Wed, 16 Dec 2020

Package statGraph updated to version 0.4.1 with previous version 0.4.0 dated 2020-11-19

Title: Statistical Methods for Graphs
Description: Contains statistical methods to analyze graphs, such as graph parameter estimation, model selection based on the GIC (Graph Information Criterion), statistical tests to discriminate two or more populations of graphs (ANOGVA - Analysis of Graph Variability), correlation between graphs, and clustering of graphs. References: Takahashi et al. (2012) <doi:10.1371/journal.pone.0049949>, Futija et al. (2017) <doi:10.3389/fnins.2017.00066>, Fujita et al. (2017) <doi:10.1016/j.csda.2016.11.016>, Tang et al. (2017) <doi:10.3150/15-BEJ789>, Tang et al. (2017) <doi:10.1080/10618600.2016.1193505>, Ghoshdastidar et al. (2017) <arXiv:1705.06168>, Ghoshdastidar et al. (2017) <arXiv:1707.00833>, Cerqueira et al. (2017) <doi:10.1109/TNSE.2017.2674026>, Fraiman and Fraiman (2018) <doi:10.1038/s41598-018-23152-5>, Fujita et al. (2019) <doi:10.1093/comnet/cnz028>.
Author: Diogo R. da Costa [aut], Taiane C. Ramos [aut], Grover E. Castro Guzman [aut], Suzana S. Santos [aut], Eduardo S. Lira [aut], Andre Fujita [aut, cre]
Maintainer: Andre Fujita <andrefujita@usp.br>

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Package gmvarkit updated to version 1.3.1 with previous version 1.3.0 dated 2020-12-16

Title: Estimate Gaussian Mixture Vector Autoregressive Model
Description: Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive (GMVAR) model, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi Virolainen (2020) <arXiv:2007.04713>.
Author: Savi Virolainen [aut, cre]
Maintainer: Savi Virolainen <savi.virolainen@helsinki.fi>

Diff between gmvarkit versions 1.3.0 dated 2020-12-16 and 1.3.1 dated 2020-12-16

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Package usefun updated to version 0.4.8 with previous version 0.4.7 dated 2020-06-10

Title: A Collection of Useful Functions by John
Description: A set of general functions that I have used in various projects and in other R packages. They support some miscellaneous operations on data frames, matrices and vectors like adding a row on a ternary (3-value) data.frame based on positive and negative vector-indicators, rearranging a list of data.frames by rownames, pruning rows or columns of a data.frame that contain only one specific value given by the user, pruning and reordering a vector according to the common elements between its names and elements of another given vector, finding the non-common elements between two vectors (outer-section), normalization of a vector, matrix or data.frame's numeric values in a specified range, pretty printing of vector names and values in an R Markdown document. Also included is a function that returns the statistics needed for plotting a ROC curve.
Author: John Zobolas [aut, cph, cre] (<https://orcid.org/0000-0002-3609-8674>)
Maintainer: John Zobolas <bblodfon@gmail.com>

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Package DSI updated to version 1.1.1 with previous version 1.1.0 dated 2020-05-18

Title: 'DataSHIELD' Interface
Description: 'DataSHIELD' is an infrastructure and series of R packages that enables the remote and 'non-disclosive' analysis of sensitive research data. This package defines the API that is to be implemented by 'DataSHIELD' compliant data repositories.
Author: Yannick Marcon [aut, cre] (<https://orcid.org/0000-0003-0138-2023>), Amadou Gaye [ctb] (<https://orcid.org/0000-0002-1180-2792>), Paul Burton [ctb]
Maintainer: Yannick Marcon <yannick.marcon@obiba.org>

Diff between DSI versions 1.1.0 dated 2020-05-18 and 1.1.1 dated 2020-12-16

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Package vdg updated to version 1.2.1 with previous version 1.2.0 dated 2016-10-18

Title: Variance Dispersion Graphs and Fraction of Design Space Plots
Description: Facilities for constructing variance dispersion graphs, fraction- of-design-space plots and similar graphics for exploring the properties of experimental designs. The design region is explored via random sampling, which allows for more flexibility than traditional variance dispersion graphs. A formula interface is leveraged to provide access to complex model formulae. Graphics can be constructed simultaneously for multiple experimental designs and/or multiple model formulae. Instead of using pointwise optimization to find the minimum and maximum scaled prediction variance curves, which can be inaccurate and time consuming, this package uses quantile regression as an alternative.
Author: Pieter Schoonees [aut, cre, cph], Niel le Roux [ctb]
Maintainer: Pieter Schoonees <schoonees@gmail.com>

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Package purrrlyr updated to version 0.0.7 with previous version 0.0.6 dated 2020-05-02

Title: Tools at the Intersection of 'purrr' and 'dplyr'
Description: Some functions at the intersection of 'dplyr' and 'purrr' that formerly lived in 'purrr'.
Author: Lionel Henry [aut, cre], Hadley Wickham [ctb], RStudio [cph]
Maintainer: Lionel Henry <lionel@rstudio.com>

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Package opalr updated to version 1.5.1 with previous version 1.5.0 dated 2020-11-03

Title: 'Opal' Data Repository Client and 'DataSHIELD' Utils
Description: Data integration Web application for biobanks by 'OBiBa'. 'Opal' is the core database application for biobanks. Participant data, once collected from any data source, must be integrated and stored in a central data repository under a uniform model. 'Opal' is such a central repository. It can import, process, validate, query, analyze, report, and export data. 'Opal' is typically used in a research center to analyze the data acquired at assessment centres. Its ultimate purpose is to achieve seamless data-sharing among biobanks. This 'Opal' client allows to interact with 'Opal' web services and to perform operations on the R server side. 'DataSHIELD' administration tools are also provided.
Author: Yannick Marcon [aut, cre] (<https://orcid.org/0000-0003-0138-2023>), Amadou Gaye [ctb] (<https://orcid.org/0000-0002-1180-2792>), OBiBa group [cph]
Maintainer: Yannick Marcon <yannick.marcon@obiba.org>

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Package morphomap updated to version 1.3 with previous version 1.2 dated 2020-06-14

Title: Morphometric Maps, Bone Landmarking and Cross Sectional Geometry
Description: Extract cross sections from long bone meshes at specified intervals along the diaphysis. Calculate two and three-dimensional morphometric maps, cross-sectional geometric parameters, and semilandmarks on the periosteal and endosteal contours of each cross section.
Author: Antonio Profico [aut, cre], Luca Bondioli [aut], Pasquale Raia [aut], Julien Claude [ctb], Paul O'Higgins [aut], Damiano Marchi [aut]
Maintainer: Antonio Profico <antonio.profico@gmail.com>

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Package fda updated to version 5.1.9 with previous version 5.1.7 dated 2020-11-28

Title: Functional Data Analysis
Description: These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. New York: Springer and in Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book. Matlab versions are available by ftp from <http://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/>.
Author: J. O. Ramsay <ramsay@psych.mcgill.ca> [aut,cre], Spencer Graves <spencer.graves@effectivedefense.org> [ctb], Giles Hooker <gjh27@cornell.edu> [ctb]
Maintainer: J. O. Ramsay <ramsay@psych.mcgill.ca>

Diff between fda versions 5.1.7 dated 2020-11-28 and 5.1.9 dated 2020-12-16

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Package broom updated to version 0.7.3 with previous version 0.7.2 dated 2020-10-20

Title: Convert Statistical Objects into Tidy Tibbles
Description: Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.
Author: David Robinson [aut], Alex Hayes [aut, cre] (<https://orcid.org/0000-0002-4985-5160>), Simon Couch [aut], Indrajeet Patil [ctb] (<https://orcid.org/0000-0003-1995-6531>), Derek Chiu [ctb], Matthieu Gomez [ctb], Boris Demeshev [ctb], Dieter Menne [ctb], Benjamin Nutter [ctb], Luke Johnston [ctb], Ben Bolker [ctb], Francois Briatte [ctb], Jeffrey Arnold [ctb], Jonah Gabry [ctb], Luciano Selzer [ctb], Gavin Simpson [ctb], Jens Preussner [ctb], Jay Hesselberth [ctb], Hadley Wickham [ctb], Matthew Lincoln [ctb], Alessandro Gasparini [ctb], Lukasz Komsta [ctb], Frederick Novometsky [ctb], Wilson Freitas [ctb], Michelle Evans [ctb], Jason Cory Brunson [ctb], Simon Jackson [ctb], Ben Whalley [ctb], Karissa Whiting [ctb], Yves Rosseel [ctb], Michael Kuehn [ctb], Jorge Cimentada [ctb], Erle Holgersen [ctb], Karl Dunkle Werner [ctb] (<https://orcid.org/0000-0003-0523-7309>), Ethan Christensen [ctb], Steven Pav [ctb], Paul PJ [ctb], Ben Schneider [ctb], Patrick Kennedy [ctb], Lily Medina [ctb], Brian Fannin [ctb], Jason Muhlenkamp [ctb], Matt Lehman [ctb], Bill Denney [ctb] (<https://orcid.org/0000-0002-5759-428X>), Nic Crane [ctb], Andrew Bates [ctb], Vincent Arel-Bundock [ctb] (<https://orcid.org/0000-0003-2042-7063>), Hideaki Hayashi [ctb], Luis Tobalina [ctb], Annie Wang [ctb], Wei Yang Tham [ctb], Clara Wang [ctb], Abby Smith [ctb] (<https://orcid.org/0000-0002-3207-0375>), Jasper Cooper [ctb] (<https://orcid.org/0000-0002-8639-3188>), E Auden Krauska [ctb] (<https://orcid.org/0000-0002-1466-5850>), Alex Wang [ctb], Malcolm Barrett [ctb] (<https://orcid.org/0000-0003-0299-5825>), Charles Gray [ctb] (<https://orcid.org/0000-0002-9978-011X>), Jared Wilber [ctb], Vilmantas Gegzna [ctb] (<https://orcid.org/0000-0002-9500-5167>), Eduard Szoecs [ctb], Frederik Aust [ctb] (<https://orcid.org/0000-0003-4900-788X>), Angus Moore [ctb], Nick Williams [ctb], Marius Barth [ctb] (<https://orcid.org/0000-0002-3421-6665>), Bruna Wundervald [ctb] (<https://orcid.org/0000-0001-8163-220X>), Joyce Cahoon [ctb] (<https://orcid.org/0000-0001-7217-4702>), Grant McDermott [ctb] (<https://orcid.org/0000-0001-7883-8573>), Kevin Zarca [ctb], Shiro Kuriwaki [ctb] (<https://orcid.org/0000-0002-5687-2647>), Lukas Wallrich [ctb] (<https://orcid.org/0000-0003-2121-5177>), James Martherus [ctb] (<https://orcid.org/0000-0002-8285-3300>), Chuliang Xiao [ctb] (<https://orcid.org/0000-0002-8466-9398>), Joseph Larmarange [ctb], Max Kuhn [ctb], Michal Bojanowski [ctb], Hakon Malmedal [ctb], Clara Wang [ctb], Sergio Oller [ctb], Luke Sonnet [ctb], Jim Hester [ctb], Cory Brunson [ctb], Ben Schneider [ctb], Bernie Gray [ctb] (<https://orcid.org/0000-0001-9190-6032>), Mara Averick [ctb], Aaron Jacobs [ctb], Andreas Bender [ctb], Sven Templer [ctb], Paul-Christian Buerkner [ctb], Matthew Kay [ctb], Erwan Le Pennec [ctb], Johan Junkka [ctb], Hao Zhu [ctb], Benjamin Soltoff [ctb], Zoe Wilkinson Saldana [ctb], Tyler Littlefield [ctb], Charles T. Gray [ctb], Shabbh E. Banks [ctb], Serina Robinson [ctb], Roger Bivand [ctb], Riinu Ots [ctb], Nicholas Williams [ctb], Nina Jakobsen [ctb], Michael Weylandt [ctb], Lisa Lendway [ctb], Karl Hailperin [ctb], Josue Rodriguez [ctb], Jenny Bryan [ctb], Chris Jarvis [ctb], Greg Macfarlane [ctb], Brian Mannakee [ctb], Drew Tyre [ctb], Shreyas Singh [ctb], Laurens Geffert [ctb], Hong Ooi [ctb], Henrik Bengtsson [ctb], Eduard Szocs [ctb], David Hugh-Jones [ctb], Matthieu Stigler [ctb], Hugo Tavares [ctb] (<https://orcid.org/0000-0001-9373-2726>), R. Willem Vervoort [ctb], Brenton M. Wiernik [ctb]
Maintainer: Alex Hayes <alexpghayes@gmail.com>

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Package BIGL updated to version 1.6.0 with previous version 1.5.3 dated 2020-09-10

Title: Biochemically Intuitive Generalized Loewe Model
Description: Response surface methods for drug synergy analysis. Available methods include generalized and classical Loewe formulations as well as Highest Single Agent methodology. Response surfaces can be plotted in an interactive 3-D plot and formal statistical tests for presence of synergistic effects are available. Implemented methods and tests are described in the article "BIGL: Biochemically Intuitive Generalized Loewe null model for prediction of the expected combined effect compatible with partial agonism and antagonism" by Koen Van der Borght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas, Maxim Nazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017) <doi:10.1038/s41598-017-18068-5>.
Author: Heather Turner, Annelies Tourny, Olivier Thas, Maxim Nazarov, Rytis Bagdziunas, Stijn Hawinkel
Maintainer: Maxim Nazarov <maxim.nazarov@openanalytics.eu>

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Package tau updated to version 0.0-23 with previous version 0.0-22 dated 2020-12-03

Title: Text Analysis Utilities
Description: Utilities for text analysis.
Author: Christian Buchta [aut], Kurt Hornik [aut, cre] (<https://orcid.org/0000-0003-4198-9911>), Ingo Feinerer [aut], David Meyer [aut]
Maintainer: Kurt Hornik <Kurt.Hornik@R-project.org>

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Package sumFREGAT updated to version 1.2.0 with previous version 1.1.0 dated 2018-11-15

Title: Fast Region-Based Association Tests on Summary Statistics
Description: An adaptation of classical region/gene-based association analysis techniques to the use of summary statistics (P values and effect sizes) and correlations between genetic variants as input. It is a tool to perform the most popular and efficient gene-based tests using the results of genome-wide association (meta-)analyses without having the original genotypes and phenotypes at hand.
Author: Nadezhda M. Belonogova <belon@bionet.nsc.ru> and Gulnara R. Svishcheva <gulsvi@bionet.nsc.ru> with contributions from: Seunggeun Lee (kernel functions), Pierre Lafaye de Micheaux ('davies' method), Minghui Wang, Yiyuan Liu, Shizhong Han (simpleM function), Yaowu Liu (ACAT function), Thomas Lumley ('kuonen' method), and James O. Ramsay (functional data analysis functions)
Maintainer: Nadezhda M. Belonogova <belon@bionet.nsc.ru>

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Package worcs updated to version 0.1.6 with previous version 0.1.5 dated 2020-10-09

Title: Workflow for Open Reproducible Code in Science
Description: Create reproducible and transparent research projects in 'R'. This package is based on the Workflow for Open Reproducible Code in Science (WORCS), a step-by-step procedure based on best practices for Open Science. It includes an 'RStudio' project template, several convenience functions, and all dependencies required to make your project reproducible and transparent. WORCS is explained in the tutorial paper by Van Lissa, Brandmaier, Brinkman, Lamprecht, Struiksma, & Vreede (2020). <doi:10.17605/OSF.IO/ZCVBS>.
Author: Caspar J. van Lissa [aut, cre] (<https://orcid.org/0000-0002-0808-5024>), Aaron Peikert [aut] (<https://orcid.org/0000-0001-7813-818X>), Andreas M. Brandmaier [aut] (<https://orcid.org/0000-0001-8765-6982>)
Maintainer: Caspar J. van Lissa <c.j.vanlissa@uu.nl>

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Package psych updated to version 2.0.12 with previous version 2.0.9 dated 2020-10-05

Title: Procedures for Psychological, Psychometric, and Personality Research
Description: A general purpose toolbox for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Functions for simulating and testing particular item and test structures are included. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <https://personality-project.org/r/> web page.
Author: William Revelle [aut, cre] (<https://orcid.org/0000-0003-4880-9610>)
Maintainer: William Revelle <revelle@northwestern.edu>

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Package netCoin updated to version 1.1.26 with previous version 1.1.25 dated 2020-05-26

Title: Interactive Analytic Networks
Description: Create interactive analytic networks. It joins the data analysis power of R to obtain coincidences, co-occurrences and correlations, and the visualization libraries of 'JavaScript' in one package. The methods are described in Escobar and Martinez-Uribe (2020) <doi:10.18637/jss.v093.i11>.
Author: Modesto Escobar <modesto@usal.es>, David Barrios <metal@usal.es>, Carlos Prieto <cprietos@usal.es>, Luis Martinez-Uribe <lmartinez@usal.es>.
Maintainer: Modesto Escobar <modesto@usal.es>

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Package lazytrade updated to version 0.4.3 with previous version 0.4.2 dated 2020-11-12

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 gge updated to version 1.6 with previous version 1.5 dated 2020-07-21

Title: Genotype Plus Genotype-by-Environment Biplots
Description: Create biplots for GGE (genotype plus genotype-by-environment) and GGB (genotype plus genotype-by-block-of-environments) models. See Laffont et al. (2013) <doi:10.2135/cropsci2013.03.0178>.
Author: Kevin Wright [aut, cre] (<https://orcid.org/0000-0002-0617-8673>), Jean-Louis Laffont [aut]
Maintainer: Kevin Wright <kw.stat@gmail.com>

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Package ftrCOOL updated to version 1.1.0 with previous version 0.1.0 dated 2020-08-31

Title: Feature Extraction from Biological Sequences
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>.
Author: Sare Amerifar
Maintainer: Sare Amerifar <sare.ameri.01@gmail.com>

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 ftrCOOL-0.1.0/ftrCOOL/man/AutoCorDiNuc.Rd                       |only
 ftrCOOL-0.1.0/ftrCOOL/man/AutoCorTriNuc.Rd                      |only
 ftrCOOL-0.1.0/ftrCOOL/man/CkSNUCpair.Rd                         |only
 ftrCOOL-0.1.0/ftrCOOL/man/CodonUsage.Rd                         |only
 ftrCOOL-0.1.0/ftrCOOL/man/DiNucIndex.Rd                         |only
 ftrCOOL-0.1.0/ftrCOOL/man/Dinuc2Binary.Rd                       |only
 ftrCOOL-0.1.0/ftrCOOL/man/ENUComposition.Rd                     |only
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 ftrCOOL-0.1.0/ftrCOOL/man/LocalPoSpKaaF.Rd                      |only
 ftrCOOL-0.1.0/ftrCOOL/man/LocalPoSpKnucF.Rd                     |only
 ftrCOOL-0.1.0/ftrCOOL/man/NUC2Binary.Rd                         |only
 ftrCOOL-0.1.0/ftrCOOL/man/NUCKpartComposition.Rd                |only
 ftrCOOL-0.1.0/ftrCOOL/man/PSEkNCTri.Rd                          |only
 ftrCOOL-0.1.0/ftrCOOL/man/PSEkNCdi.Rd                           |only
 ftrCOOL-0.1.0/ftrCOOL/man/TriNucIndex.Rd                        |only
 ftrCOOL-0.1.0/ftrCOOL/man/kNUComposition.Rd                     |only
 ftrCOOL-0.1.0/ftrCOOL/man/maxORFlength.Rd                       |only
 ftrCOOL-0.1.0/ftrCOOL/man/novel_PseKNC.Rd                       |only
 ftrCOOL-0.1.0/ftrCOOL/tests/testthat/test_novelPseNuc.R         |only
 ftrCOOL-1.1.0/ftrCOOL/DESCRIPTION                               |    8 
 ftrCOOL-1.1.0/ftrCOOL/MD5                                       |  533 ++++++----
 ftrCOOL-1.1.0/ftrCOOL/NAMESPACE                                 |   81 +
 ftrCOOL-1.1.0/ftrCOOL/R/AA2Binary.R                             |   16 
 ftrCOOL-1.1.0/ftrCOOL/R/AAKpartComposition.R                    |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/AAindex.R                               |    9 
 ftrCOOL-1.1.0/ftrCOOL/R/AAutoCor.R                              |   37 
 ftrCOOL-1.1.0/ftrCOOL/R/ANF_DNA.R                               |only
 ftrCOOL-1.1.0/ftrCOOL/R/ANF_RNA.R                               |only
 ftrCOOL-1.1.0/ftrCOOL/R/APAAC.R                                 |    8 
 ftrCOOL-1.1.0/ftrCOOL/R/APkNUCTri_DNA.R                         |only
 ftrCOOL-1.1.0/ftrCOOL/R/APkNUCdi_DNA.R                          |only
 ftrCOOL-1.1.0/ftrCOOL/R/APkNUCdi_RNA.R                          |only
 ftrCOOL-1.1.0/ftrCOOL/R/ASA.R                                   |only
 ftrCOOL-1.1.0/ftrCOOL/R/AutoCorDiNUC_DNA.R                      |only
 ftrCOOL-1.1.0/ftrCOOL/R/AutoCorDiNUC_RNA.R                      |only
 ftrCOOL-1.1.0/ftrCOOL/R/AutoCorTriNUC_DNA.R                     |only
 ftrCOOL-1.1.0/ftrCOOL/R/Blosum62.R                              |    9 
 ftrCOOL-1.1.0/ftrCOOL/R/CTD.R                                   |   29 
 ftrCOOL-1.1.0/ftrCOOL/R/CTDC.R                                  |   33 
 ftrCOOL-1.1.0/ftrCOOL/R/CTDD.R                                  |   29 
 ftrCOOL-1.1.0/ftrCOOL/R/CTDT.R                                  |   33 
 ftrCOOL-1.1.0/ftrCOOL/R/CkSAApair.R                             |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/CkSGAApair.R                            |    4 
 ftrCOOL-1.1.0/ftrCOOL/R/CkSNUCpair_DNA.R                        |only
 ftrCOOL-1.1.0/ftrCOOL/R/CkSNUCpair_RNA.R                        |only
 ftrCOOL-1.1.0/ftrCOOL/R/CodonFraction.R                         |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/CodonUsage_DNA.R                        |only
 ftrCOOL-1.1.0/ftrCOOL/R/CodonUsage_RNA.R                        |only
 ftrCOOL-1.1.0/ftrCOOL/R/DDE.R                                   |    3 
 ftrCOOL-1.1.0/ftrCOOL/R/DiNUC2Binary_DNA.R                      |only
 ftrCOOL-1.1.0/ftrCOOL/R/DiNUC2Binary_RNA.R                      |only
 ftrCOOL-1.1.0/ftrCOOL/R/DiNUCindex_DNA.R                        |only
 ftrCOOL-1.1.0/ftrCOOL/R/DiNUCindex_RNA.R                        |only
 ftrCOOL-1.1.0/ftrCOOL/R/DisorderB.R                             |only
 ftrCOOL-1.1.0/ftrCOOL/R/DisorderC.R                             |only
 ftrCOOL-1.1.0/ftrCOOL/R/DisorderS.R                             |only
 ftrCOOL-1.1.0/ftrCOOL/R/EAAComposition.R                        |   37 
 ftrCOOL-1.1.0/ftrCOOL/R/EGAAComposition.R                       |  124 ++
 ftrCOOL-1.1.0/ftrCOOL/R/EIIP.R                                  |   15 
 ftrCOOL-1.1.0/ftrCOOL/R/ENUComposition_DNA.R                    |only
 ftrCOOL-1.1.0/ftrCOOL/R/ENUComposition_RNA.R                    |only
 ftrCOOL-1.1.0/ftrCOOL/R/EffectiveNumberOfCodon.R                |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/ExpectedValKmerNUC_DNA.R                |only
 ftrCOOL-1.1.0/ftrCOOL/R/ExpectedValKmerNUC_RNA.R                |only
 ftrCOOL-1.1.0/ftrCOOL/R/ExpectedValueAA.R                       |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/ExpectedValueGAA.R                      |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/ExpectedValueGKmerAA.R                  |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/ExpectedValueKmerAA.R                   |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/GAAKpartComposition.R                   |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/G_Ccontent_DNA.R                        |only
 ftrCOOL-1.1.0/ftrCOOL/R/G_Ccontent_RNA.R                        |only
 ftrCOOL-1.1.0/ftrCOOL/R/GrpDDE.R                                |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/KNNPeptide.R                            |only
 ftrCOOL-1.1.0/ftrCOOL/R/KNNProtein.R                            |only
 ftrCOOL-1.1.0/ftrCOOL/R/LocalPoSpKAAF.R                         |only
 ftrCOOL-1.1.0/ftrCOOL/R/LocalPoSpKNUCF_DNA.R                    |only
 ftrCOOL-1.1.0/ftrCOOL/R/LocalPoSpKNUCF_RNA.R                    |only
 ftrCOOL-1.1.0/ftrCOOL/R/NCP_DNA.R                               |only
 ftrCOOL-1.1.0/ftrCOOL/R/NCP_RNA.R                               |only
 ftrCOOL-1.1.0/ftrCOOL/R/NUC2Binary_DNA.R                        |only
 ftrCOOL-1.1.0/ftrCOOL/R/NUC2Binary_RNA.R                        |only
 ftrCOOL-1.1.0/ftrCOOL/R/NUCKpartComposition_DNA.R               |only
 ftrCOOL-1.1.0/ftrCOOL/R/NUCKpartComposition_RNA.R               |only
 ftrCOOL-1.1.0/ftrCOOL/R/PCPseDNC.R                              |only
 ftrCOOL-1.1.0/ftrCOOL/R/PSEAAC.R                                |    7 
 ftrCOOL-1.1.0/ftrCOOL/R/PSEkNUCTri_DNA.R                        |only
 ftrCOOL-1.1.0/ftrCOOL/R/PSEkNUCdi_DNA.R                         |only
 ftrCOOL-1.1.0/ftrCOOL/R/PSEkNUCdi_RNA.R                         |only
 ftrCOOL-1.1.0/ftrCOOL/R/PSSM.R                                  |only
 ftrCOOL-1.1.0/ftrCOOL/R/PSTNPds.R                               |only
 ftrCOOL-1.1.0/ftrCOOL/R/PSTNPss_DNA.R                           |only
 ftrCOOL-1.1.0/ftrCOOL/R/PSTNPss_RNA.R                           |only
 ftrCOOL-1.1.0/ftrCOOL/R/PseEIIP.R                               |   14 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T1.R                           |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T10.R                          |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T11.R                          |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T12.R                          |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T13.R                          |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T14.R                          |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T15.R                          |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T16.R                          |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T2.R                           |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T3A.R                          |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T3B.R                          |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T4.R                           |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T5.R                           |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T6A.R                          |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T6B.R                          |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T7.R                           |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T8.R                           |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/PseKRAAC_T9.R                           |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/QSOrder.R                               |    3 
 ftrCOOL-1.1.0/ftrCOOL/R/SAAC.R                                  |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/SGAAC.R                                 |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/SOCNumber.R                             |    3 
 ftrCOOL-1.1.0/ftrCOOL/R/SSEB.R                                  |only
 ftrCOOL-1.1.0/ftrCOOL/R/SSEC.R                                  |only
 ftrCOOL-1.1.0/ftrCOOL/R/SSES.R                                  |only
 ftrCOOL-1.1.0/ftrCOOL/R/TorsionAngle.R                          |only
 ftrCOOL-1.1.0/ftrCOOL/R/TriNUCindex_DNA.R                       |only
 ftrCOOL-1.1.0/ftrCOOL/R/codonAdaptionIndex.R                    |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/conjointTrKS.R                          |    4 
 ftrCOOL-1.1.0/ftrCOOL/R/conjointTriad.R                         |    5 
 ftrCOOL-1.1.0/ftrCOOL/R/fa.read.R                               |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/fickettScore.R                          |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/kAAComposition.R                        |    3 
 ftrCOOL-1.1.0/ftrCOOL/R/kGAAComposition.R                       |    5 
 ftrCOOL-1.1.0/ftrCOOL/R/kNUComposition_DNA.R                    |only
 ftrCOOL-1.1.0/ftrCOOL/R/kNUComposition_RNA.R                    |only
 ftrCOOL-1.1.0/ftrCOOL/R/maxORF.R                                |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/maxORF_RNA.R                            |only
 ftrCOOL-1.1.0/ftrCOOL/R/maxORFlength_DNA.R                      |only
 ftrCOOL-1.1.0/ftrCOOL/R/maxORFlength_RNA.R                      |only
 ftrCOOL-1.1.0/ftrCOOL/R/nameKmer.R                              |    4 
 ftrCOOL-1.1.0/ftrCOOL/R/needleman.R                             |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/nonStandardSeq.R                        |    2 
 ftrCOOL-1.1.0/ftrCOOL/R/readASAdir.R                            |only
 ftrCOOL-1.1.0/ftrCOOL/R/readDisDir.R                            |only
 ftrCOOL-1.1.0/ftrCOOL/R/readPSSMdir.R                           |only
 ftrCOOL-1.1.0/ftrCOOL/R/readTorsionDir.R                        |only
 ftrCOOL-1.1.0/ftrCOOL/R/readss2Dir.R                            |only
 ftrCOOL-1.1.0/ftrCOOL/R/revComp.R                               |   10 
 ftrCOOL-1.1.0/ftrCOOL/R/zSCALE.R                                |    4 
 ftrCOOL-1.1.0/ftrCOOL/inst/extdata/Carica_papaya101RNA.txt      |only
 ftrCOOL-1.1.0/ftrCOOL/inst/extdata/Carica_papaya101RNATotal.txt |only
 ftrCOOL-1.1.0/ftrCOOL/inst/extdata/DI_DNA.csv                   |only
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 ftrCOOL-1.1.0/ftrCOOL/inst/extdata/ncp.csv                      |only
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 ftrCOOL-1.1.0/ftrCOOL/inst/testForder                           |only
 ftrCOOL-1.1.0/ftrCOOL/man/AA2Binary.Rd                          |   10 
 ftrCOOL-1.1.0/ftrCOOL/man/AAKpartComposition.Rd                 |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/AAindex.Rd                            |    9 
 ftrCOOL-1.1.0/ftrCOOL/man/AAutoCor.Rd                           |    6 
 ftrCOOL-1.1.0/ftrCOOL/man/ANF_DNA.Rd                            |only
 ftrCOOL-1.1.0/ftrCOOL/man/ANF_RNA.Rd                            |only
 ftrCOOL-1.1.0/ftrCOOL/man/APAAC.Rd                              |    8 
 ftrCOOL-1.1.0/ftrCOOL/man/APkNUCTri_DNA.Rd                      |only
 ftrCOOL-1.1.0/ftrCOOL/man/APkNUCdi_DNA.Rd                       |only
 ftrCOOL-1.1.0/ftrCOOL/man/APkNUCdi_RNA.Rd                       |only
 ftrCOOL-1.1.0/ftrCOOL/man/ASA.Rd                                |only
 ftrCOOL-1.1.0/ftrCOOL/man/AutoCorDiNUC_DNA.Rd                   |only
 ftrCOOL-1.1.0/ftrCOOL/man/AutoCorDiNUC_RNA.Rd                   |only
 ftrCOOL-1.1.0/ftrCOOL/man/AutoCorTriNUC_DNA.Rd                  |only
 ftrCOOL-1.1.0/ftrCOOL/man/BLOSUM62.Rd                           |    9 
 ftrCOOL-1.1.0/ftrCOOL/man/CTD.Rd                                |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/CTDC.Rd                               |    4 
 ftrCOOL-1.1.0/ftrCOOL/man/CTDD.Rd                               |    4 
 ftrCOOL-1.1.0/ftrCOOL/man/CTDT.Rd                               |    4 
 ftrCOOL-1.1.0/ftrCOOL/man/CkSAApair.Rd                          |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/CkSGAApair.Rd                         |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/CkSNUCpair_DNA.Rd                     |only
 ftrCOOL-1.1.0/ftrCOOL/man/CkSNUCpair_RNA.Rd                     |only
 ftrCOOL-1.1.0/ftrCOOL/man/CodonFraction.Rd                      |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/CodonUsage_DNA.Rd                     |only
 ftrCOOL-1.1.0/ftrCOOL/man/CodonUsage_RNA.Rd                     |only
 ftrCOOL-1.1.0/ftrCOOL/man/DDE.Rd                                |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/DiNUC2Binary_DNA.Rd                   |only
 ftrCOOL-1.1.0/ftrCOOL/man/DiNUC2Binary_RNA.Rd                   |only
 ftrCOOL-1.1.0/ftrCOOL/man/DiNUCindex_DNA.Rd                     |only
 ftrCOOL-1.1.0/ftrCOOL/man/DiNUCindex_RNA.Rd                     |only
 ftrCOOL-1.1.0/ftrCOOL/man/DisorderB.Rd                          |only
 ftrCOOL-1.1.0/ftrCOOL/man/DisorderC.Rd                          |only
 ftrCOOL-1.1.0/ftrCOOL/man/DisorderS.Rd                          |only
 ftrCOOL-1.1.0/ftrCOOL/man/EAAComposition.Rd                     |   14 
 ftrCOOL-1.1.0/ftrCOOL/man/EGAAComposition.Rd                    |   12 
 ftrCOOL-1.1.0/ftrCOOL/man/EIIP.Rd                               |   12 
 ftrCOOL-1.1.0/ftrCOOL/man/ENUComposition_DNA.Rd                 |only
 ftrCOOL-1.1.0/ftrCOOL/man/ENUComposition_RNA.Rd                 |only
 ftrCOOL-1.1.0/ftrCOOL/man/EffectiveNumberCodon.Rd               |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/ExpectedValKmerNUC_DNA.Rd             |only
 ftrCOOL-1.1.0/ftrCOOL/man/ExpectedValKmerNUC_RNA.Rd             |only
 ftrCOOL-1.1.0/ftrCOOL/man/ExpectedValueAA.Rd                    |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/ExpectedValueGAA.Rd                   |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/ExpectedValueGKmerAA.Rd               |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/ExpectedValueKmerAA.Rd                |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/GAAKpartComposition.Rd                |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/G_Ccontent_DNA.Rd                     |only
 ftrCOOL-1.1.0/ftrCOOL/man/G_Ccontent_RNA.Rd                     |only
 ftrCOOL-1.1.0/ftrCOOL/man/GrpDDE.Rd                             |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/KNNPeptide.Rd                         |only
 ftrCOOL-1.1.0/ftrCOOL/man/KNNProtein.Rd                         |only
 ftrCOOL-1.1.0/ftrCOOL/man/LocalPoSpKAAF.Rd                      |only
 ftrCOOL-1.1.0/ftrCOOL/man/LocalPoSpKNUCF_DNA.Rd                 |only
 ftrCOOL-1.1.0/ftrCOOL/man/LocalPoSpKNUCF_RNA.Rd                 |only
 ftrCOOL-1.1.0/ftrCOOL/man/NCP_DNA.Rd                            |only
 ftrCOOL-1.1.0/ftrCOOL/man/NCP_RNA.Rd                            |only
 ftrCOOL-1.1.0/ftrCOOL/man/NUC2Binary_DNA.Rd                     |only
 ftrCOOL-1.1.0/ftrCOOL/man/NUC2Binary_RNA.Rd                     |only
 ftrCOOL-1.1.0/ftrCOOL/man/NUCKpartComposition_DNA.Rd            |only
 ftrCOOL-1.1.0/ftrCOOL/man/NUCKpartComposition_RNA.Rd            |only
 ftrCOOL-1.1.0/ftrCOOL/man/PCPseDNC.Rd                           |only
 ftrCOOL-1.1.0/ftrCOOL/man/PSEAAC.Rd                             |    6 
 ftrCOOL-1.1.0/ftrCOOL/man/PSEkNUCTri_DNA.Rd                     |only
 ftrCOOL-1.1.0/ftrCOOL/man/PSEkNUCdi_DNA.Rd                      |only
 ftrCOOL-1.1.0/ftrCOOL/man/PSEkNUCdi_RNA.Rd                      |only
 ftrCOOL-1.1.0/ftrCOOL/man/PSSM.Rd                               |only
 ftrCOOL-1.1.0/ftrCOOL/man/PSTNPds.Rd                            |only
 ftrCOOL-1.1.0/ftrCOOL/man/PSTNPss_DNA.Rd                        |only
 ftrCOOL-1.1.0/ftrCOOL/man/PSTNPss_RNA.Rd                        |only
 ftrCOOL-1.1.0/ftrCOOL/man/PseEIIP.Rd                            |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T1.Rd                        |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T10.Rd                       |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T11.Rd                       |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T12.Rd                       |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T13.Rd                       |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T14.Rd                       |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T15.Rd                       |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T16.Rd                       |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T2.Rd                        |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T3A.Rd                       |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T3B.Rd                       |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T4.Rd                        |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T5.Rd                        |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T6A.Rd                       |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T6B.Rd                       |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T7.Rd                        |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T8.Rd                        |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/PseKRAAC_T9.Rd                        |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/QSOrder.Rd                            |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/SAAC.Rd                               |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/SGAAC.Rd                              |    2 
 ftrCOOL-1.1.0/ftrCOOL/man/SOCNumber.Rd                          |    2 
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Package deepgp updated to version 0.2.0 with previous version 0.1.0 dated 2020-10-29

Title: Sequential Design for Deep Gaussian Processes using MCMC
Description: Performs model fitting and sequential design for deep Gaussian processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>. Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Sequential design criteria include integrated mean-squared error (IMSE), active learning Cohn (ALC), and expected improvement (EI). Covariance structure is based on inverse exponentiated squared euclidean distance. Applicable to noisy and deterministic functions. Incorporates SNOW parallelization and utilizes C under the hood.
Author: Annie Sauer <anniees@vt.edu>
Maintainer: Annie Sauer <anniees@vt.edu>

Diff between deepgp versions 0.1.0 dated 2020-10-29 and 0.2.0 dated 2020-12-16

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New package TwoArmSurvSim with initial version 0.1
Package: TwoArmSurvSim
Title: Simulate Survival Data for Randomized Clinical Trials
Version: 0.1
Authors@R: c( person("Bo", "Zhang", email = "bzhang@pumabiotechnology.com", role = c("cre","aut")), person("Yi", "Zhong", email = "yi.zhong1006@gmail.com", role = c("aut","ctb")), person("Ginny", "Peng", email = "gpeng@pumabiotechnology.com", role = c("aut","ctb")), person("Bin", "Yao", email = "BYao@pumabiotechnology.com", role = c("aut","ctb")) )
Description: A system to simulate clinical trials with time to event endpoints. Event simulation is based on Cox models allowing for covariates in addition to the treatment or group factor. Specific drop-out rates (separate from administrative censoring) can be controlled in the simulation. Other features include stratified randomization, non-proportional hazards, different accrual patterns, and event projection (timing to reach the target event) based on interim data.
License: GPL (>= 2)
Depends: R (>= 4.0.0),blockrand,dplyr,survival
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-12-15 01:55:55 UTC; bzhang
Author: Bo Zhang [cre, aut], Yi Zhong [aut, ctb], Ginny Peng [aut, ctb], Bin Yao [aut, ctb]
Maintainer: Bo Zhang <bzhang@pumabiotechnology.com>
Repository: CRAN
Date/Publication: 2020-12-16 15:20:02 UTC

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New package shinyfullscreen with initial version 1.0.0
Package: shinyfullscreen
Title: Display 'HTML' Elements on Full Screen in 'Shiny' Apps
Version: 1.0.0
Authors@R: person(given = "Etienne", family = "Bacher", role = c("aut", "cre", "cph"), email = "etienne.bacher@protonmail.com")
Description: In 'Shiny' apps, it is sometimes useful to see a plot or a table in full screen. Using 'Shinyfullscreen', you can easily designate the 'HTML' elements that can be displayed on fullscreen and use buttons to trigger the fullscreen view.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: shiny, jsonlite
URL: https://github.com/etiennebacher/shinyfullscreen
BugReports: https://github.com/etiennebacher/shinyfullscreen/issues
Suggests: testthat, spelling
Language: en-US
NeedsCompilation: no
Packaged: 2020-12-15 12:41:04 UTC; etienne
Author: Etienne Bacher [aut, cre, cph]
Maintainer: Etienne Bacher <etienne.bacher@protonmail.com>
Repository: CRAN
Date/Publication: 2020-12-16 16:00:02 UTC

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New package RHSDB with initial version 0.1.0
Package: RHSDB
Type: Package
Title: Ryan-Holm Step-Down Bonferroni or Sidak Procedure
Version: 0.1.0
Authors@R: c(person("Zhicheng Du", "Developer", role = c("aut", "cre", "cph"), email = "dgdzc@hotmail.com"), person("Hailin Feng", "Developer", role = "aut"))
Description: The Ryan-Holm step-down Bonferroni or Sidak procedure is to control the family-wise (experiment-wise) type I error rate in the multiple comparisons. This procedure provides the adjusting p-values and adjusting CIs. The methods used in this package are referenced from John Ludbrook (2000) <doi:10.1046/j.1440-1681.2000.03223.x>.
License: GPL-3
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2020-12-15 09:37:37 UTC; dgdzc
Author: Zhicheng Du Developer [aut, cre, cph], Hailin Feng Developer [aut]
Maintainer: Zhicheng Du Developer <dgdzc@hotmail.com>
Repository: CRAN
Date/Publication: 2020-12-16 15:30:09 UTC

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New package ptm with initial version 0.2.2
Package: ptm
Type: Package
Title: Analyses of Protein Post-Translational Modifications
Version: 0.2.2
Author: Juan Carlos Aledo [au, cre]
Maintainer: Juan Carlos Aledo <caledo@uma.es>
Description: Contains utilities for the analysis of post-translational modifications (PTMs) in proteins, with particular emphasis on the sulfoxidation of methionine residues. Features include the ability to download, filter and analyze data from the sulfoxidation database 'MetOSite', and integrate data from other main PTMs (other databases). Utilities to search and characterize S-aromatic motifs in proteins are also provided. In addition, functions to analyze sequence environments around modifiable residues in proteins can be found. For instance, 'ptm' allows to search for amino acids either overrepresented or avoided around the modifiable residues from the proteins of interest. Functions tailored to test statistical hypothesis related to these differential sequence environments are also implemented. A number of utilities to assess the effect of the modification/mutation of a given residue on the protein stability, have also been included in this package. Further and detailed information regarding the methods in this package can be found in (Aledo (2020) <https://metositeptm.com>).
License: GPL (>= 2)
URL: https://bitbucket.org/jcaledo/ptm, https://metositeptm.com
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Depends: R (>= 3.5.0)
Imports: bio3d (>= 2.3-4), Biostrings, graphics, httr (>= 1.3.1), igraph, jsonlite, muscle, RCurl, seqinr, stats, utils, xml2, XML
Suggests: KEGGREST, knitr, markdown, rmarkdown, stringr, testthat,
NeedsCompilation: no
Packaged: 2020-12-15 12:44:24 UTC; JCA
Repository: CRAN
Date/Publication: 2020-12-16 16:00:05 UTC

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Package ggrepel updated to version 0.9.0 with previous version 0.8.2 dated 2020-03-08

Title: Automatically Position Non-Overlapping Text Labels with 'ggplot2'
Description: Provides text and label geoms for 'ggplot2' that help to avoid overlapping text labels. Labels repel away from each other and away from the data points.
Author: Kamil Slowikowski [aut, cre] (<https://orcid.org/0000-0002-2843-6370>), Alicia Schep [ctb] (<https://orcid.org/0000-0002-3915-0618>), Sean Hughes [ctb] (<https://orcid.org/0000-0002-9409-9405>), Trung Kien Dang [ctb] (<https://orcid.org/0000-0001-7562-6495>), Saulius Lukauskas [ctb], Jean-Olivier Irisson [ctb] (<https://orcid.org/0000-0003-4920-3880>), Zhian N Kamvar [ctb] (<https://orcid.org/0000-0003-1458-7108>), Thompson Ryan [ctb] (<https://orcid.org/0000-0002-0450-8181>), Dervieux Christophe [ctb] (<https://orcid.org/0000-0003-4474-2498>), Yutani Hiroaki [ctb], Pierre Gramme [ctb], Amir Masoud Abdol [ctb], Malcolm Barrett [ctb] (<https://orcid.org/0000-0003-0299-5825>), Robrecht Cannoodt [ctb] (<https://orcid.org/0000-0003-3641-729X>), Michał Krassowski [ctb] (<https://orcid.org/0000-0002-9638-7785>)
Maintainer: Kamil Slowikowski <kslowikowski@gmail.com>

Diff between ggrepel versions 0.8.2 dated 2020-03-08 and 0.9.0 dated 2020-12-16

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New package brolgar with initial version 0.1.0
Package: brolgar
Title: Browse Over Longitudinal Data Graphically and Analytically in R
Version: 0.1.0
Authors@R: c(person(given = "Nicholas", family = "Tierney", role = c("aut", "cre"), email = "nicholas.tierney@gmail.com", comment = c(ORCID = "https://orcid.org/0000-0003-1460-8722")), person(given = "Di", family = "Cook", role = "aut", email = "dicook@monash.edu", comment = c(ORCID = "https://orcid.org/0000-0002-3813-7155")), person(given = "Tania", family = "Prvan", role = "aut", email = "tania.prvan@mq.edu.au"), person(given = "Stuart", family = "Lee", role = "ctb"), person(given = "Earo", family = "Wang", role = "ctb"))
Description: Provides a framework of tools to summarise, visualise, and explore longitudinal data. It builds upon the tidy time series data frames used in the 'tsibble' package, and is designed to integrate within the 'tidyverse', and 'tidyverts' (for time series) ecosystems. The methods implemented include calculating features for understanding longitudinal data, including calculating summary statistics such as quantiles, medians, and numeric ranges, sampling individual series, identifying individual series representative of a group, and extending the facet system in 'ggplot2' to facilitate exploration of samples of data. These methods are fully described in the paper "brolgar: An R package to Browse Over Longitudinal Data Graphically and Analytically in R", Nicholas Tierney, Dianne Cook, Tania Prvan (2020) <arXiv:2012.01619>.
License: MIT + file LICENSE
URL: https://github.com/njtierney/brolgar
BugReports: https://github.com/njtierney/brolgar/issues
Depends: R (>= 3.5.0)
Imports: dplyr (>= 0.8.3), fabletools, ggplot2 (>= 3.2.0), glue (>= 1.3.1), magrittr (>= 1.5), purrr (>= 0.3.2), rlang (>= 0.4.0), stats, tibble (>= 2.1.3), tidyr (>= 0.8.3), tsibble (>= 0.8.2), vctrs
Suggests: covr, gapminder, gghighlight (>= 0.1.0), knitr (>= 1.23), lme4, modelr, readr (>= 1.3.1), rmarkdown (>= 1.14), spelling (>= 2.1), testthat (>= 2.1.0), tsibbledata, vdiffr (>= 0.3.1)
VignetteBuilder: knitr
Encoding: UTF-8
Language: en-US
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-12-15 07:55:37 UTC; ntie0001
Author: Nicholas Tierney [aut, cre] (<https://orcid.org/0000-0003-1460-8722>), Di Cook [aut] (<https://orcid.org/0000-0002-3813-7155>), Tania Prvan [aut], Stuart Lee [ctb], Earo Wang [ctb]
Maintainer: Nicholas Tierney <nicholas.tierney@gmail.com>
Repository: CRAN
Date/Publication: 2020-12-16 15:30:05 UTC

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Package sparta updated to version 0.7.0 with previous version 0.6.1 dated 2020-11-24

Title: Sparse Tables
Description: Fast Multiplication and Marginalization of Sparse Tables.
Author: Mads Lindskou [aut, cre]
Maintainer: Mads Lindskou <mads@math.aau.dk>

Diff between sparta versions 0.6.1 dated 2020-11-24 and 0.7.0 dated 2020-12-16

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Package sctransform updated to version 0.3.2 with previous version 0.3.1 dated 2020-10-08

Title: Variance Stabilizing Transformations for Single Cell UMI Data
Description: A normalization method for single-cell UMI count data using a variance stabilizing transformation. The transformation is based on a negative binomial regression model with regularized parameters. As part of the same regression framework, this package also provides functions for batch correction, and data correction. See Hafemeister and Satija 2019 <doi:10.1186/s13059-019-1874-1> for more details.
Author: Christoph Hafemeister [aut, cre] (<https://orcid.org/0000-0001-6365-8254>)
Maintainer: Christoph Hafemeister <christoph.hafemeister@nyu.edu>

Diff between sctransform versions 0.3.1 dated 2020-10-08 and 0.3.2 dated 2020-12-16

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 21 files changed, 854 insertions(+), 165 deletions(-)

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Package jti updated to version 0.6.0 with previous version 0.5.2 dated 2020-11-24

Title: Junction Tree Inference
Description: Minimal and memory efficient implementation of the junction tree algorithm using the Lauritzen-Spiegelhalter scheme; S. L. Lauritzen and D. J. Spiegelhalter (1988) <https://www.jstor.org/stable/2345762?seq=1>.
Author: Mads Lindskou [aut, cre]
Maintainer: Mads Lindskou <mads@math.aau.dk>

Diff between jti versions 0.5.2 dated 2020-11-24 and 0.6.0 dated 2020-12-16

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Package gmvarkit updated to version 1.3.0 with previous version 1.2.3 dated 2020-11-03

Title: Estimate Gaussian Mixture Vector Autoregressive Model
Description: Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive (GMVAR) model, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi Virolainen (2020) <arXiv:2007.04713>.
Author: Savi Virolainen [aut, cre]
Maintainer: Savi Virolainen <savi.virolainen@helsinki.fi>

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Package Rquake updated to version 2.4-4 with previous version 2.4-0 dated 2016-08-20

Title: Seismic Hypocenter Determination
Description: Hypocenter estimation and analysis of seismic data collected continuously, or in trigger mode. The functions organize other functions from RSEIS and GEOmap to help researchers pick, locate, and store hypocenters for detailed seismic investigation.
Author: Jonathan M. Lees [aut, cre], Baptiste Auguie [ctb]
Maintainer: Jonathan M. Lees <jonathan.lees@unc.edu>

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Package mlr3db updated to version 0.3.0 with previous version 0.2.0 dated 2020-09-28

Title: Data Base Backend for 'mlr3'
Description: Extends the 'mlr3' package with a backend to transparently work with databases. Includes two extra backends: One relies on relies on the abstraction of package 'dbplyr' to interact with one of the many supported database management systems (DBMS). The other one is specialized for package 'duckdb'.
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>)
Maintainer: Michel Lang <michellang@gmail.com>

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Package Markovchart updated to version 1.1.1 with previous version 1.1.0 dated 2020-11-07

Title: Markov Chain-Based Cost-Optimal Control Charts
Description: Functions for cost-optimal control charts with a focus on health care applications. Compared to assumptions in traditional control chart theory, here, we allow random shift sizes, random repair and random sampling times. The package focuses on X-bar charts with a sample size of 1 (representing the monitoring of a single patient at a time). The methods are described in Zempleni et al. (2004) <doi:10.1002/asmb.521>, Dobi and Zempleni (2019) <doi:10.1002/qre.2518> and Dobi and Zempleni (2019) <http://ac.inf.elte.hu/Vol_049_2019/129_49.pdf>.
Author: Balazs Dobi & Andras Zempleni
Maintainer: Balazs Dobi <dobib@cs.elte.hu>

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Package lori updated to version 2.2.2 with previous version 2.2.1 dated 2020-11-26

Title: Imputation of High-Dimensional Count Data using Side Information
Description: Analysis, imputation, and multiple imputation of count data using covariates. LORI uses a log-linear Poisson model where main row and column effects, as well as effects of known covariates and interaction terms can be fitted. The estimation procedure is based on the convex optimization of the Poisson loss penalized by a Lasso type penalty and a nuclear norm. LORI returns estimates of main effects, covariate effects and interactions, as well as an imputed count table. The package also contains a multiple imputation procedure. The methods are described in Robin, Josse, Moulines and Sardy (2019) <arXiv:1703.02296v4>.
Author: Genevieve Robin [aut, cre]
Maintainer: Genevieve Robin <genevieve.robin@cnrs.fr>

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Package ggquickeda updated to version 0.1.9 with previous version 0.1.8 dated 2020-11-05

Title: Quickly Explore Your Data Using 'ggplot2' and 'table1' Summary Tables
Description: Quickly and easily perform exploratory data analysis by uploading your data as a 'csv' file. Start generating insights using 'ggplot2' plots and 'table1' tables with descriptive stats, all using an easy-to-use point and click 'Shiny' interface.
Author: Samer Mouksassi [aut, cre] (<https://orcid.org/0000-0002-7152-6654>), Dean Attali [aut], Benjamin Rich [aut] (provided summary stats tables tab code), Michael Sachs [aut] (provided ggkm code)
Maintainer: Samer Mouksassi <samermouksassi@gmail.com>

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Package amen updated to version 1.4.4 with previous version 1.3 dated 2017-05-24

Title: Additive and Multiplicative Effects Models for Networks and Relational Data
Description: Analysis of dyadic network and relational data using additive and multiplicative effects (AME) models. The basic model includes regression terms, the covariance structure of the social relations model (Warner, Kenny and Stoto (1979) <DOI:10.1037/0022-3514.37.10.1742>, Wong (1982) <DOI:10.2307/2287296>), and multiplicative factor models (Hoff(2009) <DOI:10.1007/s10588-008-9040-4>). Several different link functions accommodate different relational data structures, including binary/network data, normal relational data, zero-inflated positive outcomes using a tobit model, ordinal relational data and data from fixed-rank nomination schemes. Several of these link functions are discussed in Hoff, Fosdick, Volfovsky and Stovel (2013) <DOI:10.1017/nws.2013.17>. Development of this software was supported in part by NIH grant R01HD067509.
Author: Peter Hoff [aut, cre], Bailey Fosdick [aut], Alex Volfovsky [aut], Yanjun He [ctb]
Maintainer: Peter Hoff <peter.hoff@duke.edu>

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More information about amen at CRAN
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Package sNPLS updated to version 1.0.27 with previous version 0.3.31 dated 2018-02-20

Title: NPLS Regression with L1 Penalization
Description: Tools for performing variable selection in three-way data using N-PLS in combination with L1 penalization, Selectivity Ratio and VIP scores. The N-PLS model (Rasmus Bro, 1996 <DOI:10.1002/(SICI)1099-128X(199601)10:1%3C47::AID-CEM400%3E3.0.CO;2-C>) is the natural extension of PLS (Partial Least Squares) to N-way structures, and tries to maximize the covariance between X and Y data arrays. The package also adds variable selection through L1 penalization, Selectivity Ratio and VIP scores.
Author: David Hervas
Maintainer: David Hervas <ddhervas@yahoo.es>

Diff between sNPLS versions 0.3.31 dated 2018-02-20 and 1.0.27 dated 2020-12-16

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Package msir updated to version 1.3.3 with previous version 1.3.2 dated 2019-03-15

Title: Model-Based Sliced Inverse Regression
Description: An R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.
Author: Luca Scrucca [aut, cre] (<https://orcid.org/0000-0003-3826-0484>)
Maintainer: Luca Scrucca <luca.scrucca@unipg.it>

Diff between msir versions 1.3.2 dated 2019-03-15 and 1.3.3 dated 2020-12-16

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Package mlr3benchmark updated to version 0.1.1 with previous version 0.1.0 dated 2020-11-19

Title: Analysis and Visualisation of Benchmark Experiments
Description: Implements methods for post-hoc analysis and visualisation of benchmark experiments, for 'mlr3' and beyond.
Author: Sonabend Raphael [cre, aut] (<https://orcid.org/0000-0001-9225-4654>), Florian Pfisterer [aut] (<https://orcid.org/0000-0001-8867-762X>), Michel Lang [ctb] (<https://orcid.org/0000-0001-9754-0393>), Bernd Bischl [ctb] (<https://orcid.org/0000-0001-6002-6980>)
Maintainer: Sonabend Raphael <raphael.sonabend.15@ucl.ac.uk>

Diff between mlr3benchmark versions 0.1.0 dated 2020-11-19 and 0.1.1 dated 2020-12-16

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Package dynamichazard updated to version 0.6.7 with previous version 0.6.6 dated 2019-10-14

Title: Dynamic Hazard Models using State Space Models
Description: Contains functions that lets you fit dynamic hazard models using state space models. The first implemented model is described in Fahrmeir (1992) <doi:10.1080/01621459.1992.10475232> and Fahrmeir (1994) <doi:10.1093/biomet/81.2.317>. Extensions hereof are available where the Extended Kalman filter is replaced by an unscented Kalman filter and other options including particle filters. The implemented particle filters support more general state space models.
Author: Benjamin Christoffersen [cre, aut], Alan Miller [cph], Anthony Williams [cph], Boost developers [cph], R-core [cph]
Maintainer: Benjamin Christoffersen <boennecd@gmail.com>

Diff between dynamichazard versions 0.6.6 dated 2019-10-14 and 0.6.7 dated 2020-12-16

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New package webfakes with initial version 1.1.1
Package: webfakes
Title: Fake Web Apps for HTTP Testing
Version: 1.1.1
Authors@R: c(person(given = "Gábor", family = "Csárdi", role = c("aut", "cre"), email = "csardi.gabor@gmail.com"), person(family = "RStudio, Pbc.", role = "cph"), person(family = "Civetweb contributors", role = "ctb", comment = "see inst/credits/ciwetweb.md"), person(family = "Redoc contributors", role = "ctb", comment = "see inst/credits/redoc.md"), person(given = "L. Peter", family = "Deutsch", role = "ctb", comment = "src/md5.h"), person(given = "Martin", family = "Purschke", role = "ctb", comment = "src/md5.h"), person(family = "Aladdin Enterprises", role = "cph", comment = "src/md5.h"), person(given = "Maëlle", family = "Salmon", role = "ctb", email = "maelle.salmon@yahoo.se", comment = c(ORCID = "0000-0002-2815-0399")))
Description: Create a web app that makes it easier to test web clients without using the internet. It includes a web app framework with path matching, parameters and templates. Can parse various 'HTTP' request bodies. Can send 'JSON' data or files from the disk. Includes a web app that implements the <https://httpbin.org> web service.
License: MIT + file LICENSE
LazyData: true
URL: https://r-lib.github.io/webfakes/, https://github.com/r-lib/webfakes#readme
BugReports: https://github.com/r-lib/webfakes/issues
RoxygenNote: 7.1.1.9000
Config/testthat/edition: 3
Imports: stats, tools, utils
Suggests: callr, curl, glue, jsonlite, httr, httpuv, testthat (>= 3.0.0), withr, xml2
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2020-12-14 10:25:38 UTC; gaborcsardi
Author: Gábor Csárdi [aut, cre], RStudio, Pbc. [cph], Civetweb contributors [ctb] (see inst/credits/ciwetweb.md), Redoc contributors [ctb] (see inst/credits/redoc.md), L. Peter Deutsch [ctb] (src/md5.h), Martin Purschke [ctb] (src/md5.h), Aladdin Enterprises [cph] (src/md5.h), Maëlle Salmon [ctb] (<https://orcid.org/0000-0002-2815-0399>)
Maintainer: Gábor Csárdi <csardi.gabor@gmail.com>
Repository: CRAN
Date/Publication: 2020-12-16 10:20:02 UTC

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New package polyqtlR with initial version 0.0.4
Package: polyqtlR
Type: Package
Title: QTL Analysis in Autopolyploid Bi-Parental F1 Populations
Version: 0.0.4
Date: 2020-12-04
Authors@R: c(person("Peter","Bourke", email = "pbourkey@gmail.com", role = c("aut","cre")), person("Christine","Hackett", role = "aut"), person("Chris","Maliepaard", role = "ctb"), person("Geert","van Geest", role = "aut"), person("Roeland","Voorrips", role = "ctb"), person("Johan","Willemsen", role = "ctb"))
Maintainer: Peter Bourke <pbourkey@gmail.com>
Description: Quantitative trait loci (QTL) analysis in polyploid bi-parental F1 populations. For all ploidy levels, identity-by-descent (IBD) probabilities can be estimated. Significance thresholds, exploring QTL allele effects and visualising results are provided. The methods have been largely described in the dissertation of P.M. Bourke in 2018 <doi:10.18174/444415>.
Depends: R (>= 3.5.0)
Imports: abind, doParallel, foreach, Hmisc, knitr, nlme, RColorBrewer, Rcpp (>= 0.12.19), reshape2, rmarkdown
Suggests: polymapR, mappoly
License: GPL-3
Encoding: UTF-8
SystemRequirements: C++11
RoxygenNote: 7.1.1
VignetteBuilder: knitr
LazyData: true
LinkingTo: Rcpp, RcppArmadillo
NeedsCompilation: yes
Packaged: 2020-12-14 19:35:34 UTC; bourk001
Author: Peter Bourke [aut, cre], Christine Hackett [aut], Chris Maliepaard [ctb], Geert van Geest [aut], Roeland Voorrips [ctb], Johan Willemsen [ctb]
Repository: CRAN
Date/Publication: 2020-12-16 10:40:02 UTC

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New package noisySBM with initial version 0.1.4
Package: noisySBM
Type: Package
Title: Noisy Stochastic Block Mode: Graph Inference by Multiple Testing
Version: 0.1.4
Authors@R: c(person(given = "Tabea", family = "Rebafka", role = c("aut", "cre"), email = "tabea.rebafka@sorbonne-universite.fr"), person(given = "Etienne", family = "Roquain", role = "ctb"), person(given = "Fanny", family = "Villers", role = "aut"))
Author: Tabea Rebafka [aut, cre], Etienne Roquain [ctb], Fanny Villers [aut]
Maintainer: Tabea Rebafka <tabea.rebafka@sorbonne-universite.fr>
Description: Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying binary graph. This procedure comes with a control of the false discovery rate. The method is described in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, E. Roquain, F. Villers (2020) <arXiv:1907.10176>.
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: parallel, gtools, ggplot2, RColorBrewer
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2020-12-14 21:10:07 UTC; tabea
Repository: CRAN
Date/Publication: 2020-12-16 10:40:06 UTC

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New package ib with initial version 0.1.0
Package: ib
Type: Package
Title: Bias Correction via Iterative Bootstrap
Version: 0.1.0
Description: An implementation of the iterative bootstrap procedure of Kuk (1995) <doi:10.1111/j.2517-6161.1995.tb02035.x> to correct the estimation bias of a fitted model object. This procedure has better bias correction properties than the bootstrap bias correction technique.
Authors@R: c( person("Samuel", "Orso", email="Samuel.Orso@unige.ch", role=c("aut","cre")), person("Stéphane", "Guerrier", role="ctb") )
Maintainer: Samuel Orso <Samuel.Orso@unige.ch>
Depends: R (>= 4.0.0)
Imports: lme4, MASS, Matrix, methods, Rdpack (>= 0.7), stats, utils, VGAM
Suggests: knitr, nlraa, rmarkdown
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RdMacros: Rdpack
RoxygenNote: 7.1.1
URL: https://github.com/SMAC-Group/ib/
BugReports: https://github.com/SMAC-Group/ib/issues/
NeedsCompilation: no
Packaged: 2020-12-14 13:42:44 UTC; orsos
Author: Samuel Orso [aut, cre], Stéphane Guerrier [ctb]
Repository: CRAN
Date/Publication: 2020-12-16 10:10:02 UTC

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New package DoubleML with initial version 0.1.1
Package: DoubleML
Type: Package
Title: Double Machine Learning in R
Version: 0.1.1
Authors@R: c( person("Philipp", "Bach", email = "philipp.bach@uni-hamburg.de", role="aut"), person("Victor", "Chernozhukov", role="aut"), person("Malte S.", "Kurz", email = "malte.simon.kurz@uni-hamburg.de", role=c("aut", "cre")), person("Martin", "Spindler", email="martin.spindler@gmx.de", role="aut"))
Description: Implementation of the double/debiased machine learning framework of Chernozhukov et al. (2018) <doi:10.1111/ectj.12097> for partially linear regression models, partially linear instrumental variable regression models, interactive regression models and interactive instrumental variable regression models. 'DoubleML' allows estimation of the nuisance parts in these models by machine learning methods and computation of the Neyman orthogonal score functions. 'DoubleML' is built on top of 'mlr3' and the 'mlr3' ecosystem. The object-oriented implementation of 'DoubleML' based on the 'R6' package is very flexible.
License: MIT + file LICENSE
URL: https://github.com/DoubleML/doubleml-for-r/
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0)
Imports: R6 (>= 2.4.1), data.table (>= 1.12.8), stats, checkmate, mlr3 (>= 0.5.0), mlr3tuning (>= 0.3.0), mvtnorm, utils, clusterGeneration, readstata13
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown, testthat, patrick, mlr3learners (>= 0.3.0), paradox (>= 0.4.0), dplyr, glmnet, lgr, ranger, sandwich, AER, rpart
VignetteBuilder: knitr
Collate: 'double_ml.R' 'double_ml_data.R' 'double_ml_iivm.R' 'double_ml_irm.R' 'double_ml_pliv.R' 'double_ml_plr.R' 'helper.R' 'datasets.R' 'zzz.R'
NeedsCompilation: no
Packaged: 2020-12-14 12:44:53 UTC; runner
Author: Philipp Bach [aut], Victor Chernozhukov [aut], Malte S. Kurz [aut, cre], Martin Spindler [aut]
Maintainer: Malte S. Kurz <malte.simon.kurz@uni-hamburg.de>
Repository: CRAN
Date/Publication: 2020-12-16 10:30:06 UTC

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New package cmcR with initial version 0.1.3
Package: cmcR
Type: Package
Title: An Implementation of the 'Congruent Matching Cells' Method
Version: 0.1.3
Authors@R: c(person(given = "Joe",family = "Zemmels",role = c("aut","cre"),email = "jzemmels@iastate.edu"), person(given = "Heike",family = "Hofmann",email = "hofmann@iastate.edu",role = "aut"), person(given = "Susan",family = "VanderPlas", email ="susan.vanderplas@unl.edu",role= "aut"))
Maintainer: Joe Zemmels <jzemmels@iastate.edu>
Description: An open-source implementation of the 'Congruent Matching Cells' method for cartridge case identification as proposed by Song (2013) <https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=911193> as well as an extension of the method proposed by Tong et al. (2015) <doi:10.6028/jres.120.008>. Provides a wide range of pre, inter, and post-processing options when working with cartridge case scan data and their associated comparisons. See the cmcR package website for more details and examples.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: magrittr, x3ptools, dplyr, ggplot2, imager, purrr, zoo, stringr, assertthat, stats, utils, scales, ggnewscale, quantreg, tibble, tidyr, rlang
Suggests: knitr, rmarkdown, markdown, testthat, DT, magick, rgl, covr, gridExtra, cowplot
VignetteBuilder: knitr
Depends: R (>= 3.5.0)
NeedsCompilation: no
Packaged: 2020-12-11 18:14:07 UTC; jzemm
Author: Joe Zemmels [aut, cre], Heike Hofmann [aut], Susan VanderPlas [aut]
Repository: CRAN
Date/Publication: 2020-12-16 10:30:03 UTC

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Package BTSPAS updated to version 2021.1.1 with previous version 2020.9.1 dated 2020-09-05

Title: Bayesian Time-Stratified Population Analysis
Description: Provides advanced Bayesian methods to estimate abundance and run-timing from temporally-stratified Petersen mark-recapture experiments. Methods include hierarchical modelling of the capture probabilities and spline smoothing of the daily run size.
Author: Carl J Schwarz <cschwarz.stat.sfu.ca@gmail.com> and Simon J Bonner <sbonner6@uwo.ca>
Maintainer: Carl J Schwarz <cschwarz.stat.sfu.ca@gmail.com>

Diff between BTSPAS versions 2020.9.1 dated 2020-09-05 and 2021.1.1 dated 2020-12-16

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 vignettes/c-Non-diagonal-model.Rmd                  |   62 
 vignettes/d-Non-diagonal-with-fall-back-model.Rmd   |   29 
 vignettes/e-Bias-from-incomplete-sampling.Rmd       |   32 
 vignettes/f-Interpolating-run-earlier-and-later.Rmd |   17 
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New package yamlme with initial version 0.1.0
Package: yamlme
Version: 0.1.0
Encoding: UTF-8
Title: Writing 'YAML' Headers for 'R-Markdown' Documents
Authors@R: c( person("Miguel", "Alvarez", email="kamapu78@gmail.com", role=c("aut","cre"), comment=c(ORCID="0000-0003-1500-1834")), person("Bisrat Haile", "Gebrekidan", role=c("ctb")))
Depends: R(>= 3.0.0)
Imports: knitr, methods, testthat
Suggests: devtools, distill, roxygen2, rmarkdown, covr
Description: Setting layout through 'YAML' headers in 'R-Markdown' documents, enabling their automatic generation. Functions and methods may summarize R-objects in automatic reports, for instance check-lists and further reports applied to the packages 'taxlist' and 'vegtable'.
LazyData: true
License: GPL (>= 2)
URL: https://github.com/kamapu/yamlme, https://kamapu.github.io/rpkg/yamlme/
BugReports: https://github.com/kamapu/yamlme/issues
VignetteBuilder: knitr
RoxygenNote: 7.1.1.9000
NeedsCompilation: no
Packaged: 2020-12-14 10:37:19 UTC; miguel
Author: Miguel Alvarez [aut, cre] (<https://orcid.org/0000-0003-1500-1834>), Bisrat Haile Gebrekidan [ctb]
Maintainer: Miguel Alvarez <kamapu78@gmail.com>
Repository: CRAN
Date/Publication: 2020-12-16 09:50:03 UTC

More information about yamlme at CRAN
Permanent link

Package shadow updated to version 0.7.0 with previous version 0.6.9 dated 2020-10-10

Title: Geometric Shadow Calculations
Description: Functions for calculating: (1) shadow height, (2) logical shadow flag, (3) shadow footprint, (4) Sky View Factor and (5) radiation load. Basic required inputs include a polygonal layer of obstacle outlines along with their heights (i.e. "extruded polygons"), sun azimuth and sun elevation. The package also provides functions for related preliminary calculations: breaking polygons into line segments, determining azimuth of line segments, shifting segments by azimuth and distance, constructing the footprint of a line-of-sight between an observer and the sun, and creating a 3D grid covering the surface area of extruded polygons.
Author: Michael Dorman [aut, cre], Evyatar Erell [ctb], Itai Kloog [ctb], Adi Vulkan [ctb], Roger Bivand [ctb]
Maintainer: Michael Dorman <dorman@post.bgu.ac.il>

Diff between shadow versions 0.6.9 dated 2020-10-10 and 0.7.0 dated 2020-12-16

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More information about shadow at CRAN
Permanent link

New package readrba with initial version 0.1.0
Package: readrba
Title: Download and Tidy Data from the Reserve Bank of Australia
Version: 0.1.0
Authors@R: person(given = "Matt", family = "Cowgill", role = c("aut", "cre"), email = "mattcowgill@gmail.com", comment = c(ORCID = "0000-0003-0422-3300"))
Description: Download up-to-date data from the Reserve Bank of Australia in a tidy data frame. Package includes functions to download current and historical statistical tables (<https://www.rba.gov.au/statistics/tables/>) and forecasts (<https://www.rba.gov.au/publications/smp/forecasts-archive.html>). Data includes a broad range of Australian macroeconomic and financial time series.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
URL: https://mattcowgill.github.io/readrba/index.html
BugReports: https://github.com/MattCowgill/readrba/issues
Suggests: covr, testthat, knitr, rmarkdown, ggplot2
Imports: readxl (>= 1.3.0), tidyr (>= 1.0.0), dplyr (>= 1.0.0), purrr, rlang, xml2, rvest (>= 0.3.6), stringr, httr, lubridate, curl
Depends: R (>= 3.6.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-12-14 13:35:32 UTC; mcowgill
Author: Matt Cowgill [aut, cre] (<https://orcid.org/0000-0003-0422-3300>)
Maintainer: Matt Cowgill <mattcowgill@gmail.com>
Repository: CRAN
Date/Publication: 2020-12-16 10:00:02 UTC

More information about readrba at CRAN
Permanent link

New package DescrTab2 with initial version 2.0.3
Package: DescrTab2
Type: Package
Title: Publication Quality Descriptive Statistics Tables
Version: 2.0.3
Date: 2020-12-10
Authors@R: c(person("Jan", "Meis", role = c("aut", "cre"), email="meis@imbi.uni-heidelberg.de", comment = c(ORCID = "0000-0001-5407-7220")), person("Lukas", "Baumann", role = c("aut"), comment = c(ORCID = "0000-0001-7931-7470")), person("Maximilian", "Pilz", role = c("aut"), comment = c(ORCID = "0000-0002-9685-1613")), person("Lorenz", "Uhlmann", role = c("ctb")), person("Csilla", "van Lunteren", role = c("ctb")), person("Kevin", "Kunzmann", role = c("ctb")))
Description: Provides functions to create descriptive statistics tables for continuous and categorical variables. By default, summary statistics such as mean, standard deviation, quantiles, minimum and maximum for continuous variables and relative and absolute frequencies for categorical variables are calculated. 'DescrTab2' features a sophisticated algorithm to choose appropriate test statistics for your data and provides p-values. On top of this, confidence intervals for group differences of appropriated summary measures are automatically produces for two-group comparison. Tables generated by 'DescrTab2' can be integrated in a variety of document formats, including .html, .tex and .docx documents. 'DescrTab2' also allows printing tables to console and saving table objects for later use.
Depends: R (>= 4.0.0)
Suggests: testthat, covr, knitr, rmarkdown, tidyverse, here, shiny, Exact
Imports: stats, utils, nlme, exact2x2, DescTools, dplyr, rlang, tibble, stringr, forcats, magrittr, tidyselect, scales, cli, kableExtra, flextable, officer
VignetteBuilder: knitr
License: GPL (>= 3)
Copyright: This package contains codes copyrighted by third parites. See file COPYRIGHTS for details.
LazyLoad: true
RoxygenNote: 7.1.1
Encoding: UTF-8
URL: https://imbi-heidelberg.github.io/DescrTab2/
BugReports: https://github.com/imbi-heidelberg/DescrTab2/issues
NeedsCompilation: no
Packaged: 2020-12-14 11:57:54 UTC; janme
Author: Jan Meis [aut, cre] (<https://orcid.org/0000-0001-5407-7220>), Lukas Baumann [aut] (<https://orcid.org/0000-0001-7931-7470>), Maximilian Pilz [aut] (<https://orcid.org/0000-0002-9685-1613>), Lorenz Uhlmann [ctb], Csilla van Lunteren [ctb], Kevin Kunzmann [ctb]
Maintainer: Jan Meis <meis@imbi.uni-heidelberg.de>
Repository: CRAN
Date/Publication: 2020-12-16 10:00:05 UTC

More information about DescrTab2 at CRAN
Permanent link

New package POFIBGE with initial version 0.1.0
Package: POFIBGE
Type: Package
Title: Downloading, Reading and Analysing POF Microdata
Version: 0.1.0
Authors@R: c(person("Gabriel", "Assuncao", email = "pacotesipd@ibge.gov.br", role = c("aut", "cre")), person("Luna", "Hidalgo", email = "pacotesipd@ibge.gov.br", role = c("aut")), person("Douglas", "Braga", email = "pacotesipd@ibge.gov.br", role = c("ctb")))
Description: Provides tools for downloading, reading, and analysing the POF household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website <https://www.ibge.gov.br/>. Further analyses must be made using package 'survey'.
Depends: R (>= 3.2.0)
Imports: survey, readr, dplyr, magrittr, RCurl, utils, timeDate, readxl, tibble
Suggests: tidyverse, convey
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.1
NeedsCompilation: no
Author: Gabriel Assuncao [aut, cre], Luna Hidalgo [aut], Douglas Braga [ctb]
Maintainer: Gabriel Assuncao <pacotesipd@ibge.gov.br>
Packaged: 2020-12-11 17:12:18 UTC; gabriel.assuncao
Repository: CRAN
Date/Publication: 2020-12-16 08:30:02 UTC

More information about POFIBGE at CRAN
Permanent link

Package htsr updated to version 1.1.0 with previous version 1.0.4 dated 2020-11-25

Title: Hydro-Meteorology Time-Series
Description: Functions for the management and treatment of hydrology and meteorology time-series stored in a 'Sqlite' data base.
Author: Pierre Chevallier [aut, cre]
Maintainer: Pierre Chevallier <pierre.chevallier@ird.fr>

Diff between htsr versions 1.0.4 dated 2020-11-25 and 1.1.0 dated 2020-12-16

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More information about htsr at CRAN
Permanent link

New package COVIDIBGE with initial version 0.1.0
Package: COVIDIBGE
Type: Package
Title: Downloading, Reading and Analysing PNAD COVID19 Microdata
Version: 0.1.0
Authors@R: c(person("Gabriel", "Assuncao", email = "pacotesipd@ibge.gov.br", role = c("aut", "cre")), person("Luna", "Hidalgo", email = "pacotesipd@ibge.gov.br", role = c("aut")), person("Douglas", "Braga", email = "pacotesipd@ibge.gov.br", role = c("ctb")))
Description: Provides tools for downloading, reading, and analysing the PNAD COVID19 household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website <https://www.ibge.gov.br/>. Further analyses must be made using package 'survey'.
Depends: R (>= 3.2.0)
Imports: survey, readr, dplyr, magrittr, RCurl, utils, timeDate, readxl, tibble
Suggests: tidyverse, convey
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.1
NeedsCompilation: no
Author: Gabriel Assuncao [aut, cre], Luna Hidalgo [aut], Douglas Braga [ctb]
Maintainer: Gabriel Assuncao <pacotesipd@ibge.gov.br>
Packaged: 2020-12-11 17:19:29 UTC; gabriel.assuncao
Repository: CRAN
Date/Publication: 2020-12-16 08:30:06 UTC

More information about COVIDIBGE at CRAN
Permanent link

Package clustvarsel updated to version 2.3.4 with previous version 2.3.3 dated 2018-11-19

Title: Variable Selection for Gaussian Model-Based Clustering
Description: Variable selection for Gaussian model-based clustering as implemented in the 'mclust' package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting 'mclust' models. By default the algorithm uses a sequential search, but parallelisation is also available.
Author: Nema Dean [aut] (<https://orcid.org/0000-0002-5080-2517>), Adrian E. Raftery [aut], Luca Scrucca [aut, cre] (<https://orcid.org/0000-0003-3826-0484>)
Maintainer: Luca Scrucca <luca.scrucca@unipg.it>

Diff between clustvarsel versions 2.3.3 dated 2018-11-19 and 2.3.4 dated 2020-12-16

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More information about clustvarsel at CRAN
Permanent link

Package rosqp (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:

2018-02-07 0.1.0

Permanent link
Package tidytable updated to version 0.5.7 with previous version 0.5.6 dated 2020-09-30

Title: Tidy Interface to 'data.table'
Description: A tidy interface to 'data.table' that is 'rlang' compatible, giving users the speed of 'data.table' with the clean syntax of the tidyverse.
Author: Mark Fairbanks [aut, cre], Tyson Barrett [ctb], Ivan Leung [ctb], Ross Kennedy [ctb], Lionel Henry [ctb], Matt Carlson [ctb], Abdessabour Moutik [ctb]
Maintainer: Mark Fairbanks <mark.t.fairbanks@gmail.com>

Diff between tidytable versions 0.5.6 dated 2020-09-30 and 0.5.7 dated 2020-12-16

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More information about tidytable at CRAN
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Package spatstat.data updated to version 1.7-0 with previous version 1.5-2 dated 2020-11-12

Title: Datasets for 'spatstat'
Description: Contains all the datasets for the 'spatstat' package.
Author: Adrian Baddeley [aut, cre] (<https://orcid.org/0000-0001-9499-8382>), Rolf Turner [aut] (<https://orcid.org/0000-0001-5521-5218>), Ege Rubak [aut] (<https://orcid.org/0000-0002-6675-533X>)
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>

Diff between spatstat.data versions 1.5-2 dated 2020-11-12 and 1.7-0 dated 2020-12-16

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More information about spatstat.data at CRAN
Permanent link

Package RMariaDB updated to version 1.0.11 with previous version 1.0.10 dated 2020-08-27

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

Diff between RMariaDB versions 1.0.10 dated 2020-08-27 and 1.0.11 dated 2020-12-16

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Package LambertW updated to version 0.6.6 with previous version 0.6.5 dated 2020-06-08

Title: Probabilistic Models to Analyze and Gaussianize Heavy-Tailed, Skewed Data
Description: Lambert W x F distributions are a generalized framework to analyze skewed, heavy-tailed data. It is based on an input/output system, where the output random variable (RV) Y is a non-linearly transformed version of an input RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed). The transformed RV Y has a Lambert W x F distribution. This package contains functions to model and analyze skewed, heavy-tailed data the Lambert Way: simulate random samples, estimate parameters, compute quantiles, and plot/ print results nicely. The most useful function is 'Gaussianize', which works similarly to 'scale', but actually makes the data Gaussian. A do-it-yourself toolkit allows users to define their own Lambert W x 'MyFavoriteDistribution' and use it in their analysis right away.
Author: Georg M. Goerg [aut, cre]
Maintainer: Georg M. Goerg <im@gmge.org>

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Package frechet updated to version 0.2.0 with previous version 0.1.0 dated 2020-06-19

Title: Statistical Analysis for Random Objects and Non-Euclidean Data
Description: Provides implementation of statistical methods for random objects lying in various metric spaces, which are not necessarily linear spaces. The core of this package is Fréchet regression for random objects with Euclidean predictors, which allows one to perform regression analysis for non-Euclidean responses under some mild conditions. Examples include distributions in L^2-Wasserstein space, covariance matrices endowed with power metric (with Frobenius metric as a special case), Cholesky and log-Cholesky metrics. References: Petersen, A., & Müller, H.-G. (2019) <doi:10.1214/17-AOS1624>.
Author: Yaqing Chen [aut, cre], Alvaro Gajardo [aut], Jianing Fan [aut], Qixian Zhong [aut], Paromita Dubey [aut], Kyunghee Han [aut], Satarupa Bhattacharjee [aut], Hans-Georg Müller [cph, ths, aut]
Maintainer: Yaqing Chen <yaqchen@ucdavis.edu>

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Package fgeo.plot updated to version 1.1.10 with previous version 1.1.9 dated 2019-06-18

Title: Plot ForestGEO Data
Description: To help you access, transform, analyze, and visualize ForestGEO data, we developed a collection of R packages (<https://forestgeo.github.io/fgeo/>). This package, in particular, helps you to plot ForestGEO data. To learn more about ForestGEO visit <https://forestgeo.si.edu/>.
Author: Mauro Lepore [aut, ctr, cre] (<https://orcid.org/0000-0002-1986-7988>), CTFS-ForestGEO [cph, fnd]
Maintainer: Mauro Lepore <maurolepore@gmail.com>

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Package yotover updated to version 0.3.2 with previous version 0.1 dated 2020-11-05

Title: An Advanced Guide to Trade Policy Analysis
Description: On-disk embedded database with SQL versions of the original datasets from Yotov, et al (2016, ISBN: 978-92-870-4367-2) and functions to report regressions with clustered robust standard errors.
Author: Mauricio Vargas [aut, cre] (<https://orcid.org/0000-0003-1017-7574>), Alexey Kravchenko [ths], The United Nations [dtc, cph, fnd]
Maintainer: Mauricio Vargas <mvargas@dcc.uchile.cl>

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Package RXshrink updated to version 1.5 with previous version 1.4.3 dated 2020-11-01

Title: Maximum Likelihood Shrinkage using Generalized Ridge or Least Angle Regression Methods
Description: Functions are provided to calculate and display ridge TRACE diagnostics for a variety of shrinkage Paths. TRACEs identify the m-Extent of shrinkage most likely, under Normal-theory, to produce optimally biased estimates of beta-coefficients with minimum MSE Risk. The unr.ridge() function implements the "Unrestricted Path" introduced in Obenchain (2020) <arXiv:2005.14291>. This Shrinkage-Path is more efficient than the Paths used by the qm.ridge(), aug.lars() and uc.lars() functions. Optimally biased predictions can be made using RXpredict() for all six types of RXshrink linear model estimation methods. Functions MLboot(), MLcalc(), MLhist() and MLtrue() provide insights into the true bias and MSE risk characteristics of non-linear Shrinkage estimators. Functions unr.aug() and unr.biv() augment the calculations made by unr.ridge() to provide plots of the bivariate confidence ellipses corresponding to any of the p*(p-1) possible pairs of shrunken regression coefficients. The correct.signs() function provides estimates with "correct" numerical signs when ill-conditioned (nearly multicollinear) models yield OLS estimates that disagree with the signs of the observed correlations between the y-outcome and the selected x-predictor variables. Finally, the YonX() function displays Shrinkage statistics and graphics for "simple" linear regression (p = 1) models.
Author: Bob Obenchain
Maintainer: Bob Obenchain <wizbob@att.net>

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Package ffscrapr updated to version 1.2.1 with previous version 1.2.0 dated 2020-11-27

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

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Package censo2017 updated to version 0.2 with previous version 0.1 dated 2020-11-11

Title: Base de Datos de Facil Acceso del Censo 2017 de Chile (2017 Chilean Census Easy Access Database)
Description: Provee un acceso conveniente a mas de 17 millones de registros de la base de datos del Censo 2017. Los datos fueron importados desde el DVD oficial del INE usando el Convertidor REDATAM creado por Pablo De Grande y ademas se proporcionan los mapas que acompanian a estos datos. Esta paquete esta documentado intencionalmente en castellano asciificado para que funcione sin problema en diferentes plataformas. (Provides convenient access to more than 17 million records from the Chilean Census 2017 database. The datasets were imported from the official DVD provided by the Chilean National Bureau of Statistics by using the REDATAM converter created by Pablo De Grande and in addition it includes the maps accompanying these datasets.)
Author: Mauricio Vargas [aut, cre] (<https://orcid.org/0000-0003-1017-7574>), Juan Correa [ctb], Instituto Nacional de Estadisticas (INE) [dtc]
Maintainer: Mauricio Vargas <mvargas@dcc.uchile.cl>

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Package languageserver (with last version 0.3.8) was removed from CRAN

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

2020-12-01 0.3.8
2020-09-15 0.3.7
2020-05-25 0.3.6
2020-03-21 0.3.5
2020-02-24 0.3.4
2020-01-08 0.3.3
2019-10-26 0.3.2
2019-09-27 0.3.1
2019-09-09 0.3.0
2019-03-28 0.2.9
2019-03-26 0.2.8
2019-03-07 0.2.7
2019-02-24 0.2.6
2018-09-10 0.2.5
2018-08-27 0.2.4
2018-06-16 0.2.3
2018-05-09 0.2.2
2018-04-24 0.2.0
2018-04-24 0.2.1
2018-03-01 0.1.0

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Package PCRedux (with last version 1.0-6) was removed from CRAN

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2020-03-06 1.0-6
2020-01-07 1.0-4

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Package chipPCR (with last version 0.0.8-10) was removed from CRAN

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2015-04-09 0.0.8-10
2015-03-12 0.0.8-8
2015-02-22 0.0.8-6
2014-09-29 0.0.8-3
2014-08-26 0.0.8-1
2014-06-25 0.0.7
2014-04-24 0.0.6
2013-12-11 0.0.5

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Package MBmca (with last version 0.0.3-5) was removed from CRAN

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2015-01-13 0.0.3-5
2014-08-26 0.0.3-4
2014-06-25 0.0.3-3
2014-04-25 0.0.3-2
2013-05-30 0.0.2-1
2013-05-28 0.0.2
2012-08-31 0.0.1

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