Thu, 26 May 2022

Package huito updated to version 0.1.4 with previous version 0.1.3 dated 2022-04-01

Title: Flexible and Reproducible Label Designs
Description: An open-source R package to deploys flexible and reproducible labels using layers. The 'huito' package is part of the 'inkaverse' project for developing different procedures and tools used in plant science and experimental designs. Learn more about the 'inkaverse' project at <https://inkaverse.com/>.
Author: Flavio Lozano-Isla [aut, cre] , Inkaverse [cph]
Maintainer: Flavio Lozano-Isla <flavjack@gmail.com>

Diff between huito versions 0.1.3 dated 2022-04-01 and 0.1.4 dated 2022-05-26

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More information about huito at CRAN
Permanent link

Package renv updated to version 0.15.5 with previous version 0.15.4 dated 2022-03-02

Title: Project Environments
Description: A dependency management toolkit for R. Using 'renv', you can create and manage project-local R libraries, save the state of these libraries to a 'lockfile', and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible.
Author: Kevin Ushey [aut, cre], RStudio, PBC [cph]
Maintainer: Kevin Ushey <kevin@rstudio.com>

Diff between renv versions 0.15.4 dated 2022-03-02 and 0.15.5 dated 2022-05-26

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More information about renv at CRAN
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Package OmicNavigator updated to version 1.11.5 with previous version 1.8.0 dated 2021-12-01

Title: Open-Source Software for 'Omic' Data Analysis and Visualization
Description: A tool for interactive exploration of the results from 'omics' experiments to facilitate novel discoveries from high-throughput biology. The software includes R functions for the 'bioinformatician' to deposit study metadata and the outputs from statistical analyses (e.g. differential expression, enrichment). These results are then exported to an interactive JavaScript dashboard that can be interrogated on the user's local machine or deployed online to be explored by collaborators. The dashboard includes 'sortable' tables, interactive plots including network visualization, and fine-grained filtering based on statistical significance.
Author: Terrence Ernst [aut] , John Blischak [aut, cre] , Paul Nordlund [aut] , Justin Moore [aut] , Joe Dalen [aut] , Akshay Bhamidipati [aut] , Brett Engelmann [aut], Marco Curado [aut] , Joe LoGrasso [aut] , AbbVie Inc. [cph, fnd]
Maintainer: John Blischak <jdblischak@gmail.com>

Diff between OmicNavigator versions 1.8.0 dated 2021-12-01 and 1.11.5 dated 2022-05-26

 DESCRIPTION                                  |   17 
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 132 files changed, 20309 insertions(+), 19901 deletions(-)

More information about OmicNavigator at CRAN
Permanent link

Package reactable updated to version 0.3.0 with previous version 0.2.3 dated 2020-10-04

Title: Interactive Data Tables Based on 'React Table'
Description: Interactive data tables for R, based on the 'React Table' JavaScript library. Provides an HTML widget that can be used in 'R Markdown' documents and 'Shiny' applications, or viewed from an R console.
Author: Greg Lin [aut, cre], Tanner Linsley [ctb, cph] , Emotion team and other contributors [ctb, cph]
Maintainer: Greg Lin <glin@glin.io>

Diff between reactable versions 0.2.3 dated 2020-10-04 and 0.3.0 dated 2022-05-26

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More information about reactable at CRAN
Permanent link

Package sbtools updated to version 1.1.18 with previous version 1.1.17 dated 2021-07-20

Title: USGS ScienceBase Tools
Description: Tools for interacting with U.S. Geological Survey ScienceBase <https://www.sciencebase.gov> interfaces. ScienceBase is a data cataloging and collaborative data management platform. Functions included for querying ScienceBase, and creating and fetching datasets.
Author: David Blodgett [cre], Luke Winslow [aut], Scott Chamberlain [ctb], Alison Appling [ctb], Jordan Read [ctb]
Maintainer: David Blodgett <dblodgett@usgs.gov>

Diff between sbtools versions 1.1.17 dated 2021-07-20 and 1.1.18 dated 2022-05-26

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More information about sbtools at CRAN
Permanent link

Package packrat updated to version 0.8.0 with previous version 0.7.0 dated 2021-08-20

Title: A Dependency Management System for Projects and their R Package Dependencies
Description: Manage the R packages your project depends on in an isolated, portable, and reproducible way.
Author: Kevin Ushey, Jonathan McPherson, Joe Cheng, Aron Atkins, JJ Allaire
Maintainer: Aron Atkins <aron@rstudio.com>

Diff between packrat versions 0.7.0 dated 2021-08-20 and 0.8.0 dated 2022-05-26

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 25 files changed, 310 insertions(+), 114 deletions(-)

More information about packrat at CRAN
Permanent link

Package r4ss updated to version 1.44.0 with previous version 1.36.1 dated 2019-10-18

Title: R Code for Stock Synthesis
Description: A collection of R functions for use with Stock Synthesis, a fisheries stock assessment modeling platform written in ADMB by Dr. Richard D. Methot at the NOAA Northwest Fisheries Science Center. The functions include tools for summarizing and plotting results, manipulating files, visualizing model parameterizations, and various other common stock assessment tasks. This version of '{r4ss}' is compatible with Stock Synthesis versions 3.24 through 3.30 (specifically version 3.30.19.01, from April 2022).
Author: Ian G. Taylor [aut, cre], Ian J. Stewart [aut], Allan C. Hicks [aut], Tommy M. Garrison [aut], Andre E. Punt [aut], John R. Wallace [aut], Chantel R. Wetzel [aut], James T. Thorson [aut], Yukio Takeuchi [aut], Kotaro Ono [aut], Cole C. Monnahan [aut] [...truncated...]
Maintainer: Ian G. Taylor <Ian.Taylor@noaa.gov>

Diff between r4ss versions 1.36.1 dated 2019-10-18 and 1.44.0 dated 2022-05-26

 r4ss-1.36.1/r4ss/R/SS_makedatlist.R                       |only
 r4ss-1.36.1/r4ss/R/SStimeseries.R                         |only
 r4ss-1.36.1/r4ss/R/rich.colors.short.R                    |only
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 r4ss-1.36.1/r4ss/inst/Shiny                               |only
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 r4ss-1.36.1/r4ss/man/SS_makedatlist.Rd                    |only
 r4ss-1.36.1/r4ss/man/SStimeseries.Rd                      |only
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 r4ss-1.44.0/r4ss/DESCRIPTION                              |   99 
 r4ss-1.44.0/r4ss/MD5                                      |  447 
 r4ss-1.44.0/r4ss/NAMESPACE                                |   26 
 r4ss-1.44.0/r4ss/NEWS.md                                  |   22 
 r4ss-1.44.0/r4ss/R/NegLogInt_Fn.R                         |  654 -
 r4ss-1.44.0/r4ss/R/PinerPlot.R                            |  444 
 r4ss-1.44.0/r4ss/R/RebuildPlot.R                          | 1040 +-
 r4ss-1.44.0/r4ss/R/SSMethod.Cond.TA1.8.R                  |  412 
 r4ss-1.44.0/r4ss/R/SSMethod.TA1.8.R                       |  516 -
 r4ss-1.44.0/r4ss/R/SS_ForeCatch.R                         |  100 
 r4ss-1.44.0/r4ss/R/SS_RunJitter.R                         |  242 
 r4ss-1.44.0/r4ss/R/SS_Sensi_plot.R                        |only
 r4ss-1.44.0/r4ss/R/SS_changepars.R                        |  529 -
 r4ss-1.44.0/r4ss/R/SS_decision_table_stuff.R              |   61 
 r4ss-1.44.0/r4ss/R/SS_doRetro.R                           |  230 
 r4ss-1.44.0/r4ss/R/SS_fitbiasramp.R                       |  661 -
 r4ss-1.44.0/r4ss/R/SS_html.R                              |  519 -
 r4ss-1.44.0/r4ss/R/SS_makeHTMLdiagnostictable.R           |  188 
 r4ss-1.44.0/r4ss/R/SS_output.R                            | 6773 +++++++-------
 r4ss-1.44.0/r4ss/R/SS_parlines.R                          |  127 
 r4ss-1.44.0/r4ss/R/SS_plots.R                             | 2654 +++--
 r4ss-1.44.0/r4ss/R/SS_profile.R                           |  672 -
 r4ss-1.44.0/r4ss/R/SS_read.R                              |only
 r4ss-1.44.0/r4ss/R/SS_read_summary.R                      |  126 
 r4ss-1.44.0/r4ss/R/SS_readctl.R                           |  288 
 r4ss-1.44.0/r4ss/R/SS_readctl_3.24.R                      | 2086 ++--
 r4ss-1.44.0/r4ss/R/SS_readctl_3.30.R                      | 2923 +++---
 r4ss-1.44.0/r4ss/R/SS_readdat.R                           |  317 
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 r4ss-1.44.0/r4ss/R/SS_readdat_3.30.R                      | 1510 +--
 r4ss-1.44.0/r4ss/R/SS_readforecast.R                      |  553 -
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 r4ss-1.44.0/r4ss/R/SS_readwtatage.R                       |   57 
 r4ss-1.44.0/r4ss/R/SS_recdevs.R                           |  268 
 r4ss-1.44.0/r4ss/R/SS_splitdat.R                          |  135 
 r4ss-1.44.0/r4ss/R/SS_tune_comps.R                        |  754 +
 r4ss-1.44.0/r4ss/R/SS_varadjust.R                         |  200 
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 r4ss-1.44.0/r4ss/R/SS_writectl.R                          |   76 
 r4ss-1.44.0/r4ss/R/SS_writectl_3.24.R                     |  599 -
 r4ss-1.44.0/r4ss/R/SS_writectl_3.30.R                     |  959 +
 r4ss-1.44.0/r4ss/R/SS_writedat.R                          |   39 
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 r4ss-1.44.0/r4ss/R/SS_writedat_3.30.R                     |  293 
 r4ss-1.44.0/r4ss/R/SS_writeforecast.R                     |  290 
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 r4ss-1.44.0/r4ss/R/SSbootstrap.R                          |   63 
 r4ss-1.44.0/r4ss/R/SSexecutivesummary.r                   | 1947 ++--
 r4ss-1.44.0/r4ss/R/SSgetMCMC.R                            |  165 
 r4ss-1.44.0/r4ss/R/SSgetoutput.R                          |  193 
 r4ss-1.44.0/r4ss/R/SSmakeMmatrix.R                        |  162 
 r4ss-1.44.0/r4ss/R/SSmohnsrho.R                           |  180 
 r4ss-1.44.0/r4ss/R/SSplotAgeMatrix.R                      |  238 
 r4ss-1.44.0/r4ss/R/SSplotBiology.R                        | 2865 +++--
 r4ss-1.44.0/r4ss/R/SSplotCatch.R                          | 1018 +-
 r4ss-1.44.0/r4ss/R/SSplotCohortCatch.R                    |  451 
 r4ss-1.44.0/r4ss/R/SSplotComparisons.R                    | 4122 ++++----
 r4ss-1.44.0/r4ss/R/SSplotComps.R                          | 3901 ++++----
 r4ss-1.44.0/r4ss/R/SSplotData.R                           |  489 -
 r4ss-1.44.0/r4ss/R/SSplotDiscard.R                        |  315 
 r4ss-1.44.0/r4ss/R/SSplotDynamicB0.R                      |only
 r4ss-1.44.0/r4ss/R/SSplotIndices.R                        | 1513 +--
 r4ss-1.44.0/r4ss/R/SSplotMCMC_ExtraSelex.R                |   74 
 r4ss-1.44.0/r4ss/R/SSplotMnwt.R                           |  224 
 r4ss-1.44.0/r4ss/R/SSplotMovementMap.R                    |  162 
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 r4ss-1.44.0/r4ss/R/SSplotNumbers.R                        | 1511 +--
 r4ss-1.44.0/r4ss/R/SSplotPars.R                           | 1017 +-
 r4ss-1.44.0/r4ss/R/SSplotProfile.R                        |  477 
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 r4ss-1.44.0/r4ss/R/SSplotSPR.R                            |  631 -
 r4ss-1.44.0/r4ss/R/SSplotSelex.R                          | 1962 ++--
 r4ss-1.44.0/r4ss/R/SSplotSexRatio.R                       |  380 
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 r4ss-1.44.0/r4ss/R/SSplotSummaryF.R                       |  134 
 r4ss-1.44.0/r4ss/R/SSplotTags.R                           |  813 -
 r4ss-1.44.0/r4ss/R/SSplotTimeseries.R                     | 1123 +-
 r4ss-1.44.0/r4ss/R/SSplotYield.R                          |  452 
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 r4ss-1.44.0/r4ss/R/SStableComparisons.R                   |  256 
 r4ss-1.44.0/r4ss/R/SSunavailableSpawningOutput.R          |  699 -
 r4ss-1.44.0/r4ss/R/TSCplot.R                              |  253 
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 r4ss-1.44.0/r4ss/R/mcmc.nuisance.R                        |  356 
 r4ss-1.44.0/r4ss/R/mcmc.out.R                             |  656 -
 r4ss-1.44.0/r4ss/R/mountains.R                            |  114 
 r4ss-1.44.0/r4ss/R/plotCI.R                               |   26 
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 r4ss-1.44.0/r4ss/R/r4ss-package.R                         |   44 
 r4ss-1.44.0/r4ss/R/r4ss_logo.R                            |   23 
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 251 files changed, 40647 insertions(+), 30436 deletions(-)

More information about r4ss at CRAN
Permanent link

Package BoomSpikeSlab updated to version 1.2.5 with previous version 1.2.4 dated 2021-04-06

Title: MCMC for Spike and Slab Regression
Description: Spike and slab regression with a variety of residual error distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a few others. Spike and slab regression is Bayesian regression with prior distributions containing a point mass at zero. The posterior updates the amount of mass on this point, leading to a posterior distribution that is actually sparse, in the sense that if you sample from it many coefficients are actually zeros. Sampling from this posterior distribution is an elegant way to handle Bayesian variable selection and model averaging. See <DOI:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.
Author: Steven L. Scott <steve.the.bayesian@gmail.com>
Maintainer: Steven L. Scott <steve.the.bayesian@gmail.com>

Diff between BoomSpikeSlab versions 1.2.4 dated 2021-04-06 and 1.2.5 dated 2022-05-26

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New package ri2 with initial version 0.4.0
Package: ri2
Title: Randomization Inference for Randomized Experiments
Version: 0.4.0
Description: Randomization inference procedures for simple and complex randomized designs, including multi-armed trials, as described in Gerber and Green (2012, ISBN: 978-0393979954). Users formally describe their randomization procedure and test statistic. The randomization distribution of the test statistic under some null hypothesis is efficiently simulated.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: generics, ggplot2, pbapply
Depends: randomizr (>= 0.16.0), estimatr
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-05-26 16:05:55 UTC; ac2595
Author: Alexander Coppock [aut, cre]
Maintainer: Alexander Coppock <acoppock@gmail.com>
Repository: CRAN
Date/Publication: 2022-05-26 16:20:02 UTC

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Package rego updated to version 1.5.2 with previous version 1.5.1 dated 2022-05-26

Title: Automatic Time Series Forecasting and Missing Value Imputation
Description: Machine learning algorithm for predicting and imputing time series. It can automatically set all the parameters needed, thus in the minimal configuration it only requires the target variable and the dependent variables if present. It can address large problems with hundreds or thousands of dependent variables and problems in which the number of dependent variables is greater than the number of observations. Moreover it can be used not only for time series but also for any other real valued target variable. The algorithm implemented includes a Bayesian stochastic search methodology for model selection and a robust estimation based on bootstrapping. 'rego' is fast because all the code is C++.
Author: Davide Altomare [cre, aut], David Loris [aut]
Maintainer: Davide Altomare <info@channelattribution.io>

Diff between rego versions 1.5.1 dated 2022-05-26 and 1.5.2 dated 2022-05-26

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Package QuClu updated to version 1.0.1 with previous version 0.1.0 dated 2018-07-30

Title: Quantile-Based Clustering Algorithms
Description: Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, C., Viroli, C., Anderlucci, L. (2019) "Quantile-based clustering." Electronic Journal of Statistics. 13 (2) 4849 - 4883 <doi:10.1214/19-EJS1640>.
Author: Christian Hennig, Cinzia Viroli and Laura Anderlucci
Maintainer: Laura Anderlucci <laura.anderlucci@unibo.it>

Diff between QuClu versions 0.1.0 dated 2018-07-30 and 1.0.1 dated 2022-05-26

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Package nnlib2Rcpp updated to version 0.1.9 with previous version 0.1.8 dated 2021-10-30

Title: A Tool for Creating Custom Neural Networks in C++ and using Them in R
Description: Contains a module to define neural networks from custom components and versions of Autoencoder, BP, LVQ, MAM NN.
Author: Vasilis Nikolaidis [aut, cph, cre]
Maintainer: Vasilis Nikolaidis <vnikolaidis@us.uop.gr>

Diff between nnlib2Rcpp versions 0.1.8 dated 2021-10-30 and 0.1.9 dated 2022-05-26

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Package LSDsensitivity updated to version 1.2.2 with previous version 1.2.1 dated 2022-05-15

Title: Sensitivity Analysis Tools for LSD Simulations
Description: Tools for sensitivity analysis of LSD simulation models. Reads object-oriented data produced by LSD simulation models and performs screening and global sensitivity analysis (Sobol decomposition method, Saltelli et al. (2008) ISBN:9780470725177). A Kriging or polynomial meta-model (Kleijnen (2009) <doi:10.1016/j.ejor.2007.10.013>) is estimated using the simulation data to provide the data required by the Sobol decomposition. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at <https://www.labsimdev.org/>).
Author: Marcelo C. Pereira [aut, cre]
Maintainer: Marcelo C. Pereira <mcper@unicamp.br>

Diff between LSDsensitivity versions 1.2.1 dated 2022-05-15 and 1.2.2 dated 2022-05-26

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Package sparklyr updated to version 1.7.6 with previous version 1.7.5 dated 2022-02-02

Title: R Interface to Apache Spark
Description: R interface to Apache Spark, a fast and general engine for big data processing, see <https://spark.apache.org/>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.
Author: Javier Luraschi [aut], Kevin Kuo [aut] , Kevin Ushey [aut], JJ Allaire [aut], Samuel Macedo [ctb], Hossein Falaki [aut], Lu Wang [aut], Andy Zhang [aut], Yitao Li [aut] , Jozef Hajnala [ctb], Maciej Szymkiewicz [ctb] , Wil Davis [ctb], Edgar Ruiz [au [...truncated...]
Maintainer: Edgar Ruiz <edgar@rstudio.com>

Diff between sparklyr versions 1.7.5 dated 2022-02-02 and 1.7.6 dated 2022-05-26

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Package rlas updated to version 1.6.0 with previous version 1.5.3 dated 2022-03-18

Title: Read and Write 'las' and 'laz' Binary File Formats Used for Remote Sensing Data
Description: Read and write 'las' and 'laz' binary file formats. The LAS file format is a public file format for the interchange of 3-dimensional point cloud data between data users. The LAS specifications are approved by the American Society for Photogrammetry and Remote Sensing <https://www.asprs.org/divisions-committees/lidar-division/laser-las-file-format-exchange-activities>. The LAZ file format is an open and lossless compression scheme for binary LAS format versions 1.0 to 1.4 <https://laszip.org/>.
Author: Jean-Romain Roussel [aut, cre, cph], Florian De Boissieu [aut, ctb] , Martin Isenburg [cph] , David Auty [ctb] , Pierrick Marie [ctb] , Tiago de Conto [ctb]
Maintainer: Jean-Romain Roussel <jean-romain.roussel.1@ulaval.ca>

Diff between rlas versions 1.5.3 dated 2022-03-18 and 1.6.0 dated 2022-05-26

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Package nloptr updated to version 2.0.3 with previous version 2.0.2 dated 2022-05-19

Title: R Interface to NLopt
Description: Solve optimization problems using an R interface to NLopt. NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. See <https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/> for more information on the available algorithms. Building from included sources requires 'CMake'. On Linux and 'macOS', if a suitable system build of NLopt (2.7.0 or later) is found, it is used; otherwise, it is built from included sources via 'CMake'. On Windows, NLopt is obtained through 'rwinlib' for 'R <= 4.1.x' or grabbed from the 'Rtools42 toolchain' for 'R >= 4.2.0'.
Author: Jelmer Ypma [aut], Steven G. Johnson [aut] , Hans W. Borchers [ctb], Dirk Eddelbuettel [ctb], Brian Ripley [ctb] , Kurt Hornik [ctb] , Julien Chiquet [ctb], Avraham Adler [ctb] , Xiongtao Dai [ctb], Aymeric Stamm [ctb, cre] , Jeroen Ooms [ctb]
Maintainer: Aymeric Stamm <aymeric.stamm@math.cnrs.fr>

Diff between nloptr versions 2.0.2 dated 2022-05-19 and 2.0.3 dated 2022-05-26

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Package pbo updated to version 1.3.5 with previous version 1.3.4 dated 2014-05-31

Title: Probability of Backtest Overfitting
Description: Following the method of Bailey et al., computes for a collection of candidate models the probability of backtest overfitting, the performance degradation and probability of loss, and the stochastic dominance.
Author: Matt Barry [aut, cre]
Maintainer: Matt Barry <mrb@softisms.com>

Diff between pbo versions 1.3.4 dated 2014-05-31 and 1.3.5 dated 2022-05-26

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New package MonteCarloSEM with initial version 0.0.4
Package: MonteCarloSEM
Title: Monte Carlo Data Simulation Package
Version: 0.0.4
Description: Monte Carlo simulation allows to test different conditions given to the correct structural equation models. This package runs Monte Carlo simulations under different conditions (such as sample size or normality of data). Within the package data sets can be simulated and run based on the given model. First, continuous and normal data sets are generated based on the given model. Later Fleishman's power method (1978) <DOI:10.1007/BF02293811> is used to add non-normality if exists. When data generation is completed (or when generated data sets are given) model test can also be run. Please cite as "Orçan, F. (2021). MonteCarloSEM: An R Package to Simulate Data for SEM. International Journal of Assessment Tools in Education, 8 (3), 704-713."
License: GPL-3
Encoding: UTF-8
Imports: Matrix, stats, utils, lavaan
Copyright: Fatih Orcan, Turkey
NeedsCompilation: no
Packaged: 2022-05-25 07:30:29 UTC; PC110
Author: Fatih Orcan [aut, cre]
Maintainer: Fatih Orcan <fatihorcan84@gmail.com>
Repository: CRAN
Date/Publication: 2022-05-26 14:30:07 UTC

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New package latrend with initial version 1.3.0
Package: latrend
Version: 1.3.0
Date: 2022-05-25
Title: A Framework for Clustering Longitudinal Data
Description: A framework for clustering longitudinal datasets in a standardized way. Provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease.
Maintainer: Niek Den Teuling <niek.den.teuling@philips.com>
URL: https://github.com/philips-software/latrend
BugReports: https://github.com/philips-software/latrend/issues
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Language: en-US
Depends: R (>= 3.6.0)
Imports: stats, methods, Rdpack, R.utils, assertthat (>= 0.2.1), foreach, data.table (>= 1.12.0), magrittr, matrixStats, rlang, rmarkdown (>= 1.18)
Suggests: testthat (>= 3.0.0), roxygen2 (>= 7.1.0), knitr (>= 1.24), rcmdcheck, pkgdown, devtools, evaluate, lme4, covr, lintr, tinytex, longitudinalData (>= 2.4.1), kml (>= 2.4.1), lcmm (>= 1.9.3), mixtools, flexmix, fda, funFEM, gridExtra, igraph, crimCV, akmedoids (>= 1.3.0), dtwclust, mixAK, clusterCrit, mclust, mclustcomp, psych, qqplotr, doParallel, simTool, dplyr, ggplot2, caret, tibble
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-05-25 22:57:28 UTC; 310083353
Author: Niek Den Teuling [aut, cre] , Steffen Pauws [ctb], Edwin van den Heuvel [ctb], Copyright © 2022 Koninklijke Philips N.V. [cph]
Repository: CRAN
Date/Publication: 2022-05-26 14:40:05 UTC

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Package HACSim updated to version 1.0.6 with previous version 1.0.5 dated 2020-01-07

Title: Iterative Extrapolation of Species' Haplotype Accumulation Curves for Genetic Diversity Assessment
Description: Performs iterative extrapolation of species' haplotype accumulation curves using a nonparametric stochastic (Monte Carlo) optimization method for assessment of specimen sampling completeness based on the approach of Phillips et al. (2015) <doi:10.1515/dna-2015-0008>, Phillips et al. (2019) <doi:10.1002/ece3.4757> and Phillips et al. (2020) <doi: 10.7717/peerj-cs.243>. 'HACSim' outputs a number of useful summary statistics of sampling coverage ("Measures of Sampling Closeness"), including an estimate of the likely required sample size (along with desired level confidence intervals) necessary to recover a given number/proportion of observed unique species' haplotypes. Any genomic marker can be targeted to assess likely required specimen sample sizes for genetic diversity assessment. The method is particularly well-suited to assess sampling sufficiency for DNA barcoding initiatives. Users can also simulate their own DNA sequences according to various models of nucleotide substitution. A Shiny app is also available.
Author: Jarrett D. Phillips [aut, cre], Steven H. French [ctb], Navdeep Singh [ctb]
Maintainer: Jarrett D. Phillips <phillipsjarrett1@gmail.com>

Diff between HACSim versions 1.0.5 dated 2020-01-07 and 1.0.6 dated 2022-05-26

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Package ergm.ego updated to version 1.0.1 with previous version 1.0.0 dated 2021-06-23

Title: Fit, Simulate and Diagnose Exponential-Family Random Graph Models to Egocentrically Sampled Network Data
Description: Utilities for managing egocentrically sampled network data and a wrapper around the 'ergm' package to facilitate ERGM inference and simulation from such data. See Krivitsky and Morris (2017) <doi:10.1214/16-AOAS1010>.
Author: Pavel N. Krivitsky [aut, cre] , Steven M. Goodreau [ctb], Martina Morris [ctb], Kirk Li [ctb], Emily N. Beylerian [ctb], Michal Bojanowski [ctb] , Chad Klumb [ctb]
Maintainer: Pavel N. Krivitsky <pavel@statnet.org>

Diff between ergm.ego versions 1.0.0 dated 2021-06-23 and 1.0.1 dated 2022-05-26

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Permanent link

Package ctmcd updated to version 1.4.2 with previous version 1.4.1 dated 2019-01-02

Title: Estimating the Parameters of a Continuous-Time Markov Chain from Discrete-Time Data
Description: Functions for estimating Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal adjustment, weighted adjustment and quasi-optimization of matrix logarithm based candidate solutions, an expectation-maximization algorithm as well as a Gibbs sampler.
Author: Marius Pfeuffer [aut,cre], Greig Smith [ctb], Goncalo dos Reis [ctb], Linda Moestel [ctb], Matthias Fischer [ctb]
Maintainer: Marius Pfeuffer <marius.pfeuffer@fau.de>

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Permanent link

Package cchsflow updated to version 2.1.0 with previous version 2.0.0 dated 2021-10-25

Title: Transforming and Harmonizing CCHS Variables
Description: Supporting the use of the Canadian Community Health Survey (CCHS) by transforming variables from each cycle into harmonized, consistent versions that span survey cycles (currently, 2001 to 2018). CCHS data used in this library is accessed and adapted in accordance to the Statistics Canada Open Licence Agreement. This package uses rec_with_table(), which was developed from 'sjmisc' rec(). Lüdecke D (2018). "sjmisc: Data and Variable Transformation Functions". Journal of Open Source Software, 3(26), 754. <doi:10.21105/joss.00754>.
Author: Doug Manuel [aut, cph] , Warsame Yusuf [aut], Rostyslav Vyuha [aut], Kitty Chen [aut, cre], Carol Bennett [aut], Yulric Sequeira [ctb], The Ottawa Hospital [cph]
Maintainer: Kitty Chen <kitchen@ohri.ca>

Diff between cchsflow versions 2.0.0 dated 2021-10-25 and 2.1.0 dated 2022-05-26

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More information about cchsflow at CRAN
Permanent link

Package Boom updated to version 0.9.8 with previous version 0.9.7 dated 2021-02-23

Title: Bayesian Object Oriented Modeling
Description: A C++ library for Bayesian modeling, with an emphasis on Markov chain Monte Carlo. Although boom contains a few R utilities (mainly plotting functions), its primary purpose is to install the BOOM C++ library on your system so that other packages can link against it.
Author: Steven L. Scott is the sole author and creator of the BOOM project. Some code in the BOOM libraries has been modified from other open source projects. These include Cephes , NEWUOA , and a modified version of the R math libraries . Original copyrig [...truncated...]
Maintainer: Steven L. Scott <steve.the.bayesian@gmail.com>

Diff between Boom versions 0.9.7 dated 2021-02-23 and 0.9.8 dated 2022-05-26

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 Boom-0.9.8/Boom/DESCRIPTION                                                                                 |    8 
 Boom-0.9.8/Boom/MD5                                                                                         |  522 ++---
 Boom-0.9.8/Boom/NAMESPACE                                                                                   |   12 
 Boom-0.9.8/Boom/R/prior_specification.R                                                                     |   14 
 Boom-0.9.8/Boom/inst/include/LinAlg/Array.hpp                                                               |    7 
 Boom-0.9.8/Boom/inst/include/LinAlg/ArrayIterator.hpp                                                       |   24 
 Boom-0.9.8/Boom/inst/include/LinAlg/Matrix.hpp                                                              |   18 
 Boom-0.9.8/Boom/inst/include/LinAlg/Selector.hpp                                                            |    7 
 Boom-0.9.8/Boom/inst/include/LinAlg/SpdMatrix.hpp                                                           |   12 
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 Boom-0.9.8/Boom/inst/include/LinAlg/Vector.hpp                                                              |    6 
 Boom-0.9.8/Boom/inst/include/LinAlg/VectorView.hpp                                                          |    2 
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 Boom-0.9.8/Boom/inst/include/Models/DataTypes.hpp                                                           |   14 
 Boom-0.9.8/Boom/inst/include/Models/Glm/ChoiceData.hpp                                                      |    3 
 Boom-0.9.8/Boom/inst/include/Models/Glm/Glm.hpp                                                             |    3 
 Boom-0.9.8/Boom/inst/include/Models/Glm/GlmCoefs.hpp                                                        |    8 
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 Boom-0.9.8/Boom/inst/include/Models/Glm/MultinomialLogitModel.hpp                                           |    7 
 Boom-0.9.8/Boom/inst/include/Models/Glm/MvnGivenX.hpp                                                       |   66 
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 Boom-0.9.8/Boom/inst/include/Models/Glm/RegressionModel.hpp                                                 |  106 +
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 Boom-0.9.8/Boom/inst/include/Models/Hierarchical/HierarchicalZeroInflatedPoissonModel.hpp                   |    2 
 Boom-0.9.8/Boom/inst/include/Models/IRT/IRT.hpp                                                             |    3 
 Boom-0.9.8/Boom/inst/include/Models/Impute/MixedDataImputerWithErrorCorrection.hpp                          |    2 
 Boom-0.9.8/Boom/inst/include/Models/MarkovModel.hpp                                                         |    2 
 Boom-0.9.8/Boom/inst/include/Models/Mixtures/DirichletProcessMvnModel.hpp                                   |   26 
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 Boom-0.9.8/Boom/inst/include/Models/ModelTypes.hpp                                                          |   18 
 Boom-0.9.8/Boom/inst/include/Models/ParamTypes.hpp                                                          |    2 
 Boom-0.9.8/Boom/inst/include/Models/Policies/IID_DataPolicy.hpp                                             |    2 
 Boom-0.9.8/Boom/inst/include/Models/Policies/PriorPolicy.hpp                                                |   16 
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 Boom-0.9.8/Boom/inst/include/Models/StateSpace/DynamicInterceptRegression.hpp                               |   39 
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 Boom-0.9.8/Boom/inst/include/Models/StateSpace/Filters/ConditionalIidKalmanFilter.hpp                       |   69 
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 Boom-0.9.8/Boom/inst/include/Models/StateSpace/Filters/KalmanFilterBase.hpp                                 |   30 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/Filters/KalmanTools.hpp                                      |   26 
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 Boom-0.9.8/Boom/inst/include/Models/StateSpace/Filters/ScalarKalmanFilter.hpp                               |   19 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/Filters/SparseKalmanTools.hpp                                |   10 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/Filters/SparseMatrix.hpp                                     |  731 ++++++-
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 Boom-0.9.8/Boom/inst/include/Models/StateSpace/PosteriorSamplers/DynamicRegressionDirectGibbs.hpp           |   49 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/PosteriorSamplers/DynamicRegressionPosteriorSampler.hpp      |    7 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/PosteriorSamplers/StateSpaceLogitPosteriorSampler.hpp        |    3 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/PosteriorSamplers/StateSpacePoissonPosteriorSampler.hpp      |    3 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/PosteriorSamplers/StateSpacePosteriorSampler.hpp             |    4 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/StateModelVector.hpp                                         |  146 -
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/StateModels/GeneralSeasonalStateModel.hpp                    |  164 +
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/StateModels/LocalLevelStateModel.hpp                         |  170 -
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/StateModels/PosteriorSamplers                                |only
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/StateModels/RegressionHolidayStateModel.hpp                  |   15 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/StateModels/SemilocalLinearTrend.hpp                         |    5 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/StateModels/StateModel.hpp                                   |   27 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/StateSpaceLogitModel.hpp                                     |   48 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/StateSpaceModel.hpp                                          |   38 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/StateSpaceModelBase.hpp                                      |  161 +
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/StateSpacePoissonModel.hpp                                   |   13 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/StateSpaceRegressionModel.hpp                                |   38 
 Boom-0.9.8/Boom/inst/include/Models/StateSpace/StateSpaceStudentRegressionModel.hpp                         |   30 
 Boom-0.9.8/Boom/inst/include/Models/TimeSeries/PosteriorSamplers/ArPosteriorSampler.hpp                     |    4 
 Boom-0.9.8/Boom/inst/include/Models/TimeSeries/PosteriorSamplers/ArSpikeSlabSampler.hpp                     |    3 
 Boom-0.9.8/Boom/inst/include/cpputil/Date.hpp                                                               |   10 
 Boom-0.9.8/Boom/inst/include/cpputil/Ptr.hpp                                                                |   12 
 Boom-0.9.8/Boom/inst/include/cpputil/ThreadTools.hpp                                                        |    3 
 Boom-0.9.8/Boom/inst/include/cpputil/string_utils.hpp                                                       |    8 
 Boom-0.9.8/Boom/inst/include/distributions.hpp                                                              |    2 
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 Boom-0.9.8/Boom/inst/include/r_interface/prior_specification.hpp                                            |   34 
 Boom-0.9.8/Boom/inst/include/stats/DataTable.hpp                                                            |   33 
 Boom-0.9.8/Boom/inst/include/uint.hpp                                                                       |    4 
 Boom-0.9.8/Boom/man/MvnGivenSigmaMatrixPrior.Rd                                                             |only
 Boom-0.9.8/Boom/src/Bmath/mlutils.cpp                                                                       |   30 
 Boom-0.9.8/Boom/src/Bmath/plogis.cpp                                                                        |   11 
 Boom-0.9.8/Boom/src/LinAlg/Array.cpp                                                                        |   31 
 Boom-0.9.8/Boom/src/LinAlg/Eigen.cpp                                                                        |    3 
 Boom-0.9.8/Boom/src/LinAlg/LU.cpp                                                                           |   22 
 Boom-0.9.8/Boom/src/LinAlg/Matrix.cpp                                                                       |   56 
 Boom-0.9.8/Boom/src/LinAlg/SWEEP.cpp                                                                        |    3 
 Boom-0.9.8/Boom/src/LinAlg/Selector.cpp                                                                     |   12 
 Boom-0.9.8/Boom/src/LinAlg/SpdMatrix.cpp                                                                    |   28 
 Boom-0.9.8/Boom/src/LinAlg/SubMatrix.cpp                                                                    |   88 
 Boom-0.9.8/Boom/src/LinAlg/Vector.cpp                                                                       |    9 
 Boom-0.9.8/Boom/src/LinAlg/VectorView.cpp                                                                   |   16 
 Boom-0.9.8/Boom/src/Makevars                                                                                |   11 
 Boom-0.9.8/Boom/src/Models/BinomialModel.cpp                                                                |    5 
 Boom-0.9.8/Boom/src/Models/FiniteMixtureModel.cpp                                                           |    2 
 Boom-0.9.8/Boom/src/Models/GaussianModel.cpp                                                                |    2 
 Boom-0.9.8/Boom/src/Models/Glm/GlmCoefs.cpp                                                                 |   18 
 Boom-0.9.8/Boom/src/Models/Glm/MultinomialLogitModel.cpp                                                    |    2 
 Boom-0.9.8/Boom/src/Models/Glm/PoissonRegressionModel.cpp                                                   |    2 
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 Boom-0.9.8/Boom/src/Models/Glm/PosteriorSamplers/BregVsSampler.cpp                                          |    2 
 Boom-0.9.8/Boom/src/Models/Glm/PosteriorSamplers/PoissonRegressionAuxMixSampler.cpp                         |    9 
 Boom-0.9.8/Boom/src/Models/Glm/PosteriorSamplers/PoissonRegressionSpikeSlabSampler.cpp                      |   14 
 Boom-0.9.8/Boom/src/Models/Glm/PosteriorSamplers/RegressionSemiconjugateSampler.cpp                         |    4 
 Boom-0.9.8/Boom/src/Models/Glm/PosteriorSamplers/SpikeSlabDaRegressionSampler.cpp                           |    2 
 Boom-0.9.8/Boom/src/Models/Glm/PosteriorSamplers/SpikeSlabSampler.cpp                                       |   14 
 Boom-0.9.8/Boom/src/Models/Glm/PosteriorSamplers/ZeroInflatedLognormalRegressionPosteriorSampler.cpp        |    2 
 Boom-0.9.8/Boom/src/Models/Glm/RegressionModel.cpp                                                          |  102 
 Boom-0.9.8/Boom/src/Models/Glm/RegressionSlabPrior.cpp                                                      |only
 Boom-0.9.8/Boom/src/Models/Glm/VariableSelectionPrior.cpp                                                   |    3 
 Boom-0.9.8/Boom/src/Models/HMM/HmmDataImputer.cpp                                                           |    3 
 Boom-0.9.8/Boom/src/Models/IRT/PartialCreditModel.cpp                                                       |    9 
 Boom-0.9.8/Boom/src/Models/Impute/MixedDataImputer.cpp                                                      |   12 
 Boom-0.9.8/Boom/src/Models/Impute/MixedDataImputerWithErrorCorrection.cpp                                   |    4 
 Boom-0.9.8/Boom/src/Models/Impute/MvRegCopulaDataImputer.cpp                                                |    3 
 Boom-0.9.8/Boom/src/Models/MarkovModel.cpp                                                                  |   11 
 Boom-0.9.8/Boom/src/Models/Mixtures/DirichletProcessMixture.cpp                                             |    8 
 Boom-0.9.8/Boom/src/Models/Mixtures/DirichletProcessMvnModel.cpp                                            |   11 
 Boom-0.9.8/Boom/src/Models/Mixtures/PosteriorSamplers/DirichletProcessMvnCollapsedGibbsSampler.cpp          |    7 
 Boom-0.9.8/Boom/src/Models/Mixtures/identify_permutation.cpp                                                |only
 Boom-0.9.8/Boom/src/Models/MultinomialModel.cpp                                                             |    2 
 Boom-0.9.8/Boom/src/Models/PoissonModel.cpp                                                                 |    2 
 Boom-0.9.8/Boom/src/Models/Policies/PriorPolicy.cpp                                                         |   29 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/AbsorbingMarkovConjSampler.cpp                                 |    8 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/BetaBinomialPosteriorSampler.cpp                               |    7 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/BetaBinomialSampler.cpp                                        |    6 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/BetaPosteriorSampler.cpp                                       |    8 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/CompositeModelSampler.cpp                                      |    5 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/CorrelationSampler.cpp                                         |    8 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/DirichletPosteriorSampler.cpp                                  |    7 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/ExchangeableDirichletSampler.cpp                               |    7 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/ExponentialGammaSampler.cpp                                    |   33 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/FixedProbBinomialSampler.cpp                                   |    9 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/GammaPosteriorSampler.cpp                                      |   19 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/GaussianConjSampler.cpp                                        |   11 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/GaussianMeanSampler.cpp                                        |   28 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/GaussianVarSampler.cpp                                         |    8 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/GenericGaussianVarianceSampler.cpp                             |    8 
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 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/IndependentMvnConjSampler.cpp                                  |   16 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/IndependentMvnVarSampler.cpp                                   |   20 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/MarkovConjSampler.cpp                                          |   13 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/MultinomialDirichletSampler.cpp                                |   14 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/MvnConjSampler.cpp                                             |    7 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/MvnIndependentVarianceSampler.cpp                              |   22 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/MvnMeanSampler.cpp                                             |   14 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/MvnVarSampler.cpp                                              |   15 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/PoissonGammaPosteriorSampler.cpp                               |   15 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/PoissonGammaSampler.cpp                                        |   44 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/PosteriorSampler.cpp                                           |    6 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/ProductDirichletPosteriorSampler.cpp                           |   16 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/SepStratSampler.cpp                                            |   18 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/ZeroInflatedGammaPosteriorSampler.cpp                          |   13 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/ZeroInflatedLognormalPosteriorSampler.cpp                      |    8 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/ZeroInflatedPoissonSampler.cpp                                 |    9 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/ZeroMeanGaussianConjSampler.cpp                                |   17 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/ZeroMeanMvnConjSampler.cpp                                     |    7 
 Boom-0.9.8/Boom/src/Models/PosteriorSamplers/ZeroMeanMvnIndependenceSampler.cpp                             |   10 
 Boom-0.9.8/Boom/src/Models/ProductVectorModel.cpp                                                           |    2 
 Boom-0.9.8/Boom/src/Models/SpdModel.cpp                                                                     |    6 
 Boom-0.9.8/Boom/src/Models/StateSpace/AggregatedStateSpaceRegression.cpp                                    |   27 
 Boom-0.9.8/Boom/src/Models/StateSpace/DynamicInterceptRegression.cpp                                        |   26 
 Boom-0.9.8/Boom/src/Models/StateSpace/DynamicRegression.cpp                                                 |   68 
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 Boom-0.9.8/Boom/src/Models/StateSpace/Filters/KalmanFilterBase.cpp                                          |   17 
 Boom-0.9.8/Boom/src/Models/StateSpace/Filters/KalmanTools.cpp                                               |   31 
 Boom-0.9.8/Boom/src/Models/StateSpace/Filters/MultivariateKalmanFilterBase.cpp                              |  196 +
 Boom-0.9.8/Boom/src/Models/StateSpace/Filters/ScalarKalmanFilter.cpp                                        |   74 
 Boom-0.9.8/Boom/src/Models/StateSpace/Filters/SparseKalmanTools.cpp                                         |  122 -
 Boom-0.9.8/Boom/src/Models/StateSpace/Filters/SparseMatrix.cpp                                              | 1027 ++++++++--
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 Boom-0.9.8/Boom/src/Models/StateSpace/StateModelVector.cpp                                                  |   88 
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 Boom-0.9.8/Boom/src/Models/StateSpace/StateModels/Holiday.cpp                                               |    1 
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 Boom-0.9.8/Boom/src/Models/StateSpace/StateSpacePoissonModel.cpp                                            |   68 
 Boom-0.9.8/Boom/src/Models/StateSpace/StateSpaceRegressionModel.cpp                                         |   59 
 Boom-0.9.8/Boom/src/Models/StateSpace/StateSpaceStudentRegressionModel.cpp                                  |   61 
 Boom-0.9.8/Boom/src/Models/TimeSeries/ArModel.cpp                                                           |    4 
 Boom-0.9.8/Boom/src/Models/ZeroInflatedGammaModel.cpp                                                       |    7 
 Boom-0.9.8/Boom/src/Models/ZeroInflatedLognormalModel.cpp                                                   |    9 
 Boom-0.9.8/Boom/src/boom_r_tools.cpp                                                                        |    4 
 Boom-0.9.8/Boom/src/cpputil/ThreadTools.cpp                                                                 |    4 
 Boom-0.9.8/Boom/src/cpputil/report_error.cpp                                                                |    1 
 Boom-0.9.8/Boom/src/cpputil/split.cpp                                                                       |    8 
 Boom-0.9.8/Boom/src/distributions/dirichlet.cpp                                                             |   15 
 Boom-0.9.8/Boom/src/distributions/rng.cpp                                                                   |    3 
 Boom-0.9.8/Boom/src/distributions/student_fix.cpp                                                           |   10 
 Boom-0.9.8/Boom/src/distributions/trun_exp.cpp                                                              |    3 
 Boom-0.9.8/Boom/src/distributions/trun_logit.cpp                                                            |   14 
 Boom-0.9.8/Boom/src/math/cephes/cephes_rgamma.cpp                                                           |only
 Boom-0.9.8/Boom/src/numopt/LinearAssignment.cpp                                                             |only
 Boom-0.9.8/Boom/src/numopt/MarkovDecisionProcess.cpp                                                        |only
 Boom-0.9.8/Boom/src/numopt/Qlearning.cpp                                                                    |only
 Boom-0.9.8/Boom/src/prior_specification.cpp                                                                 |   22 
 Boom-0.9.8/Boom/src/stats/DataTable.cpp                                                                     |   58 
 Boom-0.9.8/Boom/src/stats/Design.cpp                                                                        |    2 
 272 files changed, 5449 insertions(+), 2321 deletions(-)

More information about Boom at CRAN
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Package sboost updated to version 0.1.2 with previous version 0.1.1 dated 2019-04-08

Title: Machine Learning with AdaBoost on Decision Stumps
Description: Creates classifier for binary outcomes using Adaptive Boosting (AdaBoost) algorithm on decision stumps with a fast C++ implementation. For a description of AdaBoost, see Freund and Schapire (1997) <doi:10.1006/jcss.1997.1504>. This type of classifier is nonlinear, but easy to interpret and visualize. Feature vectors may be a combination of continuous (numeric) and categorical (string, factor) elements. Methods for classifier assessment, predictions, and cross-validation also included.
Author: Jadon Wagstaff [aut, cre]
Maintainer: Jadon Wagstaff <jadonw@gmail.com>

Diff between sboost versions 0.1.1 dated 2019-04-08 and 0.1.2 dated 2022-05-26

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 sboost-0.1.2/sboost/NEWS.md                          |    8 +-
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Package ProjectionBasedClustering updated to version 1.1.8 with previous version 1.1.6 dated 2020-12-11

Title: Projection Based Clustering
Description: A clustering approach applicable to every projection method is proposed here. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on Thrun and Ultsch, "Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data" <DOI:10.1007/s00357-020-09373-2>. Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package, and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R.
Author: Michael Thrun [aut, cre, cph], Florian Lerch [aut], Felix Pape [aut], Tim Schreier [aut], Luis Winckelmann [aut], Kristian Nybo [cph], Jarkko Venna [cph]
Maintainer: Michael Thrun <m.thrun@gmx.net>

Diff between ProjectionBasedClustering versions 1.1.6 dated 2020-12-11 and 1.1.8 dated 2022-05-26

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 NAMESPACE                                      |    3 
 R/PlotProjectedPoints.R                        |    4 
 R/ProjectionBasedClustering.R                  |    6 
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 man/CCA.Rd                                     |    4 
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 man/KruskalStress.Rd                           |    2 
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 man/PCA.Rd                                     |    2 
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 man/Projection2Bestmatches.Rd                  |    2 
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 man/SammonsMapping.Rd                          |    2 
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 man/interactiveGeneralizedUmatrixIsland.Rd     |  117 +-
 man/interactiveProjectionBasedClustering.Rd    |   22 
 man/tSNE.Rd                                    |    2 
 src/RcppExports.cpp                            |    5 
 30 files changed, 561 insertions(+), 1832 deletions(-)

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Package plotROC updated to version 2.3.0 with previous version 2.2.1 dated 2018-06-23

Title: Generate Useful ROC Curve Charts for Print and Interactive Use
Description: Most ROC curve plots obscure the cutoff values and inhibit interpretation and comparison of multiple curves. This attempts to address those shortcomings by providing plotting and interactive tools. Functions are provided to generate an interactive ROC curve plot for web use, and print versions. A Shiny application implementing the functions is also included.
Author: Michael C. Sachs [aut, cre], Robert W. Corty [ctb], Luis Crouch [ctb]
Maintainer: Michael C. Sachs <sachsmc@gmail.com>

Diff between plotROC versions 2.2.1 dated 2018-06-23 and 2.3.0 dated 2022-05-26

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 NAMESPACE                     |   97 -
 NEWS                          |  209 +--
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More information about plotROC at CRAN
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New package OPTtesting with initial version 1.0.0
Package: OPTtesting
Title: Optimal Testing
Version: 1.0.0
Maintainer: Lijia Wang <lijiawan@usc.edu>
Description: Optimal testing under general dependence. The R package implements procedures proposed in Wang, Han, and Tong (2022). The package includes parameter estimation procedures, the computation for the posterior probabilities, and the testing procedure.
Encoding: UTF-8
Imports: rootSolve, quantreg, RSpectra, stats
Suggests: MASS
License: GPL-2
NeedsCompilation: no
Packaged: 2022-05-26 00:51:46 UTC; cocow
Author: Lijia Wang [aut, cre, cph], Xu Han [aut], Xin Tong [aut]
Repository: CRAN
Date/Publication: 2022-05-26 13:30:09 UTC

More information about OPTtesting at CRAN
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New package mapbayr with initial version 0.7.3
Package: mapbayr
Title: MAP-Bayesian Estimation of PK Parameters
Version: 0.7.3
Description: Performs maximum a posteriori Bayesian estimation of individual pharmacokinetic parameters from a model defined in 'mrgsolve', typically for model-based therapeutic drug monitoring. Internally computes an objective function value from model and data, performs optimization and returns predictions in a convenient format. The performance of the package was described by Le Louedec et al (2021) <doi:10.1002/psp4.12689>.
License: GPL-3
Encoding: UTF-8
Imports: dplyr, ggplot2, magrittr, mrgsolve (>= 1.0.0), optimx, progress, purrr, rlang, stringr, tibble, tidyr
URL: https://github.com/FelicienLL/mapbayr
BugReports: https://github.com/FelicienLL/mapbayr/issues
Suggests: knitr, minqa, testthat (>= 3.0.0)
Depends: R (>= 2.10)
LazyData: true
NeedsCompilation: no
Packaged: 2022-05-25 15:13:49 UTC; le_louedec
Author: Felicien Le Louedec [aut, cre] , Kyle T Baron [ctb]
Maintainer: Felicien Le Louedec <felicienlelouedec@proton.me>
Repository: CRAN
Date/Publication: 2022-05-26 13:30:02 UTC

More information about mapbayr at CRAN
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Package effectsize updated to version 0.7.0 with previous version 0.6.0.1 dated 2022-01-26

Title: Indices of Effect Size and Standardized Parameters
Description: Provide utilities to work with indices of effect size and standardized parameters for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), allowing computation of and conversion between indices such as Cohen's d, r, odds, etc.
Author: Mattan S. Ben-Shachar [aut, cre] , Dominique Makowski [aut] , Daniel Luedecke [aut] , Indrajeet Patil [aut] , Brenton M. Wiernik [aut] , Ken Kelley [ctb], David Stanley [ctb], Jessica Burnett [rev] , Johannes Karreth [rev]
Maintainer: Mattan S. Ben-Shachar <matanshm@post.bgu.ac.il>

Diff between effectsize versions 0.6.0.1 dated 2022-01-26 and 0.7.0 dated 2022-05-26

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 effectsize-0.7.0/effectsize/tests/testthat/test-effectsize.R               |  161 +-
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New package FAVAR with initial version 0.1.3
Package: FAVAR
Title: Bayesian Analysis of a FAVAR Model
Version: 0.1.3
Description: Estimate a FAVAR model by a Bayesian method, based on Bernanke et al. (2005) <DOI:10.1162/0033553053327452>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: ggplot2, bvartools, foreach, magrittr, MCMCpack, coda, dplyr, doParallel, Matrix
Depends: R (>= 3.5.0)
Suggests: testthat, vars, patchwork
NeedsCompilation: no
Packaged: 2022-05-26 02:32:20 UTC; yangnay
Author: Pu Chen [aut, cre] , Chen Chen [aut], Gary Koop [cph], Dimitris Korobilis [cph]
Maintainer: Pu Chen <shengnehs@qq.com>
Repository: CRAN
Date/Publication: 2022-05-26 13:30:12 UTC

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New package BiCausality with initial version 0.1.1
Package: BiCausality
Title: Binary Causality Inference Framework
Version: 0.1.1
Maintainer: Chainarong Amornbunchornvej <grandca@gmail.com>
Description: A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E) where V is a set of nodes representing binary variables and there is an edge from i to j in E if the variable i causes j. The framework determines dependency among variables as well as analyzing confounding factors before deciding whether i causes j. The publication of this package is at <arXiv:2205.06131>.
License: MIT + file LICENSE
URL: https://github.com/DarkEyes/BiCausality
BugReports: https://github.com/DarkEyes/BiCausality/issues
Depends: R (>= 3.5.0)
Encoding: UTF-8
LazyData: TRUE
Suggests: knitr, rmarkdown, markdown, igraph
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-05-26 03:53:52 UTC; 006005
Author: Chainarong Amornbunchornvej [aut, cre]
Repository: CRAN
Date/Publication: 2022-05-26 13:30:15 UTC

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Package ergm.userterms (with last version 3.10.0) was removed from CRAN

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

2019-05-15 3.10.0
2018-08-25 3.9.0
2013-11-28 3.1.1
2013-04-27 3.1-0
2013-01-30 3.0-2
2012-03-08 3.0-1
2011-02-03 1.0

Permanent link
Package rego updated to version 1.5.1 with previous version 1.4.1 dated 2022-05-10

Title: Automatic Time Series Forecasting and Missing Value Imputation
Description: Machine learning algorithm for predicting and imputing time series. It can automatically set all the parameters needed, thus in the minimal configuration it only requires the target variable and the dependent variables if present. It can address large problems with hundreds or thousands of dependent variables and problems in which the number of dependent variables is greater than the number of observations. Moreover it can be used not only for time series but also for any other real valued target variable. The algorithm implemented includes a Bayesian stochastic search methodology for model selection and a robust estimation based on bootstrapping. 'rego' is fast because all the code is C++.
Author: Davide Altomare [cre, aut], David Loris [aut]
Maintainer: Davide Altomare <info@channelattribution.io>

Diff between rego versions 1.4.1 dated 2022-05-10 and 1.5.1 dated 2022-05-26

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 7 files changed, 408 insertions(+), 233 deletions(-)

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Package SoupX updated to version 1.6.1 with previous version 1.5.2 dated 2021-05-17

Title: Single Cell mRNA Soup eXterminator
Description: Quantify, profile and remove ambient mRNA contamination (the "soup") from droplet based single cell RNA-seq experiments. Implements the method described in Young et al. (2018) <doi:10.1101/303727>.
Author: Matthew Daniel Young
Maintainer: Matthew Daniel Young <my4@sanger.ac.uk>

Diff between SoupX versions 1.5.2 dated 2021-05-17 and 1.6.1 dated 2022-05-26

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Package DendroSync updated to version 0.1.4 with previous version 0.1.3 dated 2019-05-30

Title: A Set of Tools for Calculating Spatial Synchrony Between Tree-Ring Chronologies
Description: Provides functions for the calculation and plotting of synchrony in tree growth from tree-ring width chronologies (TRW index). It combines variance-covariance (VCOV) mixed modelling with functions that quantify the degree to which the TRW chronologies contain a common temporal signal. It also implements temporal trends in spatial synchrony using a moving window. These methods can also be used with other kind of ecological variables that have temporal autocorrelation corrected.
Author: Josu G. Alday , Tatiana A. Shestakova , Victor Resco de Dios , Jordi Voltas
Maintainer: Josu G. Alday <josucham@gmail.com>

Diff between DendroSync versions 0.1.3 dated 2019-05-30 and 0.1.4 dated 2022-05-26

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New package FGLMtrunc with initial version 0.1.0
Package: FGLMtrunc
Title: Truncated Functional Generalized Linear Models
Version: 0.1.0
Date: 2022-05-21
Author: Xi Liu [aut], Chau Tran [aut, cre], Alexander Petersen [aut]
Maintainer: Chau Tran <chautran@ucsb.edu>
Description: An implementation of the methodologies described in Xi Liu, Afshin A. Divani, and Alexander Petersen (2022) <doi:10.1016/j.csda.2022.107421>, including truncated functional linear and truncated functional logistic regression models.
License: GPL-2
Depends: R (>= 3.6.0)
Imports: foreach, glmnet, splines2
LinkingTo: Rcpp, RcppArmadillo
Encoding: UTF-8
Suggests: knitr, xfun, rmarkdown
VignetteBuilder: knitr
LazyData: true
NeedsCompilation: yes
Packaged: 2022-05-25 14:52:03 UTC; chautran
Repository: CRAN
Date/Publication: 2022-05-26 08:20:02 UTC

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New package BioRssay with initial version 1.0.0
Package: BioRssay
Title: Analyze Bioassays and Probit Graphs
Version: 1.0.0
Maintainer: Piyal Karunarathne <piyalkarumail@yahoo.com>
Description: A robust framework for analyzing mortality data from bioassays for one or several strains/lines/populations.
License: AGPL (>= 3)
Imports: colorspace
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5)
Suggests: markdown, rmarkdown, knitr, testthat (>= 2.0.0), covr
VignetteBuilder: knitr
URL: https://milesilab.github.io/BioRssay/
BugReports: https://github.com/milesilab/BioRssay/issues/
NeedsCompilation: no
Packaged: 2022-05-25 09:24:39 UTC; piyalkarunarathne
Author: Piyal Karunarathne [aut, cre] , Pascal Milesi [aut] , Pierrick Labbe [aut]
Repository: CRAN
Date/Publication: 2022-05-26 08:20:05 UTC

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New package async with initial version 0.2.1
Package: async
Title: Asynchronous Code Constructs: Generators, Yield, Async, Await
Version: 0.2.1
Date: 2022-05-15
URL: https://crowding.github.io/async/, https://github.com/crowding/async/
BugReports: https://github.com/crowding/async/issues
Description: Write sequential-looking code that pauses and resumes. gen() creates a generator, an iterator that returns a value and pauses each time it reaches a yield() call. async() creates a promise, which runs until it reaches a call to await(), then resumes when information is available. These work similarly to generator and async constructs from 'Python' or 'JavaScript'. Objects produced are compatible with the 'iterators' and 'promises' packages.
License: GPL-2
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: nseval (>= 0.4), iterators, itertools, later, promises
Suggests: rmarkdown, testthat (>= 3.0.0), knitr, magrittr, audio, profvis, covr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-05-25 05:52:07 UTC; peter
Author: Peter Meilstrup [aut, cre]
Maintainer: Peter Meilstrup <peter.meilstrup@gmail.com>
Repository: CRAN
Date/Publication: 2022-05-26 08:30:02 UTC

More information about async at CRAN
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Package Neighboot (with last version 1.0.0) was removed from CRAN

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

2020-09-21 1.0.0

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Package MorphoTools2 updated to version 1.0.0.0 with previous version 0.9.1.2 dated 2022-02-03

Title: Multivariate Morphometric Analysis
Description: Tools for multivariate analyses of morphological data, wrapped in one package, to make the workflow convenient and fast. Statistical and graphical tools provide a comprehensive framework for checking and manipulating input data, statistical analyses, and visualization of results. Several methods are provided for the analysis of raw data, to make the dataset ready for downstream analyses. Integrated statistical methods include hierarchical classification, principal component analysis, principal coordinates analysis, non-metric multidimensional scaling, and multiple discriminant analyses: canonical, stepwise, and classificatory (linear, quadratic, and the non-parametric k nearest neighbours). The philosophy of the package will be described in Šlenker et al. (in prep).
Author: Marek Šlenker [aut, cre] Petr Koutecký [ctb] Karol Marhold [ctb]
Maintainer: Marek Šlenker <marek.slenker@savba.sk>

Diff between MorphoTools2 versions 0.9.1.2 dated 2022-02-03 and 1.0.0.0 dated 2022-05-26

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 tests/testthat/test.delete.population.R                 |   40 
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 tests/testthat/test.delete.taxon.R                      |   40 
 tests/testthat/test.descrByGroup.R                      |   80 
 tests/testthat/test.export.res.R                        |  102 
 tests/testthat/test.formatDescrStatistic.R              |   64 
 tests/testthat/test.giveMeNiceBoxPlot.R                 |   42 
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 tests/testthat/test.keep.population.R                   |   66 
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 tests/testthat/test.keep.taxon.R                        |   76 
 tests/testthat/test.missingCharactersTable.R            |   72 
 tests/testthat/test.missingSamplesTable.R               |   70 
 tests/testthat/test.morphodataFromDataFrame.R           |   78 
 tests/testthat/test.na.meanSubst.R                      |   72 
 tests/testthat/test.newCdadata.R                        |   34 
 tests/testthat/test.newMorphodata.R                     |   30 
 tests/testthat/test.newPcadata.R                        |   28 
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 tests/testthat/test.pca.calc.R                          |   92 
 tests/testthat/test.pcoa.calc.R                         |   62 
 tests/testthat/test.plot.addEllipse.R                   |   68 
 tests/testthat/test.plot.addLabels.characters_hist.R    |   48 
 tests/testthat/test.plot.addLabels.characters_scatter.R |   62 
 tests/testthat/test.plot.addLabels.points_hist.R        |   36 
 tests/testthat/test.plot.addSpiders.R                   |   66 
 tests/testthat/test.plot.characters.cdadata_hist.R      |   58 
 tests/testthat/test.plot.characters.cdadata_scatter.R   |   32 
 tests/testthat/test.plot.characters.pcadata.R           |   56 
 tests/testthat/test.plot.points.cdadata_hist.R          |  102 
 tests/testthat/test.plot.points.cdadata_scatter.R       |   52 
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 tests/testthat/test.popul.otu.R                         |   52 
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 tests/testthat/test.summary.cdadata.R                   |   38 
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 tests/testthat/test.transformCharacter.R                |   36 
 vignettes/MorphoTools2_tutorial.Rmd                     | 2666 ++++++++--------
 178 files changed, 16861 insertions(+), 16859 deletions(-)

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2013-11-22 1.36
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2012-04-17 1.18
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