Sun, 18 Oct 2026

Package summata updated to version 0.13.0 with previous version 0.12.0 dated 2026-08-21

Title: Publication-Ready Summary Tables and Forest Plots
Description: A comprehensive framework for descriptive statistics and regression analysis that produces publication-ready tables and forest plots. Provides a unified interface from descriptive statistics through multivariable modeling, with support for linear models, generalized linear models, Cox proportional hazards, and mixed-effects models. Also includes univariable screening, multivariate regression, model comparison, and export to multiple formats including PDF, DOCX, PPTX, 'LaTeX', HTML, and RTF. Built on 'data.table' for computational efficiency.
Author: Paul Hsin-ti McClelland [aut, cre, cph]
Maintainer: Paul Hsin-ti McClelland <PaulHMcClelland@protonmail.com>

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Package DENSaftertransform updated to version 1.0 with previous version 0.1 dated 2026-06-02

Title: Estimating Density after Logarithmic or Power Transformation of Data
Description: Functions for computing: (1) the adaptive normal plug-in(PI) density estimate for data after the logarithmic transformation; (2) the adaptive log-normal PI density estimate for data after the power transformation with an exponent ranging from zero to one; (3) the single-bandwidth PI density estimate for data after the logarithmic transformation; (4) the single-bandwidth PI density estimate for data after the power transformation; (5) the data-driven bandwidth for the single-bandwidth PI estimator for data after the logarithmic transformation; (6) the rule-of-thumb bandwidth for the single-bandwidth PI estimator for data after the power transformation. For details, see the articles: (1) Savchuk, O. (2026) (submitted). Adaptive log-normal plug-in density estimator for power transformations. (2) Savchuk, O. (2026). Density estimation for log-transformed data. Computational Statistics, 41(98) <doi:10.1007/s00180-026-01766-y>. (3) Savchuk, O., Schick A. (2013). Density estimation for [...truncated...]
Author: Olga Savchuk [aut, cre, cph]
Maintainer: Olga Savchuk <olga.y.savchuk@gmail.com>

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Package graDiEnt updated to version 1.1.0 with previous version 1.0.1 dated 2022-05-10

Title: Stochastic Quasi-Gradient Differential Evolution Optimization
Description: An optim-style implementation of the Stochastic Quasi-Gradient Differential Evolution (SQG-DE) optimization algorithm first published by Sala, Baldanzini, and Pierini (2018; <doi:10.1007/978-3-319-72926-8_27>). This optimization algorithm fuses the robustness of the population-based global optimization algorithm "Differential Evolution" with the efficiency of gradient-based optimization. The derivative-free algorithm uses population members to build stochastic gradient estimates, without any additional objective function evaluations. Sala, Baldanzini, and Pierini argue this algorithm is useful for 'difficult optimization problems under a tight function evaluation budget.' This package can run SQG-DE in parallel and sequentially.
Author: Brendan Matthew Galdo [aut, cre]
Maintainer: Brendan Matthew Galdo <Brendan.m.galdo@gmail.com>

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Package HistDAWass updated to version 1.0.9 with previous version 1.0.8 dated 2024-01-24

Title: Histogram-Valued Data Analysis
Description: In the framework of Symbolic Data Analysis, a relatively new approach to the statistical analysis of multi-valued data, we consider histogram-valued data, i.e., data described by univariate histograms. The methods and the basic statistics for histogram-valued data are mainly based on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric between quantile functions. The package contains unsupervised classification techniques, least square regression and tools for histogram-valued data and for histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi: 10.1007/s11634-014-0176-4>.
Author: Antonio Irpino [aut, cre]
Maintainer: Antonio Irpino <antonio.irpino@unicampania.it>

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Package brfssdata updated to version 0.1.1 with previous version 0.1.0 dated 2026-08-21

Title: Access CDC Behavioral Risk Factor Surveillance System Data
Description: Download, cache, and analyze annual microdata from the United States Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance System (BRFSS) <https://www.cdc.gov/brfss/>. Each requested survey year is downloaded once as a compact file hosted on public releases, verified against a published checksum, and cached locally; queries then run through 'DuckDB' (via the 'duckdb' package), so column selection and repeat analyses never re-transfer data. Survey-design helpers construct 'srvyr' design objects with year-appropriate weights, strata, and primary sampling units, including explicit handling of the 2011 weighting methodology change and of the codes CDC uses for missing-type answers.
Author: Muntasir Masum [aut, cre, cph]
Maintainer: Muntasir Masum <muntasir.1124@gmail.com>

Diff between brfssdata versions 0.1.0 dated 2026-08-21 and 0.1.1 dated 2026-10-18

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Permanent link

Package accessr updated to version 1.1.4 with previous version 1.1.3 dated 2025-03-30

Title: Command Line Tools to Produce Accessible Documents using 'R Markdown'
Description: Provides functions to produce accessible 'HTML' slides, 'HTML', 'Word' and 'PDF' documents from input 'R markdown' files. Accessible 'PDF' files are produced only on a 'Windows' Operating System. One aspect of accessibility is providing a headings structure that is recognised by a screen reader, providing a navigational tool for a blind or partially-sighted person. A key aim is to produce documents of different formats easily from each of a collection of 'R markdown' source files. Input 'R markdown' files are rendered using the render() function from the 'rmarkdown' package <https://cran.r-project.org/package=rmarkdown>. A 'zip' file containing multiple output files can be produced from one function call. A user-supplied template 'Word' document can be used to determine the formatting of an output 'Word' document. Accessible 'PDF' files are produced from 'Word' documents using 'OfficeToPDF' <https://github.com/cognidox/OfficeToPDF>. A convenience function, install_otp() is [...truncated...]
Author: Paul J. Northrop [aut, cre, cph]
Maintainer: Paul J. Northrop <p.northrop@ucl.ac.uk>

Diff between accessr versions 1.1.3 dated 2025-03-30 and 1.1.4 dated 2026-10-18

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Package mpath updated to version 0.4-2.28 with previous version 0.4-2.27 dated 2026-07-12

Title: Regularized Linear Models
Description: Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016) <doi:10.1177/0962280214530608>, Wang (2021) <doi:10.1007/s11749-021-00770-2>, Wang (2024) <doi:10.1111/anzs.12409>.
Author: Zhu Wang [aut, cre], Achim Zeileis [aut], Simon Jackman [aut], Brian Ripley [aut], Patrick Breheny [aut]
Maintainer: Zhu Wang <zwang145@uthsc.edu>

Diff between mpath versions 0.4-2.27 dated 2026-07-12 and 0.4-2.28 dated 2026-10-18

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Package future.batchtools updated to version 0.30.0 with previous version 0.22.0 dated 2026-04-24

Title: A Future API for Parallel and Distributed Processing using 'batchtools'
Description: Implementation of the Future API <doi:10.32614/RJ-2021-048> on top of the 'batchtools' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, not only on your local machine or ad-hoc cluster of machines, but also via high-performance compute ('HPC') job schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS', e.g. 'y <- lapply(files, FUN = process) |> futurize::futurize()'.
Author: Henrik Bengtsson [aut, cre, cph] , Michel Lang [ctb, cph] , Bernd Bischl [ctb, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between future.batchtools versions 0.22.0 dated 2026-04-24 and 0.30.0 dated 2026-10-18

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Permanent link

New package seowebchecker with initial version 1.0.0
Package: seowebchecker
Title: Comprehensive Website Search Engine Optimization Audit and Analysis Tools
Version: 1.0.0
Author: Rahul Gupta [aut, cre], SEOWebChecker [cph]
Maintainer: Rahul Gupta <jaiganesh6999@gmail.com>
Description: Provides a lightweight client software development kit and utility suite for automated website technical search engine optimization audits, meta tag validations, Core Web Vitals checks, and optimization scoring. Official web audit portal and tools available at <https://seowebchecker.com/>.
License: MIT + file LICENSE
URL: https://seowebchecker.com/, https://github.com/jaiganesh6999/seowebchecker-seoaudit-sdk
BugReports: https://github.com/jaiganesh6999/seowebchecker-seoaudit-sdk/issues
Depends: R (>= 3.5.0)
Imports: utils
Suggests: testthat (>= 3.0.0)
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-10-08 10:55:09 UTC; runner
Repository: CRAN
Date/Publication: 2026-10-18 16:20:02 UTC

More information about seowebchecker at CRAN
Permanent link

Package rWind readmission to version 1.2.0 with previous version 1.1.7 dated 2021-10-19

Title: Download, Edit and Include Wind and Sea Currents Data in Ecological and Evolutionary Analysis
Description: Downloads and processes surface wind data from the Global Forecast System <https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast> and sea-current data from Ocean Surface Current Analyses Real-time (OSCAR). Converts flow speed and direction into spatial rasters and directed movement costs and conductances for connectivity and least-cost path analyses.
Author: Javier Fernandez-Lopez [aut, cre], Klaus Schliep [aut], Yurena Arjona [aut]
Maintainer: Javier Fernandez-Lopez <jflopez.bio@gmail.com>

This is a re-admission after prior archival of version 1.1.7 dated 2021-10-19

Diff between rWind versions 1.1.7 dated 2021-10-19 and 1.2.0 dated 2026-10-18

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More information about rWind at CRAN
Permanent link

Package progressr updated to version 1.1.0 with previous version 1.0.0 dated 2026-07-03

Title: An Inclusive, Unifying API for Progress Updates
Description: A minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing. The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it. The end user has full control of how, where, and when to render these progress updates, e.g. in the terminal using utils::txtProgressBar(), cli::cli_progress_bar(), in a graphical user interface using utils::winProgressBar(), tcltk::tkProgressBar() or shiny::withProgress(), via the speakers using beepr::beep(), or on a file system via the size of a file. Anyone can add additional, customized, progression handlers. The 'progressr' package uses R's condition framework for signaling progress updated. Because of this, progress can be reported from almost anywhere in R, e.g. from classical for and while loops, from map-reduce API:s like the lapply() family of functions, 'purrr', 'plyr', and 'foreach'. It will als [...truncated...]
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between progressr versions 1.0.0 dated 2026-07-03 and 1.1.0 dated 2026-10-18

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 progressr-1.1.0/progressr/DESCRIPTION                                                                           |   18 
 progressr-1.1.0/progressr/MD5                                                                                   |  231 ++++++----
 progressr-1.1.0/progressr/NAMESPACE                                                                             |   21 
 progressr-1.1.0/progressr/NEWS.md                                                                               |   49 ++
 progressr-1.1.0/progressr/R/global_progression_handler.R                                                        |   14 
 progressr-1.1.0/progressr/R/handler_callback.R                                                                  |only
 progressr-1.1.0/progressr/R/handlers.R                                                                          |   60 ++
 progressr-1.1.0/progressr/R/options.R                                                                           |   18 
 progressr-1.1.0/progressr/R/rstudio-console.R                                                                   |    5 
 progressr-1.1.0/progressr/R/utils-stealth_sample.int.R                                                          |only
 progressr-1.1.0/progressr/R/utils-uuid.R                                                                        |only
 progressr-1.1.0/progressr/R/utils.R                                                                             |   12 
 progressr-1.1.0/progressr/inst/doc/progressr-91-appendix.html                                                   |   18 
 progressr-1.1.0/progressr/inst/doc/progressr-91-appendix.md                                                     |   29 +
 progressr-1.1.0/progressr/inst/testme/test-exceptions.R                                                         |   37 +
 progressr-1.1.0/progressr/inst/testme/test-handler_callback.R                                                   |only
 progressr-1.1.0/progressr/inst/testme/test-handler_filesize.R                                                   |   27 +
 progressr-1.1.0/progressr/inst/testme/test-handlers.R                                                           |   55 ++
 progressr-1.1.0/progressr/inst/testme/test-progression.R                                                        |   10 
 progressr-1.1.0/progressr/inst/testme/test-progressor.R                                                         |   27 +
 progressr-1.1.0/progressr/inst/testme/test-rstudio-console.R                                                    |only
 progressr-1.1.0/progressr/inst/testme/test-slow.R                                                               |   14 
 progressr-1.1.0/progressr/inst/testme/test-update_package_options,handlers.R                                    |only
 progressr-1.1.0/progressr/inst/testme/test-utils.R                                                              |   51 ++
 progressr-1.1.0/progressr/inst/testme/test-with_progress,delay.R                                                |    9 
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New package multigrain with initial version 1.0.0
Package: multigrain
Title: Optimise Graphical Approaches to Multiple Testing Procedures
Version: 1.0.0
Description: Find the optimal graphical multiple testing procedure for confirmatory clinical trials that test several hypotheses across endpoints, doses, or subpopulations. Choose the graph maximising an explicit gain-function: a user-defined measure of trial success that scores each possible set of rejected hypotheses. Given simulated p-values, search over hypothesis weights and transition weights to maximise expected trial success. Conventional power criteria are recovered and the family-wise error rate is strongly controlled due to the search being confined to graphical procedures. Described in Spiers, Grayling, Wheeler, and Mander (2026) "Gain-function optimisation of graphical multiple testing procedures for confirmatory clinical trials" <doi:10.48550/arXiv.2609.19994>.
License: LGPL (>= 3)
URL: https://github.com/GSK-Biostatistics/multigrain, https://gsk-biostatistics.github.io/multigrain/
BugReports: https://github.com/GSK-Biostatistics/multigrain/issues
Depends: R (>= 4.1.0)
Imports: cli, dplyr, GA, ggplot2, ggraph, glue, igraph, mvtnorm, nloptr, purrr, Rcpp, RcppParallel (>= 5.1.10), rlang (>= 1.2.0), stats, tibble, tidyr, utils, vctrs, withr
Suggests: gMCPLite, spelling, svglite, testthat (>= 3.0.0), vdiffr
LinkingTo: Rcpp, RcppParallel
Encoding: UTF-8
Language: en-GB
LazyData: true
SystemRequirements: GNU make
NeedsCompilation: yes
Packaged: 2026-10-08 08:26:04 UTC; dvm44402
Author: Alex Spiers [aut, cre] , Dragoș Moldovan-Gruenfeld [aut], GlaxoSmithKline Research & Development Limited [cph, fnd]
Maintainer: Alex Spiers <alex.d.spiers@gsk.com>
Repository: CRAN
Date/Publication: 2026-10-18 16:20:14 UTC

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Package modelbased updated to version 0.18.0 with previous version 0.17.0 dated 2026-08-28

Title: Estimation of Model-Based Predictions, Contrasts and Means
Description: Implements a general interface for model-based estimations for a wide variety of models, used in the computation of marginal means, contrast analysis and predictions. For a list of supported models, see 'insight::supported_models()'.
Author: Dominique Makowski [aut, cre] , Daniel Luedecke [aut] , Mattan S. Ben-Shachar [aut] , Indrajeet Patil [aut] , Remi Theriault [aut]
Maintainer: Dominique Makowski <officialeasystats@gmail.com>

Diff between modelbased versions 0.17.0 dated 2026-08-28 and 0.18.0 dated 2026-10-18

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New package geomattR with initial version 0.3.1
Package: geomattR
Title: Calculate Geometric Attributes of Spatial Polygons
Version: 0.3.1
Description: Calculate geometric attributes of spatial polygons including area, perimeter, compactness, elongation, orientation (bearing), fractal dimension, and various shape indices. The package relies heavily on 'terra' methods and vector data objects <https://github.com/rspatial/terra>.
License: MIT + file LICENSE
URL: https://github.com/gortegasolis/geomattR
BugReports: https://github.com/gortegasolis/geomattR/issues
Encoding: UTF-8
Depends: R (>= 4.1)
Imports: terra (>= 1.8.70), geosphere (>= 1.5.20), methods, parallel, stats, cli, rlang
Suggests: testthat (>= 3.2.3), sf, knitr (>= 1.50), rmarkdown (>= 2.30)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-08 12:52:47 UTC; gabriel
Author: Gabriel Ortega-Solis [aut, cre, cph]
Maintainer: Gabriel Ortega-Solis <g.ortega.solis@geomattr.org>
Repository: CRAN
Date/Publication: 2026-10-18 16:30:08 UTC

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New package flowz with initial version 0.1.0
Package: flowz
Title: Local Latent Flow Forecasting with Empirical Calibration
Version: 0.1.0
Description: Fits conditional flow-matching models to univariate time series. Combines a selectable exponentially weighted moving average (EWMA), autoregressive integrated moving average (ARIMA), autoregressive fractionally integrated moving average (ARFIMA), or structural time-series baseline with direct multi-step residual paths and a random-feature extreme learning machine (ELM) velocity field. Spectral dispersion calibration uses a chronological calibration block, with a separate test block for forecast scoring. Provides simulated forecast paths and smooth marginal distributions. Methodological background is given by Lipman et al. (2023) <doi:10.48550/arXiv.2210.02747>, Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, Hyndman and Khandakar (2008) <doi:10.18637/jss.v027.i03>, and Haslett and Raftery (1989) <doi:10.2307/2347679>.
URL: https://rpubs.com/giancarlo_vercellino/flowz
License: GPL-3
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: forecast, fracdiff, graphics, grDevices, parallel, stats, utils
Suggests: testthat (>= 3.0.0), pkgload
NeedsCompilation: no
Packaged: 2026-10-08 10:23:30 UTC; gianc
Author: Giancarlo Vercellino [aut, cre]
Maintainer: Giancarlo Vercellino <giancarlo.vercellino@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-18 16:20:21 UTC

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New package divoRce with initial version 1.0-2
Package: divoRce
Title: Diagnosing Separation Phenomena in Latent Polyhedral Categorical Response Models
Version: 1.0-2
Maintainer: Thomas Rusch <thomas.rusch@wu.ac.at>
Description: Utilities for checking and diagnosing separation phenomena (complete separation, quasi-complete separation, overlap) for latent polyhedral categorical response models including binary, nominal and ordinal data models with link construction as baseline-category, adjacent-category, cumulative, sequential (continuation-ratio), ordered stereotype and arbitrary link error distribution (including logit, probit, cauchit, cloglog, loglog, log amongst others) based on structure vectors as described in Sablica, Hornik, Rusch (2026) <doi:10.48550/arXiv.2610.08100> and Rusch, Sablica, Hornik (2026) <doi:10.48550/arXiv.2610.09951>. The package functionality includes a check for separation and a check for overlap with a linear program, a quick check for overlap, a sequential and a parallel check for overlap based on subsamples, detailed separation diagnostics such as identification of columns in the design matrix/structure vector matrix that lead to separation, identification of design m [...truncated...]
Depends: R (>= 3.5.0)
Imports: rcdd
Suggests: clustord, detectseparation, MASS, nnet, ordinal, ROI
License: GPL-2 | GPL-3
LazyData: true
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-10-08 11:38:25 UTC; trusch
Author: Thomas Rusch [aut, cre] , Lukas Sablica [aut] , Kurt Hornik [aut]
Repository: CRAN
Date/Publication: 2026-10-18 16:30:14 UTC

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Package lmomco updated to version 2.5.8 with previous version 2.5.7 dated 2026-07-03

Title: L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments, and Many Distributions
Description: Extensive functions for Lmoments (LMs) and probability-weighted moments (PWMs), distribution parameter estimation, LMs for distributions, LM ratio diagrams, multivariate Lcomoments, and asymmetric (asy) trimmed LMs (TLMs). Maximum likelihood and maximum product spacings estimation are available. Right-tail and left-tail LM censoring by threshold or indicator variable are available. LMs of residual (resid) and reversed (rev) residual life are implemented along with 13 quantile operators for reliability analyses. Exact analytical bootstrap estimates of order statistics, LMs, and LM var-covars are available. Harri-Coble Tau34-squared Normality Test is available. Distributions with L, TL, and added (+) support for right-tail censoring (RC) encompass: Asy Exponential (Exp) Power [L], Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp Poisson [L], Gen Extreme Value [L], Gen Lambda [L, TL], Gen Logistic [L], Gen Normal [L], Gen Pareto [L+RC, TL], Govindara [...truncated...]
Author: William Asquith [aut, cre]
Maintainer: William Asquith <william.asquith@ttu.edu>

Diff between lmomco versions 2.5.7 dated 2026-07-03 and 2.5.8 dated 2026-10-18

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New package forecastsuite with initial version 1.0.2
Package: forecastsuite
Title: Multi-Model Time Series Forecasting Toolkit and Local App
Version: 1.0.2
Description: Fits, compares and visualizes univariate time series forecasts through an extensible model registry built largely on the 'forecast' package: AutoRegressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) models (Hyndman and Khandakar (2008) <doi:10.18637/jss.v027.i03>), Error, Trend, Seasonal (ETS) exponential smoothing (Hyndman et al. (2002) <doi:10.1016/S0169-2070(01)00110-8>), Trigonometric seasonality, Box-Cox transformation, AutoRegressive Moving Average (ARMA) errors, Trend and Seasonal components (TBATS) models (De Livera, Hyndman and Snyder (2011) <doi:10.1198/jasa.2011.tm09771>), Neural Network AutoRegression (NNETAR) and Holt-Winters exponential smoothing (Winters (1960) <doi:10.1287/mnsc.6.3.324>), plus optional 'Prophet' models via the 'prophet' package (Taylor and Letham (2018) <doi:10.1080/00031305.2017.1380080>) and Long Short-Term Memory (LSTM) networks via the 'torch' package (Hochreiter and Schmidhuber (1997) <doi:10.1162 [...truncated...]
License: MIT + file LICENSE
URL: https://github.com/SpaRroW410/Forecast_dashboard/tree/main/forecastsuite
BugReports: https://github.com/SpaRroW410/Forecast_dashboard/issues
Encoding: UTF-8
Depends: R (>= 4.1)
Imports: shiny, dplyr, lubridate, tibble, tidyr, stringr, plotly, DT, forecast, zoo, shinycssloaders, shinyjs, colourpicker, stats, utils, grDevices, graphics
Suggests: prophet, torch, readxl, writexl, testthat (>= 3.0.0), withr, rmarkdown, knitr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-07 08:52:02 UTC; runner
Author: Mukul Maheshwari [aut, cre, cph]
Maintainer: Mukul Maheshwari <ryan00700@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-18 16:00:02 UTC

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New package dmrfit with initial version 0.1.0
Package: dmrfit
Date: 2026-10-07
Title: Scalable Bayesian Inference for Discrete Markov Random Fields
Version: 0.1.0
Description: Estimation of discrete Markov random fields through the pseudo-likelihood, for both Ising models (Ising (1925) <doi:10.1007/BF02980577>) and ordinal Markov random fields (Marsman et al. (2025) <doi:10.1017/psy.2024.4>). Point estimates, either maximum pseudo-likelihood or maximum a posteriori, are obtained through a trust region optimization algorithm, with robust standard errors from the Godambe-Huber-White (GHW, or sandwich) estimator, for both full and constrained network structures. Bayesian inference corrects the pseudo-posterior through coordinate rescaling (Arena and Marsman (2026) <doi:10.48550/arXiv.2601.17205>), which matches its covariance to the GHW covariance, and samples from it with the Fisher adaptive Metropolis-adjusted Langevin algorithm (Titsias (2024) <doi:10.48550/arXiv.2305.14442>). For comparison, the posterior based on the full likelihood can also be sampled, exactly for small networks or with the double Metropolis-Hastings algorithm (Lia [...truncated...]
License: MIT + file LICENSE
Copyright: The dataset 'rads2' is licensed under CC BY 4.0; see file COPYRIGHTS.
URL: https://jupepis.github.io/dmrfit/, https://github.com/jupepis/dmrfit
BugReports: https://github.com/jupepis/dmrfit/issues
Depends: R (>= 4.0.0)
Imports: ggplot2, grDevices, graphics, parallel, Rcpp (>= 1.1.0), stats
Suggests: igraph, knitr, rmarkdown, tinytest
VignetteBuilder: knitr
LinkingTo: Rcpp, RcppArmadillo, RcppProgress
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
Packaged: 2026-10-07 21:10:23 UTC; giuseppearena
Author: Giuseppe Arena [aut, cre]
Maintainer: Giuseppe Arena <g.arena@uva.nl>
Repository: CRAN
Date/Publication: 2026-10-18 15:10:02 UTC

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Package distanceHD updated to version 1.4 with previous version 1.2 dated 2025-01-31

Title: Distance Metrics for High-Dimensional Clustering
Description: We provide three distance metrics for measuring the separation between two clusters in high-dimensional spaces. The first metric is the centroid distance, which calculates the Euclidean distance between the centers of the two groups. The second is a ridge Mahalanobis distance, which incorporates a ridge correction constant, alpha, to ensure that the covariance matrix is invertible. The third metric is the maximal data piling distance, which computes the orthogonal distance between the affine spaces spanned by each class. These three distances are asymptotically interconnected and are applicable in tasks such as discrimination, clustering, and outlier detection in high-dimensional settings. The package also provides a distance-based outlier detection procedure for high-dimensional, low-sample-size data, as described in Ahn, Lee and Lee (2018).
Author: Jung Ae Lee [aut, cre], Jeongyoun Ahn [aut]
Maintainer: Jung Ae Lee <jungaeleeb@gmail.com>

Diff between distanceHD versions 1.2 dated 2025-01-31 and 1.4 dated 2026-10-18

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Package BioFTF readmission to version 3.0.0 with previous version 1.2-0 dated 2016-09-26

Title: Biodiversity Assessment Using Hill-Number Functional Tools
Description: Tools for biodiversity assessment through Hill-number diversity profiles treated as functions of the order q. The package computes the profiles of Hill (1973) <doi:10.2307/1934352>, their analytical first and second derivatives, curvature, radius of curvature, arc length, cumulative integral functions, profile areas, area-based rankings and relative changes over time, together with static and interactive three-dimensional biodiversity surfaces. The functional approach follows Di Battista, Fortuna and Maturo (2017) <doi:10.1016/j.ecolind.2016.10.032>, Maturo and Di Battista (2018) <doi:10.1016/j.ecolind.2017.08.016> and Maturo (2018) <doi:10.1016/j.ecolind.2018.03.013>.
Author: Fabrizio Maturo [aut, cre], Francesca Fortuna [aut], Tonio Di Battista [aut]
Maintainer: Fabrizio Maturo <fabrizio.maturo@unimercatorum.it>

This is a re-admission after prior archival of version 1.2-0 dated 2016-09-26

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New package BDIB with initial version 0.1.1
Package: BDIB
Title: Bayesian Dynamic Information Borrowing for Longitudinal Data with Informative Dropout
Version: 0.1.1
Description: Implements 'BDIB' (Bayesian dynamic information borrowing) for longitudinal outcomes with informative dropout. The package provides the mixture prior for longitudinal data borrowing (MLB) and the self-adapting mixture prior for longitudinal data information borrowing (SLB). Both methods use a shared-parameter model for outcomes and dropout.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 4.0.0)
Imports: RBesT, jagsUI, stats
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2026-10-08 00:45:36 UTC; saijun
Author: Saijun Zhao [aut, cre]
Maintainer: Saijun Zhao <saijzhao@iu.edu>
Repository: CRAN
Date/Publication: 2026-10-18 15:40:02 UTC

More information about BDIB at CRAN
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Package amorem updated to version 1.2.0 with previous version 1.1.0 dated 2026-10-17

Title: Augmented Modelling of Relational Events
Description: An integrated workflow for relational event models, in which timestamped interactions among actors arrive in continuous time and the event rate depends on the history of the network itself. Event streams are simulated by an exact Gillespie kernel or an approximate tau-leap kernel; non-events are drawn by case-control sampling in the manner of Lerner and Lomi (2020) <doi:10.1017/nws.2019.57>, or from a time-shifted copy of the process so that covariates shared by all dyads at a given time remain estimable (Lembo et al. 2025, <doi:10.1093/jrsssc/qlaf058>); the timing- and closure-based endogenous statistics of Juozaitiene and Wit (2024) <doi:10.1093/jrsssa/qnae132> are computed from that history; and one front-end fits the resulting case-control data by conditional-logistic partial likelihood, by penalised regression on its degenerate one-control binomial form (which admits smooth, time-varying and actor random effects), or by stochastic gradient descent, including the [...truncated...]
Author: Francisco Richter [aut, cre], Martina Boschi [aut], Ernst C. Wit [aut], Melania Lembo [aut]
Maintainer: Francisco Richter <richtf@usi.ch>

Diff between amorem versions 1.1.0 dated 2026-10-17 and 1.2.0 dated 2026-10-18

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New package XSLmodels with initial version 0.4.0
Package: XSLmodels
Title: Run and Optimize Cross-Situational Word Learning Models
Version: 0.4.0
Maintainer: George Kachergis <george.kachergis@gmail.com>
Description: A collection of cognitive models of cross-situational word learning, together with methods and datasets for fitting and cross-validating them against experimental and corpus data, and for plotting the results.
URL: https://www.kachergis.com/XSLmodels/, https://github.com/kachergis/XSLmodels
BugReports: https://github.com/kachergis/XSLmodels/issues
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: DEoptim, dplyr, ggplot2, purrr, rlang, tibble, tidyr
Suggests: gganimate, knitr, rmarkdown, testthat (>= 3.0.0), viridis
VignetteBuilder: knitr
LazyData: true
License: GPL (>= 3)
NeedsCompilation: no
Packaged: 2026-10-07 17:46:52 UTC; gkacherg
Author: George Kachergis [aut, cre] , Mika Braginsky [aut] , Kevin Liang [aut]
Repository: CRAN
Date/Publication: 2026-10-18 14:50:07 UTC

More information about XSLmodels at CRAN
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Package ROI.plugin.coinclp updated to version 0.1.1 with previous version 0.1.0 dated 2026-09-28

Title: 'COIN-OR' 'Clp' Plugin for the 'R' Optimization Infrastructure
Description: Registers the 'COIN-OR' 'Clp' linear programming solver with 'ROI', the 'R' Optimization Infrastructure, through the 'coinclp' package. Linear programs with continuous variables are then solved by 'ROI' with the 'coinclp' solver, keeping sparse constraint matrices sparse all the way to the solver, and returning dual values, reduced costs and row activities alongside the primal solution.
Author: Sam Lovick [aut, cre]
Maintainer: Sam Lovick <sam@lovickconsulting.com>

Diff between ROI.plugin.coinclp versions 0.1.0 dated 2026-09-28 and 0.1.1 dated 2026-10-18

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More information about ROI.plugin.coinclp at CRAN
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New package namegender with initial version 0.7.1
Package: namegender
Title: Client for the 'NameGender' Name-to-Gender API
Version: 0.7.1
Description: Infer the likely gender of first names, email addresses and usernames, and the country distribution of a name, through the 'NameGender' web API <https://namegender.com>. Each answer carries the share and number of records behind it and the data version that produced it, so results can be reported and reproduced in research. Also returns letter salutations, checks whether a name looks like a real person's name, runs 'CSV' and 'XLSX' files as file jobs, and verifies the signature of 'NameGender' webhooks. An API key is needed.
License: MIT + file LICENSE
Depends: R (>= 4.1.0)
Encoding: UTF-8
Imports: jsonlite, httr2, openssl, utils
Suggests: testthat (>= 3.0.0)
URL: https://namegender.com/docs, https://github.com/anpekesen/namegender-r
BugReports: https://github.com/anpekesen/namegender-r/issues
NeedsCompilation: no
Packaged: 2026-10-07 14:50:29 UTC; anilpekesen
Author: Anil Pekesen [aut, cre], NameGender [cph]
Maintainer: Anil Pekesen <info@namegender.com>
Repository: CRAN
Date/Publication: 2026-10-18 14:30:02 UTC

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Package myman updated to version 0.12.0 with previous version 0.10.0 dated 2026-10-08

Title: Draw from Sequence of 'My Man' Posts by Kevin Kruse
Description: Starting on the afternoon of July 17, 2026, Kevin Kruse fired off an astonishing array of 'BlueSky' replies to an initial post of his featuring a certain government figure: <https://bsky.app/profile/did:plc:cnpe7qvcyjrhm6w7w7e4atur/post/3mqum4mxsuk2g>. This lasted a week and generated nearly seven hundred posts. Over the next few weeks, several more waves followed, each taking aim at a different prominent figure. All of the by now over two thousand posts from these series start with 'My man ...' and make for excellent input to a 'fortunes'-like function. So this small package obliges and offers a random draw each time its myman() function is called. The overall package structure follows package 'fortunes', and 'atrrr' was used to (bulk-)retrieve posts. Neither package is required to run this package to display random selections.
Author: Dirk Eddelbuettel [aut, cre]
Maintainer: Dirk Eddelbuettel <edd@debian.org>

Diff between myman versions 0.10.0 dated 2026-10-08 and 0.12.0 dated 2026-10-18

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New package lwpls with initial version 0.3.0
Package: lwpls
Title: Locally-Weighted Partial Least Squares for 'tidymodels'
Version: 0.3.0
Description: Locally-weighted partial least squares (LW-PLS) is a just-in-time learning method that builds a dedicated weighted partial least squares model around every new sample, where training samples are weighted by their similarity to that sample (Kim et al. (2011) <doi:10.1016/j.ijpharm.2011.10.007>). Regression (single or multiple outcomes) and classification (locally weighted partial least squares-discriminant analysis; Bevilacqua and Marini (2014) <doi:10.1016/j.aca.2014.05.057>) are supported. The local models can be robust to outliers, by partial robust M-regression (Serneels et al. (2005) <doi:10.1016/j.chemolab.2005.04.007>), and sparse (Hoffmann et al. (2015) <doi:10.1016/j.chemolab.2015.09.019>). The prediction kernel is written in C++ with 'RcppArmadillo', and the model is integrated with the 'parsnip', 'dials', 'tune' and 'yardstick' packages so it can be used within the 'tidymodels' framework. Diagnostics of the local models (prediction reliability, local r [...truncated...]
License: MIT + file LICENSE
URL: https://github.com/ChristianGoueguel/lwpls, https://christiangoueguel.com/lwpls/
BugReports: https://github.com/ChristianGoueguel/lwpls/issues
Depends: R (>= 4.1.0)
Imports: cli, dials (>= 1.2.0), generics, ggplot2 (>= 3.5.0), hardhat (>= 1.3.0), parsnip (>= 1.3.0), Rcpp, rlang (>= 1.1.0), scales (>= 1.3.0), stats, tibble, vctrs, yardstick (>= 1.2.0)
Suggests: dplyr, knitr, modeldata, plotly, pls, recipes, rmarkdown, rsample, spelling, testthat (>= 3.0.0), tune (>= 1.1.0), withr, workflows
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
Encoding: UTF-8
Language: en-US
NeedsCompilation: yes
Packaged: 2026-10-07 19:35:25 UTC; christiangoueguel
Author: Christian L. Goueguel [aut, cre], Hiromasa Kaneko [cph]
Maintainer: Christian L. Goueguel <christian.goueguel@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-18 15:00:02 UTC

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New package irw with initial version 1.3.1
Package: irw
Title: Access and Analyze Data from the Item Response Warehouse
Version: 1.3.1
Maintainer: Ben Domingue <ben.domingue@gmail.com>
URL: https://itemresponsewarehouse.github.io/Rpkg/
Contact: mailto:itemresponsewarehouse@stanford.edu
Description: Find, download, and analyze datasets from the Item Response Warehouse, a harmonized collection of item response data from educational and psychological measurement described in Nadela et al. (2026) <doi:10.59863/cijg4549>. Functions search tables by metadata, fetch them in a common long format, reshape between long and wide layouts, simulate item response data, and compare the predictive accuracy of fitted models with the InterModel Vigorish.
Depends: R (>= 4.0.0)
Imports: glue, httr, imv, tibble, tools, utils, stats
Suggests: arrow, redivis, testthat (>= 3.0.0), withr, evaluate, mirt, gnm, BradleyTerry2
Additional_repositories: https://redivis.r-universe.dev
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
BugReports: https://github.com/itemresponsewarehouse/Rpkg/issues
NeedsCompilation: no
Packaged: 2026-10-07 16:29:59 UTC; ben
Author: Hansol Lee [aut, cph], Michael Hardy [aut, cph], Ben Domingue [aut, cre, cph]
Repository: CRAN
Date/Publication: 2026-10-18 14:40:07 UTC

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New package CUNYsports with initial version 0.1.0
Package: CUNYsports
Title: Accessible Collegiate Athletics Data from the City University of New York (CUNY)
Version: 0.1.0
Description: Provides access to collegiate athletics data from the City University of New York (CUNY), the largest urban public university system in the United States. Includes tools for discovering and retrieving sports data from an included SQLite database. The data covers individual CUNY schools, players, games, seasons, and other aspects of CUNY athletics, providing accessible datasets for teaching, student learning, and sports analytics.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 4.1.0)
Imports: DBI, dplyr, RSQLite, stringr, rlang, tibble
Suggests: ggplot2, knitr, rmarkdown, dbplyr
VignetteBuilder: knitr
URL: https://nyc-open-data-lab.github.io/CUNYsports/, https://github.com/nyc-open-data-lab/CUNYsports
BugReports: https://github.com/nyc-open-data-lab/CUNYsports/issues
NeedsCompilation: no
Packaged: 2026-10-07 15:32:40 UTC; christianmartinez
Author: Christian Martinez [aut, cre]
Maintainer: Christian Martinez <c.martinez0@outlook.com>
Repository: CRAN
Date/Publication: 2026-10-18 14:20:08 UTC

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Package codriver updated to version 1.1.0 with previous version 1.0.0 dated 2026-08-08

Title: Context-Aware AI Assistant for 'RStudio'
Description: A context-aware AI assistant for 'RStudio' that works directly in the source editor without switching context or opening a separate chat window. Reads the cursor position and selection to automatically choose the right action - generate, complete, continue, edit, or comment. Results are presented as ghost text or an in-place diff suggestion. Supports configuration of multiple large language model providers and can be invoked via the keyboard shortcut or Addins menu.
Author: S.A. van der Wulp [aut, cre, cph]
Maintainer: S.A. van der Wulp <vdwulp@gmail.com>

Diff between codriver versions 1.0.0 dated 2026-08-08 and 1.1.0 dated 2026-10-18

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New package POEMED with initial version 1.0.0
Package: POEMED
Title: Power Enhancement for High-Dimensional Mediation Analysis
Version: 1.0.0
Date: 2026-10-05
Description: Provides powerful global tests for high-dimensional mediation analysis with heterogeneous (and even contrasting) mediation effects, for continuous, binary, and count outcomes. The power enhancement (PE) idea augments a Wald test on the total indirect effect with a component that captures marginal signals from individual mediators, so that active mediators are detected even when their individual indirect effects cancel and the total indirect effect is zero. The package also identifies which individual mediators are active with familywise error rate or false discovery rate control. The methods implement Yu and Kelley (in press), "Power Enhancement in High-Dimensional Heterogeneous Mediation Analysis" (Journal of the American Statistical Association); the package also simulates mediation data under the homogeneous, heterogeneous, and contrasting patterns that the article studies, ships its empirical health-expenditure data as a benchmark and reruns that empirical analysis, and runs the de [...truncated...]
Imports: generics, glmnet, graphics, ncvreg, parallel, stats, utils, withr (>= 3.0.3)
Suggests: knitr, rmarkdown, testthat (>= 3.2.0), tools
License: GPL (>= 3)
Copyright: Xiufan Yu and Ken Kelley, except the WHO_health_mediation and WHO_indicator_codebook data sets, which redistribute World Health Organization (Global Health Expenditure Database) and World Bank (World Development Indicators) data under the terms stated on the help page of WHO_health_mediation and, in the source package, in the file LICENSE.note.
URL: https://github.com/yelleKneK/POEMED, https://yelleknek.github.io/POEMED/
BugReports: https://github.com/yelleKneK/POEMED/issues
Encoding: UTF-8
Language: en-US
LazyData: true
LazyDataCompression: xz
VignetteBuilder: knitr
Depends: R (>= 4.1.0)
NeedsCompilation: no
Packaged: 2026-10-05 11:34:16 UTC; kkelley
Author: Xiufan Yu [aut], Ken Kelley [aut, cre]
Maintainer: Ken Kelley <kkelley@nd.edu>
Repository: CRAN
Date/Publication: 2026-10-18 14:00:02 UTC

More information about POEMED at CRAN
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Package tidypredict updated to version 1.2.2 with previous version 1.2.1 dated 2026-09-09

Title: Run Predictions Inside the Database
Description: It parses a fitted 'R' model object, and returns a formula in 'Tidy Eval' code that calculates the predictions. It works with several databases back-ends because it leverages 'dplyr' and 'dbplyr' for the final 'SQL' translation of the algorithm. Dozens of model classes are supported; see the "Supported models" article at <https://tidypredict.tidymodels.org/articles/models.html> for the current list.
Author: Emil Hvitfeldt [aut, cre] , Edgar Ruiz [aut], Max Kuhn [aut] , Posit Software, PBC [cph, fnd]
Maintainer: Emil Hvitfeldt <emil.hvitfeldt@posit.co>

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Package SpectralMM updated to version 0.2.0 with previous version 0.1.0 dated 2026-10-15

Title: Spectral Majorization for Statistical Estimation
Description: Fits generalized linear, asymmetric and robust regression models using spectral majorization and iterative quadratic subproblem solvers. A shared C++ computational core supports dense and sparse designs, automatic intercepts, optional Wald inference and detailed iteration diagnostics. Native code is compiled automatically during package installation.
Author: Xun-Jian Li [aut, cre]
Maintainer: Xun-Jian Li <xunjianli@ucla.edu>

Diff between SpectralMM versions 0.1.0 dated 2026-10-15 and 0.2.0 dated 2026-10-18

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Package shaman updated to version 2.1.1 with previous version 2.1.0 dated 2026-10-16

Title: Sampling Hi-C Contact Matrices for Aparametric Normalization
Description: Generate expected Hi-C (genome-wide chromosome conformation capture) data maintaining marginal coverage, and the decay in contact probability with increasing genomic distance. Expected Hi-C data is used for normalization and graphical presentation of statistical significance of observed Hi-C contact enrichments. The method is described in Mendelson Cohen et al. (2017) <doi:10.1101/187203>.
Author: Amos Tanay [aut], Netta Mendelson Cohen [aut], Aviezer Lifshitz [aut, cre], Weizmann Institute of Science [cph]
Maintainer: Aviezer Lifshitz <aviezer.lifshitz@weizmann.ac.il>

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Package plotbb updated to version 1.0.0 with previous version 0.1.2 dated 2026-05-21

Title: Grammar of Graphics on 'base' Graphics
Description: Implements a grammar of graphics on top of the 'base' graphics system. A 'bbplot' object is built incrementally with a '+' operator from data, aesthetic mappings and layers, then rendered with base graphics, so plotbb layers can be mixed with ordinary base plotting calls. The package provides layers for points, lines, bars, histograms, densities, boxplots, violins, ribbons, smooths, error bars and tiles, together with colour and other aesthetic scales, automatic legends, faceting, themes, coordinate flipping and significance annotations.
Author: Guangchuang Yu [aut, cre, cph]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>

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Package performance updated to version 0.19.0 with previous version 0.18.2 dated 2026-09-10

Title: Assessment of Regression Models Performance
Description: Utilities for computing measures to assess model quality, which are not directly provided by R's 'base' or 'stats' packages. These include e.g. measures like r-squared, intraclass correlation coefficient (Nakagawa, Johnson & Schielzeth (2017) <doi:10.1098/rsif.2017.0213>), root mean squared error or functions to check models for overdispersion, singularity or zero-inflation and more. Functions apply to a large variety of regression models, including generalized linear models, mixed effects models and Bayesian models. References: Lüdecke et al. (2021) <doi:10.21105/joss.03139>.
Author: Daniel Luedecke [aut, cre] , Dominique Makowski [aut, ctb] , Mattan S. Ben-Shachar [aut, ctb] , Indrajeet Patil [aut, ctb] , Philip Waggoner [aut, ctb] , Brenton M. Wiernik [aut, ctb] , Remi Theriault [aut, ctb] , Vincent Arel-Bundock [ctb] , Martin J [...truncated...]
Maintainer: Daniel Luedecke <officialeasystats@gmail.com>

Diff between performance versions 0.18.2 dated 2026-09-10 and 0.19.0 dated 2026-10-18

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Package emhawkes updated to version 1.0.1 with previous version 1.0.0 dated 2026-10-17

Title: Exponential Multivariate Hawkes Model
Description: Simulate and fit multivariate Hawkes models with exponential kernels, introduced by Hawkes (1971) <doi:10.2307/2334319>. Includes marked, state-dependent, and multi-kernel specifications. Parameter estimation uses maximum likelihood, following Ozaki (1979) <doi:10.1007/BF02480272>, through the optional 'maxLik' package or the optimization functions supplied with R.
Author: Kyungsub Lee [aut, cre]
Maintainer: Kyungsub Lee <kyungsub@gmail.com>

Diff between emhawkes versions 1.0.0 dated 2026-10-17 and 1.0.1 dated 2026-10-18

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Package censusindia updated to version 0.2.1 with previous version 0.2.0 dated 2026-10-15

Title: Census of India Data, 1901-2011, with Boundaries and Projections
Description: Tables from every decennial Census of India from 1901 to 2011: population time series, Primary Census Abstracts at state, district and sub-district level, mother tongue tables, Scheduled Caste and Scheduled Tribe populations, and the 1931 caste tables by religion, together with Ministry of Health and Family Welfare population projections to 2036. Historical series are drawn from Jolad and Singh (2026) <doi:10.7910/DVN/ON8CP8>. Census variables can be returned with state or district boundaries of the matching census year attached for mapping. Boundary files and the largest village-level tables are downloaded on first use and cached locally.
Author: Saket Choudhary [aut, cre, cph]
Maintainer: Saket Choudhary <saketc@iitb.ac.in>

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Package AHTauDesign updated to version 1.0 with previous version 0.1.0 dated 2026-09-10

Title: Prospective Trial Planning for Average Hazard Estimands
Description: Provides a simulation-based prospective planning workflow for average hazard estimands in two-arm time-to-event trials. Supports calibration and freezing of design-specific information-support rules, assessment of clinically preferred truncation horizons, conditional selection of clinically acceptable alternative horizons, and reverse design over sample size and additional follow-up. Separates information support from statistical power and supports auditable planning reports.
Author: Xinyue Zhao [aut, cre], Haitao Pan [aut]
Maintainer: Xinyue Zhao <xinyuezhao27@gwu.edu>

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Package swash updated to version 3.0.2 with previous version 3.0.1 dated 2026-10-11

Title: Health Geography Toolbox for Model-Based Analysis of Infections Panel Data
Description: Within epidemic outbreaks, infections grow and decline differently between regions, and the velocity of spatial spread differs between countries. The swash library offers a set of model-based analyses for these topics. Spread velocity may be analysed with the Swash-Backwash Model for the Single Epidemic Wave by Cliff and Haggett (2006) <doi:10.1007/s10109-006-0027-8> and corresponding functions for bootstrap confidence intervals, country comparison, and visualization of results. Differences in epidemic growth between regions may be analysed using logistic growth models, exponential growth models, Hawkes processes and breakpoint analyses. Cluster and hotspot analyses can be conducted using neighborhood matrices. See Wieland (2020) <doi:10.18335/region.v7i2.324> for an application of some of the mentioned analysis methods. All functionalities are accessed by the class "infpan" for infections panel data defined in this package, which is built from a data.frame provided by the [...truncated...]
Author: Thomas Wieland [aut, cre]
Maintainer: Thomas Wieland <geowieland@googlemail.com>

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Package IssueTrackeR updated to version 2.0.0 with previous version 1.5.0 dated 2026-08-21

Title: List Things to Do
Description: Manage a 'GitHub' problem using R: wrangle issues, labels and milestones. It includes functions for storing, prioritizing (sorting), displaying, adding, deleting, and selecting (filtering) issues based on qualitative and quantitative information. Issues (labels and milestones) are written in lists and categorized into the S3 class to be easily manipulated as datasets in R.
Author: Tanguy Barthelemy [aut, cre, art, cph]
Maintainer: Tanguy Barthelemy <tanguy.barthelemy@insee.fr>

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Package pigauto updated to version 0.11.0 with previous version 0.10.0 dated 2026-07-30

Title: Fill in Missing Species Traits Using a Phylogenetic Tree
Description: Imputes missing species trait data for comparative analyses by combining three sources of information: phylogenetic similarity (closely related species share similar traits), cross-trait correlations (observed traits inform missing ones), and optional environmental covariates (climate, habitat, geography). Handles continuous measurements, counts, binary variables, ordered categories, unordered categories, bounded proportions, zero-inflated counts, and compositional multi-proportion data in a single call. The default method is a phylogenetic baseline calibrated on held-out data; an optional graph neural network correction, which is needed to use covariates, is blended in through a per-trait gate that retains a baseline fallback. Provides conformal prediction intervals for continuous, count, ordinal, and proportion traits and an experimental analysis-aware multiple-imputation workflow for one missing continuous covariate in Gaussian linear, binomial-logit, and Gaussian random-intercept m [...truncated...]
Author: Shinichi Nakagawa [aut, cre, cph]
Maintainer: Shinichi Nakagawa <itchyshin@gmail.com>

Diff between pigauto versions 0.10.0 dated 2026-07-30 and 0.11.0 dated 2026-10-18

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Package iglm (with last version 1.2.6) was removed from CRAN

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

2026-09-23 1.2.6
2026-07-08 1.2.5
2026-04-23 1.2.4
2026-04-10 1.2.3
2026-03-30 1.2.2
2026-03-18 1.2.1
2026-03-15 1.2
2025-11-28 1.1
2025-11-25 1.0

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Package OmopViewer (with last version 0.7.0) was removed from CRAN

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

2026-02-09 0.7.0
2025-12-10 0.6.0
2025-11-16 0.5.0
2025-07-08 0.4.0
2025-04-15 0.3.0
2024-11-27 0.2.0

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