle Salmon [ctb], Maria Paula Caldas [ctb], Nicolas Roelandt [ctb], Oliver Madsen [ctb], Raniere Silva [ctb], TC Zhang [ctb], Xianying Tan [ctb], RStudio, PBC [cph]
Maintainer: Yihui Xie <xie@yihui.name>

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Package tidyBF updated to version 0.4.3 with previous version 0.4.2 dated 2020-12-08

Title: Tidy Wrapper for 'BayesFactor' Package
Description: Provides helper functions that make it easy to run 'BayesFactor' package tests on a data which is in a tidy format. Additionally, it provides a more consistent syntax and by default returns a dataframe with rich details. These functions can also return expressions containing results from Bayes Factor tests that can then be displayed on custom plots. Posterior estimation is carried out using the 'bayestestR' package.
Author: Indrajeet Patil [aut, cre, cph] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets)
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

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Package mongolite updated to version 2.3.0 with previous version 2.2.1 dated 2020-11-21

Title: Fast and Simple 'MongoDB' Client for R
Description: High-performance MongoDB client based on 'mongo-c-driver' and 'jsonlite'. Includes support for aggregation, indexing, map-reduce, streaming, encryption, enterprise authentication, and GridFS. The online user manual provides an overview of the available methods in the package: <https://jeroen.github.io/mongolite/>.
Author: Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>), MongoDB, Inc [cph] (Bundled mongo-c-driver, see AUTHORS file)
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

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Package ggstatsplot updated to version 0.6.8 with previous version 0.6.6 dated 2020-12-03

Title: 'ggplot2' Based Plots with Statistical Details
Description: Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier API to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses.
Author: Indrajeet Patil [cre, aut, cph] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets)
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

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Package BOIN updated to version 2.7.2 with previous version 2.7.1 dated 2020-11-19

Title: Bayesian Optimal INterval (BOIN) Design for Single-Agent and Drug- Combination Phase I Clinical Trials
Description: The Bayesian optimal interval (BOIN) design is a novel phase I clinical trial design for finding the maximum tolerated dose (MTD). It can be used to design both single-agent and drug-combination trials. The BOIN design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. The prominent advantage of the BOIN design is that it achieves simplicity and superior performance at the same time. The BOIN design is algorithm-based and can be implemented in a simple way similar to the traditional 3+3 design. The BOIN design yields an average performance that is comparable to that of the continual reassessment method (CRM, one of the best model-based designs) in terms of selecting the MTD, but has a substantially lower risk of assigning patients to subtherapeutic or overly toxic doses. For tutorial, please check Yan et al. (2020) <doi:10.18637/jss.v094.i13>.
Author: Ying Yuan and Suyu Liu
Maintainer: Ying Yuan <yyuan@mdanderson.org>

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Package VIM updated to version 6.1.0 with previous version 6.0.0 dated 2020-05-08

Title: Visualization and Imputation of Missing Values
Description: New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.
Author: Matthias Templ [aut, cre], Alexander Kowarik [aut] (<https://orcid.org/0000-0001-8598-4130>), Andreas Alfons [aut], Gregor de Cillia [aut], Bernd Prantner [ctb], Wolfgang Rannetbauer [aut]
Maintainer: Matthias Templ <matthias.templ@gmail.com>

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Package UCSCXenaTools updated to version 1.4.0 with previous version 1.3.6 dated 2020-11-23

Title: Download and Explore Datasets from UCSC Xena Data Hubs
Description: Download and explore datasets from UCSC Xena data hubs, which are a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. Databases are normalized so they can be combined, linked, filtered, explored and downloaded.
Author: Shixiang Wang [aut, cre] (<https://orcid.org/0000-0001-9855-7357>), Xue-Song Liu [aut] (<https://orcid.org/0000-0002-7736-0077>), Martin Morgan [ctb], Christine Stawitz [rev] (Christine reviewed the package for ropensci, see <https://github.com/ropensci/software-review/issues/315>), Carl Ganz [rev] (Carl reviewed the package for ropensci, see <https://github.com/ropensci/software-review/issues/315>)
Maintainer: Shixiang Wang <w_shixiang@163.com>

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Package statsExpressions updated to version 0.7.0 with previous version 0.6.2 dated 2020-12-08

Title: Expressions and Dataframes with Statistical Details
Description: Statistical processing backend for 'ggstatsplot', this package creates expressions with details from statistical tests. It can additionally return dataframes with these results, which also make these functions a more pipe-friendly way to do statistical analysis. Currently, it supports only the most common types of statistical tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, and meta-analysis.
Author: Indrajeet Patil [cre, aut, cph] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets)
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

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New package ftrCOOL with initial version 1.1.2
Package: ftrCOOL
Type: Package
Title: Feature Extraction from Biological Sequences
Version: 1.1.2
Author: Sare Amerifar
Maintainer: Sare Amerifar <sare.ameri.01@gmail.com>
Description: Extracts features from biological sequences. It contains most features which are presented in related work and also includes features which have never been introduced before. It extracts numerous features from nucleotide and peptide sequences. Each feature converts the input sequences to discrete numbers in order to use them as predictors in machine learning models. There are many features and information which are hidden inside a sequence. Utilizing the package, users can convert biological sequences to discrete models based on chosen properties. References: 'iLearn' 'Z. Chen et al.' (2019) <DOI:10.1093/bib/bbz041>. 'iFeature' 'Z. Chen et al.' (2018) <DOI:10.1093/bioinformatics/bty140>. <https://CRAN.R-project.org/package=rDNAse>. 'PseKRAAC' 'Y. Zuo et al.' 'PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition' (2017) <DOI:10.1093/bioinformatics/btw564>. 'iDNA6mA-PseKNC' 'P. Feng et al.' 'iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC' (2019) <DOI:10.1016/j.ygeno.2018.01.005>. 'I. Dubchak et al.' 'Prediction of protein folding class using global description of amino acid sequence' (1995) <DOI:10.1073/pnas.92.19.8700>. 'W. Chen et al.' 'Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome' (2015) <DOI:10.1038/srep13859>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Imports: stats, utils
Suggests: testthat
NeedsCompilation: no
Packaged: 2021-01-18 17:18:29 UTC; sareameri
Repository: CRAN
Date/Publication: 2021-01-19 17:00:02 UTC

More information about ftrCOOL at CRAN
Permanent link

Package boxr updated to version 0.3.6 with previous version 0.3.5 dated 2019-11-19

Title: Interface for the 'Box.com API'
Description: An R interface for the remote file hosting service 'Box' (<https://www.box.com/>). In addition to uploading and downloading files, this package includes functions which mirror base R operations for local files, (e.g. box_load(), box_save(), box_read(), box_setwd(), etc.), as well as 'git' style functions for entire directories (e.g. box_fetch(), box_push()).
Author: Brendan Rocks [aut], Ian Lyttle [aut, cre] (<https://orcid.org/0000-0001-9962-4849>), Nathan Day [aut] (<https://orcid.org/0000-0002-6714-8611>), Vincent Fulco [ctb], Alec Wong [ctb], Alex Brodersen [ctb]
Maintainer: Ian Lyttle <ian.lyttle@se.com>

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Package stars updated to version 0.5-0 with previous version 0.4-3 dated 2020-07-08

Title: Spatiotemporal Arrays, Raster and Vector Data Cubes
Description: Reading, manipulating, writing and plotting spatiotemporal arrays (raster and vector data cubes) in 'R', using 'GDAL' bindings provided by 'sf', and 'NetCDF' bindings by 'ncmeta' and 'RNetCDF'.
Author: Edzer Pebesma [aut, cre] (<https://orcid.org/0000-0001-8049-7069>), Michael Sumner [ctb] (<https://orcid.org/0000-0002-2471-7511>), Etienne Racine [ctb], Adriano Fantini [ctb], David Blodgett [ctb]
Maintainer: Edzer Pebesma <edzer.pebesma@uni-muenster.de>

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Package pspline.inference updated to version 1.0.4 with previous version 1.0.2 dated 2020-07-17

Title: Estimation of Characteristics of Seasonal and Sporadic Infectious Disease Outbreaks Using Generalized Additive Modeling with Penalized Basis Splines
Description: Inference of infectious disease outcomes using generalized additive (mixed) models with penalized basis splines (P-Splines). See <https://medrxiv.org/cgi/content/short/2020.07.14.20138180v1>.
Author: Ben Artin [aut, cre, cph]
Maintainer: Ben Artin <ben@artins.org>

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New package RGENERATEPREC with initial version 1.2.8
Package: RGENERATEPREC
Maintainer: Emanuele Cordano <emanuele.cordano@gmail.com>
License: GPL (>= 2)
Title: Tools to Generate Daily-Precipitation Time Series
Type: Package
Author: Emanuele Cordano
Description: The method 'generate()' is extended for spatial multi-site stochastic generation of daily precipitation. It generates precipitation occurrence in several sites using logit regression (Generalized Linear Models) and the approach by D.S. Wilks (1998) <doi:10.1016/S0022-1694(98)00186-3> .
Version: 1.2.8
Repository: CRAN
Date: 2021-01-19
Depends: R (>= 3.0), copula, RGENERATE, blockmatrix, Matrix, stringr
Imports: RMAWGEN
Suggests: knitr,rmarkdown,lubridate,leaflet,lmom,ggplot2,reshape2,RefManageR
VignetteBuilder: knitr
URL: https://github.com/ecor/RGENERATEPREC
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-01-19 14:04:34 UTC; ecor
Date/Publication: 2021-01-19 15:40:19 UTC

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Permanent link

Package meta updated to version 4.16-1 with previous version 4.16-0 dated 2021-01-18

Title: General Package for Meta-Analysis
Description: User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker <DOI:10.1007/978-3-319-21416-0>, "Meta-Analysis with R" (2015): - fixed effect and random effects meta-analysis; - several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble); - statistical tests and trim-and-fill method to evaluate bias in meta-analysis; - import data from 'RevMan 5'; - prediction interval, Hartung-Knapp method for random effects model; - cumulative meta-analysis and leave-one-out meta-analysis; - meta-regression; - generalised linear mixed models; - produce forest plot summarising several (subgroup) meta-analyses.
Author: Guido Schwarzer [cre, aut] (<https://orcid.org/0000-0001-6214-9087>)
Maintainer: Guido Schwarzer <sc@imbi.uni-freiburg.de>

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Package measures updated to version 0.3 with previous version 0.2 dated 2018-07-25

Title: Performance Measures for Statistical Learning
Description: Provides the biggest amount of statistical measures in the whole R world. Includes measures of regression, (multiclass) classification and multilabel classification. The measures come mainly from the 'mlr' package and were programed by several 'mlr' developers.
Author: Philipp Probst [aut, cre]
Maintainer: Philipp Probst <philipp_probst@gmx.de>

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Package LipidMS updated to version 2.0.0 with previous version 1.2.0 dated 2020-11-20

Title: Lipid Annotation for LC-MS/MS DDA or DIA Data
Description: Lipid annotation in untargeted liquid chromatography mass spectrometry lipidomics based on fragmentation rules. Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) <doi:10.1021/acs.analchem.8b03409>.
Author: M Isabel Alcoriza-Balaguer, J Carlos Garcia-Canaveras
Maintainer: M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

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Package hisse updated to version 1.9.13 with previous version 1.9.10 dated 2020-10-28

Title: Hidden State Speciation and Extinction
Description: Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification. Beaulieu and O'Meara (2016) <doi:10.1093/sysbio/syw022>.
Author: Jeremy Beaulieu [aut, cre], Brian O'Meara [aut], Daniel Caetano [aut], James Boyko [aut]
Maintainer: Jeremy Beaulieu <jmbeauli@uark.edu>

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Package bibliometrix updated to version 3.0.4 with previous version 3.0.3 dated 2020-09-28

Title: Comprehensive Science Mapping Analysis
Description: Tool for quantitative research in scientometrics and bibliometrics. It provides various routines for importing bibliographic data from 'SCOPUS' (<https://scopus.com>), 'Clarivate Analytics Web of Science' (<https://www.webofknowledge.com/>), 'Digital Science Dimensions' (<https://www.dimensions.ai/>), 'Cochrane Library' (<https://www.cochranelibrary.com/>) and 'PubMed' (<https://pubmed.ncbi.nlm.nih.gov/>) databases, performing bibliometric analysis and building networks for co-citation, coupling, scientific collaboration and co-word analysis.
Author: Massimo Aria [cre, aut], Corrado Cuccurullo [aut]
Maintainer: Massimo Aria <aria@unina.it>

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Package attachment updated to version 0.2.0 with previous version 0.1.0 dated 2020-03-15

Title: Deal with Dependencies
Description: Tools to help manage dependencies during package development. This can retrieve all dependencies that are used in R files in the "R" directory, in Rmd files in "vignettes" directory and in 'roxygen2' documentation of functions. There is a function to update the Description file of your package and a function to create a file with the R commands to install all dependencies of your package. All functions to retrieve dependencies of R scripts and Rmd files can be used independently of a package development.
Author: Sébastien Rochette [cre, aut] (<https://orcid.org/0000-0002-1565-9313>), Vincent Guyader [aut] (<https://orcid.org/0000-0003-0671-9270>, previous maintainer), ThinkR [cph, fnd]
Maintainer: Sébastien Rochette <sebastien@thinkr.fr>

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Package Rsmlx updated to version 3.0.0 with previous version 2.0.2 dated 2019-04-30

Title: R Speaks 'Monolix'
Description: Provides methods for model building and model evaluation of mixed effects models using 'Monolix' <http://monolix.lixoft.com>. 'Monolix' is a software tool for nonlinear mixed effects modeling that must have been installed in order to use 'Rsmlx'. Among other tasks, 'Rsmlx' provides a powerful tool for automatic PK model building, performs statistical tests for model assessment, bootstrap simulation and likelihood profiling for computing confidence intervals. 'Rsmlx' also proposes several automatic covariate search methods for mixed effects models.
Author: Marc Lavielle [aut, cre], Jonathan Chauvin [ctb], Clémence Pinaud [ctb]
Maintainer: Marc Lavielle <Marc.Lavielle@inria.fr>

Diff between Rsmlx versions 2.0.2 dated 2019-04-30 and 3.0.0 dated 2021-01-19

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Package RPostgres updated to version 1.3.1 with previous version 1.3.0 dated 2021-01-07

Title: 'Rcpp' Interface to 'PostgreSQL'
Description: Fully 'DBI'-compliant 'Rcpp'-backed interface to 'PostgreSQL' <https://www.postgresql.org/>, an open-source relational database.
Author: Hadley Wickham [aut], Jeroen Ooms [aut], Kirill Müller [aut, cre] (<https://orcid.org/0000-0002-1416-3412>), RStudio [cph], R Consortium [fnd], Tomoaki Nishiyama [ctb] (Code for encoding vectors into strings derived from RPostgreSQL)
Maintainer: Kirill Müller <krlmlr+r@mailbox.org>

Diff between RPostgres versions 1.3.0 dated 2021-01-07 and 1.3.1 dated 2021-01-19

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Package NMOF updated to version 2.3-1 with previous version 2.2-2 dated 2020-10-20

Title: Numerical Methods and Optimization in Finance
Description: Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). The package provides implementations of optimisation heuristics (Differential Evolution, Genetic Algorithms, Particle Swarm Optimisation, Simulated Annealing and Threshold Accepting), and other optimisation tools, such as grid search and greedy search. There are also functions for the valuation of financial instruments such as bonds and options, for portfolio selection and functions that help with stochastic simulations.
Author: Enrico Schumann [aut, cre] (<https://orcid.org/0000-0001-7601-6576>)
Maintainer: Enrico Schumann <es@enricoschumann.net>

Diff between NMOF versions 2.2-2 dated 2020-10-20 and 2.3-1 dated 2021-01-19

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Package mlxR updated to version 4.2.0 with previous version 4.1.3 dated 2020-06-02

Title: Simulation of Longitudinal Data
Description: Simulation and visualization of complex models for longitudinal data. The models are encoded using the model coding language 'Mlxtran' and automatically converted into C++ codes. That allows one to implement very easily complex ODE-based models and complex statistical models, including mixed effects models, for continuous, count, categorical, and time-to-event data.
Author: Marc Lavielle [aut, cre], Esther Ilinca [ctb], Raphael Kuate [ctb]
Maintainer: Marc Lavielle <Marc.Lavielle@inria.fr>

Diff between mlxR versions 4.1.3 dated 2020-06-02 and 4.2.0 dated 2021-01-19

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Package bda updated to version 15.2.2 with previous version 15.1.7 dated 2020-09-30

Title: Binned Data Analysis
Description: Algorithms developed for binned data analysis, gene expression data analysis and measurement error models for ordinal data analysis.
Author: Bin Wang <bwang831@gmail.com>
Maintainer: Bin Wang <bwang831@gmail.com>

Diff between bda versions 15.1.7 dated 2020-09-30 and 15.2.2 dated 2021-01-19

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Package linemap updated to version 0.2.0 with previous version 0.1.0 dated 2017-09-15

Title: Line Maps
Description: Create maps made of lines. The package contains two functions: linemap() and getgrid(). linemap() displays a map made of lines using a data frame of gridded data. getgrid() transforms a set of polygons (sf objects) into a suitable data frame for linemap().
Author: Timothée Giraud [cre, aut] (<https://orcid.org/0000-0002-1932-3323>)
Maintainer: Timothée Giraud <timothee.giraud@cnrs.fr>

Diff between linemap versions 0.1.0 dated 2017-09-15 and 0.2.0 dated 2021-01-19

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Package geoaxe (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:

2016-02-19 0.1.0

Permanent link
Package groupedstats (with last version 2.0.0) was removed from CRAN

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

2020-12-01 2.0.0
2020-10-30 1.0.2
2020-05-29 1.0.1
2020-05-05 1.0.0
2020-04-05 0.2.2
2020-03-17 0.2.1
2020-02-28 0.2.0
2020-01-14 0.1.1
2019-11-21 0.1.0
2019-08-28 0.0.9
2019-08-07 0.0.8
2019-06-05 0.0.7
2019-03-20 0.0.6
2019-01-21 0.0.5
2018-12-01 0.0.4
2018-10-05 0.0.3
2018-07-19 0.0.2
2018-07-05 0.0.1

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New package unstruwwel with initial version 0.1.0
Package: unstruwwel
Title: Detect and Parse Historic Dates
Version: 0.1.0
Authors@R: person('Stefanie Schneider', email = 'stefanie.schneider@itg.uni-muenchen.de', role = c('cre', 'aut'), comment = c(ORCID = "0000-0003-4915-6949"))
Maintainer: Stefanie Schneider <stefanie.schneider@itg.uni-muenchen.de>
Description: Automatically converts language-specific verbal information, e.g., "1st half of the 19th century," to its standardized numerical counterparts, e.g., "1801-01-01/1850-12-31." It follows the recommendations of the 'MIDAS' ('Marburger Informations-, Dokumentations- und Administrations-System'), see <doi:10.11588/artdok.00003770>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
URL: https://github.com/stefanieschneider/unstruwwel
BugReports: https://github.com/stefanieschneider/unstruwwel/issues
Suggests: testthat, roxygen2
Imports: R6, assertthat, lubridate, magrittr, stringr, tibble, tidyr, purrr, dplyr, rlang
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2021-01-15 20:08:09 UTC; sschneider
Author: Stefanie Schneider [cre, aut] (<https://orcid.org/0000-0003-4915-6949>)
Repository: CRAN
Date/Publication: 2021-01-19 10:10:02 UTC

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New package R6P with initial version 0.1.1
Package: R6P
Type: Package
Title: Design Patterns in R
URL: https://tidylab.github.io/R6P/, https://github.com/tidylab/R6P
BugReports: https://github.com/tidylab/R6P/issues
Version: 0.1.1
Date: 2021-01-01
Authors@R: c( person("Harel", "Lustiger", email = "tidylab@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-2953-9598")) )
Maintainer: Harel Lustiger <tidylab@gmail.com>
Description: Build robust and maintainable software with object-oriented design patterns in R. Design patterns abstract and present in neat, well-defined components and interfaces the experience of many software designers and architects over many years of solving similar problems. These are solutions that have withstood the test of time with respect to re-usability, flexibility, and maintainability. 'R6P' provides abstract base classes with examples for a few known design patterns. The patterns were selected by their applicability to analytic projects in R. Using these patterns in R projects have proven effective in dealing with the complexity that data-driven applications possess.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Language: en-GB
Depends: R (>= 3.5)
Suggests: testthat, collections, dplyr, DBI, RSQLite, tibble, pryr
Imports: R6, purrr, stringr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2021-01-16 06:53:00 UTC; Alpha
Author: Harel Lustiger [aut, cre] (<https://orcid.org/0000-0003-2953-9598>)
Repository: CRAN
Date/Publication: 2021-01-19 10:20:02 UTC

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Package gMOIP updated to version 1.4.6 with previous version 1.4.3 dated 2020-02-20

Title: Tools for 2D and 3D Plots of Single and Multi-Objective Linear/Integer Programming Models
Description: Make 2D and 3D plots of linear programming (LP), integer linear programming (ILP), or mixed integer linear programming (MILP) models with up to three objectives. Plots of both the solution and criterion space are possible. For instance the non-dominated (Pareto) set for bi-objective LP/ILP/MILP programming models (see vignettes for an overview). The package also contains an function for checking if a point is inside the convex hull.
Author: Lars Relund Nielsen [aut, cre] (<https://orcid.org/0000-0002-4802-3071>)
Maintainer: Lars Relund Nielsen <lars@relund.dk>

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New package ubms with initial version 1.0.1
Package: ubms
Version: 1.0.1
Date: 2021-01-15
Title: Bayesian Models for Data from Unmarked Animals using 'Stan'
Authors@R: person("Ken", "Kellner", email="contact@kenkellner.com", role=c("cre","aut"))
Depends: R (>= 3.4.0), unmarked
Imports: ggplot2 (>= 2.0.0), gridExtra, lme4, loo, Matrix, methods, Rcpp (>= 0.12.0), rstan (>= 2.18.1), rstantools (>= 2.0.0), stats
Suggests: covr, devtools, knitr, pkgdown, raster, rmarkdown, roxygen2, testthat
VignetteBuilder: knitr
Description: Fit Bayesian hierarchical models of animal abundance and occurrence via the 'rstan' package, the R interface to the 'Stan' C++ library. Supported models include single-season occupancy, dynamic occupancy, and N-mixture abundance models. Covariates on model parameters are specified using a formula-based interface similar to package 'unmarked', while also allowing for estimation of random slope and intercept terms. References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
License: GPL (>= 3)
URL: https://kenkellner.com/ubms/
BugReports: https://github.com/kenkellner/ubms/issues
Encoding: UTF-8
RoxygenNote: 7.1.1
Biarch: true
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppArmadillo (>= 0.9.300.2.0), RcppEigen (>= 0.3.3.3.0), rstan (>= 2.18.1), StanHeaders (>= 2.18.0)
SystemRequirements: GNU make
Collate: 'RcppExports.R' 'submodel.R' 'response.R' 'inputs.R' 'fit.R' 'posterior_predict.R' 'posterior_linpred.R' 'fitted.R' 'gof.R' 'occu.R' 'colext.R' 'missing.R' 'distamp.R' 'fitlist.R' 'occuRN.R' 'mb_chisq.R' 'multinomPois.R' 'occuTTD.R' 'pcount.R' 'plot_marginal.R' 'predict.R' 'ranef.R' 'residuals.R' 'stanmodels.R' 'ubms-package.R' 'ubmsFit-methods.R' 'ubmsFitList-methods.R' 'umf.R' 'utils.R'
NeedsCompilation: yes
Packaged: 2021-01-15 18:00:03 UTC; ken
Author: Ken Kellner [cre, aut]
Maintainer: Ken Kellner <contact@kenkellner.com>
Repository: CRAN
Date/Publication: 2021-01-19 09:30:03 UTC

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New package torchdatasets with initial version 0.0.1
Package: torchdatasets
Title: Ready to Use Extra Datasets for Torch
Version: 0.0.1
Authors@R: c( person(given = "Daniel", family = "Falbel", role = c("aut", "cre"), email = "daniel@rstudio.com" ), person(family = "RStudio", role = c("cph")) )
Description: Provides datasets in a format that can be easily consumed by torch 'dataloaders'. Handles data downloading from multiple sources, caching and pre-processing so users can focus only on their model implementations.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: torch, fs, zip, pins, torchvision, stringr
Suggests: testthat, readr
URL: https://mlverse.github.io/torchdatasets/, https://github.com/mlverse/torchdatasets
BugReports: https://github.com/mlverse/torchdatasets/issues
NeedsCompilation: no
Packaged: 2021-01-15 19:00:00 UTC; dfalbel
Author: Daniel Falbel [aut, cre], RStudio [cph]
Maintainer: Daniel Falbel <daniel@rstudio.com>
Repository: CRAN
Date/Publication: 2021-01-19 09:40:02 UTC

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New package sgstar with initial version 0.1.0
Package: sgstar
Type: Package
Title: Seasonal Generalized Space Time Autoregressive (S-GSTAR) Model
Version: 0.1.0
Authors@R: c(person("M. Yoga Satria Utama","Developer", role = c("aut","cre"),email = "221709801@stis.ac.id"), person("Ernawati Pasaribu","Developer",role = "aut"))
Description: A set of function that implements for seasonal multivariate time series analysis based on Seasonal Generalized Space Time Autoregressive with Seemingly Unrelated Regression (S-GSTAR-SUR) Model by Setiawan(2016)<https://www.researchgate.net/publication/316517889_S-GSTAR-SUR_model_for_seasonal_spatio_temporal_data_forecasting>.
License: GPL-3
Imports: dplyr,ggplot2,nlme,tidyr
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Suggests: knitr,rmarkdown
Depends: R (>= 3.4)
URL: https://github.com/yogasatria30/sgstar
BugReports: https://github.com/yogasatria30/sgstar/issues
NeedsCompilation: no
Packaged: 2021-01-15 17:00:16 UTC; Lenovo
Author: M. Yoga Satria Utama Developer [aut, cre], Ernawati Pasaribu Developer [aut]
Maintainer: M. Yoga Satria Utama Developer <221709801@stis.ac.id>
Repository: CRAN
Date/Publication: 2021-01-19 09:20:03 UTC

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New package safetyData with initial version 1.0.0
Package: safetyData
Title: Clinical Trial Data
Description: Example clinical trial data sets formatted for easy use in R.
Version: 1.0.0
Authors@R: c( person( given = "Jeremy", family = "Wildfire", role = c("aut", "cre"), email = "jwildfire@gmail.com" ), person( given = "Renan", family = "Escalante Chong", role = c("aut"), email = "raescalan@gmail.com" ) )
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Depends: R (>= 3.0)
NeedsCompilation: no
Packaged: 2021-01-15 19:37:09 UTC; jeremy
Author: Jeremy Wildfire [aut, cre], Renan Escalante Chong [aut]
Maintainer: Jeremy Wildfire <jwildfire@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-19 09:50:02 UTC

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New package randChecks with initial version 0.1.0
Package: randChecks
Type: Package
Title: Covariate Balance Checks: Randomization Tests and Graphical Diagnostics
Version: 0.1.0
Author: Zach Branson
Maintainer: Zach Branson <zach@stat.cmu.edu>
Description: Provides randomization tests and graphical diagnostics for assessing randomized assignment and covariate balance for a binary treatment variable. See Branson (2021) <arXiv:1804.08760> for details.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2021-01-15 16:25:12 UTC; zjbranson
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2021-01-19 09:50:06 UTC

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New package qut with initial version 2.2
Package: qut
Type: Package
Title: Quantile Universal Threshold
Version: 2.2
Authors@R: c(person("Jairo", "Diaz-Rodriguez", role = c("aut", "cre","cph"), email = "adjairo@uninorte.edu.co"), person("Sylvain", "Sardy", role = c("aut", "ths"), email = "sylvain.sardy@unige.ch"), person("Caroline", "Giacobino", role = c("aut")), person("Nick", "Hengartner", role = c("aut")))
Description: Thresholding based tests for null hypothesis of the form A beta =c, and the Quantile Universal Threshold (QUT) for lasso regularization of Generalized Linear Models (GLM) and square-root lasso to obtain a sparse model with a good compromise between high true positive rate and low false discovery rate. Giacobino et al. (2017) <doi:10.1214/17-EJS1366>. Sardy et al. (2017) <arXiv:1708.02908>.
License: GPL-2
Depends: Matrix, glmnet, lars, flare
NeedsCompilation: no
Packaged: 2021-01-15 19:40:31 UTC; jairo
Author: Jairo Diaz-Rodriguez [aut, cre, cph], Sylvain Sardy [aut, ths], Caroline Giacobino [aut], Nick Hengartner [aut]
Maintainer: Jairo Diaz-Rodriguez <adjairo@uninorte.edu.co>
Repository: CRAN
Date/Publication: 2021-01-19 10:00:02 UTC

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New package MOSS with initial version 0.1.0
Package: MOSS
Title: Multi-Omic Integration via Sparse Singular Value Decomposition
Version: 0.1.0
Authors@R: c( person("Agustin", "Gonzalez-Reymundez", email = "agugonrey@gmail.com", role = c("aut","cre")), person("Alexander", "Grueneberg", email = "cran@agrueneberg.info", role = c("aut")), person("Ana", "Vazquez", email = "avazquez@msu.edu", role = c("ctb")))
Description: High dimensionality, noise and heterogeneity among samples and features challenge the omic integration task. Here we present an omic integration method based on sparse singular value decomposition (SVD) to deal with these limitations, by: a. obtaining the main axes of variation of the combined omics, b. imposing sparsity constraints at both subjects (rows) and features (columns) levels using Elastic Net type of shrinkage, and d. allowing both linear and non-linear projections (via t-Stochastic Neighbor Embedding) of the omic data to detect clusters in very convoluted data (Gonzalez-Reymundez & Vazquez, 2020) <doi:10.1038/s41598-020-65119-5>.
License: GPL-2
URL: https://github.com/agugonrey/MOSS
BugReports: https://github.com/agugonrey/MOSS/issues
Imports: cluster, dbscan, Rtsne, stats
Suggests: annotate, bigparallelr, bigstatsr, clValid, ComplexHeatmap, fpc, ggplot2, ggpmisc, ggthemes, gridExtra, irlba, knitr, MASS, rmarkdown, testthat, viridis
VignetteBuilder: knitr
Encoding: UTF-8
RoxygenNote: 7.1.0
NeedsCompilation: no
Packaged: 2021-01-15 17:18:02 UTC; epibio
Author: Agustin Gonzalez-Reymundez [aut, cre], Alexander Grueneberg [aut], Ana Vazquez [ctb]
Maintainer: Agustin Gonzalez-Reymundez <agugonrey@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-19 09:20:06 UTC

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New package httpgd with initial version 1.0.0
Package: httpgd
Type: Package
Title: A 'HTTP' Server Graphics Device
Version: 1.0.0
Authors@R: c( person(given = "Florian", family = "Rupprecht", email = "floruppr@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-1795-8624")), person(given = "Kun", family = "Ren", role = "ctb", email = "mail@renkun.me"), person("Jeroen", "Ooms", role = c("ctb"), email = "jeroen@berkeley.edu", comment = c(ORCID = "0000-0002-4035-0289")), person("Hadley", "Wickham", email = "hadley@rstudio.com", role = "cph", comment = "Author of included svglite code"), person("Lionel", "Henry", email = "lionel@rstudio.com", role = "cph", comment = "Author of included svglite code"), person("Thomas Lin", "Pedersen", email = "thomas.pedersen@rstudio.com", role = "cph", comment = "Author and creator of included svglite code"), person("T Jake", "Luciani", email = "jake@apache.org", role = "cph", comment = "Author of included svglite code"), person("Matthieu", "Decorde", email = "matthieu.decorde@ens-lyon.fr", role = "cph", comment = "Author of included svglite code"), person("Vaudor", "Lise", email = "lise.vaudor@ens-lyon.fr", role = "cph", comment = "Author of included svglite code"), person("Tony", "Plate", role = "cph", comment = "Contributor to included svglite code"), person("David", "Gohel", role = "cph", comment = "Contributor to included svglite code"), person("Yixuan", "Qiu", role = "cph", comment = "Contributor to included svglite code"), person("Håkon", "Malmedal", role = "cph", comment = "Contributor to included svglite code"), person("RStudio", role = "cph", comment = "Copyright holder of included svglite code"), person("Brett", "Robinson", role = "cph", comment = "Author of included belle library"), person("Google", role = "cph", comment = "Copyright holder of included material design icons"), person("Victor", "Zverovich", role = "cph", comment = "Author of included fmt library") )
Description: A graphics device for R that is accessible via network protocols. This package was created to make it easier to embed live R graphics in integrated development environments and other applications. The included 'HTML/JavaScript' client (plot viewer) aims to provide a better overall user experience when dealing with R graphics. The device asynchronously serves 'SVG' graphics via 'HTTP' and 'WebSockets'.
License: GPL (>= 2)
Depends: R (>= 4.0.0)
Imports: cpp11 (>= 0.2.4), later (>= 1.1.0), systemfonts (>= 0.2.3)
LinkingTo: cpp11, BH (>= 1.75.0), later, systemfonts
Suggests: testthat, xml2 (>= 1.0.0), fontquiver (>= 0.2.0)
RoxygenNote: 7.1.1
Encoding: UTF-8
SystemRequirements: C++17, libpng
URL: https://github.com/nx10/httpgd
BugReports: https://github.com/nx10/httpgd/issues
NeedsCompilation: yes
Packaged: 2021-01-15 16:58:31 UTC; floru
Author: Florian Rupprecht [aut, cre] (<https://orcid.org/0000-0002-1795-8624>), Kun Ren [ctb], Jeroen Ooms [ctb] (<https://orcid.org/0000-0002-4035-0289>), Hadley Wickham [cph] (Author of included svglite code), Lionel Henry [cph] (Author of included svglite code), Thomas Lin Pedersen [cph] (Author and creator of included svglite code), T Jake Luciani [cph] (Author of included svglite code), Matthieu Decorde [cph] (Author of included svglite code), Vaudor Lise [cph] (Author of included svglite code), Tony Plate [cph] (Contributor to included svglite code), David Gohel [cph] (Contributor to included svglite code), Yixuan Qiu [cph] (Contributor to included svglite code), Håkon Malmedal [cph] (Contributor to included svglite code), RStudio [cph] (Copyright holder of included svglite code), Brett Robinson [cph] (Author of included belle library), Google [cph] (Copyright holder of included material design icons), Victor Zverovich [cph] (Author of included fmt library)
Maintainer: Florian Rupprecht <floruppr@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-19 09:10:02 UTC

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Package Compositional updated to version 4.3 with previous version 4.2 dated 2020-11-12

Title: Compositional Data Analysis
Description: Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include a) Tsagris M.T., Preston S. and Wood A.T.A. (2011) A data-based power transformation for compositional data. Fourth International International Workshop on Compositional Data Analysis. b) Tsagris M. (2014). The k-NN algorithm for compositional data: a revised approach with and without zero values present. Journal of Data Science, 12(3):519--534. c) Tsagris M. (2015). A novel, divergence based, regression for compositional data. Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. d) Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2):47--57. e) Tsagris M., Preston S. and Wood A.T.A. (2016). Improved supervised classification for compositional data using the alpha-transformation. Journal of Classification, 33(2):243--261. <doi:10.1007/s00357-016-9207-5>. f) Tsagris M., Preston S. and Wood A.T.A. (2017). Nonparametric hypothesis testing for equality of means on the simplex. Journal of Statistical Computation and Simulation, 87(2): 406--422. <doi:10.1080/00949655.2016.1216554> g) Tsagris M. and Stewart C. (2018). A Dirichlet regression model for compositional data with zeros. Lobachevskii Journal of Mathematics, 39(3): 398--412. <doi:10.1134/S1995080218030198>. h) Alenazi A. (2019). Regression for compositional data with compositional data as predictor variables with or without zero values. Journal of Data Science, 17(1): 219--238. <doi:10.6339/JDS.201901_17(1).0010>. i) Tsagris M. and Stewart C. (2020). A folded model for compositional data analysis. Australian and New Zealand Journal of Statistics, 62(2):249--277. <doi:10.1111/anzs.12289>. j) Tsagris M., Alenazi A. and Stewart C. (2020). The alpha-k-NN regression for compositional data. <arXiv:2002.05137>. We further include functions for percentages (or proportions).
Author: Michail Tsagris [aut, cre], Giorgos Athineou [aut], Abdulaziz Alenazi [ctb]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>

Diff between Compositional versions 4.2 dated 2020-11-12 and 4.3 dated 2021-01-19

 Compositional-4.2/Compositional/R/js.prop.reg.R              |only
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 Compositional-4.3/Compositional/MD5                          |   79 ++++++-----
 Compositional-4.3/Compositional/NAMESPACE                    |    6 
 Compositional-4.3/Compositional/R/aknn.reg.R                 |    4 
 Compositional-4.3/Compositional/R/compknn.tune.R             |    4 
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 Compositional-4.3/Compositional/R/js.compreg.R               |   25 +--
 Compositional-4.3/Compositional/R/kent.contour.R             |only
 Compositional-4.3/Compositional/R/kl.compreg.R               |   14 -
 Compositional-4.3/Compositional/R/norm.contour.R             |   27 +--
 Compositional-4.3/Compositional/R/ols.compcomp.R             |only
 Compositional-4.3/Compositional/R/ols.compreg.R              |   35 ++--
 Compositional-4.3/Compositional/R/probout.R                  |only
 Compositional-4.3/Compositional/R/symkl.compreg.R            |   23 +--
 Compositional-4.3/Compositional/R/ternary.R                  |   16 +-
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 Compositional-4.3/Compositional/R/unitweib.reg.R             |only
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 Compositional-4.3/Compositional/man/a.est.Rd                 |    3 
 Compositional-4.3/Compositional/man/alfa.Rd                  |    7 
 Compositional-4.3/Compositional/man/alfa.rda.Rd              |    2 
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 Compositional-4.3/Compositional/man/kl.alfapcr.Rd            |    4 
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 Compositional-4.3/Compositional/man/ternary.Rd               |   39 ++---
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 49 files changed, 230 insertions(+), 234 deletions(-)

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New package ccmReportR with initial version 0.0.2
Package: ccmReportR
Title: Wraps 'CCM' with Utility Functions
Version: 0.0.2
Authors@R: person(given = "James", family = "Lane", role = c("aut", "cre"), email = "lanejames35@gmail.com")
Description: Provides a set of functions to perform queries against the 'CCM' API <https://mohcontacttracing.my.salesforce.com>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: dplyr (>= 1.0.2), httr (>= 1.4.2), jsonlite (>= 1.7.1), keyring (>= 1.1.0), lubridate (>= 1.7.9), purrr (>= 0.3.4)
NeedsCompilation: no
Packaged: 2021-01-15 14:41:52 UTC; lane_j
Author: James Lane [aut, cre]
Maintainer: James Lane <lanejames35@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-19 09:40:05 UTC

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Package PMwR updated to version 0.16-0 with previous version 0.15-0 dated 2020-10-19

Title: Portfolio Management with R
Description: Functions and examples for 'Portfolio Management with R': backtesting investment and trading strategies, computing profit/loss and returns, analysing trades, handling lists of transactions, reporting, and more.
Author: Enrico Schumann [aut, cre] (<https://orcid.org/0000-0001-7601-6576>)
Maintainer: Enrico Schumann <es@enricoschumann.net>

Diff between PMwR versions 0.15-0 dated 2020-10-19 and 0.16-0 dated 2021-01-19

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New package logitr with initial version 0.1.0
Package: logitr
Title: Logit Models w/Preference & WTP Space Utility Parameterizations
Version: 0.1.0
Authors@R: c( person(given = "John", family = "Helveston", role = c("aut", "cre", "cph"), email = "john.helveston@gmail.com", comment = c(ORCID = "0000-0002-2657-9191")))
Description: Estimation of multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. An option is available to run a multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for simulating the expected shares of a set of alternatives using an estimated model. MXL models assume uncorrelated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) "Discrete Choice Methods with Simulation, 2nd Edition" <doi:10.1017/CBO9780511805271>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
VignetteBuilder: knitr
Depends: R (>= 3.5.0)
Suggests: knitr, rmarkdown, here, ggplot2
Imports: nloptr, stats, randtoolbox, MASS
URL: https://github.com/jhelvy/logitr
BugReports: https://github.com/jhelvy/logitr/issues
NeedsCompilation: no
Packaged: 2021-01-15 13:02:49 UTC; jhelvy
Author: John Helveston [aut, cre, cph] (<https://orcid.org/0000-0002-2657-9191>)
Maintainer: John Helveston <john.helveston@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-19 08:40:03 UTC

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New package feamiR with initial version 0.1.0
Package: feamiR
Type: Package
Title: Classification and Feature Selection for microRNA/mRNA Interactions
Version: 0.1.0
Authors@R: c(person(given = "Eleanor", family = "Williams", role = c("aut","cre"), email = "ecw63@cam.ac.uk"), person(given="Irina", family = "Mohorianu", role = c("aut"), email = "iim22@cam.ac.uk"))
Maintainer: Eleanor Williams <ecw63@cam.ac.uk>
Description: Comprises a pipeline for predicting microRNA/mRNA interactions, as detailed in Williams, Calinescu, Mohorianu (2020) <doi:10.1101/2020.12.23.424130>. Its input consists of [a] a messenger RNA (mRNA) dataset (either in fasta format, focused on 3' UTRs or in gtf format; for the latter, the sequences of the 3’ UTRs are generated using the genomic coordinates), [b] a microRNA dataset (in fasta format, retrieved from miRBase, <http://www.mirbase.org/>) and [c] an interaction dataset (in csv format, from miRTarBase <http://mirtarbase.cuhk.edu.cn/php/index.php>). To characterise and predict microRNA/mRNA interactions, we use [a] statistical analyses based on Chi-squared and Fisher exact tests and [b] Machine Learning classifiers (decision trees, random forests and support vector machines). To enhance the accuracy of the classifiers we also employ feature selection approaches used in on conjunction with the classifiers. The feature selection approaches include a voting scheme for decision trees, a measure based on Gini index for random forests, forward feature selection and Genetic Algorithms on SVMs. The pipeline also includes a novel approach based on embryonic Genetic Algorithms which combines and optimises the forward feature selection and Genetic Algorithms. All analyses, including the classification and feature selection, are applicable on the microRNA seed features (default), on the full microRNA features and/or flanking features on the mRNA. The sets of features can be combined.
Encoding: UTF-8
Depends: R (>= 3.1.2)
Imports: stringr, randomForest, rpart, rpart.plot, GA, e1071, ggplot2, magrittr, tibble, dplyr, reticulate
Config/reticulate: list( packages = list( list(package = "os"), list(package = "argparse"), list(package = "gzip"), list(package = "pandas"), list(package = "numpy"), list(package = "math"), list(package = "scipy.stats"), list(package = "matplotlib.pyplot"), list(package = "seaborn"), list(package = "statistics"), list(package = "logging"), list(package = "Bio") ) )
Suggests: parallel, doParallel
SystemRequirements: Python (>=3.6) sreformat patman
URL: https://github.com/Core-Bioinformatics/feamiR
BugReports: https://github.com/Core-Bioinformatics/feamiR/issues
LazyData: true
RoxygenNote: 7.1.1.9000
License: GPL-2
NeedsCompilation: no
Packaged: 2021-01-15 12:03:40 UTC; ecw63
Author: Eleanor Williams [aut, cre], Irina Mohorianu [aut]
Repository: CRAN
Date/Publication: 2021-01-19 08:30:02 UTC

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New package eemdTDNN with initial version 0.1.0
Package: eemdTDNN
Type: Package
Title: EEMD and Its Variant Based Time Delay Neural Network Model
Version: 0.1.0
Authors@R: c(person(given = "Kapil", family = "Choudhary", role = c("aut", "cre"), email = "choudharykapil832@gmail.com"), person(given = "Girish Kumar", family = "Jha", role = c("aut", "ths", "ctb")), person(given = "Rajeev Ranjan", family = "Kumar", role = c("aut", "ctb")), person(given = "Ronit", family = "Jaiswal", role = "ctb"))
Maintainer: Kapil Choudhary <choudharykapil832@gmail.com>
Description: Forecasting univariate time series with different decomposition based time delay neural network models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: forecast, Rlibeemd
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2021-01-15 13:53:56 UTC; Rajeev-PC
Author: Kapil Choudhary [aut, cre], Girish Kumar Jha [aut, ths, ctb], Rajeev Ranjan Kumar [aut, ctb], Ronit Jaiswal [ctb]
Repository: CRAN
Date/Publication: 2021-01-19 09:00:02 UTC

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Package rflexscan updated to version 0.4.0 with previous version 0.3.1 dated 2020-08-24

Title: The Flexible Spatial Scan Statistic
Description: Functions for the detection of spatial clusters using the flexible spatial scan statistic developed by Tango and Takahashi (2005) <doi:10.1186/1476-072X-4-11>. This package implements a wrapper for the C routine used in the FleXScan 3.1.2 <https://sites.google.com/site/flexscansoftware/home> developed by Takahashi, Yokoyama, and Tango.
Author: Takahiro Otani, Kunihiko Takahashi
Maintainer: Takahiro Otani <otani@med.nagoya-cu.ac.jp>

Diff between rflexscan versions 0.3.1 dated 2020-08-24 and 0.4.0 dated 2021-01-19

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Package fabletools updated to version 0.3.0 with previous version 0.2.1 dated 2020-09-03

Title: Core Tools for Packages in the 'fable' Framework
Description: Provides tools, helpers and data structures for developing models and time series functions for 'fable' and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis.
Author: Mitchell O'Hara-Wild [aut, cre], Rob Hyndman [aut], Earo Wang [aut], Di Cook [ctb], George Athanasopoulos [ctb], David Holt [ctb]
Maintainer: Mitchell O'Hara-Wild <mail@mitchelloharawild.com>

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Package CVXR updated to version 1.0-9 with previous version 1.0-8 dated 2020-09-13

Title: Disciplined Convex Optimization
Description: An object-oriented modeling language for disciplined convex programming (DCP) as described in Fu, Narasimhan, and Boyd (2020, <doi:10.18637/jss.v094.i14>). It allows the user to formulate convex optimization problems in a natural way following mathematical convention and DCP rules. The system analyzes the problem, verifies its convexity, converts it into a canonical form, and hands it off to an appropriate solver to obtain the solution. Interfaces to solvers on CRAN and elsewhere are provided, both commercial and open source.
Author: Anqi Fu [aut, cre], Balasubramanian Narasimhan [aut], David W Kang [aut], Steven Diamond [aut], John Miller [aut], Stephen Boyd [ctb], Paul Kunsberg Rosenfield [ctb]
Maintainer: Anqi Fu <anqif@stanford.edu>

Diff between CVXR versions 1.0-8 dated 2020-09-13 and 1.0-9 dated 2021-01-19

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Package chest updated to version 0.3.5 with previous version 0.3.4 dated 2020-09-30

Title: Change-in-Estimate Approach to Assess Confounding Effects
Description: Applies the change-in-effect estimate method to assess confounding effects in medical and epidemiological research (Greenland & Pearce (2016) <doi:10.1146/annurev-publhealth-031914-122559> ). It starts with a crude model including only the outcome and exposure variables. At each of the subsequent steps, one variable which creates the largest change among the remaining variables is selected. This process is repeated until all variables have been entered into the model (Wang Z (2007) <doi:10.1177/1536867X0700700203> ). Currently, the 'chest' package has functions for linear regression, logistic regression, negative binomial regression, Cox proportional hazards model and conditional logistic regression.
Author: Zhiqiang Wang [aut, cre]
Maintainer: Zhiqiang Wang <zhiqiang.wang@menzies.edu.au>

Diff between chest versions 0.3.4 dated 2020-09-30 and 0.3.5 dated 2021-01-19

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Package slackr updated to version 2.0.1 with previous version 1.4.2 dated 2016-07-20

Title: Send Messages, Images, R Objects and Files to 'Slack' Channels/Users
Description: 'Slack' <https://slack.com/> provides a service for teams to collaborate by sharing messages, images, links, files and more. Functions are provided that make it possible to interact with the 'Slack' platform 'API'. When you need to share information or data from R, rather than resort to copy/ paste in e-mails or other services like 'Skype' <https://www.skype.com/>, you can use this package to send well-formatted output from multiple R objects and expressions to all teammates at the same time with little effort. You can also send images from the current graphics device, R objects, and upload files.
Author: Bob Rudis [aut, cre], Jay Jacobs [ctb], David Severski [ctb], Quinn Weber [ctb], Konrad Karczewski [ctb], Shinya Uryu [ctb], Gregory Jefferis [ctb], Ed Niles [ctb], Rick Saporta [ctb], Jonathan Sidi [aut, ctb], Matt Kaye [ctb], Xinye Li [ctb]
Maintainer: Matt Kaye <mrkaye97@gmail.com>

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Package reporter updated to version 1.1.2 with previous version 1.0.6 dated 2020-11-20

Title: Creates Statistical Reports
Description: Contains functions to create regulatory-style statistical reports. Originally designed to create tables, listings, and figures for the pharmaceutical, biotechnology, and medical device industries, these reports are generalized enough that they could be used in any industry. Generates text, rich-text, and PDF file formats. The package specializes in printing wide and long tables with automatic page wrapping and splitting. Reports can be produced with a minimum of function calls, and without relying on other table packages. The package supports titles, footnotes, page header, page footers, spanning headers, page by variables, and automatic page numbering.
Author: David Bosak
Maintainer: David Bosak <dbosak01@gmail.com>

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Package psycho updated to version 0.6.1 with previous version 0.5.0 dated 2020-01-22

Title: Efficient and Publishing-Oriented Workflow for Psychological Science
Description: The main goal of the psycho package is to provide tools for psychologists, neuropsychologists and neuroscientists, to facilitate and speed up the time spent on data analysis. It aims at supporting best practices and tools to format the output of statistical methods to directly paste them into a manuscript, ensuring statistical reporting standardization and conformity.
Author: Dominique Makowski [aut, cre, cph] (<https://orcid.org/0000-0001-5375-9967>), Hugo Najberg [ctb], Viliam Simko [ctb], Sasha Epskamp [rev] (Sasha reviewed the package for JOSS, see https://github.com/openjournals/joss-reviews/issues/470)
Maintainer: Dominique Makowski <dom.makowski@gmail.com>

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Package memor updated to version 0.2.3 with previous version 0.2.2 dated 2020-05-10

Title: A 'rmarkdown' Template that Can be Highly Customized
Description: A 'rmarkdown' template that supports company logo, contact info, watermarks and more. Currently restricted to 'Latex'/'Markdown'; a similar 'HTML' theme will be added in the future.
Author: Hao Zhu [aut, cre] (<https://orcid.org/0000-0002-3386-6076>), Timothy Tsai [aut] (<https://orcid.org/0000-0002-0274-8042>), Thomas Travison [aut], Mikhail Popov [ctb]
Maintainer: Hao Zhu <haozhu233@gmail.com>

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Package dataverse updated to version 0.3.0 with previous version 0.2.0 dated 2017-06-15

Title: Client for Dataverse 4 Repositories
Description: Provides access to Dataverse APIs <https://dataverse.org/> (versions 4-5), enabling data search, retrieval, and deposit. For Dataverse versions <= 3.0, use the archived 'dvn' package <https://cran.r-project.org/package=dvn>.
Author: Will Beasley [aut, cre] (<https://orcid.org/0000-0002-5613-5006>), Thomas J. Leeper [aut] (<https://orcid.org/0000-0003-4097-6326>), Shiro Kuriwaki [aut] (<https://orcid.org/0000-0002-5687-2647>), Philip Durbin [aut] (<https://orcid.org/0000-0002-9528-9470>), Sebastian Karcher [aut] (<https://orcid.org/0000-0001-8249-7388>), Jan Kanis [ctb]
Maintainer: Will Beasley <wibeasley@hotmail.com>

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Package BMTAR updated to version 0.1.1 with previous version 0.1.0 dated 2020-06-09

Title: Bayesian Approach for MTAR Models with Missing Data
Description: Implements parameter estimation using a Bayesian approach for Multivariate Threshold Autoregressive (MTAR) models with missing data using Markov Chain Monte Carlo methods. Performs the simulation of MTAR processes (mtarsim()), estimation of matrix parameters and the threshold values (mtarns()), identification of the autoregressive orders using Bayesian variable selection (mtarstr()), identification of the number of regimes using Metropolised Carlin and Chib (mtarnumreg()) and estimate missing data, coefficients and covariance matrices conditional on the autoregressive orders, the threshold values and the number of regimes (mtarmissing()). Calderon and Nieto (2017) <doi:10.1080/03610926.2014.990758>.
Author: Valeria Bejarano Salcedo <vbejaranos@unal.edu.co>, Sergio Alejandro Calderon Villanueva <sacalderonv@unal.edu.co> Andrey Duvan Rincon Torres <adrincont@unal.edu.co>
Maintainer: Andrey Duvan Rincon Torres <adrincont@unal.edu.co>

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