Mon, 08 Jun 2026

Package simcross updated to version 0.10 with previous version 0.8 dated 2026-05-01

Title: Simulate Experimental Crosses
Description: Simulate and plot general experimental crosses. The focus is on simulating genotypes with an aim towards flexibility rather than speed. Meiosis is simulated following the Stahl model, in which chiasma locations are the superposition of two processes: a proportion p coming from a process exhibiting no interference, and the remainder coming from a process following the chi-square model.
Author: Karl W Broman [aut, cre]
Maintainer: Karl W Broman <broman@wisc.edu>

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Package qtl2 updated to version 0.42 with previous version 0.40 dated 2026-05-04

Title: Quantitative Trait Locus Mapping in Experimental Crosses
Description: Provides a set of tools to perform quantitative trait locus (QTL) analysis in experimental crosses. It is a reimplementation of the 'R/qtl' package to better handle high-dimensional data and complex cross designs. Broman et al. (2019) <doi:10.1534/genetics.118.301595>.
Author: Karl W Broman [aut, cre] , R Core Team [ctb]
Maintainer: Karl W Broman <broman@wisc.edu>

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Package broman updated to version 0.94 with previous version 0.92 dated 2025-12-08

Title: Karl Broman's R Code
Description: Miscellaneous R functions, including functions related to graphics (mostly for base graphics), permutation tests, running mean/median, and general utilities.
Author: Karl W Broman [aut, cre] , Aimee Teo Broman [ctb]
Maintainer: Karl W Broman <broman@wisc.edu>

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Package rbbnp updated to version 1.1.0 with previous version 0.3.0 dated 2025-04-30

Title: A Bias Bound Approach to Non-Parametric Inference
Description: A novel bias-bound approach for non-parametric inference is introduced, focusing on both density and conditional expectation estimation. It constructs valid confidence intervals that account for the presence of a non-negligible bias and thus make it possible to perform inference with optimal mean squared error minimizing bandwidths. This package is based on Schennach (2020) <doi:10.1093/restud/rdz065>.
Author: Xinyu DAI [aut, cre], Susanne M Schennach [aut]
Maintainer: Xinyu DAI <xinyu_dai@brown.edu>

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Package cgmguru updated to version 1.1.0 with previous version 1.0.0 dated 2026-05-14

Title: Advanced Continuous Glucose Monitoring Analysis with High-Performance C++ Backend
Description: Tools for advanced analysis of continuous glucose monitoring (CGM) time-series, implementing GRID (Glucose Rate Increase Detector) and GRID-based algorithms for postprandial peak detection, and detection of hypoglycemic and hyperglycemic episodes (Levels 1/2/Extended) aligned with international consensus CGM metrics. Core algorithms are implemented in optimized C++ using 'Rcpp' to provide accurate and fast analysis on large datasets.
Author: Sang Ho Park [aut, cre], Rosa Oh [aut, ctb], Sang-Man Jin [aut, ctb]
Maintainer: Sang Ho Park <shstat1729@gmail.com>

Diff between cgmguru versions 1.0.0 dated 2026-05-14 and 1.1.0 dated 2026-06-08

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Package bravo updated to version 4.1.0 with previous version 4.0.0 dated 2026-05-27

Title: Bayesian Screening and Variable Selection
Description: Performs Bayesian variable screening and selection for ultra-high dimensional linear regression models.Also contains an user friendly web application to perform multi trait GWAS.
Author: Dongjin Li [aut, cre], Debarshi Chakraborty [aut], Somak Dutta [aut], Vivekananda Roy [ctb]
Maintainer: Dongjin Li <liyangxiaobei@gmail.com>

Diff between bravo versions 4.0.0 dated 2026-05-27 and 4.1.0 dated 2026-06-08

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Package stochtree updated to version 0.4.4 with previous version 0.4.2 dated 2026-04-28

Title: Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference
Description: Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) (Chipman, George, McCulloch (2010) <doi:10.1214/09-AOAS285>) for supervised learning and Bayesian Causal Forests (BCF) (Hahn, Murray, Carvalho (2020) <doi:10.1214/19-BA1195>) for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers. Includes the grow-from-root algorithm for accelerated forest sampling (He and Hahn (2021) <doi:10.1080/01621459.2021.1942012>), a log-linear leaf model for forest-based heteroskedasticity (Murray (2020) <doi:10.1080/01621459.2020.1813587>), and the cloglog BART model of Alam and Linero (2025) <doi:10.48550/arXiv.2502.00606> for ordinal outcomes.
Author: Drew Herren [aut, cre] , Richard Hahn [aut], Jared Murray [aut], Carlos Carvalho [aut], Jingyu He [aut], Pedro Lima [ctb], Entejar Alam [ctb], stochtree contributors [cph], Eigen contributors [cph] , xgboost contributors [cph] , treelite contributors [...truncated...]
Maintainer: Drew Herren <drewherrenopensource@gmail.com>

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Package semlbci updated to version 0.11.6 with previous version 0.11.5 dated 2025-12-06

Title: Likelihood-Based Confidence Interval in Structural Equation Models
Description: Forms likelihood-based confidence intervals (LBCIs) for parameters in structural equation modeling, introduced in Cheung and Pesigan (2023) <doi:10.1080/10705511.2023.2183860>. Currently implements the algorithm illustrated by Pek and Wu (2018) <doi:10.1037/met0000163>, and supports the robust LBCI proposed by Falk (2018) <doi:10.1080/10705511.2017.1367254>.
Author: Shu Fai Cheung [aut, cre] , Ivan Jacob Agaloos Pesigan [ctb]
Maintainer: Shu Fai Cheung <shufai.cheung@gmail.com>

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Package tinycodet updated to version 0.7.0 with previous version 0.6.2 dated 2026-03-05

Title: Functions to Help in your Coding Etiquette
Description: Adds some functions to help in your coding etiquette. 'tinycodet' primarily focuses on 4 aspects. 1) Safer decimal (in)equality testing, standard-evaluated alternatives to with() and aes(), and other functions for safer coding. 2) A new package import system, that attempts to combine the benefits of using a package without attaching it, with the benefits of attaching a package. 3) Extending the string manipulation capabilities of the 'stringi' R package. 4) Reducing repetitive code. Besides linking to 'Rcpp', 'tinycodet' has only one other dependency, namely 'stringi'.
Author: Tony Wilkes [aut, cre, cph]
Maintainer: Tony Wilkes <tony_a_wilkes@outlook.com>

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Package StatisticTeach1 updated to version 0.1.2 with previous version 0.1.0 dated 2026-05-30

Title: Interactive Tool for Statistics and Probability
Description: A Shiny application designed to support the learning of basic concepts in statistics and probability. The tool provides an interactive interface that allows students to explore and visualize different statistical concepts intuitively, including descriptive statistics for continuous and qualitative variables, and probability distributions.
Author: Javier De La Hoz Maestre [cre, aut] , Humberto Llinas Solano [aut] , Alexander Rangel Vizcaino [aut]
Maintainer: Javier De La Hoz Maestre <jdelahoz@unimagdalena.edu.co>

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Package SHARK4R updated to version 1.2.0 with previous version 1.1.1 dated 2026-03-12

Title: Accessing and Validating Marine Environmental Data from 'SHARK' and Related Databases
Description: Provides functions to retrieve, process, analyze, and quality-control marine physical, chemical, and biological data. The main focus is on Swedish monitoring data available through the 'SHARK' database <https://shark.smhi.se/en/>, with additional API support for 'Nordic Microalgae' <https://nordicmicroalgae.org/>, 'Dyntaxa' <https://artfakta.se/>, World Register of Marine Species ('WoRMS') <https://www.marinespecies.org>, 'AlgaeBase' <https://www.algaebase.org>, OBIS 'xylookup' web service <https://iobis.github.io/xylookup/> and Intergovernmental Oceanographic Commission (IOC) - UNESCO databases on harmful algae <https://www.marinespecies.org/hab/> and toxins <https://toxins.hais.ioc-unesco.org/>.
Author: Markus Lindh [aut] , Anders Torstensson [aut, cre] , Mikael Hedblom [ctb] , Bengt Karlson [ctb] , Peter Thor [ctb] , Marie Johansen [ctb] , SHARK [cph], SBDI [fnd]
Maintainer: Anders Torstensson <anders.torstensson@smhi.se>

Diff between SHARK4R versions 1.1.1 dated 2026-03-12 and 1.2.0 dated 2026-06-08

 DESCRIPTION                                                                        |   25 
 MD5                                                                                |  140 +-
 NAMESPACE                                                                          |   18 
 NEWS.md                                                                            |   19 
 R/SHARK4R-package.R                                                                |   14 
 R/algaebase_api_functions.R                                                        |   16 
 R/check_codes.R                                                                    |   17 
 R/check_depth.R                                                                    |    4 
 R/check_fields.R                                                                   |  138 +-
 R/check_logical.R                                                                  |   85 -
 R/check_onland.R                                                                   |   17 
 R/check_outliers.R                                                                 |   22 
 R/check_stations.R                                                                 |   41 
 R/defunct.R                                                                        |   59 
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 R/hab_api_functions.R                                                              |   88 +
 R/iRfcb_functions.R                                                                |  202 +--
 R/nua_api_functions.R                                                              |   48 
 R/pie_map.R                                                                        |only
 R/plankton_toolbox.R                                                               |   30 
 R/plot_map_leaflet.R                                                               |    7 
 R/read_delivery_template.R                                                         |   12 
 R/run_qc_app.R                                                                     |   19 
 R/scatterplot.R                                                                    |   20 
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 R/shark_read.R                                                                     |   26 
 R/util.R                                                                           |  167 +-
 R/worms_api_functions.R                                                            |   80 -
 R/xylookup.R                                                                       |   10 
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 R/zzz.R                                                                            |  123 -
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 man/find_required_fields.Rd                                                        |    2 
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 man/which_basin.Rd                                                                 |    6 
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 tests/testthat/test-run_qc_app.R                                                   |   12 
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 tests/testthat/test-shark_read.R                                                   |    8 
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Package nlmixr2autoinit updated to version 1.0.1 with previous version 1.0.0 dated 2025-11-13

Title: Automatic Generation of Initial Estimates for Population Pharmacokinetic Modeling
Description: Provides automated methods for generating initial parameter estimates in population pharmacokinetic modeling. The pipeline integrates adaptive single-point methods, naive pooled graphic approaches, noncompartmental analysis methods, and parameter sweeping across pharmacokinetic models. It estimates residual unexplained variability using either data-driven or fixed-fraction approaches and assigns pragmatic initial values for inter-individual variability. These strategies are designed to improve model robustness and convergence in 'nlmixr2' workflows. For more details see Huang Z, Fidler M, Lan M, Cheng IL, Kloprogge F, Standing JF (2025) <doi:10.1007/s10928-025-10000-z>.
Author: Zhonghui Huang [aut, cre], Joseph Standing [ctb], Matthew Fidler [ctb], Frank Kloprogge [ctb]
Maintainer: Zhonghui Huang <huangzhonghui22@gmail.com>

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Permanent link

Package clarify updated to version 0.2.3 with previous version 0.2.2 dated 2025-09-19

Title: Simulation-Based Inference for Regression Models
Description: Performs simulation-based inference as an alternative to the delta method for obtaining valid confidence intervals and p-values for regression post-estimation quantities, such as average marginal effects and predictions at representative values. This framework for simulation-based inference is especially useful when the resulting quantity is not normally distributed and the delta method approximation fails. The methodology is described in Greifer, et al. (2025) <doi:10.32614/RJ-2024-015>. 'clarify' is meant to replace some of the functionality of the archived package 'Zelig'; see the vignette "Translating Zelig to clarify" for replicating this functionality.
Author: Noah Greifer [aut, cre] , Steven Worthington [aut] , Stefano Iacus [aut] , Gary King [aut]
Maintainer: Noah Greifer <ngreifer@iq.harvard.edu>

Diff between clarify versions 0.2.2 dated 2025-09-19 and 0.2.3 dated 2026-06-08

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More information about clarify at CRAN
Permanent link

Package wrMisc updated to version 2.1.0 with previous version 2.0.2 dated 2026-04-29

Title: Analyze Experimental High-Throughput (Omics) Data
Description: The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of larg [...truncated...]
Author: Wolfgang Raffelsberger [aut, cre]
Maintainer: Wolfgang Raffelsberger <w.raffelsberger@gmail.com>

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New package vizClust with initial version 0.1.0
Package: vizClust
Title: Visualization and Exploration of Cluster Transitions
Version: 0.1.0
Description: Provides tools to explore and visualize transitions between clusters in multivariate data. The package generates pseudo-samples by interpolating between cluster medoids, enabling the study of gradual changes in feature space. It also computes k-nearest neighbors (KNN)-based statistics to relate pseudo-samples to real data and summarize variable behavior using mean, median, or standard deviation. Finally, the package offers interactive visualizations of variable trajectories along cluster transitions, including both direct trajectory plots and bootstrap-based interactive plots with confidence intervals to assess variability and uncertainty across the transition path.
License: GPL-3
Encoding: UTF-8
Imports: ggplot2, ggiraph, reshape2, FNN, dplyr, tibble
Suggests: webshot, cluster, webshot2, phenomap
NeedsCompilation: no
Packaged: 2026-06-03 09:31:09 UTC; elsaa
Author: Elsa Arribas [aut, cre], YingHong Chen [ctb], Ferran Reverter [ctb]
Maintainer: Elsa Arribas <elsaarribasg@gmail.com>
Depends: R (>= 4.1.0)
Repository: CRAN
Date/Publication: 2026-06-08 19:40:02 UTC

More information about vizClust at CRAN
Permanent link

New package Uno with initial version 2.7.3
Package: Uno
Title: R Interface to the 'Uno' Nonlinear Optimization Solver
Version: 2.7.3
Description: Bindings to 'Uno' (Unifying Nonlinear Optimization), a C++ solver for smooth nonlinearly constrained optimization. 'Uno' unifies Lagrange-Newton methods, including sequential quadratic programming and interior-point methods, by decomposing them into interacting building blocks (constraint-relaxation, inequality-handling, Hessian, and globalization strategies) that can be freely combined, either through options or through presets that reproduce established solvers such as 'filterSQP' and 'IPOPT'. The framework is described in Vanaret and Leyffer (2024) <doi:10.48550/arXiv.2406.13454>.
License: MIT + file LICENSE
Copyright: file inst/COPYRIGHTS
URL: https://bnaras.github.io/Uno/, https://github.com/bnaras/Uno
BugReports: https://github.com/bnaras/Uno/issues
SystemRequirements: CMake (>= 3.16), C++17, GNU make
Imports: rmumps (>= 5.2.1-41)
LinkingTo: cpp11, rmumps
Suggests: cpp11, tinytest, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Encoding: UTF-8
Packaged: 2026-06-03 04:37:30 UTC; naras
Author: Balasubramanian Narasimhan [aut, cre], Charlie Vanaret [aut, cph] , Sven Leyffer [aut, cph] , HiGHS development team [cph] ; see inst/COPYRIGHTS)
Maintainer: Balasubramanian Narasimhan <naras@stanford.edu>
Repository: CRAN
Date/Publication: 2026-06-08 19:20:02 UTC

More information about Uno at CRAN
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New package twinsvm with initial version 0.0.1
Package: twinsvm
Title: Twin Support Vector Machines
Version: 0.0.1
Author: Shamika Tissera [aut, cre]
Maintainer: Shamika Tissera <nimeshshamika@gmail.com>
Description: Provides twin support vector machine classifiers and visualization tools for small to moderate classification problems. Includes one-vs-one multi-class classification and a standard support vector machine baseline for comparison.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 3.5.0)
LinkingTo: Rcpp, RcppArmadillo
Imports: ggplot2, Rcpp, rlang
Suggests: e1071, gganimate, knitr, plotly, rmarkdown, shiny, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-06-03 07:12:12 UTC; Shamika
Repository: CRAN
Date/Publication: 2026-06-08 19:30:02 UTC

More information about twinsvm at CRAN
Permanent link

New package sparsediff with initial version 0.4.0
Package: sparsediff
Title: R Interface to the 'SparseDiffEngine' Sparse Differentiation Backend
Version: 0.4.0
Description: Bindings for the 'SparseDiffEngine' C library, the sparse Jacobian and Hessian differentiation backend used by 'CVXPY' for its Disciplined Nonlinear Programming (DNLP) extension. Provides low-level routines for building nonlinear expression graphs and evaluating sparse derivatives, intended as a backend for higher-level modeling layers such as 'CVXR'. This is the R analog of the 'sparsediffpy' Python package and wraps the same C library.
License: Apache License (== 2.0)
Copyright: file inst/COPYRIGHTS
URL: https://bnaras.github.io/sparsediff/, https://github.com/bnaras/sparsediff
BugReports: https://github.com/bnaras/sparsediff/issues
Encoding: UTF-8
SystemRequirements: GNU make
LinkingTo: cpp11
Suggests: cpp11, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-06-03 04:46:07 UTC; naras
Author: Balasubramanian Narasimhan [aut, cre], Daniel Cederberg [aut, cph] , William Zijie Zhang [aut, cph]
Maintainer: Balasubramanian Narasimhan <naras@stanford.edu>
Repository: CRAN
Date/Publication: 2026-06-08 19:30:08 UTC

More information about sparsediff at CRAN
Permanent link

Package shinyds updated to version 0.3.0 with previous version 0.2.2 dated 2026-06-04

Title: 'Shiny' Bindings for Designsystemet Components
Description: Provides 'R' wrappers for the Designsystemet component library <https://designsystemet.no>, enabling use of Norwegian government design system components in 'Shiny' applications. Includes web components and CSS-based HTML components with full 'Shiny' input binding support.
Author: Novica Nakov [aut, cre, cph]
Maintainer: Novica Nakov <nnovica@gmail.com>

Diff between shinyds versions 0.2.2 dated 2026-06-04 and 0.3.0 dated 2026-06-08

 DESCRIPTION                          |   10 
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 NAMESPACE                            |    5 
 NEWS.md                              |    6 
 R/ds-button.R                        |   36 +-
 R/ds-dependencies.R                  |   64 +++
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 R/ds-misc.R                          |   68 +++-
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 README.md                            |    4 
 inst/doc/getting-started.R           |   62 +++
 inst/doc/getting-started.html        |  561 +++++++++++++++++++----------------
 inst/doc/getting-started.qmd         |   90 +++++
 inst/doc/reactivity-patterns.R       |   45 +-
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 inst/doc/reactivity-patterns.qmd     |   59 +--
 inst/examples/faithful/app.R         |    3 
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 inst/www/js/ds-bindings-generated.js |   78 ----
 inst/www/js/ds-bindings.js           |   12 
 man/ds_badge.Rd                      |   13 
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 tests/testthat/test-showcase.R       |   27 +
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 vignettes/getting-started.qmd        |   90 +++++
 vignettes/reactivity-patterns.qmd    |   59 +--
 37 files changed, 1185 insertions(+), 664 deletions(-)

More information about shinyds at CRAN
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Package rsconnect updated to version 1.10.0 with previous version 1.8.0 dated 2026-04-10

Title: Deploy Docs, Apps, and APIs to 'Posit Connect', 'shinyapps.io', and 'RPubs'
Description: Programmatic deployment interface for 'RPubs', 'shinyapps.io', and 'Posit Connect'. Supported content types include R Markdown documents, Shiny applications, Plumber APIs, plots, and static web content.
Author: Aron Atkins [aut, cre], Toph Allen [aut], Hadley Wickham [aut], Jonathan McPherson [aut], JJ Allaire [aut], Posit Software, PBC [cph, fnd]
Maintainer: Aron Atkins <aron@posit.co>

Diff between rsconnect versions 1.8.0 dated 2026-04-10 and 1.10.0 dated 2026-06-08

 DESCRIPTION                                |   16 +-
 MD5                                        |   98 ++++++-------
 NAMESPACE                                  |    1 
 NEWS.md                                    |   42 +++++
 R/accounts.R                               |  169 +++++++++++++++++++----
 R/appDependencies.R                        |   16 +-
 R/appMetadata.R                            |   56 +++++++
 R/bundle.R                                 |   36 ++++
 R/bundleFiles.R                            |    3 
 R/bundlePackage.R                          |   17 +-
 R/bundlePackageRenv.R                      |   35 +++-
 R/client-cloudAuth.R                       |   19 ++
 R/client-connectCloud.R                    |   33 +++-
 R/config.R                                 |   12 +
 R/deployApp.R                              |  107 +++++++++++++-
 R/deployments.R                            |   31 +++-
 R/http-httr2.R                             |    2 
 R/http-libcurl.R                           |    2 
 R/http.R                                   |    4 
 R/servers.R                                |   17 ++
 R/writeManifest.R                          |    9 +
 README.md                                  |    2 
 man/accounts.Rd                            |    9 -
 man/appDependencies.Rd                     |   17 +-
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 man/deployDoc.Rd                           |   12 -
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 man/deployTFModel.Rd                       |   12 -
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 tests/integration/test-deploy.R            |    8 -
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 tests/testthat/_snaps/servers.md           |    1 
 tests/testthat/helper.R                    |    1 
 tests/testthat/test-accounts.R             |  157 +++++++++++++++++++++
 tests/testthat/test-appDependencies.R      |    5 
 tests/testthat/test-appMetadata.R          |  211 ++++++++++++++++++++++++++++-
 tests/testthat/test-bundleNodejs.R         |only
 tests/testthat/test-bundlePackageRenv.R    |   60 +++++++-
 tests/testthat/test-client-connectCloud.R  |   75 ++++++++++
 tests/testthat/test-deployApp.R            |  107 ++++++++++++++
 tests/testthat/test-deployments.R          |   43 +++++
 tests/testthat/test-servers.R              |   49 ++++++
 tests/testthat/test-writeManifest.R        |  125 +++++++++++++++++
 51 files changed, 1582 insertions(+), 228 deletions(-)

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New package rrstools with initial version 1.1.0
Package: rrstools
Title: Analyzing RoboCupRescue Simulation Data
Version: 1.1.0
Description: Tools for analyzing data from RoboCupRescue Simulation (RRS) <https://rescuesim.robocup.org>, a disaster rescue simulation platform. Supports reading virtual city map and disaster scenario files into analyzable data structures and provides functions for their visualization.
License: MIT + file LICENSE
URL: https://nononoexe.github.io/rrstools/
Depends: R (>= 3.5)
Imports: graphics, jsonlite, sf, stats, utils, xml2
Suggests: spelling, testthat (>= 3.3.0), withr
LinkingTo: cpp11
Encoding: UTF-8
Language: en-US
NeedsCompilation: yes
Packaged: 2026-06-03 05:18:54 UTC; ando
Author: Keisuke Ando [aut, cre]
Maintainer: Keisuke Ando <nononoexe@ymail.ne.jp>
Repository: CRAN
Date/Publication: 2026-06-08 19:30:14 UTC

More information about rrstools at CRAN
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New package onnxr with initial version 0.1.2
Package: onnxr
Title: Bindings to 'ONNX' Runtime
Version: 0.1.2
Description: Provides native access to the 'Open Neural Network Exchange' (ONNX) Runtime <https://onnxruntime.ai/>, which is a performant engine for running machine learning models that are saved to a standardized format. Rather than interfacing with 'ONNX' via 'Python', as in the official 'onnx' package, 'onnxr' directly interfaces with the runtime's 'C++' API via 'cpp11'. Models saved to '.onnx' files can be loaded and run on various backends, including CPUs and Apple's 'CoreML' library.
License: MIT + file LICENSE
LinkingTo: cpp11
Suggests: jpeg, knitr, rmarkdown, testthat (>= 3.0.0)
URL: https://corymccartan.com/onnxr/, https://github.com/CoryMcCartan/onnxr
BugReports: https://github.com/CoryMcCartan/onnxr/issues
Language: en-US
Encoding: UTF-8
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-06-03 17:23:23 UTC; cmccartan
Author: Cory McCartan [aut, cre, cph], Caleb Carr [cph] , Microsoft Corporation [cph]
Maintainer: Cory McCartan <mccartan@psu.edu>
Repository: CRAN
Date/Publication: 2026-06-08 20:00:10 UTC

More information about onnxr at CRAN
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Package lnmixsurv readmission to version 3.1.7 with previous version 3.1.6 dated 2024-09-03

Title: Bayesian Mixture Log-Normal Survival Model
Description: Bayesian Survival models via the mixture of Log-Normal distribution extends the well-known survival models and accommodates different behaviour over time and considers higher censored survival times. The proposal combines mixture distributions Fruhwirth-Schnatter(2006) <doi:10.1007/s11336-009-9121-4>, and data augmentation techniques Tanner and Wong (1987) <doi:10.1080/01621459.1987.10478458>.
Author: Viviana das Gracas Ribeiro Lobo [cre], Thais Cristina Oliveira da Fonseca [aut], Mariane Branco Alves [aut], Vitor Capdeville [aut], Victor Hugo Soares Ney [aut]
Maintainer: Viviana das Gracas Ribeiro Lobo <viviana@dme.ufrj.br>

This is a re-admission after prior archival of version 3.1.6 dated 2024-09-03

Diff between lnmixsurv versions 3.1.6 dated 2024-09-03 and 3.1.7 dated 2026-06-08

 DESCRIPTION                                                   |   28 -
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 NAMESPACE                                                     |    1 
 R/fit_metrics.R                                               |only
 R/join_empirical_hazard.R                                     |    2 
 R/simulate_data.R                                             |    2 
 R/survival_ln_mixture-fit.R                                   |    5 
 R/survival_ln_mixture-predict.R                               |    6 
 R/survival_ln_mixture-print-summary.R                         |    4 
 R/survival_ln_mixture_em-fit.R                                |   63 +-
 R/survival_ln_mixture_em-methods.R                            |    8 
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 tests/testthat/_snaps/survival_ln_mixture-print-summary.md    |   16 
 tests/testthat/_snaps/survival_ln_mixture_em-print-summary.md |   64 +-
 tests/testthat/fixtures/test-fixture.R                        |   20 
 tests/testthat/test-join_empirical_hazard.R                   |    2 
 tests/testthat/test-plot_fit_on_data.R                        |   64 +-
 tests/testthat/test-simulate_data.R                           |    5 
 tests/testthat/test-survival_ln_mixture-fit.R                 |    9 
 tests/testthat/test-survival_ln_mixture-predict.R             |   94 ++-
 tests/testthat/test-survival_ln_mixture_em-fit.R              |   20 
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 tests/testthat/test-tidiers.R                                 |    2 
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 vignettes/expectation_maximization.Rmd                        |   18 
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 vignettes/lnmixsurv.Rmd                                       |   15 
 vignettes/parallel_computation.Rmd                            |   20 
 51 files changed, 790 insertions(+), 743 deletions(-)

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New package DrivePlotR with initial version 0.1.0
Package: DrivePlotR
Title: Linked Plot Maps for Multivariate High-Resolution Spatio-Temporal Data
Version: 0.1.0
Description: Create interactive, linked plot maps for multivariate high-resolution spatio-temporal data, such as vehicle trajectories. You can explore the spatial, temporal, and multivariate aspects of the data simultaneously.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: leaflet, plotly (>= 4.10.4), crosstalk, dplyr, ggplot2 (>= 3.5.2), htmltools, rlang (>= 1.1.6), viridisLite
Depends: R (>= 4.1)
LazyData: true
URL: https://hardtme.github.io/DrivePlotR/, https://github.com/hardtme/DrivePlotR
Suggests: testthat (>= 3.0.0), sf
BugReports: https://github.com/hardtme/DrivePlotR/issues
NeedsCompilation: no
Packaged: 2026-06-03 14:43:59 UTC; MarieHardt
Author: Marie Hardt [aut, cre, cph] , Heike Hofmann [ctb] , Guillermo Basulto-Elias [ctb]
Maintainer: Marie Hardt <hardtme@iastate.edu>
Repository: CRAN
Date/Publication: 2026-06-08 19:50:10 UTC

More information about DrivePlotR at CRAN
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New package data.checker with initial version 2.0.0
Package: data.checker
Title: Data Checker for Validating Data Frames Against Defined Schema
Version: 2.0.0
Description: Validates data frames against a defined schema. Produces a report of the checks performed and any issues found, with index and entry value where appropriate. Backend checks are performed using pointblank Richard Iannone et al (2025) <doi:10.32614/CRAN.package.pointblank>.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: dplyr, glue, jsonlite, knitr, lubridate, magrittr, tools, yaml, tomledit, utils, cli, rlang, pointblank, tidyselect, tidyr, stringr, hms
Suggests: rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
URL: https://onsdigital.github.io/data.checker/
NeedsCompilation: no
Packaged: 2026-06-03 13:24:41 UTC; dayj1
Author: Crown Copyright [cph], Analysis Standards and Pipelines Team [cre, aut]
Maintainer: Analysis Standards and Pipelines Team (ONS) <ASAP@ons.gov.uk>
Repository: CRAN
Date/Publication: 2026-06-08 19:40:07 UTC

More information about data.checker at CRAN
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New package bvars with initial version 1.0
Package: bvars
Title: Bayesian Forecasting with Large Vector Autoregressions
Version: 1.0
Date: 2026-06-03
Maintainer: Tomasz Wozniak <wozniak.tom@pm.me>
Description: Provides fast and efficient procedures for Bayesian estimation and forecasting using state-of-the-art Vector Autoregressions. This package includes the model proposed by Chan (2020) <doi:10.1080/07350015.2018.1451336>, that is, a Bayesian Vector Autoregression with Minnesota priors and a flexible structure of the error term specification. The latter includes: conditional multivariate normal or Student’s t distributions, as well as homoskedastic or heteroskedastic specifications with a common volatility modelled by centred or non-centred Stochastic Volatility. Additionally, the package facilitates predictive analyses using density forecasting and forecast-error variance decompositions. All this is complemented by simple workflows, useful plots and summary functions, and comprehensive documentation. The 'bvars' package aligns with R packages 'bsvars' by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, 'bsvarSIGNs' by Wang & Woźniak (2025) <doi:10.32614/CRAN.package.b [...truncated...]
License: GPL (>= 3)
Depends: R (>= 4.1.0), RcppArmadillo, bsvars
Imports: generics, Rcpp (>= 1.0.14), RcppProgress, RcppTN, R6
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, RcppTN, bsvars
URL: https://bsvars.org/bvars/
BugReports: https://github.com/bsvars/bvars/issues
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
Packaged: 2026-06-03 08:54:49 UTC; twozniak
Author: Rui Liu [aut] , Andres Ramirez Hassan [aut] , Tomasz Wozniak [aut, cre, cph]
Repository: CRAN
Date/Publication: 2026-06-08 19:40:12 UTC

More information about bvars at CRAN
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New package AdmixPoly with initial version 1.0.0
Package: AdmixPoly
Title: Global and Local Admixture Inference in Polyploids
Version: 1.0.0
Description: Provides functions to perform global (genome-wide) and local admixture inference from bi- and multi-allelic marker dosages (discrete or continuous) in polyploid species.
License: GPL (>= 3)
Encoding: UTF-8
NeedsCompilation: yes
Imports: Rcpp, purrr, utils, dplyr (>= 1.1.0), tidyr, tibble, ggplot2, magrittr, Matrix, data.table
LinkingTo: Rcpp, RcppArmadillo
Depends: R (>= 3.6.0)
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Language: en-US
SystemRequirements: A system with a C++ compiler supporting OpenMP
Packaged: 2026-06-03 07:00:29 UTC; rio
Author: Simon Rio [aut, cre] , Tristan Mary-Huard [aut] , Franck Gauthier [aut]
Maintainer: Simon Rio <simon.rio@cirad.fr>
Repository: CRAN
Date/Publication: 2026-06-08 19:30:20 UTC

More information about AdmixPoly at CRAN
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Package surveycore updated to version 1.0.0 with previous version 0.8.3 dated 2026-05-05

Title: Core Survey Analysis Infrastructure
Description: A modern, 'S7'-based foundation for survey analysis spanning both probability and non-probability samples. Probability sample designs include Taylor series linearization, replicate weights (BRR, Fay, jackknife, bootstrap), and two-phase estimation, following 'Lumley' (2004) <doi:10.18637/jss.v009.i08>. Non-probability sample designs support bootstrap and jackknife variance estimation for opt-in panels and convenience samples. Provides a unified estimator interface for means, frequencies, totals, quantiles, ratios, correlations, regression, and t-tests, with weighted 'polychoric' and 'polyserial' correlation following 'Mannan' (2025) <doi:10.2139/ssrn.6580480>. A metadata system preserves 'haven'-style variable labels, value labels, and question-preface attributes through all operations. Uses a 'tidyselect' interface throughout.
Author: Jacob Dennen [aut, cre, cph] , Thomas Lumley [ctb, cph]
Maintainer: Jacob Dennen <jdenn0514@gmail.com>

Diff between surveycore versions 0.8.3 dated 2026-05-05 and 1.0.0 dated 2026-06-08

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 NAMESPACE                                              |    7 
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 R/analysis-corr-helpers.R                              |   20 
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 R/analysis-effective-n.R                               |only
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 R/metadata-infer.R                                     |   11 
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 R/srr-stats-standards.R                                |    8 
 R/survey-collection.R                                  |   78 
 R/update-design.R                                      |   59 
 R/utils.R                                              |   45 
 R/variance-replicate.R                                 |   31 
 R/variance-taylor.R                                    |   72 
 R/variance-twophase.R                                  |    9 
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 README.md                                              |    2 
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 inst/doc/surveycore-vs-survey.html                     |  375 +++-
 man/SURVEYCORE_DOMAIN_COL.Rd                           |    2 
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 173 files changed, 12263 insertions(+), 3733 deletions(-)

More information about surveycore at CRAN
Permanent link

New package saferDev with initial version 1.0.0
Package: saferDev
Title: Function and Pipeline Development
Version: 1.0.0
Date: 2026-06-02
Encoding: UTF-8
Maintainer: Gael Millot <gael.millot@pasteur.fr>
Description: Set of functions that perform checks according to the safer-r project recommendations for R function development (see <https://github.com/safer-r>). This includes checking argument values, ensuring correct specification of all mandatory arguments for embedded functions, as well as their explicit package namespace qualification, among other things.
URL: <https://github.com/safer-r/saferDev>, <https://safer-r.github.io/saferDev/>
License: GPL-3
Imports: ggplot2
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-06-02 16:14:25 UTC; gmillot
Author: Haiding Wang [ctb], Yushi Han [ctb], Mia Legras [ctb], Gael Millot [cre, aut, ctb]
Repository: CRAN
Date/Publication: 2026-06-08 18:10:03 UTC

More information about saferDev at CRAN
Permanent link

New package rsgl with initial version 0.1.0
Package: rsgl
Title: An Implementation of the 'SGL' Graphics Language
Version: 0.1.0
Description: Generates plots from a database connection and a 'SGL' statement. 'SGL' is a graphics language designed to look and feel like 'SQL'. It is especially useful for those familiar with 'SQL' who want to specify plots in a similar manner. The 'SGL' language is described in Chapman (2025) <doi:10.48550/arXiv.2505.14690>.
License: MIT + file LICENSE
URL: https://github.com/sgl-projects/rsgl, https://sgl-projects.github.io/rsgl/, https://arxiv.org/abs/2505.14690
BugReports: https://github.com/sgl-projects/rsgl/issues
Encoding: UTF-8
Suggests: testthat (>= 3.0.0), vdiffr, lubridate, knitr, rmarkdown, patrick, tibble, withr
Imports: DBI, dplyr, duckdb, ggplot2, purrr, Rcpp, rlang
LinkingTo: Rcpp
VignetteBuilder: knitr, rmarkdown
Depends: R (>= 4.1)
LazyData: true
NeedsCompilation: yes
Packaged: 2026-06-02 16:46:56 UTC; jochapjo
Author: Jon Chapman [aut, cre, cph], Free Software Foundation, Inc. [cph]
Maintainer: Jon Chapman <jochapjo@icloud.com>
Repository: CRAN
Date/Publication: 2026-06-08 18:20:02 UTC

More information about rsgl at CRAN
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New package PMLE4SCR with initial version 0.1.0
Package: PMLE4SCR
Title: Pseudo Maximum Likelihood Estimation for Semi-Competing Risks Data
Version: 0.1.0
Description: Implements two-stage pseudo maximum likelihood estimation (PMLE) for copula-based regression models with semi-competing risks data. The marginal distributions are modeled by semiparametric transformation regression models, and the dependence between bivariate event times is specified by a parametric copula function. See Arachchige, Chen and Zhou (2025) <doi:10.1007/s10985-024-09640-z> for details.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: trust, dplyr, rlang, VineCopula
Suggests: knitr, rmarkdown, kableExtra, SemiCompRisks
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-06-02 18:03:10 UTC; KHAN GADGET
Author: Qian M. Zhou [aut], Md. Ismail Hossain [aut, cre]
Maintainer: Md. Ismail Hossain <mr2618@msstate.edu>
Repository: CRAN
Date/Publication: 2026-06-08 18:20:19 UTC

More information about PMLE4SCR at CRAN
Permanent link

Package miclust updated to version 1.3.0 with previous version 1.2.8 dated 2022-02-07

Title: Multiple Imputation in Cluster Analysis
Description: Implementation of a framework for cluster analysis with selection of the final number of clusters and an optional variable selection procedure. The package is designed to integrate the results of multiple imputed datasets while accounting for the uncertainty that the imputations introduce in the final results. In addition, the package can also be used for a cluster analysis of the complete cases of a single dataset. The package also includes specific methods to summarize and plot the results. The methods are described in Basagana et al. (2013) <doi:10.1093/aje/kws289>.
Author: Jose Barrera-Gomez [aut, cre] , Xavier Basagana [aut]
Maintainer: Jose Barrera-Gomez <jose.barrera@isglobal.org>

Diff between miclust versions 1.2.8 dated 2022-02-07 and 1.3.0 dated 2026-06-08

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More information about miclust at CRAN
Permanent link

Package Iscores readmission to version 1.2.0 with previous version 1.1.0 dated 2022-01-04

Title: Proper Scoring Rules for Missing Value Imputation
Description: Provides tools for evaluating and ranking missing value imputation methods using proper scoring rules. Implements the Energy-I-Score and the DR-I-Score for the assessment of deterministic, stochastic and multiple imputation methods for numerical and mixed datasets, following Näf et al. (2022) <doi:10.48550/arXiv.2106.03742> and Näf et al. (2025) <doi:10.48550/arXiv.2507.11297>.
Author: Krystyna Grzesiak [aut, cre] , Loris Michel [aut, ctb], Meta-Lina Spohn [aut, ctb], Jeffrey Naef [aut, ctb]
Maintainer: Krystyna Grzesiak <krygrz11@gmail.com>

This is a re-admission after prior archival of version 1.1.0 dated 2022-01-04

Diff between Iscores versions 1.1.0 dated 2022-01-04 and 1.2.0 dated 2026-06-08

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More information about Iscores at CRAN
Permanent link

New package irtbem2pl with initial version 1.0.0
Package: irtbem2pl
Title: Marginalized Bayesian Item Parameter Estimation, 2pl Model IRT
Version: 1.0.0
Maintainer: Juan Luis Legorreta Torres <jlegorreta2002@yahoo.com.mx>
Description: Estimates item parameters of the two-parameter logistic (2PL) model in Item Response Theory (IRT) using the marginal Bayesian modal estimation via the Expectation-Maximization (EM) algorithm. The package calibrates item discrimination and difficulty parameters, yielding results comparable to software like 'BILOG-MG'.
License: GPL (>= 2)
URL: https://github.com/juanluislegorretatorres/irtbem2pl
Depends: R (>= 3.5.0)
Imports: IRTBEMM
NeedsCompilation: no
Encoding: UTF-8
Language: en-US
Packaged: 2026-06-02 16:11:16 UTC; juan.legorreta
Author: Juan Luis Legorreta Torres [aut, cre]
Repository: CRAN
Date/Publication: 2026-06-08 18:10:10 UTC

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Permanent link

Package incubate updated to version 1.4.1 with previous version 1.4.0 dated 2026-03-22

Title: Parametric Time-to-Event Analysis with Variable Incubation Phases
Description: Fit parametric models for time-to-event data that show an initial 'incubation period', i.e. a variable delay phase where no events occur. The delayed Weibull distribution serves as the foundational data model. For parameter estimation, different flavours of maximum likelihood estimation ('MLE') and the method of maximum product of spacings estimation ('MPSE') are implemented. Bootstrap confidence intervals for parameters and significance tests in a two group setting are provided.
Author: Matthias Kuhn [aut, cre, cph]
Maintainer: Matthias Kuhn <matthias.kuhn@tu-dresden.de>

Diff between incubate versions 1.4.0 dated 2026-03-22 and 1.4.1 dated 2026-06-08

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More information about incubate at CRAN
Permanent link

New package genefindr with initial version 1.0.0
Package: genefindr
Title: Rapid Gene Characterization Using Public Genomic Databases
Version: 1.0.0
Description: A user-friendly interface for characterizing gene function by disease type and tissue site, integrating curated data from publicly available genomic and proteomic databases to support candidate gene prioritization in experimental workflows.
Depends: R (>= 4.1.0)
License: GPL-3
Encoding: UTF-8
Imports: httr2, gtexr
URL: https://github.com/martincyd/genefindr
BugReports: https://github.com/martincyd/genefindr/issues
Suggests: knitr, rmarkdown, spelling
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-06-02 21:07:59 UTC; cyd
Author: Cydnie Martin [aut, cre]
Maintainer: Cydnie Martin <martincydenise@gmail.com>
Repository: CRAN
Date/Publication: 2026-06-08 18:30:02 UTC

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New package binest with initial version 0.2-1
Package: binest
Title: Estimation of Group Means and SDs from Binned Count Data
Version: 0.2-1
Date: 2026-05-27
Depends: R (>= 3.5.0), splines, stats
Suggests: knitr, rmarkdown, R2jags
Description: Estimates group-level means and standard deviations from binned (coarsened) count data, where the within-bin scores are unobserved. The package implements three methods that share a common output structure: bin_means() (a fast estimator that assumes within-district normality and uses pooled bin proportions to derive bin-conditional truncated-normal expectations), mle_hetop() (maximum likelihood for the heteroskedastic ordered probit model of Reardon, Shear, Castellano and Ho 2017 <doi:10.3102/1076998616666279>), and fh_hetop() (the Bayesian Fay-Herriot variant of Lockwood, Castellano and Shear 2018 <doi:10.3102/1076998618795124>). The mle_hetop() and fh_hetop() functions are forked from the 'HETOP' package by J. R. Lockwood ('CRAN', last released 2019). mle_hetop() has been modified to speed up the runtime via a vectorized inner loop and to remove two user-facing arguments (fixedcuts and svals) that some users found confusing; cutpoints and starting values are now derive [...truncated...]
License: GPL (>= 2)
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-06-02 16:51:41 UTC; ph3828
Author: Paul T. von Hippel [aut, cre], David J. Hunter [aut], J.R. Lockwood [aut]
Maintainer: Paul T. von Hippel <ph3828@eid.utexas.edu>
Repository: CRAN
Date/Publication: 2026-06-08 18:20:08 UTC

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Package bigmemory updated to version 4.6.6 with previous version 4.6.4 dated 2024-01-09

Title: Manage Massive Matrices with Shared Memory and Memory-Mapped Files
Description: Create, store, access, and manipulate massive matrices. Matrices are allocated to shared memory and may use memory-mapped files. Packages 'biganalytics', 'bigtabulate', 'synchronicity', and 'bigalgebra' provide advanced functionality.
Author: Michael J. Kane [aut, cre] , John W. Emerson [aut], Peter Haverty [aut], Charles Determan [aut]
Maintainer: Michael J. Kane <bigmemoryauthors@gmail.com>

Diff between bigmemory versions 4.6.4 dated 2024-01-09 and 4.6.6 dated 2026-06-08

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Package bean updated to version 0.2.1 with previous version 0.2.0 dated 2026-05-30

Title: Data Thinning of Species Occurrences in Environmental Space
Description: A suite of tools to mitigate sampling bias in species occurrence records by thinning data in the environmental space (E-space). This process can improve the accuracy and precision of species distribution models (SDM, also known as ecological niche models, ENM). The package offers a data-driven protocol to determine thinning parameters using kernel-density bandwidth selection. Two thinning methods are provided (stochastic and deterministic) to reduce over-sampled environmental conditions and down-weight outlier observations. The name 'bean' reflects the core principle of the method: each 'pod' (a grid cell in E-space) is allowed to contain only a limited number of 'beans' (occurrence points). See Silverman (1986, ISBN:978-0-412-24620-3) and Rousseeuw and Leroy (2003, ISBN:978-0-471-48855-2) for the underlying statistical methods.
Author: Paanwaris Paansri [cre, aut] , Luis E. Escobar [aut, ctb]
Maintainer: Paanwaris Paansri <paanwaris@vt.edu>

Diff between bean versions 0.2.0 dated 2026-05-30 and 0.2.1 dated 2026-06-08

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 15 files changed, 483 insertions(+), 474 deletions(-)

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Package treediff updated to version 0.2.3 with previous version 0.2.2 dated 2026-01-30

Title: Testing Differences Between Families of Trees
Description: Perform test to detect differences in structure between families of trees. The method is based on cophenetic distances and aggregated Student's tests.
Author: Nathalie Vialaneix [aut, cre] , Gwendaelle Cardenas [aut], Marie Chavent [aut] , Sylvain Foissac [aut] , Elise Maigne [aut], Pierre Neuvial [aut] , Nathanael Randriamihamison [aut]
Maintainer: Nathalie Vialaneix <nathalie.vialaneix@inrae.fr>

Diff between treediff versions 0.2.2 dated 2026-01-30 and 0.2.3 dated 2026-06-08

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 8 files changed, 89 insertions(+), 63 deletions(-)

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New package tlgarima with initial version 0.1.0
Package: tlgarima
Title: The Topp–Leone Garima Distribution
Version: 0.1.0
Description: Density, distribution function, quantile function, and random generating function of the Topp–Leone Garima distribution based on Boonmeekham, A., Supapakorn, T., & Bodhisuwan, W. (2025)<doi:10.1134/S1995080225608471>. In addition, maximum likelihood estimation for the Topp–Leone Garima distribution is provided.
License: GPL-3
Language: en-US
Encoding: UTF-8
Imports: stats, lamW
Suggests: testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2026-06-02 13:33:54 UTC; User
Author: Arin Boonmeekham [aut], Thidaporn Supapakorn [aut], Winai Bodhisuwan [aut], Atchanut Rattanalertnusorn [cre, ctb]
Maintainer: Atchanut Rattanalertnusorn <atchanut_r@rmutt.ac.th>
Repository: CRAN
Date/Publication: 2026-06-08 17:50:02 UTC

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New package spboost with initial version 0.7.0
Package: spboost
Title: Gradient Boosting for Nonlinear Spatial Autoregressive Models
Version: 0.7.0
Description: Flexible nonlinear extension of spatial autoregressive (SAR), spatial error (SEM), and spatial autoregressive with autoregressive disturbances (SARAR) models with multiple regression engines (generalized additive models ('mgcv'), gradient boosting ('mboost'), multivariate adaptive regression splines ('earth'), and 'xgboost') and two families of spatial-parameter estimators: maximum likelihood and the determinant-free Closed-Form Estimator of Smirnov (2020) <doi:10.1111/gean.12268>. See Geniaux G. (2026). "Flexible nonlinear spatial autoregressive models: a gradient boosting approach with closed-form estimation." Presented at Spatial Econometrics World Congress (SEA/SEW 2026, Paris), unpublished.
License: GPL (>= 2)
Depends: Matrix, mboost, mgcv, methods, mgwrsar
Imports: Rcpp, sf, MASS, data.table, xgboost, caret, doParallel, foreach, nabor, earth
Suggests: blockCV, knitr, rmarkdown, RSpectra, spatialreg, spdep, testthat (>= 3.0.0)
VignetteBuilder: knitr
LinkingTo: RcppEigen, Rcpp
NeedsCompilation: yes
Encoding: UTF-8
Packaged: 2026-06-02 13:54:02 UTC; geniaux
Author: Ghislain Geniaux [aut, cre]
Maintainer: Ghislain Geniaux <ghislain.geniaux@inrae.fr>
Repository: CRAN
Date/Publication: 2026-06-08 18:00:02 UTC

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Package shinylive updated to version 0.5.0 with previous version 0.4.1 dated 2026-04-07

Title: Run 'shiny' Applications in the Browser
Description: Exporting 'shiny' applications with 'shinylive' allows you to run them entirely in a web browser, without the need for a separate R server. The traditional way of deploying 'shiny' applications involves in a separate server and client: the server runs R and 'shiny', and clients connect via the web browser. When an application is deployed with 'shinylive', R and 'shiny' run in the web browser (via 'webR'): the browser is effectively both the client and server for the application. This allows for your 'shiny' application exported by 'shinylive' to be hosted by a static web server.
Author: Barret Schloerke [aut, cre] , Winston Chang [aut] , George Stagg [aut], Garrick Aden-Buie [aut] , Posit Software, PBC [cph, fnd]
Maintainer: Barret Schloerke <barret@posit.co>

Diff between shinylive versions 0.4.1 dated 2026-04-07 and 0.5.0 dated 2026-06-08

 DESCRIPTION                    |    9 +-
 MD5                            |   11 +--
 NEWS.md                        |   14 ++++
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 R/version.R                    |    4 -
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 7 files changed, 131 insertions(+), 37 deletions(-)

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New package NeutroRCDsAnalysis with initial version 0.1.0
Package: NeutroRCDsAnalysis
Title: Neutrosophic Analysis of Row Column Designs
Version: 0.1.0
Maintainer: Vinaykumar L.N. <vinaymandya123@gmail.com>
Description: Description: Provides methods for Neutrosophic Analysis of Variance (NANOVA) and Neutrosophic Analysis of Covariance (NANCOVA) for row-column designs, including Latin square designs and Youden square designs, using interval-valued observations. The package computes neutrosophic sums of squares, mean squares, interval-valued F-statistics, significance tests, and multiple comparisons using Least Significant Difference (LSD) procedures. For crisp data, users may enter identical lower and upper values of responses to obtain classical Analysis of Variance (ANOVA) results. Similarly, users may enter identical lower and upper values for both responses and covariates to obtain classical Analysis of Covariance (ANCOVA) results.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 4.0.0)
Imports: MASS
NeedsCompilation: no
Packaged: 2026-06-02 09:37:17 UTC; admin
Author: Neethu R.S. [aut, ctb], Cini Varghese [aut, ctb], Mohd Harun [aut, ctb], Anindita Datta [aut, ctb], Vinaykumar L.N. [aut, cre]
Repository: CRAN
Date/Publication: 2026-06-08 17:50:26 UTC

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New package iDIFr with initial version 1.0.1
Package: iDIFr
Title: Intersectional Differential Item Functioning Analysis
Version: 1.0.1
Description: A toolkit for detecting Differential Item Functioning (DIF) using Logistic Regression (LR) as described in Swaminathan and Rogers (1990) <doi:10.1111/j.1745-3984.1990.tb00754.x>, the IRT Likelihood Ratio Test (LRT) following Thissen, Steinberg & Wainer (1993, ISBN:0-8058-0972-4), and model-based recursive partitioning (MOB) as implemented in 'strucchange' following Strobl, Kopf and Zeileis (2015) <doi:10.1007/s11336-013-9388-3>. Designed for both standard two-group and intersectional multi-group designs, 'iDIFr' prioritises effect size reporting alongside statistical significance, clear guidance on group construction, and interpretable output suitable for applied testing contexts. Built-in Intersectional Contrast Analysis (ICA) classifies items as amplified, pure-intersection, obscured, or none by comparing single-variable and intersectional analyses.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: Rcpp (>= 1.0.0), generics, parallel, stats, cli, dplyr, ggplot2, rlang, strucchange
LinkingTo: Rcpp
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, openxlsx
VignetteBuilder: knitr
URL: https://github.com/thmsrgrs/iDIFr
BugReports: https://github.com/thmsrgrs/iDIFr/issues
NeedsCompilation: yes
Packaged: 2026-06-02 10:59:57 UTC; TMRog
Author: Thomas Rogers [aut, cre]
Maintainer: Thomas Rogers <thomas.rogers@britishcouncil.org>
Repository: CRAN
Date/Publication: 2026-06-08 17:50:06 UTC

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New package FastSegmentation with initial version 0.0.1
Package: FastSegmentation
Title: Unsupervised Cell Segmentation by Fast Gaussian Processes
Version: 0.0.1
Author: Blythe King [aut, cre], Haoran Yan [aut], Laura Baracaldo [aut], Mengyang Gu [aut]
Maintainer: Blythe King <blyking@gmail.com>
Description: Performs fast Gaussian process-based segmentation of microscopy images using spatial smoothing and data-driven thresholding. Code based on Baracaldo, L., King, B., Yan, H., Lin, Y., Miolane, N., & Gu, M. (2025). "Unsupervised cell segmentation by fast Gaussian processes." arXiv preprint <doi:10.48550/arXiv.2505.18902>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: EBImage, stats, magick, pracma, RobustGaSP
Suggests: plot3D
NeedsCompilation: no
Packaged: 2026-06-02 05:23:12 UTC; blyki
Repository: CRAN
Date/Publication: 2026-06-08 17:40:07 UTC

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New package easyRasch2 with initial version 0.8.0
Package: easyRasch2
Title: Psychometric Analysis with Rasch Measurement Theory
Version: 0.8.0
Description: Streamlines reproducible Rasch measurement theory analyses for ordinal item-response data, combining estimation routines from 'eRm', 'psychotools', 'mirt', 'iarm', and 'lavaan' with consistent diagnostic, plotting, and reporting layers. Covers the four basic psychometric criteria summarised by Christensen et al. (2021) <doi:10.1111/sms.13908> -- unidimensionality, local independence, ordered response category thresholds, and invariance across subgroups -- together with item fit, targeting, reliability, category functioning, and descriptive item-response plots. A distinguishing feature is the use of simulation-based critical values to replace rule-of-thumb cutoffs for conditional infit mean-square, Yen's Q3 local-dependence statistic, the largest residual-PCA eigenvalue, and ordinal CFA fit indices. Outputs are knitr::kable() tables and 'ggplot2' figures suitable for direct inclusion in 'Quarto' and 'R Markdown' reports.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
URL: https://github.com/pgmj/easyRasch2, https://pgmj.github.io/easyRasch2/
BugReports: https://github.com/pgmj/easyRasch2/issues
Depends: R (>= 4.1.0)
Imports: eRm, knitr, mirt, psychotools (>= 0.7-3), stats, utils, rlang
Suggests: difR, dplyr, geomtextpath, ggdist, ggtext, iarm, mirai, ggplot2 (>= 3.4.0), partykit, psychotree, stablelearner, testthat (>= 3.0.0), rmarkdown, patchwork, scales, mice, ggrepel, lavaan
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-06-02 12:03:20 UTC; magnus.johansson.3
Author: Magnus Johansson [aut, cre] , Nicklas Korsell [ctb] , Mirka Henninger [ctb] , Jan Radek [ctb]
Maintainer: Magnus Johansson <pgmj@pm.me>
Repository: CRAN
Date/Publication: 2026-06-08 17:50:12 UTC

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New package CIMEHR with initial version 0.1.0
Package: CIMEHR
Title: Gaussian Clinically Informative Visiting and Observation Processes in Electronic Health Record (EHR) Data
Version: 0.1.0
Description: Fits semiparametric joint models for longitudinal electronic health record (EHR) data that addresses two-stage hierarchical missingness mechanism. The first stage is the visiting process, and the second stage is the observation process. The core CIMEHR method (Clinical Informative Missingness for Electronic Health Records) uses a three-stage procedure: partial likelihood with log-normal frailty for visit intensity, probit regression with shared latent factor-linked random effects for observation, and weighted least squares with risk-set centering for the outcome. These three stages are connected through a shared latent factor that induces dependence across all three processes. A data simulator and implementations of common benchmark methods (linear mixed models, multiple imputation, and others) are included for comparative studies. Detailed methods are described in Yang, Shi, and Mukherjee (2026) <doi:10.48550/arXiv.2602.15374>.
License: MIT + file LICENSE
Encoding: UTF-8
Suggests: tibble, dplyr, tidyr, knitr, rmarkdown
Imports: MASS, Rcpp, nleqslv, pbivnorm, numDeriv, stats, utils, data.table, mice, nlme, slim
LinkingTo: Rcpp
URL: https://github.com/ysph-dsde/CIMEHR
BugReports: https://github.com/ysph-dsde/CIMEHR/issues
VignetteBuilder: knitr
Depends: R (>= 3.5)
LazyData: true
NeedsCompilation: yes
Packaged: 2026-06-02 13:29:18 UTC; yh785
Author: Cheng-Han Yang [aut, cre] , Yiren Hou [aut]
Maintainer: Cheng-Han Yang <chenghanyang728@gmail.com>
Repository: CRAN
Date/Publication: 2026-06-08 18:00:25 UTC

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New package chmsflow with initial version 0.1.0
Package: chmsflow
Title: Transforming and Harmonizing CHMS Variables
Version: 0.1.0
Description: Harmonizes variables from the Canadian Health Measures Survey (CHMS) across cycles 1-6 (2007-2019), producing consistent, analysis-ready variables for use with CHMS data. Recoding is data-driven through metadata tables and applied with recodeflow::rec_with_table() from the 'recodeflow' package. The recoding approach builds on sjmisc::rec() from the 'sjmisc' package (Ludecke 2018) <doi:10.21105/joss.00754>.
Depends: R (>= 4.1.0)
Imports: dplyr, haven, purrr, recodeflow
License: MIT + file LICENSE
URL: https://github.com/Big-Life-Lab/chmsflow, https://big-life-lab.github.io/chmsflow/
BugReports: https://github.com/Big-Life-Lab/chmsflow/issues
Encoding: UTF-8
Suggests: DT, kableExtra, knitr, quarto, readr, testthat (>= 3.0.0)
VignetteBuilder: quarto
LazyData: true
NeedsCompilation: no
Packaged: 2026-06-02 14:36:36 UTC; rafidulislam
Author: Rafidul Islam [aut, cre, cph] , Douglas Manuel [aut, cph] , Therese Chan [ctb] , The Ottawa Hospital Research Institute [cph]
Maintainer: Rafidul Islam <raislam@ohri.ca>
Repository: CRAN
Date/Publication: 2026-06-08 18:00:08 UTC

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Package bootPLS updated to version 1.2.0 with previous version 1.1.0 dated 2025-09-13

Title: Bootstrap Hyperparameter Selection for PLS Models and Extensions
Description: Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, 'The Multiple Facets of Partial Least Squares and Related Methods', <doi:10.1007/978-3-319-40643-5_18>) and two articles (Magnanensi et al. 2017, 'Statistics and Computing', <doi:10.1007/s11222-016-9651-4>) and (Magnanensi et al. 2021, 'Frontiers in Applied Mathematics and Statistics', <doi:10.3389/fams.2021.693126>).
Author: Frederic Bertrand [cre, aut] , Jeremy Magnanensi [aut], Myriam Maumy-Bertrand [aut]
Maintainer: Frederic Bertrand <frederic.bertrand@lecnam.net>

Diff between bootPLS versions 1.1.0 dated 2025-09-13 and 1.2.0 dated 2026-06-08

 DESCRIPTION                           |   14 +++++-----
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 build/vignette.rds                    |binary
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 12 files changed, 91 insertions(+), 41 deletions(-)

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New package BayesForge with initial version 0.0.1
Package: BayesForge
Title: Bayesian Inference using 'numpyro' and 'XLA'
Version: 0.0.1
Description: A high-performance probabilistic programming library that aims to unify the modeling experience by providing an intuitive model-building syntax together with the flexibility of low-level abstraction coding. It also includes pre-built functions for high-level abstraction and supports hardware-accelerated computation for improved scalability, including parallelization, vectorization, and execution on CPU (Central Processing Unit), GPU (Graphics Processing Unit), or TPU (Tensor Processing Unit) using 'JAX' (Just-In-Time compiled Accelerated linear algebra) as the computational backend: Sosa (2026) <doi:10.64898/2026.01.19.700318>.
URL: https://s-sosa.com/BF/
Encoding: UTF-8
Imports: reticulate, abind, methods
SystemRequirements: Python (>= 3.6), 'bayesforge' python library.
Depends: R (>= 3.5.0)
Suggests: testthat (>= 3.0.0), brms, rstan
License: GPL (>= 3)
NeedsCompilation: no
Packaged: 2026-06-02 07:55:20 UTC; sebastian_sosa
Author: Sebastian Sosa [aut, cre]
Maintainer: Sebastian Sosa <bf@s-sosa.com>
Repository: CRAN
Date/Publication: 2026-06-08 17:50:31 UTC

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New package autocodebook with initial version 0.1.0
Package: autocodebook
Title: Automatic Codebook and Tracking for 'Spark' and 'dplyr' Pipelines
Version: 0.1.0
Description: Wraps 'dplyr' verbs (mutate, summarise, filter) to automatically capture variable metadata (type, source columns, categories, and source code), producing a codebook and eligibility tracking table with zero manual documentation. Works with both 'sparklyr' (tbl_spark) and local data frames. Adds big-data optimizations (caching, assume-unique counting, checkpointing) and a standardized report module with an eligibility flowchart, editable codebook export (HTML, DOCX, XLSX), and cross-sectional or longitudinal variable inspection. The eligibility flowchart follows the CONSORT statement (Schulz, Altman and Moher (2010) <doi:10.1136/bmj.c332>) and the reporting of observational cohort studies follows the STROBE recommendations (von Elm and others (2007) <doi:10.1371/journal.pmed.0040296>).
License: MIT + file LICENSE
URL: https://github.com/patriciafortesm/autocodebook
BugReports: https://github.com/patriciafortesm/autocodebook/issues
Encoding: UTF-8
Imports: dplyr (>= 1.1.0), rlang (>= 1.0.0), tibble, gt, grid
Suggests: sparklyr, dbplyr, testthat (>= 3.0.0), tidyplots, ggplot2, patchwork, rmarkdown, knitr, officer, flextable, openxlsx, scales, rvg, devEMF, svglite
NeedsCompilation: no
Packaged: 2026-06-02 14:13:06 UTC; maced
Author: Patricia Fortes C. de Macedo [aut, cre]
Maintainer: Patricia Fortes C. de Macedo <macedopatriciafortes@gmail.com>
Repository: CRAN
Date/Publication: 2026-06-08 18:00:19 UTC

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New package aisdk.providers with initial version 0.1.0
Package: aisdk.providers
Title: Additional Model Provider Adapters for the 'aisdk' Toolkit
Version: 0.1.0
Description: Additional AI model provider adapters for the 'aisdk' toolkit, covering OpenAI-compatible and Anthropic-compatible services such as 'DeepSeek', 'Moonshot'/'Kimi', 'Stepfun', 'Volcengine', 'AiHubMix', 'xAI', 'OpenRouter', 'Bailian', and 'NVIDIA'. Providers register themselves with the core 'aisdk' provider registry on load.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: aisdk (>= 1.4.12), R6, rlang, base64enc, jsonlite, curl, utils
Suggests: withr, testthat (>= 3.0.0)
URL: https://github.com/YuLab-SMU/aisdk.providers
BugReports: https://github.com/YuLab-SMU/aisdk.providers/issues
Depends: R (>= 4.1.0)
NeedsCompilation: no
Packaged: 2026-06-02 10:47:44 UTC; xiayh
Author: Yonghe Xia [aut, cre]
Maintainer: Yonghe Xia <xiayh17@gmail.com>
Repository: CRAN
Date/Publication: 2026-06-08 17:50:20 UTC

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Package stepcount updated to version 0.5.0 with previous version 0.3.2 dated 2024-10-02

Title: Estimate Step Counts from 'Accelerometry' Data
Description: Interfaces the 'stepcount' Python module <https://github.com/OxWearables/stepcount> to estimate step counts and other activities from 'accelerometry' data.
Author: John Muschelli [aut, cre]
Maintainer: John Muschelli <muschellij2@gmail.com>

Diff between stepcount versions 0.3.2 dated 2024-10-02 and 0.5.0 dated 2026-06-08

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More information about stepcount at CRAN
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New package seine with initial version 0.1.2
Package: seine
Title: Semiparametric Ecological Inference
Version: 0.1.2
Description: Efficient and user-friendly routines for modern ecological inference. Implements the methods described in McCartan & Kuriwaki (2025+) <doi:10.48550/arXiv.2509.20194>, which generalize ecological regression as introduced by Goodman (1953) <doi:10.2307/2088121>. Includes routines for preprocessing, synthetic data generation, double/debiased machine learning (DML) estimation, partial identification bounds, and sensitivity analysis.
Depends: R (>= 3.5.0)
Imports: rlang, cli, tidyselect, tibble, hardhat, quadprog, graphics, utils, stats
Suggests: bases, knitr, rmarkdown, testthat (>= 3.0.0), xml2
LinkingTo: cpp11, cpp11armadillo, testthat
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
URL: https://corymccartan.com/seine/, https://github.com/CoryMcCartan/seine
BugReports: https://github.com/CoryMcCartan/seine/issues
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-06-02 04:06:58 UTC; cmccartan
Author: Cory McCartan [aut, cre, cph] , Shiro Kuriwaki [aut, cph]
Maintainer: Cory McCartan <mccartan@psu.edu>
Repository: CRAN
Date/Publication: 2026-06-08 15:10:02 UTC

More information about seine at CRAN
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Package releaser updated to version 1.1.1 with previous version 1.1.0 dated 2026-03-10

Title: Help with Preparing a New Version of an R Package
Description: Helps to prepare a release. Before releasing an R package it is important to update the DESCRIPTION file and the changelog. This package prepares these files and also updates the versions according to the branches. It relies heavily on the 'desc' packages.
Author: Tanguy Barthelemy [aut, cre, art]
Maintainer: Tanguy Barthelemy <tanguy.barthelemy@insee.fr>

Diff between releaser versions 1.1.0 dated 2026-03-10 and 1.1.1 dated 2026-06-08

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Package cforecast updated to version 0.1.1 with previous version 0.1.0 dated 2026-03-09

Title: Conditional Forecasting and Scenario Analysis Using VAR Models
Description: Provides tools for interpretable conditional forecasting and scenario analysis in reduced-form vector autoregressive (VAR) models. Implements a Kalman smoothing framework to generate forecasts under path restrictions on selected variables. The package enables decomposition of conditional forecasts into variable-specific contributions, and extraction of observation weights. It also computes measures of overall and marginal variable importance to enhance the economic interpretation of forecast revisions. The framework is structurally agnostic and suited for policy analysis, stress testing, and macro-financial applications. The methodology is described in more detail in Caspi and Ginker (2026) <doi:10.13140/RG.2.2.25225.51040>.
Author: Tim Ginker [aut, cre]
Maintainer: Tim Ginker <tim.ginker@gmail.com>

Diff between cforecast versions 0.1.0 dated 2026-03-09 and 0.1.1 dated 2026-06-08

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New package autoLibLoad with initial version 1.0
Package: autoLibLoad
Version: 1.0
Date: 2026-05-25
Title: Automate Retrieving, Building, Installing and Loading Specified Packages
Maintainer: Barry Zeeberg <barryz2013@gmail.com>
Author: Barry Zeeberg [aut, cre]
Depends: R (>= 4.2.0)
Imports: utils, stringr, devtools, tools, vprint
Description: Packages required for the search path may be located in the CRAN repository, the system library, or a local directory. We automate determining the disposition of each required package, retrieving it, and loading it as needed.
License: GPL (>= 2)
Encoding: UTF-8
VignetteBuilder: knitr
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2026-06-01 20:42:49 UTC; barryzeeberg
Repository: CRAN
Date/Publication: 2026-06-08 15:10:09 UTC

More information about autoLibLoad at CRAN
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New package wqrr with initial version 1.0.0
Package: wqrr
Title: Wavelet Quantile Regression Toolbox
Version: 1.0.0
Description: A comprehensive toolbox for wavelet-domain quantile analyses of bivariate and multivariate time series. Provides Wavelet Quantile Regression and Multivariate Wavelet Quantile Regression after Adebayo and Ozkan (2024) <doi:10.1016/j.jclepro.2024.140832>, Wavelet Quantile-on-Quantile regression with bootstrap p-values extending Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013>, the nonparametric Causality-in-Quantiles test of Balcilar, Gupta and Pierdzioch (2016) <doi:10.1016/j.resourpol.2016.04.004> together with its wavelet variant, Wavelet Quantile Mediation and Moderation, Wavelet Quantile Correlation, and a wavelet-based nonparametric Quantile Density estimator. The Maximal Overlap Discrete Wavelet Transform (MODWT) decomposition is performed via 'waveslim' and Short / Medium / Long band aggregation is supported throughout. For plain Quantile-on-Quantile regression see the companion CRAN package 'QuantileOnQuantile'. All interactive 3D surfaces, heatmaps and [...truncated...]
License: GPL-3
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: quantreg (>= 5.0), waveslim (>= 1.8), plotly (>= 4.0.0), stats, utils, grDevices
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
URL: https://github.com/merwanroudane/wqrr
BugReports: https://github.com/merwanroudane/wqrr/issues
NeedsCompilation: no
Packaged: 2026-06-01 19:02:19 UTC; HP
Author: Merwan Roudane [aut, cre, cph], Nicholas Sim [ctb] , Hongtao Zhou [ctb] , Tomiwa Sunday Adebayo [ctb] , Mehmet Balcilar [ctb]
Maintainer: Merwan Roudane <merwanroudane920@gmail.com>
Repository: CRAN
Date/Publication: 2026-06-08 15:00:02 UTC

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New package rsdv with initial version 0.1.0
Package: rsdv
Title: Synthetic Tabular Data Generation with Gaussian Copulas
Version: 0.1.0
Description: Generates synthetic tabular data from real datasets using Gaussian copula models, with parametric marginal selection for numerical columns and a cumulative-frequency embedding that brings categorical and boolean columns into the same joint copula. Includes a metadata system with column types and primary keys, declarative constraints enforced via rejection sampling, conditional sampling, and quality, validity and privacy reports modeled on those of the 'SDMetrics' library. Inspired by the Python 'SDV' (Synthetic Data Vault) library by 'DataCebo'; see Patki, Wedge and Veeramachaneni (2016) "The Synthetic Data Vault" <doi:10.1109/DSAA.2016.49>.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-US
URL: https://kvenkita.github.io/rsdv/, https://github.com/kvenkita/rsdv
BugReports: https://github.com/kvenkita/rsdv/issues
Depends: R (>= 4.3.0)
Imports: copula (>= 1.1-0), generics (>= 0.1.3), jsonlite (>= 1.8.0), ggplot2 (>= 3.4.0), tibble (>= 3.2.0), FNN (>= 1.1.3), rpart (>= 4.1.0), scales (>= 1.2.0), stats, utils
Suggests: testthat (>= 3.0.0), withr, knitr (>= 1.40), rmarkdown (>= 2.20)
VignetteBuilder: knitr
LazyData: true
NeedsCompilation: no
Packaged: 2026-06-01 19:36:42 UTC; kyle
Author: Kailas Venkitasubramanian [aut, cre]
Maintainer: Kailas Venkitasubramanian <kailasv@gmail.com>
Repository: CRAN
Date/Publication: 2026-06-08 15:00:07 UTC

More information about rsdv at CRAN
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New package OlinkAnalyzeVignettes with initial version 1.0.1
Package: OlinkAnalyzeVignettes
Title: Vignettes for Analyzing Data using 'OlinkAnalyze'
Version: 1.0.1
Description: Exemplifying analysis of large-scale protein data from the 'Olink platform', primarily relative protein expression data that has been exported from 'Olink NPX Software', as well as QUANT data from 'Olink'. QUANT data is log-transformed. Materials focus on reading data, demonstrating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to guide users extract biological insights from large-scale protein data run on the 'Olink platform'. More information on 'Olink' data can be found at <https://olink.com/>.
Contact: biostattools@olink.com
URL: https://olink.com/ https://github.com/Olink-Proteomics/OlinkRPackage
License: AGPL (>= 3)
Encoding: UTF-8
Depends: R (>= 4.1.0)
Suggests: dplyr (>= 1.2.0), ggplot2, ggpubr, ggrepel, kableExtra, knitr, OlinkAnalyze, stringr, tidyr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-06-01 18:27:06 UTC; kathy.nevola
Author: Kathleen Nevola [aut, cre] , Marianne Sandin [aut] , Jamey Guess [aut] , Simon Forsberg [aut] , Christoffer Cambronero [aut] , Pascal Pucholt [aut] , Boxi Zhang [aut] , Masoumeh Sheikhi [aut] , Klev Diamanti [aut] , Amrita Kar [aut] , Lei Conze [aut] [...truncated...]
Maintainer: Kathleen Nevola <biostattools@olink.com>
Repository: CRAN
Date/Publication: 2026-06-08 14:50:26 UTC

More information about OlinkAnalyzeVignettes at CRAN
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Package msmtools readmission to version 2.2.1 with previous version 2.0.1 dated 2021-04-12

Title: Building Augmented Data to Run Multi-State Models with 'msm' Package
Description: A fast and general method for restructuring classical longitudinal observational data into augmented transition data suitable for multi-state modeling with the 'msm' package. Works with any longitudinal data where subjects accumulate repeated observations with start and end times and an optional terminal outcome. Methods are described in Grossetti, Ieva and Paganoni (2018) <doi:10.1007/s10729-017-9400-z>.
Author: Francesco Grossetti [aut, cre]
Maintainer: Francesco Grossetti <francesco.grossetti@unibocconi.it>

This is a re-admission after prior archival of version 2.0.1 dated 2021-04-12

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Package mizer updated to version 3.0.0 with previous version 2.5.4 dated 2025-11-16

Title: Dynamic Multi-Species Size Spectrum Modelling
Description: A set of classes and methods to set up and run multi-species, trait based and community size spectrum ecological models, focused on the marine environment.
Author: Gustav Delius [cre, aut, cph] , Finlay Scott [aut, cph], Julia Blanchard [aut, cph] , Ken Andersen [aut, cph] , Richard Southwell [ctb, cph]
Maintainer: Gustav Delius <gustav.delius@york.ac.uk>

Diff between mizer versions 2.5.4 dated 2025-11-16 and 3.0.0 dated 2026-06-08

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 mizer-3.0.0/mizer/NAMESPACE                                                                       |  286 
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More information about mizer at CRAN
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Package MiscMetabar readmission to version 0.16.8 with previous version 0.14.4 dated 2025-09-30

Title: Miscellaneous Functions for Metabarcoding Analysis
Description: Facilitate the description, transformation, exploration, and reproducibility of metabarcoding analyses. 'MiscMetabar' is mainly built on top of the 'phyloseq', 'dada2' and 'targets' 'R' packages. It helps to build reproducible and robust bioinformatics pipelines in 'R'. 'MiscMetabar' makes ecological analysis of alpha and beta-diversity easier, more reproducible and more powerful by integrating a large number of tools. Important features are described in Taudière A. (2023) <doi:10.21105/joss.06038>.
Author: Adrien Taudiere [aut, cre, cph]
Maintainer: Adrien Taudiere <adrien.taudiere@zaclys.net>

This is a re-admission after prior archival of version 0.14.4 dated 2025-09-30

Diff between MiscMetabar versions 0.14.4 dated 2025-09-30 and 0.16.8 dated 2026-06-08

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New package DonutMap with initial version 0.1.0
Package: DonutMap
Title: Donut Maps with 'sf', 'ggplot2', and 'leaflet'
Version: 0.1.0
Description: Create donut charts positioned on maps from tidy data. The package provides helpers to compute donut polygon geometries, optional origin-destination flow lines, ready-to-use 'ggplot2' map layers, and interactive 'leaflet' widgets.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-US
Depends: R (>= 4.1.0)
Imports: dplyr, ggplot2, htmltools, leaflet, rlang, scales, sf, tibble
Suggests: ragg, knitr, rmarkdown, rnaturalearth, rnaturalearthdata, testthat (>= 3.0.0)
VignetteBuilder: knitr
URL: https://aureliennicosiaulaval.github.io/DonutMap/, https://github.com/AurelienNicosiaULaval/DonutMap
BugReports: https://github.com/AurelienNicosiaULaval/DonutMap/issues
NeedsCompilation: no
Packaged: 2026-06-01 18:11:05 UTC; aureliennicosia
Author: Aurelien Nicosia [aut, cre]
Maintainer: Aurelien Nicosia <aurelien.nicosia@mat.ulaval.ca>
Repository: CRAN
Date/Publication: 2026-06-08 14:40:02 UTC

More information about DonutMap at CRAN
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Package rclsp updated to version 2.0.0 with previous version 1.1.0 dated 2026-06-07

Title: A Modular Two-Step Convex Optimization Estimator for Ill-Posed Problems
Description: Convex Least Squares Programming (CLSP) is a two-step estimator for solving underdetermined, ill-posed, or structurally constrained least-squares problems. It combines pseudoinverse-based estimation with convex-programming correction methods inspired by Lasso, Ridge, and Elastic Net to ensure numerical stability, constraint enforcement, and interpretability. The package also provides numerical stability analysis and CLSP-specific diagnostics, including partial R^2, normalized RMSE (NRMSE), Monte Carlo t-tests for mean NRMSE, and condition-number-based confidence bands.
Author: Ilya Bolotov [aut, cre]
Maintainer: Ilya Bolotov <ilya.bolotov@vse.cz>

Diff between rclsp versions 1.1.0 dated 2026-06-07 and 2.0.0 dated 2026-06-08

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Permanent link

Package PKPDindex updated to version 0.2.1 with previous version 0.2.0 dated 2025-10-09

Title: Optimal PK/PD Index Finder
Description: Fits Emax models to pharmacokinetic/pharmacodynamic (PK/PD) data, estimate key parameters, and visualise model fits for multiple PK/PD indices. Methods are described in Macdougall J (2006) <doi:10.1007/0-387-33706-7_9>, Spiess AN, Neumeyer N (2010) <doi:10.1186/1471-2210-10-6>, and Burnham KP, Anderson DR (2004) <doi:10.1177/0049124104268644>.
Author: Najla Alabdulkarim [aut, cre], Joseph F Standing [aut]
Maintainer: Najla Alabdulkarim <naaalabdulkarim@hotmail.com>

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Permanent link

Package lulab.utils updated to version 1.0.2 with previous version 1.0.0 dated 2025-10-09

Title: Supporting Functions Maintained by Zhen Lu
Description: Miscellaneous functions commonly used by LuLab. This package aims to help more researchers on epidemiology to perform data management and visualization more efficiently.
Author: Zhen Lu [aut, cre]
Maintainer: Zhen Lu <Zhen.Lu.VA@gmail.com>

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Package ChannelAttribution updated to version 2.2.5 with previous version 2.2.4 dated 2025-11-19

Title: Markov Model for Online Multi-Channel Attribution
Description: Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++.
Author: Davide Altomare [cre, aut], David Loris [aut]
Maintainer: Davide Altomare <info@channelattribution.io>

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Permanent link

Package iRfcb updated to version 0.9.0 with previous version 0.8.1 dated 2026-03-01

Title: Tools for Managing Imaging FlowCytobot (IFCB) Data
Description: A comprehensive suite of tools for managing, processing, and analyzing data from the IFCB. I R FlowCytobot ('iRfcb') supports quality control, geospatial analysis, and preparation of IFCB data for publication in databases like <https://www.gbif.org>, <https://www.obis.org>, <https://emodnet.ec.europa.eu/en>, <https://shark.smhi.se/en/>, and <https://www.ecotaxa.org>. The package integrates with the MATLAB 'ifcb-analysis' tool, which is described in Sosik and Olson (2007) <doi:10.4319/lom.2007.5.204>, and provides features for working with raw, manually classified, and machine learning–classified image datasets. Key functionalities include image extraction, particle size distribution analysis, taxonomic data handling, and biomass concentration calculations, essential for plankton research.
Author: Anders Torstensson [aut, cre] , Kendra Hayashi [ctb] , Jamie Enslein [ctb], Raphael Kudela [ctb] , Alle Lie [ctb] , Jayme Smith [ctb] , DTO-BioFlow [fnd] , SBDI [fnd]
Maintainer: Anders Torstensson <anders.torstensson@smhi.se>

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Permanent link

Package BigDataStatMeth updated to version 2.0.2 with previous version 2.0.1 dated 2026-05-17

Title: Scalable Statistical Computing with HDF5-Backed Matrices
Description: A framework for 'scalable' statistical computing on large on-disk matrices stored in 'HDF5' files. It provides efficient block-wise implementations of core linear-algebra operations (matrix multiplication, SVD, PCA, QR decomposition, and canonical correlation analysis) written in C++ and R. These building blocks are designed not only for direct use, but also as foundational components for developing new statistical methods that must operate on datasets too large to fit in memory. The package supports data provided either as 'HDF5' files or standard R objects, and is intended for high-dimensional applications such as 'omics' and precision-medicine research.
Author: Dolors Pelegri-Siso [aut, cre] , Juan R. Gonzalez [aut]
Maintainer: Dolors Pelegri-Siso <dolors.pelegri@isglobal.org>

Diff between BigDataStatMeth versions 2.0.1 dated 2026-05-17 and 2.0.2 dated 2026-06-08

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New package HAMMER with initial version 1.0
Package: HAMMER
Title: High-Dimensional Factor-Analytic Representation Modeling and Metrics
Version: 1.0
Maintainer: Carel F.W. Peeters <carel.peeters@wur.nl>
Description: The goal of 'HAMMER' is to provide factor analytic representation learningand associated determinacy metrics for very-high-dimensional data. It projects high-dimensional data onto low-dimensional generative latent sources and assesses the uncertainty in the projection. The projection is distribution-free, scale-equivariant, and efficient.
Depends: R (>= 3.5.0)
Imports: stats, RSpectra
LazyData: true
License: GPL (>= 2)
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-06-01 14:54:33 UTC; peete064
Author: Carel F.W. Peeters [aut, cre, cph]
Repository: CRAN
Date/Publication: 2026-06-08 11:40:02 UTC

More information about HAMMER at CRAN
Permanent link

Package SNPannotator updated to version 1.4.7 with previous version 1.4.6 dated 2026-01-08

Title: Automated Functional Annotation of Genetic Variants and Linked Proxies
Description: To automated functional annotation of genetic variants and linked proxies. Linked SNPs in moderate to high linkage disequilibrium (e.g. r2>0.50) with the corresponding index SNPs will be selected for further analysis.
Author: Alireza Ani [aut, cre], Zoha Kamali [aut], Ahmad Vaez [aut]
Maintainer: Alireza Ani <a.ani@umcg.nl>

Diff between SNPannotator versions 1.4.6 dated 2026-01-08 and 1.4.7 dated 2026-06-08

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Package psc updated to version 2.1.0 with previous version 2.0.1 dated 2026-01-12

Title: Personalised Synthetic Controls
Description: Allows the comparison of data cohorts (DC) against a Counter Factual Model (CFM) and measures the difference in terms of an efficacy parameter. Allows the application of Personalised Synthetic Controls.
Author: Richard Jackson [cre, aut, cph]
Maintainer: Richard Jackson <richJ23@liverpool.ac.uk>

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Package polyglotr updated to version 1.7.4 with previous version 1.7.1 dated 2026-01-11

Title: Translate Text
Description: Provide easy methods to translate pieces of text. Functions send requests to translation services online.
Author: Tomer Iwan [aut, cre, cph]
Maintainer: Tomer Iwan <iwantomer@hotmail.com>

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Package ShinyItemAnalysis updated to version 1.6.0 with previous version 1.5.5 dated 2025-07-10

Title: Test and Item Analysis via Shiny
Description: Package including functions and interactive shiny application for the psychometric analysis of educational tests, psychological assessments, health-related and other types of multi-item measurements, or ratings from multiple raters.
Author: Patricia Martinkova [aut, cre] , Adela Hladka [aut] , Jan Netik [aut] , Ondrej Leder [ctb], Jakub Houdek [ctb], Lubomir Stepanek [ctb], Tomas Jurica [ctb], Jana Vorlickova [ctb]
Maintainer: Patricia Martinkova <martinkova@cs.cas.cz>

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 inst/ShinyItemAnalysis/server/Validity.R            |  348 +-
 inst/ShinyItemAnalysis/ui.R                         |   24 
 inst/ShinyItemAnalysis/ui/uiAbout.R                 |  333 +-
 inst/ShinyItemAnalysis/ui/uiDIF.R                   |  749 +++--
 inst/ShinyItemAnalysis/ui/uiDIF/adjacent.R          |  130 
 inst/ShinyItemAnalysis/ui/uiDIF/cumulative.R        |  169 -
 inst/ShinyItemAnalysis/ui/uiDIF/logistic.R          |  175 -
 inst/ShinyItemAnalysis/ui/uiDIF/multinomial.R       |  162 -
 inst/ShinyItemAnalysis/ui/uiDIF/uiTDIF.R            |  256 +
 inst/ShinyItemAnalysis/ui/uiData.R                  |  139 
 inst/ShinyItemAnalysis/ui/uiIRT.R                   |  127 
 inst/ShinyItemAnalysis/ui/uiIRT/uiDIRT.R            |  360 +-
 inst/ShinyItemAnalysis/ui/uiIRT/uiPolyIRT.R         |   79 
 inst/ShinyItemAnalysis/ui/uiIRT/uiPolyTraining.R    |  447 ++-
 inst/ShinyItemAnalysis/ui/uiIRT/ui_gpcm.R           |only
 inst/ShinyItemAnalysis/ui/uiIRT/ui_grm.R            |only
 inst/ShinyItemAnalysis/ui/uiModules.R               |   27 
 inst/ShinyItemAnalysis/ui/uiReferences.R            |   47 
 inst/ShinyItemAnalysis/ui/uiRegression.R            |  413 ++
 inst/ShinyItemAnalysis/ui/uiReliability.R           |  100 
 inst/ShinyItemAnalysis/ui/uiReports.R               |  111 
 inst/ShinyItemAnalysis/ui/uiScores.R                |   60 
 inst/ShinyItemAnalysis/ui/uiSetting.R               |  100 
 inst/ShinyItemAnalysis/ui/uiTraditionalAnalysis.R   |  207 -
 inst/ShinyItemAnalysis/ui/uiValidity.R              |  108 
 man/BlisClass-class.Rd                              |   14 
 man/blis2blirt.Rd                                   |    2 
 man/coef-BlisClass-method.Rd                        |   16 
 man/fa_parallel.Rd                                  |    8 
 man/fit_blis.Rd                                     |   23 
 man/get_orig_levels.Rd                              |   12 
 man/nominal_to_int.Rd                               |   12 
 man/obtain_nrm_def.Rd                               |   10 
 man/plot_corr.Rd                                    |    7 
 man/print.blis_coefs.Rd                             |   10 
 133 files changed, 9782 insertions(+), 4193 deletions(-)

More information about ShinyItemAnalysis at CRAN
Permanent link

Package manymome updated to version 0.3.6 with previous version 0.3.5 dated 2026-06-01

Title: Mediation, Moderation and Moderated-Mediation After Model Fitting
Description: Computes indirect effects, conditional effects, and conditional indirect effects in a structural equation model or path model after model fitting, with no need to define any user parameters or label any paths in the model syntax, using the approach presented in Cheung and Cheung (2024) <doi:10.3758/s13428-023-02224-z>. Can also form bootstrap confidence intervals by doing bootstrapping only once and reusing the bootstrap estimates in all subsequent computations. Supports bootstrap confidence intervals for standardized (partially or completely) indirect effects, conditional effects, and conditional indirect effects as described in Cheung (2009) <doi:10.3758/BRM.41.2.425> and Cheung, Cheung, Lau, Hui, and Vong (2022) <doi:10.1037/hea0001188>. Model fitting can be done by structural equation modeling using lavaan() or regression using lm().
Author: Shu Fai Cheung [aut, cre] , Sing-Hang Cheung [aut] , Rong Wei Sun [ctb]
Maintainer: Shu Fai Cheung <shufai.cheung@gmail.com>

Diff between manymome versions 0.3.5 dated 2026-06-01 and 0.3.6 dated 2026-06-08

 DESCRIPTION                                   |    6 +++---
 MD5                                           |   20 ++++++++++----------
 NEWS.md                                       |    2 +-
 README.md                                     |    2 +-
 inst/doc/mod_levels.html                      |    6 +++---
 tests/testthat/test_mg_boot.R                 |    3 ++-
 tests/testthat/test_sam.R                     |    8 ++++++++
 tests/testthat/test_sam_indirect_effect_std.R |    6 ++++--
 tests/testthat/test_sam_mediation_mg.R        |    3 ++-
 tests/testthat/test_sam_q_mediation.R         |   10 ++++++++--
 tests/testthat/test_sam_update_internal.R     |    7 +++++--
 11 files changed, 47 insertions(+), 26 deletions(-)

More information about manymome at CRAN
Permanent link

Package highs updated to version 1.14.0-2 with previous version 1.12.0-3 dated 2026-02-08

Title: 'HiGHS' Optimization Solver
Description: R interface to 'HiGHS', an optimization solver for solving mixed integer optimization problems with quadratic or linear objective and linear constraints.
Author: Florian Schwendinger [aut, cre], Balasubramanian Narasimhan [aut], Dirk Schumacher [aut], Julian Hall [cph], Ivet Galabova [cph], Leona Gottwald [cph], Michael Feldmeier [cph]
Maintainer: Florian Schwendinger <FlorianSchwendinger@gmx.at>

Diff between highs versions 1.12.0-3 dated 2026-02-08 and 1.14.0-2 dated 2026-06-08

 highs-1.12.0-3/highs/inst/HiGHS/HiGHS.sln                                                |only
 highs-1.12.0-3/highs/inst/HiGHS/highs/io/FilereaderEms.cpp                               |only
 highs-1.12.0-3/highs/inst/HiGHS/highs/io/FilereaderEms.h                                 |only
 highs-1.12.0-3/highs/inst/HiGHS/highs/ipm/hipo/ipm/CurtisReidScaling.cpp                 |only
 highs-1.12.0-3/highs/inst/HiGHS/highs/ipm/hipo/ipm/CurtisReidScaling.h                   |only
 highs-1.14.0-2/highs/DESCRIPTION                                                         |   15 
 highs-1.14.0-2/highs/MD5                                                                 |  442 +--
 highs-1.14.0-2/highs/NAMESPACE                                                           |   14 
 highs-1.14.0-2/highs/NEWS.md                                                             |    6 
 highs-1.14.0-2/highs/R/RcppExports.R                                                     |   44 
 highs-1.14.0-2/highs/R/constants.R                                                       |   61 
 highs-1.14.0-2/highs/R/highs.R                                                           |   32 
 highs-1.14.0-2/highs/R/options.R                                                         |  204 -
 highs-1.14.0-2/highs/R/solver-api.R                                                      |  291 ++
 highs-1.14.0-2/highs/README.md                                                           |   72 
 highs-1.14.0-2/highs/build                                                               |only
 highs-1.14.0-2/highs/cleanup                                                             |    4 
 highs-1.14.0-2/highs/configure                                                           |   14 
 highs-1.14.0-2/highs/configure.win                                                       |   13 
 highs-1.14.0-2/highs/inst/HiGHS/CMakeLists.txt                                           |   44 
 highs-1.14.0-2/highs/inst/HiGHS/CONTRIBUTING.md                                          |    4 
 highs-1.14.0-2/highs/inst/HiGHS/FEATURES.md                                              |   64 
 highs-1.14.0-2/highs/inst/HiGHS/LICENSE.txt                                              |    2 
 highs-1.14.0-2/highs/inst/HiGHS/MODULE.bazel                                             |   18 
 highs-1.14.0-2/highs/inst/HiGHS/README.md                                                |    4 
 highs-1.14.0-2/highs/inst/HiGHS/THIRD_PARTY_NOTICES.md                                   |    6 
 highs-1.14.0-2/highs/inst/HiGHS/Version.txt                                              |    2 
 highs-1.14.0-2/highs/inst/HiGHS/cmake/FindHipoDeps.cmake                                 |   14 
 highs-1.14.0-2/highs/inst/HiGHS/cmake/cpp-highs.cmake                                    |    2 
 highs-1.14.0-2/highs/inst/HiGHS/cmake/sources-python.cmake                               |   18 
 highs-1.14.0-2/highs/inst/HiGHS/cmake/sources.cmake                                      |   23 
 highs-1.14.0-2/highs/inst/HiGHS/extern/amd/SuiteSparse_config.c                          |    6 
 highs-1.14.0-2/highs/inst/HiGHS/extern/amd/SuiteSparse_config.h                          |   12 
 highs-1.14.0-2/highs/inst/HiGHS/extern/amd/amd.h                                         |   16 
 highs-1.14.0-2/highs/inst/HiGHS/extern/amd/amd_1.c                                       |    2 
 highs-1.14.0-2/highs/inst/HiGHS/extern/amd/amd_2.c                                       |    2 
 highs-1.14.0-2/highs/inst/HiGHS/extern/amd/amd_control.c                                 |   19 
 highs-1.14.0-2/highs/inst/HiGHS/extern/amd/amd_defaults.c                                |    2 
 highs-1.14.0-2/highs/inst/HiGHS/extern/amd/amd_info.c                                    |   29 
 highs-1.14.0-2/highs/inst/HiGHS/extern/amd/amd_order.c                                   |   44 
 highs-1.14.0-2/highs/inst/HiGHS/extern/amd/amd_valid.c                                   |    2 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/GKlib/error.c                               |    9 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/GKlib/gk_macros.h                           |    6 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/GKlib/mcore.c                               |   19 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/libmetis/auxapi.c                           |    2 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/libmetis/balance.c                          |   30 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/libmetis/coarsen.c                          |   18 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/libmetis/compress.c                         |    6 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/libmetis/fm.c                               |   32 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/libmetis/initpart.c                         |   23 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/libmetis/ometis.c                           |    8 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/libmetis/options.c                          |   80 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/libmetis/proto.h                            |    2 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/libmetis/sfm.c                              |   18 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/libmetis/srefine.c                          |    2 
 highs-1.14.0-2/highs/inst/HiGHS/extern/metis/metis.h                                     |    4 
 highs-1.14.0-2/highs/inst/HiGHS/extern/rcm/rcm.cpp                                       |   31 
 highs-1.14.0-2/highs/inst/HiGHS/extern/rcm/rcm.h                                         |    5 
 highs-1.14.0-2/highs/inst/HiGHS/highs/CMakeLists.txt                                     |   92 
 highs-1.14.0-2/highs/inst/HiGHS/highs/HConfig.h.bazel.in                                 |    5 
 highs-1.14.0-2/highs/inst/HiGHS/highs/Highs.h                                            |   11 
 highs-1.14.0-2/highs/inst/HiGHS/highs/highspy/highs.py                                   |    4 
 highs-1.14.0-2/highs/inst/HiGHS/highs/io/Filereader.cpp                                  |   32 
 highs-1.14.0-2/highs/inst/HiGHS/highs/io/Filereader.h                                    |    4 
 highs-1.14.0-2/highs/inst/HiGHS/highs/io/FilereaderLp.cpp                                |   10 
 highs-1.14.0-2/highs/inst/HiGHS/highs/io/HMPSIO.cpp                                      |   18 
 highs-1.14.0-2/highs/inst/HiGHS/highs/io/HMPSIO.h                                        |   12 
 highs-1.14.0-2/highs/inst/HiGHS/highs/io/HMpsFF.cpp                                      |  167 -
 highs-1.14.0-2/highs/inst/HiGHS/highs/io/HMpsFF.h                                        |    6 
 highs-1.14.0-2/highs/inst/HiGHS/highs/io/HighsIO.cpp                                     |  159 -
 highs-1.14.0-2/highs/inst/HiGHS/highs/io/HighsIO.h                                       |   15 
 highs-1.14.0-2/highs/inst/HiGHS/highs/io/LoadOptions.cpp                                 |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/io/LoadOptions.h                                   |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/io/filereaderlp/reader.cpp                         |   68 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/IpxWrapper.cpp                                 |  134 -
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/IpxWrapper.h                                   |   14 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/auxiliary/Auxiliary.cpp                   |   84 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/auxiliary/Auxiliary.h                     |   15 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/auxiliary/OrderingPrint.h                 |only
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/auxiliary/VectorOperations.cpp            |   32 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/Analyse.cpp                   |  276 +-
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/Analyse.h                     |    9 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/CliqueStack.cpp               |    5 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/DataCollector.cpp             |   59 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/FactorHiGHS.cpp               |    4 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/FactorHiGHS.h                 |   12 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/FactorHiGHSSettings.h         |    3 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/Factorise.cpp                 |   28 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/FormatHandler.h               |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/HybridHybridFormatHandler.cpp |   30 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/HybridHybridFormatHandler.h   |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/KrylovMethodsIpm.cpp          |    5 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/Symbolic.cpp                  |    3 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/Symbolic.h                    |    4 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/factorhighs/Timing.h                      |    1 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/Control.cpp                           |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/FactorHiGHSSolver.cpp                 |  469 ++-
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/FactorHiGHSSolver.h                   |   22 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/Info.h                                |    9 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/Iterate.cpp                           |  313 +-
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/Iterate.h                             |   62 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/LinearSolver.h                        |    7 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/Model.cpp                             |  529 +---
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/Model.h                               |   68 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/Options.h                             |   38 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/Parameters.h                          |    8 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/PreProcess.cpp                        |only
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/PreProcess.h                          |only
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/Refine.cpp                            |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/Solver.cpp                            |  361 +-
 highs-1.14.0-2/highs/inst/HiGHS/highs/ipm/hipo/ipm/Solver.h                              |   46 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HConst.h                                   |   53 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/Highs.cpp                                  |  401 +--
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsDebug.cpp                             |    4 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsDebug.h                               |    4 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsDeprecated.cpp                        |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsIis.cpp                               |  367 ++
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsIis.h                                 |   63 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsInfo.cpp                              |   75 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsInfo.h                                |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsInterface.cpp                         |  821 ++----
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsLpUtils.cpp                           |   18 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsLpUtils.h                             |   18 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsModelUtils.cpp                        |  182 -
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsModelUtils.h                          |   30 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsOptions.cpp                           |  196 +
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsOptions.h                             |  171 -
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsSolution.cpp                          |  335 ++
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsSolution.h                            |   11 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsSolutionDebug.cpp                     |   14 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsSolutionDebug.h                       |    4 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsSolve.cpp                             |   33 
 highs-1.14.0-2/highs/inst/HiGHS/highs/lp_data/HighsSolve.h                               |    1 
 highs-1.14.0-2/highs/inst/HiGHS/highs/meson.build                                        |    7 
 highs-1.14.0-2/highs/inst/HiGHS/highs/mip/HighsCliqueTable.cpp                           |  297 --
 highs-1.14.0-2/highs/inst/HiGHS/highs/mip/HighsCliqueTable.h                             |    8 
 highs-1.14.0-2/highs/inst/HiGHS/highs/mip/HighsDomain.cpp                                |  100 
 highs-1.14.0-2/highs/inst/HiGHS/highs/mip/HighsDomain.h                                  |    3 
 highs-1.14.0-2/highs/inst/HiGHS/highs/mip/HighsImplications.cpp                          |   60 
 highs-1.14.0-2/highs/inst/HiGHS/highs/mip/HighsImplications.h                            |    8 
 highs-1.14.0-2/highs/inst/HiGHS/highs/mip/HighsLpRelaxation.cpp                          |    4 
 highs-1.14.0-2/highs/inst/HiGHS/highs/mip/HighsMipSolver.cpp                             |    7 
 highs-1.14.0-2/highs/inst/HiGHS/highs/model/HighsHessian.cpp                             |   47 
 highs-1.14.0-2/highs/inst/HiGHS/highs/model/HighsHessian.h                               |    5 
 highs-1.14.0-2/highs/inst/HiGHS/highs/model/HighsHessianUtils.cpp                        |  363 +-
 highs-1.14.0-2/highs/inst/HiGHS/highs/parallel/HighsBinarySemaphore.h                    |    4 
 highs-1.14.0-2/highs/inst/HiGHS/highs/parallel/HighsTask.h                               |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/CupdlpWrapper.cpp                             |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/CupdlpWrapper.h                               |    2 
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 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/cupdlp/cuda/test_cuda_linalg.c                |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/cupdlp/cupdlp.h                               |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/cupdlp/cupdlp_cs.c                            |    2 
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 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/cupdlp/cupdlp_defs.h                          |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/cupdlp/cupdlp_linalg.c                        |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/cupdlp/cupdlp_linalg.h                        |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/cupdlp/cupdlp_proj.c                          |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/cupdlp/cupdlp_proj.h                          |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/cupdlp/cupdlp_restart.c                       |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/cupdlp/cupdlp_restart.h                       |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/cupdlp/cupdlp_scaling.c                       |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/cupdlp/cupdlp_scaling.h                       |    2 
 highs-1.14.0-2/highs/inst/HiGHS/highs/pdlp/cupdlp/cupdlp_solver.c                        |    2 
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Package rregm updated to version 1.2 with previous version 1.1 dated 2026-04-10

Title: Reparameterized Regression Models
Description: Provides estimation and data generation tools for several new regression models, including the gamma, beta, inverse gamma and beta prime distributions. These models can be parameterized based on the mean, median, mode, geometric mean and harmonic mean, as specified by the user. For details, see Bourguignon and Gallardo (2025a) <doi:10.1016/j.chemolab.2025.105382> and Bourguignon and Gallardo (2025b) <doi:10.1111/stan.70007>. Additional tools are provided for higher-order likelihood inference through Skovgaard-adjusted likelihood ratio statistics and for predictive shrinkage estimation in reparameterized beta regression models.
Author: Diego Gallardo [aut, cre], Marcelo Bourguignon [aut], Marcia Brandao [aut], Tiago Magalhaes [ctb], Rafael Izbicki [ctb]
Maintainer: Diego Gallardo <dgallardo@ubiobio.cl>

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Package rerddapUtils readmission to version 1.0.2 with previous version 1.0.1 dated 2026-05-21

Title: Miscellaneous Utilities for 'rerddap'
Description: The 'rerddapUtils' package is an 'R' package that is a set of four main functions designed to work with and extend the 'rerddap' package. These functions includes one for restricting by season, one for splitting large requests, and two for working with projected datasets. There are also two utility functions that provide estimates of the size of a proposed 'rerddap::griddap()' request.
Author: Roy Mendelssohn [aut, cre]
Maintainer: Roy Mendelssohn <roy.mendelssohn@noaa.gov>

This is a re-admission after prior archival of version 1.0.1 dated 2026-05-21

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Package RcppAlgos updated to version 2.10.1 with previous version 2.10.0 dated 2026-03-08

Title: High Performance Tools for Combinatorics and Computational Mathematics
Description: Provides optimized functions and flexible iterators implemented in C++ for solving problems in combinatorics and computational mathematics. Handles various combinatorial objects including combinations, permutations, integer partitions and compositions, Cartesian products, unordered Cartesian products, and partition of groups. Utilizes the RMatrix class from 'RcppParallel' for thread safety. The combination and permutation functions contain constraint parameters that allow for generation of all results of a vector meeting specific criteria (e.g. finding all combinations such that the sum is between two bounds). Capable of ranking/unranking combinatorial objects efficiently (e.g. retrieve only the nth lexicographical result) which sets up nicely for parallelization as well as random sampling. Gmp support permits exploration where the total number of results is large (e.g. comboSample(10000, 500, n = 4)). Additionally, there are several high performance number theoretic functions that are [...truncated...]
Author: Joseph Wood [aut, cre]
Maintainer: Joseph Wood <jwood000@gmail.com>

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Package LDAShiny readmission to version 1.0.0 with previous version 0.9.3 dated 2021-03-29

Title: Interactive Topic Modeling and Bibliometric Analysis via Shiny
Description: Provides a 'Shiny' graphical interface for the complete workflow of Latent Dirichlet Allocation (LDA) topic modelling on bibliometric data from Scopus and Web of Science. Steps include data import and deduplication, text preprocessing (stopword removal, stemming, n-grams, sparse-term filtering), statistical inference to select the optimal number of topics via coherence, final model training, and topic trend analysis over time using linear regression. All results can be exported as Excel files, RDS objects, and publication-quality plots.
Author: Javier De La Hoz-M [aut, cre]
Maintainer: Javier De La Hoz-M <jdelahoz@unimagdalena.edu.co>

This is a re-admission after prior archival of version 0.9.3 dated 2021-03-29

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Package ETDQualitizer updated to version 1.1.0 with previous version 1.0.0 dated 2026-05-21

Title: Automated Eye Tracking Data Quality Determination for Screen-Based Eye Trackers
Description: Compute common data quality metrics for accuracy, precision and data loss for screen-based eye trackers. The package supports gaze input in screen pixels or degrees and reports angular measures in degrees where appropriate. If you use this package, please cite Niehorster, D.C., Nyström, M., Hessels, R.S., Benjamins, J.S., Andersson, R., and Hooge, I.T.C. (2026). The fundamentals of eye tracking, Part 7: Determining data quality. Behavior Research Methods. <doi:10.3758/s13428-026-03039-4>.
Author: Diederick Niehorster [aut, cre, cph]
Maintainer: Diederick Niehorster <diederick_c.niehorster@humlab.lu.se>

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Package colocboost updated to version 1.0.9 with previous version 1.0.8 dated 2026-06-07

Title: Multi-Context Colocalization Analysis for QTL and GWAS Studies
Description: A multi-task learning approach to variable selection regression with highly correlated predictors and sparse effects, based on frequentist statistical inference. It provides statistical evidence to identify which subsets of predictors have non-zero effects on which subsets of response variables, motivated and designed for colocalization analysis across genome-wide association studies (GWAS) and quantitative trait loci (QTL) studies. The ColocBoost model is described in Cao et. al. (2025) <doi:10.1101/2025.04.17.25326042>.
Author: Xuewei Cao [cre, aut, cph], Haochen Sun [aut, cph], Ru Feng [aut, cph], Daniel Nachun [aut, cph], Kushal Dey [aut, cph], Gao Wang [aut, cph]
Maintainer: Xuewei Cao <xc2270@cumc.columbia.edu>

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Package causalweight readmission to version 1.1.5 with previous version 1.1.4 dated 2025-12-01

Title: Estimation Methods for Causal Inference Based on Inverse Probability Weighting and Doubly Robust Estimation
Description: Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects based on different identification strategies (unconfoundedness, instruments, difference-in-differences, regression discontinuity designs).
Author: Hugo Bodory [aut, cre] , Martin Huber [aut] , Jannis Kueck [aut]
Maintainer: Hugo Bodory <hugo.bodory@unisg.ch>

This is a re-admission after prior archival of version 1.1.4 dated 2025-12-01

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Package ErrorTracer (with last version 1.1.0) was removed from CRAN

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

2026-05-25 1.1.0
2026-05-04 1.0.2

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Package inti updated to version 0.7.0 with previous version 0.6.92 dated 2026-03-22

Title: Tools and Statistical Procedures in Plant Science
Description: The 'inti' package is part of the 'inkaverse' project for developing different procedures and tools used in plant science and experimental designs. The mean aim of the package is to support researchers during the planning of experiments and data collection (tarpuy()), data analysis and graphics (yupana()) , and scientific writing. Learn more about the 'inkaverse' project at <https://inkaverse.com/>.
Author: Flavio Lozano-Isla [aut, cre] , Maria Belen Kistner [ctb] , QuipoLab [ctb], Inkaverse [cph]
Maintainer: Flavio Lozano-Isla <flozanoisla@gmail.com>

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 inti-0.7.0/inti/R/tarpuy_plex.R                                          |   87 ++--
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More information about inti at CRAN
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Package dqcheckr updated to version 0.2.1 with previous version 0.2.0 dated 2026-06-01

Title: Automated Data Quality Checks for Recurring Dataset Deliveries
Description: Automates quality verification of recurring external dataset deliveries. For each new file arrival, it runs single-snapshot quality checks, compares the file to the previous delivery, writes a self-contained 'HTML' report, and records summary statistics in a local 'SQLite' database for long-term trend tracking. Supports 'CSV' and fixed-width formats. Custom organisation-specific checks can be supplied as plain R files.
Author: Mick Mioduszewski [aut, cre]
Maintainer: Mick Mioduszewski <mick@mioduszewski.net>

Diff between dqcheckr versions 0.2.0 dated 2026-06-01 and 0.2.1 dated 2026-06-08

 DESCRIPTION                          |    8 +-
 MD5                                  |   56 +++++++++----------
 NEWS.md                              |   23 +++++++
 R/checks_custom.R                    |   17 +++--
 R/checks_generic.R                   |    6 +-
 R/compare.R                          |    4 -
 R/dqcheckr-package.R                 |    9 ++-
 R/drift.R                            |   56 ++++++-------------
 R/ingest.R                           |   26 ++++++--
 R/report.R                           |   10 ++-
 R/snapshot.R                         |   30 +++++-----
 R/utils.R                            |    9 ++-
 README.md                            |only
 build/vignette.rds                   |binary
 inst/doc/specification.Rmd           |   10 +--
 inst/doc/specification.html          |    8 +-
 man/check_min_row_count.Rd           |    6 +-
 man/run_custom_checks.Rd             |    2 
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 tests/testthat/test-checks.R         |  103 +++++++++++------------------------
 tests/testthat/test-col-threshold.R  |    2 
 tests/testthat/test-compare.R        |   24 +-------
 tests/testthat/test-drift.R          |   15 -----
 tests/testthat/test-ingest.R         |    5 -
 tests/testthat/test-integration.R    |    2 
 tests/testthat/test-report-tempdir.R |    2 
 tests/testthat/test-snapshot.R       |   46 +++------------
 tests/testthat/test-type-override.R  |    2 
 tests/testthat/test-utils.R          |    2 
 vignettes/specification.Rmd          |   10 +--
 30 files changed, 212 insertions(+), 281 deletions(-)

More information about dqcheckr at CRAN
Permanent link


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