Thu, 20 Jun 2024

Package SIAtools updated to version 0.1.1 with previous version 0.1.0 dated 2024-04-24

Title: 'ShinyItemAnalysis' Modules Development Toolkit
Description: A comprehensive suite of functions designed for constructing and managing 'ShinyItemAnalysis' modules, supplemented with detailed guides, ready-to-use templates, linters, and tests. This package allows developers to seamlessly create and integrate one or more modules into their existing packages or to start a new module project from scratch.
Author: Jan Netik [cre, aut] , Patricia Martinkova [aut]
Maintainer: Jan Netik <netik@cs.cas.cz>

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Package Paris2024Colours updated to version 0.2.0 with previous version 0.1.2 dated 2024-04-16

Title: Color Palettes Inspired by Paris 2024 Olympic and Paralympic Games
Description: Palettes inspired by Paris 2024 Olympic and Paralympic Games for data visualizations. Length of color palettes is configurable.
Author: Maxime Kuntz [aut, cre]
Maintainer: Maxime Kuntz <maxime.kuntz75@gmail.com>

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Package LOPART updated to version 2024.6.19 with previous version 2020.6.29 dated 2020-06-30

Title: Labeled Optimal Partitioning
Description: Change-point detection algorithm with label constraints and a penalty for each change outside of labels. Read TD Hocking, A Srivastava (2023) <doi:10.1007/s00180-022-01238-z> for details.
Author: Toby Dylan Hocking
Maintainer: Toby Dylan Hocking <toby.hocking@r-project.org>

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Package FLOPART updated to version 2024.6.19 with previous version 2023.8.31 dated 2023-09-04

Title: Functional Labeled Optimal Partitioning
Description: Provides an efficient 'C++' code for computing an optimal segmentation model with Poisson loss, up-down constraints, and label constraints, as described by Kaufman et al. (2024) <doi:10.1080/10618600.2023.2293216>.
Author: Toby Dylan Hocking [aut, cre]
Maintainer: Toby Dylan Hocking <toby.hocking@r-project.org>

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Package BTtest updated to version 0.10.2 with previous version 0.10.1 dated 2024-01-11

Title: Estimate the Number of Factors in Large Nonstationary Datasets
Description: Large panel data sets are often subject to common trends. However, it can be difficult to determine the exact number of these common factors and analyse their properties. The package implements the Barigozzi and Trapani (2022) <doi:10.1080/07350015.2021.1901719> test, which not only provides an efficient way of estimating the number of common factors in large nonstationary panel data sets, but also gives further insights on factor classes. The routine identifies the existence of (i) a factor subject to a linear trend, (ii) the number of zero-mean I(1) and (iii) zero-mean I(0) factors. Furthermore, the package includes the Integrated Panel Criteria by Bai (2004) <doi:10.1016/j.jeconom.2003.10.022> that provide a complementary measure for the number of factors.
Author: Paul Haimerl [aut, cre]
Maintainer: Paul Haimerl <p.haimerl@student.maastrichtuniversity.nl>

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Package aum updated to version 2024.6.19 with previous version 2023.6.14 dated 2023-06-14

Title: Area Under Minimum of False Positives and Negatives
Description: Efficient algorithms <https://jmlr.org/papers/v24/21-0751.html> for computing Area Under Minimum, directional derivatives, and line search optimization of a linear model, with objective defined as either max Area Under the Curve or min Area Under Minimum.
Author: Toby Dylan Hocking [aut, cre], Jadon Fowler [aut]
Maintainer: Toby Dylan Hocking <toby.hocking@r-project.org>

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Package RNiftyReg updated to version 2.8.3 with previous version 2.8.2 dated 2024-06-04

Title: Image Registration Using the 'NiftyReg' Library
Description: Provides an 'R' interface to the 'NiftyReg' image registration tools <https://github.com/KCL-BMEIS/niftyreg>. Linear and nonlinear registration are supported, in two and three dimensions.
Author: Jon Clayden [cre, aut] , Marc Modat [aut], Benoit Presles [aut], Thanasis Anthopoulos [aut], Pankaj Daga [aut]
Maintainer: Jon Clayden <code@clayden.org>

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Package sqltargets updated to version 0.1.0 with previous version 0.0.1 dated 2024-04-24

Title: 'Targets' Extension for 'SQL' Queries
Description: Provides an extension for 'SQL' queries as separate file within 'targets' pipelines. The shorthand creates two targets, the query file and the query result.
Author: David Ranzolin [aut, cre, cph]
Maintainer: David Ranzolin <daranzolin@gmail.com>

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Package epidatr updated to version 1.2.0 with previous version 1.1.1 dated 2024-03-04

Title: Client for Delphi's 'Epidata' API
Description: The Delphi 'Epidata' API provides real-time access to epidemiological surveillance data for influenza, 'COVID-19', and other diseases for the USA at various geographical resolutions, both from official government sources such as the Center for Disease Control (CDC) and Google Trends and private partners such as Facebook and Change 'Healthcare'. It is built and maintained by the Carnegie Mellon University Delphi research group. To cite this API: David C. Farrow, Logan C. Brooks, Aaron 'Rumack', Ryan J. 'Tibshirani', 'Roni' 'Rosenfeld' (2015). Delphi 'Epidata' API. <https://github.com/cmu-delphi/delphi-epidata>.
Author: Logan Brooks [aut], Dmitry Shemetov [aut], Samuel Gratzl [aut], David Weber [ctb, cre], Nat DeFries [ctb], Alex Reinhart [ctb], Daniel McDonald [ctb], Kean Ming Tan [ctb], Will Townes [ctb], George Haff [ctb], Kathryn Mazaitis [ctb]
Maintainer: David Weber <davidweb@andrew.cmu.edu>

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Package targets updated to version 1.7.1 with previous version 1.7.0 dated 2024-04-17

Title: Dynamic Function-Oriented 'Make'-Like Declarative Pipelines
Description: Pipeline tools coordinate the pieces of computationally demanding analysis projects. The 'targets' package is a 'Make'-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, <doi:10.21105/joss.00550>).
Author: William Michael Landau [aut, cre] , Matthew T. Warkentin [ctb], Mark Edmondson [ctb] , Samantha Oliver [rev] , Tristan Mahr [rev] , Eli Lilly and Company [cph]
Maintainer: William Michael Landau <will.landau.oss@gmail.com>

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Package MBBEFDLite updated to version 0.0.3 with previous version 0.0.2 dated 2024-05-19

Title: Statistical Functions for the Maxwell-Boltzmann-Bose-Einstein-Fermi-Dirac (MBBEFD) Family of Distributions
Description: Provides probability mass, distribution, quantile, random variate generation, and method-of-moments parameter fitting for the MBBEFD family of distributions used in insurance modeling as described in Bernegger (1997) <doi:10.2143/AST.27.1.563208> without any external dependencies.
Author: Avraham Adler [aut, cre, cph]
Maintainer: Avraham Adler <Avraham.Adler@gmail.com>

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Package crew updated to version 0.9.4 with previous version 0.9.3 dated 2024-05-22

Title: A Distributed Worker Launcher Framework
Description: In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The 'NNG'-powered 'mirai' R package by Gao (2023) <doi:10.5281/zenodo.7912722> is a sleek and sophisticated scheduler that efficiently processes these intense workloads. The 'crew' package extends 'mirai' with a unifying interface for third-party worker launchers. Inspiration also comes from packages. 'future' by Bengtsson (2021) <doi:10.32614/RJ-2021-048>, 'rrq' by FitzJohn and Ashton (2023) <https://github.com/mrc-ide/rrq>, 'clustermq' by Schubert (2019) <doi:10.1093/bioinformatics/btz284>), and 'batchtools' by Lang, Bischel, and Surmann (2017) <doi:10.21105/joss.00135>.
Author: William Michael Landau [aut, cre] , Daniel Woodie [ctb], Eli Lilly and Company [cph]
Maintainer: William Michael Landau <will.landau.oss@gmail.com>

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Package clubSandwich updated to version 0.5.11 with previous version 0.5.10 dated 2023-07-19

Title: Cluster-Robust (Sandwich) Variance Estimators with Small-Sample Corrections
Description: Provides several cluster-robust variance estimators (i.e., sandwich estimators) for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator introduced by Bell and McCaffrey (2002) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2002002/article/9058-eng.pdf> and developed further by Pustejovsky and Tipton (2017) <DOI:10.1080/07350015.2016.1247004>. The package includes functions for estimating the variance- covariance matrix and for testing single- and multiple- contrast hypotheses based on Wald test statistics. Tests of single regression coefficients use Satterthwaite or saddle-point corrections. Tests of multiple- contrast hypotheses use an approximation to Hotelling's T-squared distribution. Methods are provided for a variety of fitted models, including lm() and mlm objects, glm(), geeglm() (from package 'geepack'), ivreg() (from package 'AER'), ivreg() (from package 'ivreg' when estimated by ordinary least squares), [...truncated...]
Author: James Pustejovsky [aut, cre]
Maintainer: James Pustejovsky <jepusto@gmail.com>

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 tests/testthat/test_plm-ID-variables.R   |    3 -
 19 files changed, 160 insertions(+), 118 deletions(-)

More information about clubSandwich at CRAN
Permanent link

Package psychonetrics updated to version 0.13 with previous version 0.12 dated 2024-05-21

Title: Structural Equation Modeling and Confirmatory Network Analysis
Description: Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data <doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing and fit as well as exploratory model search.
Author: Sacha Epskamp
Maintainer: Sacha Epskamp <mail@sachaepskamp.com>

Diff between psychonetrics versions 0.12 dated 2024-05-21 and 0.13 dated 2024-06-20

 DESCRIPTION                                         |    6 
 MD5                                                 |  481 ++--
 NAMESPACE                                           |  394 +--
 NEWS                                                |  352 +--
 R/00_codeOrganization.R                             |   44 
 R/00_steps_to_implement_distribution.R              |   20 
 R/00_steps_to_implement_estimator.R                 |   18 
 R/00_steps_to_implement_model.R                     |   30 
 R/01_classes.R                                      |  373 ++-
 R/02_algebrahelpers_Solve.R                         |  122 -
 R/02_algebrahelpers_blockToeplitz.R                 |   40 
 R/02_algebrahelpers_checkJacobian.R                 |  250 +-
 R/02_algebrahelpers_expected_latent_residual_covs.R |  124 -
 R/02_algebrahelpers_expectedmodel.R                 |   48 
 R/02_algebrahelpers_kronecker.R                     |    2 
 R/02_algebrahelpers_lavUtils.R                      |   48 
 R/02_algebrahelpers_matrixexponent.R                |   20 
 R/02_algebrahelpers_maxcor.R                        |   22 
 R/02_algebrahelpers_modelMatrix.R                   |   26 
 R/02_algebrahelpers_quantiletransform.R             |   20 
 R/02_algebrahelpers_spectralshift.R                 |   50 
 R/02_algebrahelpers_trysolve.R                      |   52 
 R/02_algebrahelpers_vectorize.R                     |   56 
 R/02_algebrahelpers_vectorizeMatrices.R             |  116 -
 R/02_algebrahelpers_weighted_geomean.R              |    2 
 R/02_modelformation_PCCPDC.R                        |   32 
 R/03_modelformation_adjust_p_values.R               |  116 -
 R/03_modelformation_defaultoptimizer.R              |    6 
 R/03_modelformation_factorstart.R                   |   40 
 R/03_modelformation_fixAdj.R                        |  162 -
 R/03_modelformation_fixMatrix.R                     |  160 -
 R/03_modelformation_fixMu.R                         |   82 
 R/03_modelformation_fixTau.R                        |  170 -
 R/03_modelformation_formModelMatrices.R             |  152 -
 R/03_modelformation_generateParameterTable.R        |  540 ++---
 R/03_modelformation_impliedcovstructures.R          |  272 +-
 R/03_modelformation_labtoind.R                      |   56 
 R/03_modelformation_matrixSetup_Isingbeta.R         |   50 
 R/03_modelformation_matrixSetup_Isingtau.R          |   54 
 R/03_modelformation_matrixSetup_SD.R                |  102 
 R/03_modelformation_matrixSetup_beta.R              |   76 
 R/03_modelformation_matrixSetup_cholesky.R          |  126 -
 R/03_modelformation_matrixSetup_delta.R             |  124 -
 R/03_modelformation_matrixSetup_isingomega.R        |   64 
 R/03_modelformation_matrixSetup_kappa.R             |  134 -
 R/03_modelformation_matrixSetup_lambda.R            |  452 ++--
 R/03_modelformation_matrixSetup_mu.R                |   68 
 R/03_modelformation_matrixSetup_omega.R             |  200 -
 R/03_modelformation_matrixSetup_rho.R               |  104 
 R/03_modelformation_matrixSetup_sigma.R             |  130 -
 R/03_modelformation_matrixSetup_tau.R               |   74 
 R/03_modelformation_missingpatterns.R               |  314 +-
 R/03_modelformation_missingpatterns_covs.R          |  174 -
 R/03_modelformation_samplestats.R                   |   16 
 R/03_modelformation_samplestats_norawts.R           | 1223 +++++------
 R/03_modelformation_samplestats_rawts.R             |  192 -
 R/03_modelformation_sparseordense.R                 |   90 
 R/03_modelformation_startvaluesandbounds.R          |  104 
 R/03_modelformation_tsData.R                        |  344 +--
 R/04_generalFit_fitfunction.R                       |   88 
 R/04_generalFit_gradient.R                          |  258 +-
 R/04_generalFit_logLikelihood.R                     |   36 
 R/04_generalfit_FisherInformation.R                 |  468 ++--
 R/04_generalfit_VCOV.R                              |   28 
 R/04_generalfit_impliedModel.R                      |   80 
 R/04_generalfit_loglikelihood_Ising.R               |  176 -
 R/04_generalfit_loglikelihood_gauss.R               |  232 +-
 R/04_generalfit_prepareModel.R                      |  154 -
 R/04_modelformation_matrixSetup_flexcov.R           |  138 -
 R/05_MLestimator_expected_hessian_Gauss.R           |  126 -
 R/05_MLestimator_expected_hessian_Ising.R           |  178 -
 R/05_MLestimator_fit_Gauss.R                        |   36 
 R/05_MLestimator_fit_Ising.R                        |   76 
 R/05_MLestimator_fitfunction.R                      |   22 
 R/05_MLestimator_gradient_Gauss.R                   |  146 -
 R/05_MLestimator_gradient_Ising.R                   |  164 -
 R/06_ULS_expectedHessian.R                          |   64 
 R/06_ULS_fitfunction.R                              |  154 -
 R/06_ULS_gradient.R                                 |  124 -
 R/06_weightsMatrix.R                                |   76 
 R/07_FIMLestimator_expected_hessian_Gauss.R         |  302 +-
 R/07_FIMLestimator_fit_Gauss.R                      |  194 -
 R/07_FIMLestimator_fitfunction.R                    |   20 
 R/07_FIMLestimator_gradient_Gauss.R                 |  302 +-
 R/08_outputHelpers_NAtoFALSE.R                      |    4 
 R/08_outputHelpers_NAtoTRUE.R                       |    4 
 R/08_outputHelpers_goodNum.R                        |   79 
 R/08_outputHelpers_logo.R                           |   65 
 R/09_modelmodification_clearpars.R                  |   40 
 R/09_modelmodifivation_emergencystart.R             |  166 -
 R/14_varcov_derivatives.R                           |  434 ++--
 R/14_varcov_implied.R                               |   92 
 R/14_varcov_prepare.R                               |  136 -
 R/15_lvm_derivatives.R                              |  428 +--
 R/15_lvm_identify.R                                 |  436 ++--
 R/15_lvm_implied.R                                  |  122 -
 R/15_lvm_prepare.R                                  |  122 -
 R/16_var1_derivatives.R                             |  362 +--
 R/16_var1_implied.R                                 |  150 -
 R/16_var1_prepare.R                                 |  188 -
 R/18_dlvm1_derivatives.R                            |  814 +++----
 R/18_dlvm1_identify.R                               |  518 ++--
 R/18_dlvm1_implied.R                                |  258 +-
 R/18_dlvm1_prepare.R                                |  116 -
 R/19_tsdlvm1_derivatives.R                          |  424 +--
 R/19_tsdlvm1_identify.R                             |  396 +--
 R/19_tsdlvm1_implied.R                              |  218 +-
 R/19_tsdlvm1_prepare.R                              |  116 -
 R/20_meta_varcov_derivatives.R                      |  584 ++---
 R/20_meta_varcov_implied.R                          |  148 -
 R/20_meta_varcov_prepare.R                          |   98 
 R/21_Ising_derivatives.R                            |   52 
 R/21_Ising_helperfunctions.R                        |   32 
 R/21_Ising_identify.R                               |  142 -
 R/21_Ising_implied.R                                |   20 
 R/21_Ising_prepare.R                                |  126 -
 R/22_ml_lvm_derivatives.R                           |  424 +--
 R/22_ml_lvm_identify.R                              |  576 ++---
 R/22_ml_lvm_implied.R                               |  258 +-
 R/22_ml_lvm_prepare.R                               |  116 -
 R/RcppExports.R                                     | 1342 ++++++------
 R/a_models_Ising.R                                  |  468 ++--
 R/a_models_bifactor.R                               |   90 
 R/a_models_cholesky.R                               |   18 
 R/a_models_corr.R                                   |   18 
 R/a_models_dlvm1.R                                  | 2155 ++++++++++----------
 R/a_models_frombootnet.R                            |   78 
 R/a_models_ggm.R                                    |   18 
 R/a_models_gvar.R                                   |    8 
 R/a_models_joingroups.R                             |  244 +-
 R/a_models_latentgrowth.R                           |  198 -
 R/a_models_lnm.R                                    |    6 
 R/a_models_lrnm.R                                   |    6 
 R/a_models_lvm.R                                    |  852 +++----
 R/a_models_meta_ggm.R                               |    4 
 R/a_models_meta_varcov.R                            | 1304 ++++++------
 R/a_models_ml_lnm.R                                 |    4 
 R/a_models_ml_lrnm.R                                |    4 
 R/a_models_ml_lvm.R                                 | 1190 +++++------
 R/a_models_ml_rnm.R                                 |    4 
 R/a_models_ml_tsdlvm1.R                             |  356 +--
 R/a_models_ml_tslvgvar.R                            |   44 
 R/a_models_panelgvar.R                              |  154 -
 R/a_models_precision.R                              |   18 
 R/a_models_rnm.R                                    |    6 
 R/a_models_sem.R                                    |    6 
 R/a_models_tsdlvm1.R                                |  820 +++----
 R/a_models_tslvgvar.R                               |    6 
 R/a_models_var1.R                                   |  637 ++---
 R/a_models_varcov.R                                 |  806 +++----
 R/b_modelexpansions_addMIs.R                        |  450 ++--
 R/b_modelexpansions_addSEs.R                        |  206 -
 R/b_modelexpansions_addfit.R                        |  560 ++---
 R/b_modelexpansions_identify.R                      |   70 
 R/b_modelexpansions_updateModel.R                   |   32 
 R/c_runmodel.R                                      | 1732 ++++++++--------
 R/d_stepup.R                                        |  890 ++++----
 R/e_modelmodifications_fixpar.R                     |  218 +-
 R/e_modelmodifications_fixstart.R                   |   72 
 R/e_modelmodifications_freepar.R                    |  264 +-
 R/e_modelmodifications_groupequal.R                 |  266 +-
 R/e_modelmodifications_groupfree.R                  |  252 +-
 R/e_modelmodifications_intersectionmodel.R          |  414 +--
 R/e_modelmodifications_partialprune.R               |  506 ++--
 R/e_modelmodifications_prune.R                      |  634 ++---
 R/e_modelmodifications_setequal.R                   |  110 -
 R/e_modelmodifications_transmod.R                   |  574 ++---
 R/e_modelmodifications_unionmodel.R                 |  404 +--
 R/f_conveneince_changedata.R                        |  168 -
 R/f_conveneince_fake_optimr.R                       |   60 
 R/f_conveneince_generate.R                          |   66 
 R/f_conveneince_setEstimator.R                      |    6 
 R/f_conveneince_setoptimizer.R                      |   56 
 R/f_conveneince_setverbose.R                        |    8 
 R/f_conveneince_usecpp.R                            |   22 
 R/f_convenience_CIplot.R                            |  710 ++++--
 R/f_convenience_MIs.R                               |  260 +-
 R/f_convenience_aggregate_bootstraps.R              |only
 R/f_convenience_bootstrap.R                         |  174 -
 R/f_convenience_bootstrap_SEs.R                     |   12 
 R/f_convenience_bootstrap_warning.R                 |only
 R/f_convenience_covML.R                             |   24 
 R/f_convenience_ergodicity.R                        |  450 ++--
 R/f_convenience_factorscores.R                      |  164 -
 R/f_convenience_fit.R                               |   80 
 R/f_convenience_getmatrix.R                         |  284 +-
 R/f_convenience_logbook.R                           |   84 
 R/f_convenience_parameters.R                        |  237 +-
 R/f_convenience_printMethod.R                       |  230 +-
 R/f_convenience_printMethod_bootstrap.R             |only
 R/f_convenience_residuals.R                         |   64 
 R/f_convenience_simplestructure.R                   |   22 
 R/g_simulations_replicator.R                        |  154 -
 R/h_modelsearch.R                                   |  878 ++++----
 build/partial.rdb                                   |binary
 man/CIplot.Rd                                       |  224 +-
 man/Ising.Rd                                        |  354 +--
 man/Jonas.Rd                                        |   64 
 man/MIs.Rd                                          |  134 -
 man/StarWars.Rd                                     |   76 
 man/aggregate_bootstraps.Rd                         |only
 man/algebraMatrices.Rd                              |   86 
 man/bifactor.Rd                                     |   72 
 man/bootstrap.Rd                                    |   78 
 man/changedata.Rd                                   |   82 
 man/convenience.Rd                                  |  116 -
 man/covML.Rd                                        |   94 
 man/diagnostics.Rd                                  |  106 
 man/dlvm1_family.Rd                                 |  472 ++--
 man/emergencystart.Rd                               |   54 
 man/esa.Rd                                          |  140 -
 man/factorscores.Rd                                 |   56 
 man/fit.Rd                                          |   96 
 man/fixpar_freepar.Rd                               |  126 -
 man/fixstart.Rd                                     |   68 
 man/generate.Rd                                     |   54 
 man/getVCOV.Rd                                      |   56 
 man/getmatrix.Rd                                    |  134 -
 man/groupequal_groupfree.Rd                         |  108 -
 man/latentgrowth.Rd                                 |  146 -
 man/logbook.Rd                                      |   52 
 man/lvm_family.Rd                                   | 1092 +++++-----
 man/meta_varcov.Rd                                  |  258 +-
 man/ml_lvm.Rd                                       |  386 +--
 man/ml_tsdlvm1.Rd                                   |  124 -
 man/modelsearch.Rd                                  |  182 -
 man/modelupdate.Rd                                  |  102 
 man/parameters.Rd                                   |   96 
 man/parequal.Rd                                     |   80 
 man/partialprune.Rd                                 |  106 
 man/prune.Rd                                        |  182 -
 man/psychonetrics-class.Rd                          |  122 -
 man/psychonetrics-package.Rd                        |   50 
 man/psychonetrics_bootstrap-class.Rd                |only
 man/psychonetrics_log-class.Rd                      |   68 
 man/runmodel.Rd                                     |  186 -
 man/setverbose.Rd                                   |   52 
 man/simplestructure.Rd                              |   44 
 man/stepup.Rd                                       |  204 -
 man/transmod.Rd                                     |  158 -
 man/tsdlvm1_family.Rd                               |  416 +--
 man/unionmodel_intersectionmodel.Rd                 |   94 
 man/var1_family.Rd                                  |  472 ++--
 man/varcov_family.Rd                                |  469 ++--
 244 files changed, 25988 insertions(+), 25359 deletions(-)

More information about psychonetrics at CRAN
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New package tinyplot with initial version 0.1.0
Package: tinyplot
Title: Lightweight Extension of the Base R Graphics System
Version: 0.1.0
Date: 2024-06-19
Description: Lightweight extension of the base R graphics system, with support for automatic legends, facets, and various other enhancements.
License: Apache License (>= 2)
Depends: R (>= 4.0)
Imports: graphics, grDevices, methods, stats, tools, utils
Suggests: altdoc (>= 0.3.0), basetheme, fontquiver, rsvg, svglite, tinytest, tinysnapshot (>= 0.0.3), knitr
Encoding: UTF-8
URL: https://grantmcdermott.com/tinyplot/
BugReports: https://github.com/grantmcdermott/tinyplot/issues
NeedsCompilation: no
Packaged: 2024-06-19 18:17:15 UTC; gmcd
Author: Grant McDermott [aut, cre] , Vincent Arel-Bundock [aut] , Achim Zeileis [aut] , Etienne Bacher [ctb]
Maintainer: Grant McDermott <gmcd@amazon.com>
Repository: CRAN
Date/Publication: 2024-06-20 16:40:02 UTC

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New package semlrtp with initial version 0.1.1
Package: semlrtp
Title: Likelihood Ratio Test P-Values for Structural Equation Models
Version: 0.1.1
Description: Computes likelihood ratio test (LRT) p-values for free parameters in a structural equation model. Currently supports models fitted by the 'lavaan' package by Rosseel (2012) <doi:10.18637/jss.v048.i02>.
License: GPL (>= 3)
Encoding: UTF-8
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
Imports: lavaan, utils, parallel, pbapply
VignetteBuilder: knitr
URL: https://sfcheung.github.io/semlrtp/
BugReports: https://github.com/sfcheung/semlrtp/issues
Depends: R (>= 4.0.0)
LazyData: true
NeedsCompilation: no
Packaged: 2024-06-19 13:40:40 UTC; shufa
Author: Shu Fai Cheung [aut, cre] , Mark Hok Chio Lai [aut]
Maintainer: Shu Fai Cheung <shufai.cheung@gmail.com>
Repository: CRAN
Date/Publication: 2024-06-20 16:10:02 UTC

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New package probs with initial version 0.9.9
Package: probs
Title: Elementary Probability on Finite Sample Spaces
Version: 0.9.9
Date: 2024-06-17
Description: Performs elementary probability calculations on finite sample spaces, which may be represented by data frames or lists. This package is meant to rescue some widely used functions from the archived 'prob' package (see <https://cran.r-project.org/src/contrib/Archive/prob/>). Functionality includes setting up sample spaces, counting tools, defining probability spaces, performing set algebra, calculating probability and conditional probability, tools for simulation and checking the law of large numbers, adding random variables, and finding marginal distributions. Characteristic functions for all base R distributions are included.
License: GPL (>= 3)
Encoding: UTF-8
Language: en-US
Imports: stats, utils, combinat, MASS, reshape
NeedsCompilation: no
Packaged: 2024-06-19 16:12:01 UTC; produnis
Author: G. Jay Kerns [aut, cph], Joe gr. Schlarmann [cre]
Maintainer: Joe gr. Schlarmann <schlarmann@produnis.de>
Repository: CRAN
Date/Publication: 2024-06-20 16:20:02 UTC

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New package priorsense with initial version 1.0.0
Package: priorsense
Title: Prior Diagnostics and Sensitivity Analysis
Version: 1.0.0
Description: Provides functions for prior and likelihood sensitivity analysis in Bayesian models. Currently it implements methods to determine the sensitivity of the posterior to power-scaling perturbations of the prior and likelihood.
License: GPL (>= 3)
Encoding: UTF-8
Imports: checkmate (>= 2.3.1), ggdist (>= 3.3.2), ggh4x (>= 0.2.5), ggplot2 (>= 3.5.1), matrixStats (>= 1.3.0), methods, posterior (>= 1.5.0), rlang (>= 1.1.4), stats, tibble (>= 3.2.1), utils
Suggests: bayesplot (>= 1.11.1), brms (>= 2.21.0), cmdstanr (>= 0.8.1), iwmm (>= 0.0.1), knitr (>= 1.47), philentropy (>= 0.8.0), rstan (>= 2.32.6), testthat (>= 3.0.0), transport (>= 0.15), rmarkdown (>= 2.27)
Depends: R (>= 3.6.0)
VignetteBuilder: knitr
Additional_repositories: https://topipa.r-universe.dev, https://stan-dev.r-universe.dev
URL: https://n-kall.github.io/priorsense/
NeedsCompilation: no
Packaged: 2024-06-19 18:24:03 UTC; kallion6
Author: Noa Kallioinen [aut, cre, cph], Paul-Christian Buerkner [aut], Topi Paananen [aut], Aki Vehtari [aut], Frank Weber [ctb]
Maintainer: Noa Kallioinen <noa.kallioinen@aalto.fi>
Repository: CRAN
Date/Publication: 2024-06-20 16:40:06 UTC

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New package MultRegCMP with initial version 0.1.0
Package: MultRegCMP
Title: Bayesian Multivariate Conway-Maxwell-Poisson Regression Model for Correlated Count Data
Version: 0.1.0
Description: Fits a Bayesian Regression Model for multivariate count data. This model assumes that the data is distributed according to the Conway-Maxwell-Poisson distribution, and for each response variable it is associate different covariates. This model allows to account for correlations between the counts by using latent effects based on the Chib and Winkelmann (2001) <http://www.jstor.org/stable/1392277> proposal.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: purrr, mvnfast, stats, progress, bayesplot, ggplot2, cowplot
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2024-06-19 19:25:11 UTC; User
Author: Mauro Florez [aut, cre]
Maintainer: Mauro Florez <mf53@rice.edu>
Repository: CRAN
Date/Publication: 2024-06-20 16:30:06 UTC

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Package jgsbook updated to version 1.0.6 with previous version 1.0.5 dated 2024-05-24

Title: Package of the German Book "Statistik mit R und RStudio" by Joerg grosse Schlarmann
Description: All datasets and functions used in the german book "Statistik mit R und RStudio" by grosse Schlarmann (2010-2024) <https://www.produnis.de/R/>.
Author: Joerg grosse Schlarmann [aut, cre]
Maintainer: Joerg grosse Schlarmann <schlarmann@produnis.de>

Diff between jgsbook versions 1.0.5 dated 2024-05-24 and 1.0.6 dated 2024-06-20

 DESCRIPTION                |   10 ++--
 MD5                        |   11 ++--
 NAMESPACE                  |    3 +
 NEWS.md                    |    5 ++
 R/functions.R              |  104 ++++++++++++++++++++++++++++++++++++++++++++-
 man/compare.lm.Rd          |only
 man/pairwise.chisq.test.Rd |    2 
 7 files changed, 123 insertions(+), 12 deletions(-)

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Package streamConnect updated to version 0.0-6 with previous version 0.0-4 dated 2024-06-13

Title: Connecting Stream Mining Components Using Sockets and Web Services
Description: Adds functionality to connect stream mining components from package stream using sockets and Web services. The package can be used create distributed workflows and create plumber-based Web services which can be deployed on most common cloud services.
Author: Michael Hahsler [aut, cre, cph]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>

Diff between streamConnect versions 0.0-4 dated 2024-06-13 and 0.0-6 dated 2024-06-20

 DESCRIPTION                       |    8 -
 MD5                               |   30 ++--
 NEWS.md                           |    6 
 R/DSD_ReadSocket.R                |    2 
 R/plumber.R                       |   15 ++
 R/publish_DSC_via_WebService.R    |    6 
 R/publish_DSD_via_Socket.R        |   22 ++-
 R/publish_DSD_via_WebService.R    |    2 
 inst/doc/connections.R            |    8 +
 inst/doc/connections.Rmd          |   31 +++-
 inst/doc/connections.html         |  244 ++++++++++++++++++--------------------
 man/DSD_ReadSocket.Rd             |    2 
 man/publish_DSC_via_WebService.Rd |    6 
 man/publish_DSD_via_Socket.Rd     |   13 --
 man/publish_DSD_via_WebService.Rd |    2 
 vignettes/connections.Rmd         |   31 +++-
 16 files changed, 237 insertions(+), 191 deletions(-)

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New package rush with initial version 0.1.0
Package: rush
Title: Rapid Parallel and Distributed Computing
Version: 0.1.0
Description: Parallel computing with a network of local and remote workers. Fast exchange of results between the workers through a 'Redis' database. Key features include task queues, local caching, and sophisticated error handling.
URL: https://github.com/mlr-org/rush
BugReports: https://github.com/mlr-org/rush/issues
Depends: R (>= 3.1.0)
Imports: checkmate, data.table, jsonlite, lgr, mlr3misc, parallel, processx, redux, uuid
Suggests: callr, knitr, rmarkdown, testthat (>= 3.0.0)
Encoding: UTF-8
License: MIT + file LICENSE
NeedsCompilation: no
Packaged: 2024-06-18 13:55:18 UTC; marc
Author: Marc Becker [cre, aut, cph]
Maintainer: Marc Becker <marcbecker@posteo.de>
Repository: CRAN
Date/Publication: 2024-06-20 15:40:06 UTC

More information about rush at CRAN
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Package rtables updated to version 0.6.8 with previous version 0.6.7 dated 2024-04-15

Title: Reporting Tables
Description: Reporting tables often have structure that goes beyond simple rectangular data. The 'rtables' package provides a framework for declaring complex multi-level tabulations and then applying them to data. This framework models both tabulation and the resulting tables as hierarchical, tree-like objects which support sibling sub-tables, arbitrary splitting or grouping of data in row and column dimensions, cells containing multiple values, and the concept of contextual summary computations. A convenient pipe-able interface is provided for declaring table layouts and the corresponding computations, and then applying them to data.
Author: Gabriel Becker [aut] , Adrian Waddell [aut], Daniel Sabanes Bove [ctb], Maximilian Mordig [ctb], Davide Garolini [ctb], Emily de la Rua [ctb], Abinaya Yogasekaram [ctb], Joe Zhu [ctb, cre], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Joe Zhu <joe.zhu@roche.com>

Diff between rtables versions 0.6.7 dated 2024-04-15 and 0.6.8 dated 2024-06-20

 DESCRIPTION                               |   25 -
 MD5                                       |  194 +++++----
 NAMESPACE                                 |   17 
 NEWS.md                                   |   29 +
 R/00tabletrees.R                          |  139 ++++--
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More information about rtables at CRAN
Permanent link

New package Imneuron with initial version 0.1.0
Package: Imneuron
Title: AI Powered Neural Network Solutions for Regression Tasks
Version: 0.1.0
Maintainer: M Iqbal Jeelani <jeelani.miqbal@gmail.com>
Description: It offers a sophisticated and versatile tool for creating and evaluating artificial intelligence based neural network models tailored for regression analysis on datasets with continuous target variables. Leveraging the power of neural networks, it allows users to experiment with various hidden neuron configurations across two layers, optimizing model performance through "5 fold"" or "10 fold"" cross validation. The package normalizes input data to ensure efficient training and assesses model accuracy using key metrics such as R squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Percentage Error (PER). By storing and visualizing the best performing models, it provides a comprehensive solution for precise and efficient regression modeling making it an invaluable tool for data scientists and researchers aiming to harness AI for predictive analytics.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Imports: MLmetrics, ggplot2, neuralnet
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2024-06-19 06:16:22 UTC; wani
Author: M Iqbal Jeelani [aut, cre] , Fehim Jeelani Wani [aut]
Repository: CRAN
Date/Publication: 2024-06-20 16:00:08 UTC

More information about Imneuron at CRAN
Permanent link

Package ggeffects updated to version 1.7.0 with previous version 1.6.0 dated 2024-05-18

Title: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs
Description: Compute marginal effects and adjusted predictions from statistical models and returns the result as tidy data frames. These data frames are ready to use with the 'ggplot2'-package. Effects and predictions can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The main functions are ggpredict(), ggemmeans() and ggeffect(). There is a generic plot()-method to plot the results using 'ggplot2'.
Author: Daniel Luedecke [aut, cre] , Frederik Aust [ctb] , Sam Crawley [ctb] , Mattan S. Ben-Shachar [ctb] , Sean C. Anderson [ctb]
Maintainer: Daniel Luedecke <d.luedecke@uke.de>

Diff between ggeffects versions 1.6.0 dated 2024-05-18 and 1.7.0 dated 2024-06-20

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New package crt2power with initial version 1.0.0
Package: crt2power
Title: Designing Cluster-Randomized Trials with Two Co-Primary Outcomes
Version: 1.0.0
Description: Provides methods for powering cluster-randomized trials with two co-primary outcomes using five key design techniques. Includes functions for calculating required sample size and statistical power. For more details on methodology, see Li et al. (2020) <doi:10.1111/biom.13212>, Pocock et al. (1987) <doi:10.2307/2531989>, Vickerstaff et al. (2019) <doi:10.1186/s12874-019-0754-4>, and Yang et al. (2022) <doi:10.1111/biom.13692>.
License: GPL-3
Encoding: UTF-8
URL: https://github.com/melodyaowen/crt2power
Depends: R (>= 4.3)
Imports: devtools (>= 2.4.5), knitr (>= 1.43), rootSolve (>= 1.8.2.3), tidyverse (>= 2.0.0), tableone (>= 0.13.2), foreach (>= 1.5.2), mvtnorm (>= 1.2), tibble (>= 3.2.1), dplyr (>= 1.1.4), tidyr (>= 1.3.0), stats (>= 3.6.2)
Suggests: testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2024-06-18 17:17:30 UTC; melodyowen
Author: Melody Owen [aut, cre]
Maintainer: Melody Owen <melody.owen@yale.edu>
Repository: CRAN
Date/Publication: 2024-06-20 15:50:12 UTC

More information about crt2power at CRAN
Permanent link

New package ConsensusOPLS with initial version 1.0.0
Package: ConsensusOPLS
Title: Consensus OPLS for Multi-Block Data Fusion
Description: Merging data from multiple sources is a relevant approach for comprehensively evaluating complex systems. However, the inherent problems encountered when analyzing single tables are amplified with the generation of multi-block datasets, and finding the relationships between data layers of increasing complexity constitutes a challenging task. For that purpose, a generic methodology is proposed by combining the strengths of established data analysis strategies, i.e. multi-block approaches and the Orthogonal Partial Least Squares (OPLS) framework to provide an efficient tool for the fusion of data obtained from multiple sources. The package enables quick and efficient implementation of the consensus OPLS model for any horizontal multi-block data structure (observation-based matching). Moreover, it offers an interesting range of metrics and graphics to help to determine the optimal number of components and check the validity of the model through permutation tests. Interpretation tools incl [...truncated...]
Version: 1.0.0
Depends: R (>= 4.0.0), stats, utils, graphics, grDevices, methods
Imports: parallel, reshape2
Suggests: testthat (>= 3.0.0), knitr, ggplot2, ggrepel, plotly, psych, DT, ComplexHeatmap
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-06-19 05:37:04 UTC; vandu
Author: Celine Bougel [aut] , Julien Boccard [aut] , Florence Mehl [aut] , Marie Tremblay-Franco [fnd] , Mark Ibberson [fnd] , Van Du T. Tran [aut, cre]
Maintainer: Van Du T. Tran <thuong.tran@sib.swiss>
Repository: CRAN
Date/Publication: 2024-06-20 16:00:11 UTC

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New package BrazilCrime with initial version 0.1
Package: BrazilCrime
Title: Accesses Brazilian Public Security Data from SINESP Since 2015
Version: 0.1
Maintainer: Igor Laltuf <igorlaltuf@gmail.com>
Description: Allows access to data from the Brazilian Public Security Information System (SINESP) by state and municipality. <https://www.gov.br/mj/pt-br/assuntos/sua-seguranca/seguranca-publica/sinesp-1>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: dplyr, geobr, janitor, openxlsx, tidyr
Depends: R (>= 2.10)
Suggests: testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2024-06-18 18:22:34 UTC; laltuf
Author: Giovanni Vargette [aut] , Igor Laltuf [aut, cre] , Marcelo Justus [aut]
Repository: CRAN
Date/Publication: 2024-06-20 16:00:16 UTC

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New package beastt with initial version 0.0.1
Package: beastt
Title: Bayesian Evaluation, Analysis, and Simulation Software Tools for Trials
Version: 0.0.1
Description: Bayesian dynamic borrowing with covariate adjustment via inverse probability weighting for simulations and data analyses in clinical trials. This makes it easy to use propensity score methods to balance covariate distributions between external and internal data.
License: Apache License (>= 2)
URL: https://gsk-biostatistics.github.io/beastt/, https://github.com/GSK-Biostatistics/beastt
BugReports: https://github.com/GSK-Biostatistics/beastt/issues
Suggests: knitr, rmarkdown, spelling, testthat (>= 3.0.0), tibble
Encoding: UTF-8
Imports: cli, cobalt, dplyr, generics, ggplot2, purrr, rlang, stringr, distributional, tidyr, ggdist, mixtools
VignetteBuilder: knitr
Depends: R (>= 2.10)
LazyData: true
Language: en-US
NeedsCompilation: no
Packaged: 2024-06-18 16:07:16 UTC; christinafillmore
Author: Christina Fillmore [aut, cre] , Ben Arancibia [aut], Nate Bean [aut] , GlaxoSmithKline Research & Development Limited [cph, fnd]
Maintainer: Christina Fillmore <christina.e.fillmore@gsk.com>
Repository: CRAN
Date/Publication: 2024-06-20 15:50:16 UTC

More information about beastt at CRAN
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Package DIDmultiplegtDYN updated to version 1.0.12 with previous version 1.0.11 dated 2024-05-27

Title: Estimation in Difference-in-Difference Designs with Multiple Groups and Periods
Description: Estimation of event-study Difference-in-Difference (DID) estimators in designs with multiple groups and periods, and with a potentially non-binary treatment that may increase or decrease multiple times.
Author: Diego Ciccia [aut, cre], Felix Knau [aut], Melitine Malezieux [aut], Doulo Sow [aut], Clement de Chaisemartin [aut]
Maintainer: Diego Ciccia <diego.ciccia@sciencespo.fr>

Diff between DIDmultiplegtDYN versions 1.0.11 dated 2024-05-27 and 1.0.12 dated 2024-06-20

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Package ao updated to version 1.0.0 with previous version 0.3.3 dated 2024-02-29

Title: Alternating Optimization
Description: Alternating optimization is an iterative procedure that optimizes a function by alternately performing restricted optimization over individual parameter subsets. Instead of tackling joint optimization directly, it breaks the problem down into simpler sub-problems. This approach can make optimization feasible when joint optimization is too difficult.
Author: Lennart Oelschlaeger [aut, cre] , Siddhartha Chib [ctb]
Maintainer: Lennart Oelschlaeger <oelschlaeger.lennart@gmail.com>

Diff between ao versions 0.3.3 dated 2024-02-29 and 1.0.0 dated 2024-06-20

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More information about ao at CRAN
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Package torchdatasets updated to version 0.3.1 with previous version 0.3.0 dated 2023-02-14

Title: Ready to Use Extra Datasets for Torch
Description: Provides datasets in a format that can be easily consumed by torch 'dataloaders'. Handles data downloading from multiple sources, caching and pre-processing so users can focus only on their model implementations.
Author: Daniel Falbel [aut, cre], RStudio [cph]
Maintainer: Daniel Falbel <daniel@rstudio.com>

Diff between torchdatasets versions 0.3.0 dated 2023-02-14 and 0.3.1 dated 2024-06-20

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More information about torchdatasets at CRAN
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Package crayon updated to version 1.5.3 with previous version 1.5.2 dated 2022-09-29

Title: Colored Terminal Output
Description: The crayon package is now superseded. Please use the 'cli' package for new projects. Colored terminal output on terminals that support 'ANSI' color and highlight codes. It also works in 'Emacs' 'ESS'. 'ANSI' color support is automatically detected. Colors and highlighting can be combined and nested. New styles can also be created easily. This package was inspired by the 'chalk' 'JavaScript' project.
Author: Gabor Csardi [aut, cre], Brodie Gaslam [ctb], Posit Software, PBC [cph, fnd]
Maintainer: Gabor Csardi <csardi.gabor@gmail.com>

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Package r2dii.data updated to version 0.6.0 with previous version 0.5.0 dated 2024-03-25

Title: Datasets to Measure the Alignment of Corporate Loan Books with Climate Goals
Description: These datasets support the implementation in R of the software 'PACTA' (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between corporate lending portfolios and climate scenarios (<https://www.transitionmonitor.com/>). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. Because both financial institutions and market data providers keep their data private, this package provides fake, public data to enable the development and use of 'PACTA' in R.
Author: Alex Axthelm [aut, cre, dtc] , Jackson Hoffart [aut, ctr, dtc] , Jacob Kastl [aut, ctr] , Mauro Lepore [aut, ctr] , Rocky Mountain Institute [cph, fnd]
Maintainer: Alex Axthelm <aaxthelm@rmi.org>

Diff between r2dii.data versions 0.5.0 dated 2024-03-25 and 0.6.0 dated 2024-06-20

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Package rlistings updated to version 0.2.9 with previous version 0.2.8 dated 2024-04-15

Title: Clinical Trial Style Data Readout Listings
Description: Listings are often part of the submission of clinical trial data in regulatory settings. We provide a framework for the specific formatting features often used when displaying large datasets in that context.
Author: Gabriel Becker [aut] , Adrian Waddell [aut], Joe Zhu [aut, cre], Davide Garolini [ctb], Emily de la Rua [ctb], Abinaya Yogasekaram [ctb], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Joe Zhu <joe.zhu@roche.com>

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Package MNP updated to version 3.1-5 with previous version 3.1-4 dated 2023-03-14

Title: Fitting the Multinomial Probit Model
Description: Fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP package can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005). "A Bayesian Analysis of the Multinomial Probit Model Using the Data Augmentation." Journal of Econometrics, Vol. 124, No. 2 (February), pp. 311-334. <doi:10.1016/j.jeconom.2004.02.002> Detailed examples are given in Imai and van Dyk (2005). "MNP: R Package for Fitting the Multi [...truncated...]
Author: Kosuke Imai [aut, cre], David van Dyk [aut], Hubert Jin [ctb]
Maintainer: Kosuke Imai <imai@harvard.edu>

Diff between MNP versions 3.1-4 dated 2023-03-14 and 3.1-5 dated 2024-06-20

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Package jmv updated to version 2.5.6 with previous version 2.5.5 dated 2024-05-31

Title: The 'jamovi' Analyses
Description: A suite of common statistical methods such as descriptives, t-tests, ANOVAs, regression, correlation matrices, proportion tests, contingency tables, and factor analysis. This package is also useable from the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org> for more information).
Author: Ravi Selker [aut, cph], Jonathon Love [aut, cre, cph], Damian Dropmann [aut, cph], Victor Moreno [ctb, cph], Maurizio Agosti [ctb, cph]
Maintainer: Jonathon Love <jon@thon.cc>

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Package evoTS updated to version 1.0.3 with previous version 1.0.2 dated 2023-03-06

Title: Analyses of Evolutionary Time-Series
Description: Facilitates univariate and multivariate analysis of evolutionary sequences of phenotypic change. The package extends the modeling framework available in the 'paleoTS' package. Please see <https://klvoje.github.io/evoTS/index.html> for information about the package and the implemented models.
Author: Kjetil Lysne Voje [aut, cre]
Maintainer: Kjetil Lysne Voje <k.l.voje@nhm.uio.no>

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Package DatabionicSwarm updated to version 2.0.0 with previous version 1.2.1 dated 2023-10-13

Title: Swarm Intelligence for Self-Organized Clustering
Description: Algorithms implementing populations of agents that interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here, a swarm system called Databionic swarm (DBS) is introduced which was published in Thrun, M.C., Ultsch A.: "Swarm Intelligence for Self-Organized Clustering" (2020), Artificial Intelligence, <DOI:10.1016/j.artint.2020.103237>. DBS is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method called Pswarm (Pswarm()), which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is the parameter-free high-dimensional data visualization technique, which generates projected points on the topographic map with hypsometric tints defined by the generalized U-matri [...truncated...]
Author: Michael Thrun [aut, cre, cph] , Quirin Stier [aut, rev]
Maintainer: Michael Thrun <m.thrun@gmx.net>

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Package BANAM updated to version 0.2.1 with previous version 0.2.0 dated 2024-05-29

Title: Bayesian Analysis of the Network Autocorrelation Model
Description: The network autocorrelation model (NAM) can be used for studying the degree of social influence regarding an outcome variable based on one or more known networks. The degree of social influence is quantified via the network autocorrelation parameters. In case of a single network, the Bayesian methods of Dittrich, Leenders, and Mulder (2017) <DOI:10.1016/j.socnet.2016.09.002> and Dittrich, Leenders, and Mulder (2019) <DOI:10.1177/0049124117729712> are implemented using a normal, flat, or independence Jeffreys prior for the network autocorrelation. In the case of multiple networks, the Bayesian methods of Dittrich, Leenders, and Mulder (2020) <DOI:10.1177/0081175020913899> are implemented using a multivariate normal prior for the network autocorrelation parameters. Flat priors are implemented for estimating the coefficients. For Bayesian testing of equality and order-constrained hypotheses, the default Bayes factor of Gu, Mulder, and Hoijtink, (2018) <DOI:10.1111/bms [...truncated...]
Author: Joris Mulder [aut, cre], Dino Dittrich [aut, ctb], Roger Leenders [aut, ctb]
Maintainer: Joris Mulder <j.mulder3@tilburguniversity.edu>

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Package minimaxApprox updated to version 0.4.3 with previous version 0.4.2 dated 2024-05-20

Title: Implementation of Remez Algorithm for Polynomial and Rational Function Approximation
Description: Implements the algorithm of Remez (1962) for polynomial minimax approximation and of Cody et al. (1968) <doi:10.1007/BF02162506> for rational minimax approximation.
Author: Avraham Adler [aut, cre, cph]
Maintainer: Avraham Adler <Avraham.Adler@gmail.com>

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Package revss updated to version 2.0.0 with previous version 1.0.6 dated 2024-02-01

Title: Robust Estimation in Very Small Samples
Description: Implements the estimation techniques described in Rousseeuw & Verboven (2002) <doi:10.1016/S0167-9473(02)00078-6> for the location and scale of very small samples.
Author: Avraham Adler [aut, cph, cre]
Maintainer: Avraham Adler <Avraham.Adler@gmail.com>

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 revss-2.0.0/revss/README.md                             |    7 +--
 revss-2.0.0/revss/build/partial.rdb                     |binary
 revss-2.0.0/revss/inst/CITATION                         |    4 -
 revss-2.0.0/revss/inst/NEWS.Rd                          |   37 ++++++++++++----
 revss-2.0.0/revss/inst/tinytest/test_package_metadata.R |only
 revss-2.0.0/revss/inst/tinytest/test_revss.R            |only
 revss-2.0.0/revss/man/robScale.Rd                       |    7 +--
 12 files changed, 54 insertions(+), 30 deletions(-)

More information about revss at CRAN
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Package varsExplore (with last version 0.3.0) was removed from CRAN

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

2020-07-13 0.3.0
2019-10-03 0.1.0

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Package parameters updated to version 0.22.0 with previous version 0.21.7 dated 2024-05-14

Title: Processing of Model Parameters
Description: Utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), this package implements features like bootstrapping or simulating of parameters and models, feature reduction (feature extraction and variable selection) as well as functions to describe data and variable characteristics (e.g. skewness, kurtosis, smoothness or distribution).
Author: Daniel Luedecke [aut, cre] , Dominique Makowski [aut] , Mattan S. Ben-Shachar [aut] , Indrajeet Patil [aut] , Soeren Hoejsgaard [aut], Brenton M. Wiernik [aut] , Zen J. Lau [ctb], Vincent Arel-Bundock [ctb] , Jeffrey Girard [ctb] , Christina Maimone [re [...truncated...]
Maintainer: Daniel Luedecke <d.luedecke@uke.de>

Diff between parameters versions 0.21.7 dated 2024-05-14 and 0.22.0 dated 2024-06-20

 DESCRIPTION                                          |   19 -
 MD5                                                  |  111 +++++-----
 NAMESPACE                                            |    3 
 NEWS.md                                              |   23 ++
 R/1_model_parameters.R                               |    2 
 R/bootstrap_model.R                                  |    6 
 R/display.R                                          |   14 -
 R/dof_kenward.R                                      |    2 
 R/equivalence_test.R                                 |   78 +------
 R/extract_parameters.R                               |    8 
 R/extract_parameters_anova.R                         |   17 +
 R/format.R                                           |    5 
 R/methods_BayesFactor.R                              |   61 ++---
 R/methods_DirichletReg.R                             |   30 +-
 R/methods_aov.R                                      |   86 +++----
 R/methods_glmgee.R                                   |only
 R/methods_htest.R                                    |  207 +++++++++----------
 R/methods_kmeans.R                                   |    8 
 R/methods_other.R                                    |   11 -
 R/methods_rstanarm.R                                 |    2 
 R/n_clusters_easystats.R                             |  106 +++++----
 R/principal_components.R                             |  106 +++++----
 R/print.compare_parameters.R                         |   32 ++
 R/print.parameters_model.R                           |   11 -
 R/print_html.R                                       |    3 
 R/print_md.R                                         |    9 
 R/select_parameters.R                                |   46 ++--
 R/standardize_info.R                                 |   30 +-
 R/standardize_parameters.R                           |   16 -
 R/standardize_posteriors.R                           |    2 
 R/utils_pca_efa.R                                    |   11 -
 build/partial.rdb                                    |binary
 build/vignette.rds                                   |binary
 inst/WORDLIST                                        |    2 
 man/display.parameters_model.Rd                      |   99 ---------
 man/model_parameters.BFBayesFactor.Rd                |   45 ++--
 man/model_parameters.Rd                              |    2 
 man/model_parameters.aov.Rd                          |   20 -
 man/model_parameters.cgam.Rd                         |    7 
 man/model_parameters.htest.Rd                        |   26 --
 man/parameters-package.Rd                            |    2 
 man/principal_components.Rd                          |   17 +
 man/print.compare_parameters.Rd                      |only
 man/print.parameters_model.Rd                        |  145 ++++++++++---
 man/select_parameters.Rd                             |   45 +---
 man/standardize_parameters.Rd                        |    7 
 tests/testthat/_snaps/pca.md                         |only
 tests/testthat/test-marginaleffects.R                |   10 
 tests/testthat/test-model_parameters.BFBayesFactor.R |    4 
 tests/testthat/test-model_parameters.anova.R         |    2 
 tests/testthat/test-model_parameters.aov.R           |   10 
 tests/testthat/test-model_parameters.aov_es_ci.R     |   29 +-
 tests/testthat/test-model_parameters.blmerMod.R      |   10 
 tests/testthat/test-model_parameters.fixest.R        |    2 
 tests/testthat/test-model_parameters.htest.R         |   56 +++--
 tests/testthat/test-model_parameters.mixed.R         |    2 
 tests/testthat/test-pca.R                            |   17 +
 tests/testthat/test-standardize_parameters.R         |   52 ++--
 58 files changed, 898 insertions(+), 778 deletions(-)

More information about parameters at CRAN
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Package metaCluster updated to version 0.1.1 with previous version 0.1.0 dated 2021-09-30

Title: Metagenomic Clustering
Description: Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning.
Author: Dipro Sinha [aut, cre], Sayanti Guha Majumdar [aut], Anu Sharma [aut], Dwijesh Chandra Mishra [aut], Md Yeasin [aut]
Maintainer: Dipro Sinha <diprosinha@gmail.com>

Diff between metaCluster versions 0.1.0 dated 2021-09-30 and 0.1.1 dated 2024-06-20

 DESCRIPTION |   23 ++++++++++++-----------
 MD5         |    2 +-
 2 files changed, 13 insertions(+), 12 deletions(-)

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Package adelie updated to version 1.0.1 with previous version 1.0.0 dated 2024-06-17

Title: A Fast and Flexible Group Elastic Net Solver
Description: R bindings for the Python package 'adelie'. These bindings offer a general purpose group elastic net solver, a wide range of matrix classes that can exploit special structure to allow large-scale inputs, and an assortment of generalized linear model classes for fitting various types of data. The package is an implementation of Yang, J. and Hastie, T. (2024) <doi:10.48550/arXiv.2405.08631>.
Author: James Yang [aut, cre, cph], Trevor Hastie [aut, cph, fnd], Balasubramanian Narasimhan [aut]
Maintainer: James Yang <jamesyang916@gmail.com>

Diff between adelie versions 1.0.0 dated 2024-06-17 and 1.0.1 dated 2024-06-20

 DESCRIPTION                                                              |    8 
 MD5                                                                      |   41 +-
 NAMESPACE                                                                |    1 
 NEWS.md                                                                  |    7 
 R/glm.R                                                                  |    1 
 R/solver.R                                                               |  189 +++++++++-
 R/state.R                                                                |  117 +++++-
 inst/adelie/adelie/__init__.py                                           |    2 
 inst/adelie/adelie/glm.py                                                |    2 
 inst/adelie/adelie/solver.py                                             |    4 
 inst/adelie/adelie/src/include/adelie_core/io/io_snp_base.hpp            |    4 
 inst/adelie/adelie/src/include/adelie_core/io/io_snp_phased_ancestry.hpp |  155 ++++----
 inst/adelie/adelie/src/include/adelie_core/io/io_snp_unphased.hpp        |  170 +++++---
 inst/adelie/adelie/state.py                                              |   20 -
 man/gaussian_cov.Rd                                                      |only
 man/grpnet.Rd                                                            |    2 
 src/rcpp_matrix.cpp                                                      |    2 
 src/rcpp_matrix.h                                                        |   17 
 src/rcpp_solver.cpp                                                      |   42 ++
 src/rcpp_state.cpp                                                       |   72 +++
 src/rcpp_state.h                                                         |   16 
 tests/testthat/test_solver.R                                             |   11 
 22 files changed, 695 insertions(+), 188 deletions(-)

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