Mon, 29 Dec 2025

Package proxy updated to version 0.4-29 with previous version 0.4-28 dated 2025-12-11

Title: Distance and Similarity Measures
Description: Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.
Author: David Meyer [aut, cre] , Christian Buchta [aut]
Maintainer: David Meyer <David.Meyer@R-project.org>

Diff between proxy versions 0.4-28 dated 2025-12-11 and 0.4-29 dated 2025-12-29

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Package extraDistr updated to version 1.10.0.1 with previous version 1.10.0 dated 2023-11-30

Title: Additional Univariate and Multivariate Distributions
Description: Density, distribution function, quantile function and random generation for a number of univariate and multivariate distributions. This package implements the following distributions: Bernoulli, beta-binomial, beta-negative binomial, beta prime, Bhattacharjee, Birnbaum-Saunders, bivariate normal, bivariate Poisson, categorical, Dirichlet, Dirichlet-multinomial, discrete gamma, discrete Laplace, discrete normal, discrete uniform, discrete Weibull, Frechet, gamma-Poisson, generalized extreme value, Gompertz, generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t, Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy, Laplace, location-scale t, logarithmic, Lomax, multivariate hypergeometric, multinomial, negative hypergeometric, non-standard beta, normal mixture, Poisson mixture, Pareto, power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam, slash, triangular, truncated binomial, truncated normal, truncated Poisson, Tukey lambda, Wald, zero-inflated binomial, zer [...truncated...]
Author: Tymoteusz Wolodzko [aut, cre]
Maintainer: ORPHANED

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Package nmarank updated to version 0.3-0.1 with previous version 0.3-0 dated 2023-02-24

Title: Complex Hierarchy Questions in Network Meta-Analysis
Description: Derives the most frequent hierarchies along with their probability of occurrence. One can also define complex hierarchy criteria and calculate their probability. Methodology based on Papakonstantinou et al. (2021) <DOI:10.21203/rs.3.rs-858140/v1>.
Author: Adriani Nikolakopoulou [aut] , Guido Schwarzer [aut] , Theodoros Papakonstantinou [aut, cre]
Maintainer: Theodoros Papakonstantinou <dev@tpapak.com>

Diff between nmarank versions 0.3-0 dated 2023-02-24 and 0.3-0.1 dated 2025-12-29

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Package maxLik updated to version 1.5-2.2 with previous version 1.5-2.1 dated 2024-03-24

Title: Maximum Likelihood Estimation and Related Tools
Description: Functions for Maximum Likelihood (ML) estimation, non-linear optimization, and related tools. It includes a unified way to call different optimizers, and classes and methods to handle the results from the Maximum Likelihood viewpoint. It also includes a number of convenience tools for testing and developing your own models.
Author: Ott Toomet [aut, cre], Arne Henningsen [aut], Spencer Graves [ctb], Yves Croissant [ctb], David Hugh-Jones [ctb], Luca Scrucca [ctb]
Maintainer: Ott Toomet <otoomet@gmail.com>

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Package FactoInvestigate updated to version 1.9.1 with previous version 1.9 dated 2023-11-27

Title: Automatic Description of Factorial Analysis
Description: Brings a set of tools to help and automatically realise the description of principal component analyses (from 'FactoMineR' functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).
Author: Simon Thuleau [aut], Francois Husson [aut, cre]
Maintainer: Francois Husson <francois.husson@institut-agro.fr>

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Package tesseract updated to version 5.2.4 with previous version 5.2.3 dated 2025-03-23

Title: Open Source OCR Engine
Description: Bindings to 'Tesseract': a powerful optical character recognition (OCR) engine that supports over 100 languages. The engine is highly configurable in order to tune the detection algorithms and obtain the best possible results.
Author: Jeroen Ooms [aut, cre]
Maintainer: Jeroen Ooms <jeroenooms@gmail.com>

Diff between tesseract versions 5.2.3 dated 2025-03-23 and 5.2.4 dated 2025-12-29

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Package futile.logger updated to version 1.4.9 with previous version 1.4.3 dated 2016-07-10

Title: A Logging Utility for R
Description: Provides a simple yet powerful logging utility. Based loosely on log4j, futile.logger takes advantage of R idioms to make logging a convenient and easy to use replacement for cat and print statements.
Author: Brian Lee Yung Rowe [aut, cre]
Maintainer: Brian Lee Yung Rowe <r@zatonovo.com>

Diff between futile.logger versions 1.4.3 dated 2016-07-10 and 1.4.9 dated 2025-12-29

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Wed, 24 Dec 2025

Package data.table updated to version 1.18.0 with previous version 1.17.8 dated 2025-07-10

Title: Extension of `data.frame`
Description: Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development.
Author: Tyson Barrett [aut, cre] , Matt Dowle [aut], Arun Srinivasan [aut], Jan Gorecki [aut], Michael Chirico [aut] , Toby Hocking [aut] , Benjamin Schwendinger [aut] , Ivan Krylov [aut] , Pasha Stetsenko [ctb], Tom Short [ctb], Steve Lianoglou [ctb], Eduar [...truncated...]
Maintainer: Tyson Barrett <t.barrett88@gmail.com>

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Package akc updated to version 0.9.9.3 with previous version 0.9.9.2 dated 2025-12-16

Title: Automatic Knowledge Classification
Description: A tidy framework for automatic knowledge classification and visualization. Currently, the core functionality of the framework is mainly supported by modularity-based clustering (community detection) in keyword co-occurrence network, and focuses on co-word analysis of bibliometric research. However, the designed functions in 'akc' are general, and could be extended to solve other tasks in text mining as well.
Author: Tian-Yuan Huang [aut, cre]
Maintainer: Tian-Yuan Huang <huang.tian-yuan@qq.com>

Diff between akc versions 0.9.9.2 dated 2025-12-16 and 0.9.9.3 dated 2025-12-24

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Package tlrmvnmvt updated to version 1.1.2.1 with previous version 1.1.2 dated 2022-06-09

Title: Low-Rank Methods for MVN and MVT Probabilities
Description: Implementation of the classic Genz algorithm and a novel tile-low-rank algorithm for computing relatively high-dimensional multivariate normal (MVN) and Student-t (MVT) probabilities. References used for this package: Foley, James, Andries van Dam, Steven Feiner, and John Hughes. "Computer Graphics: Principle and Practice". Addison-Wesley Publishing Company. Reading, Massachusetts (1987, ISBN:0-201-84840-6 1); Genz, A., "Numerical computation of multivariate normal probabilities," Journal of Computational and Graphical Statistics, 1, 141-149 (1992) <doi:10.1080/10618600.1992.10477010>; Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "Exploiting Low Rank Covariance Structures for Computing High-Dimensional Normal and Student- t Probabilities," Statistics and Computing, 31.1, 1-16 (2021) <doi:10.1007/s11222-020-09978-y>; Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "tlrmvnmvt: Computing High-Dimensional Multivariate Normal and Student-t Probabilit [...truncated...]
Author: Marc Genton [aut], David Keyes [aut], George Turkiyyah [aut], Jian Cao [aut, cre]
Maintainer: Jian Cao <jian.cao@kaust.edu.sa>

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Package odbc updated to version 1.6.4.1 with previous version 1.6.4 dated 2025-12-06

Title: Connect to ODBC Compatible Databases (using the DBI Interface)
Description: A DBI-compatible interface to ODBC databases.
Author: Jim Hester [aut], Hadley Wickham [aut, cre], Oliver Gjoneski [aut], Simon Couch [aut], lexicalunit [cph] , Google Inc. [cph] , Posit Software, PBC [cph, fnd]
Maintainer: Hadley Wickham <hadley@posit.co>

Diff between odbc versions 1.6.4 dated 2025-12-06 and 1.6.4.1 dated 2025-12-24

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Package archive updated to version 1.1.12.1 with previous version 1.1.12 dated 2025-03-20

Title: Multi-Format Archive and Compression Support
Description: Bindings to 'libarchive' <http://www.libarchive.org> the Multi-format archive and compression library. Offers R connections and direct extraction for many archive formats including 'tar', 'ZIP', '7-zip', 'RAR', 'CAB' and compression formats including 'gzip', 'bzip2', 'compress', 'lzma' and 'xz'.
Author: Jim Hester [aut] , Gabor Csardi [aut, cre], Ondrej Holy [cph] , RStudio [cph, fnd]
Maintainer: Gabor Csardi <csardi.gabor@gmail.com>

Diff between archive versions 1.1.12 dated 2025-03-20 and 1.1.12.1 dated 2025-12-24

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Tue, 23 Dec 2025

Package phylospatial updated to version 1.2.1 with previous version 1.2.0 dated 2025-12-20

Title: Spatial Phylogenetic Analysis
Description: Analyze spatial phylogenetic diversity patterns. Use your data on an evolutionary tree and geographic distributions of the terminal taxa to compute diversity and endemism metrics, test significance with null model randomization, analyze community turnover and biotic regionalization, and perform spatial conservation prioritizations. All functions support quantitative community data in addition to binary data.
Author: Matthew Kling [aut, cre, cph]
Maintainer: Matthew Kling <mattkling@berkeley.edu>

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Package fastTopics readmission to version 0.7-37 with previous version 0.7-30 dated 2025-10-20

Title: Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data
Description: Implements fast, scalable optimization algorithms for fitting topic models ("grade of membership" models) and non-negative matrix factorizations to count data. The methods exploit the special relationship between the multinomial topic model (also, "probabilistic latent semantic indexing") and Poisson non-negative matrix factorization. The package provides tools to compare, annotate and visualize model fits, including functions to efficiently create "structure plots" and identify key features in topics. The 'fastTopics' package is a successor to the 'CountClust' package. For more information, see <doi:10.48550/arXiv.2105.13440> and <doi:10.1186/s13059-023-03067-9>. Please also see the GitHub repository for additional vignettes not included in the package on CRAN.
Author: Peter Carbonetto [aut, cre], Kevin Luo [aut], Kushal Dey [aut], Joyce Hsiao [ctb], Abhishek Sarkar [ctb], Anthony Hung [ctb], Xihui Lin [ctb], Paul C. Boutros [ctb], Minzhe Wang [ctb], Tracy Ke [ctb], Eric Weine [ctb], Matthew Stephens [aut]
Maintainer: Peter Carbonetto <peter.carbonetto@gmail.com>

This is a re-admission after prior archival of version 0.7-30 dated 2025-10-20

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Package evprof updated to version 1.2.0 with previous version 1.1.2 dated 2024-03-14

Title: Electric Vehicle Charging Sessions Profiling and Modelling
Description: Tools for modelling electric vehicle charging sessions into generic groups with similar connection patterns called "user profiles", using Gaussian Mixture Models clustering. The clustering and profiling methodology is described in Cañigueral and Meléndez (2021, ISBN:0142-0615) <doi:10.1016/j.ijepes.2021.107195>.
Author: Marc Canigueral [aut, cre, cph]
Maintainer: Marc Canigueral <marccanyigueral@gmail.com>

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Package imager updated to version 1.0.8 with previous version 1.0.5 dated 2025-08-03

Title: Image Processing Library Based on 'CImg'
Description: Fast image processing for images in up to 4 dimensions (two spatial dimensions, one time/depth dimension, one colour dimension). Provides most traditional image processing tools (filtering, morphology, transformations, etc.) as well as various functions for easily analysing image data using R. The package wraps 'CImg', <https://cimg.eu>, a simple, modern C++ library for image processing.
Author: Simon Barthelme [aut], David Tschumperle [ctb], Jan Wijffels [ctb], Haz Edine Assemlal [ctb], Shota Ochi [ctb], Aaron Robotham [cre], Rodrigo Tobar [ctb]
Maintainer: Aaron Robotham <aaron.robotham@uwa.edu.au>

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Package exams updated to version 2.4-3 with previous version 2.4-2 dated 2025-04-29

Title: Automatic Generation of Exams in R
Description: Automatic generation of exams based on exercises in Markdown or LaTeX format, possibly including R code for dynamic generation of exercise elements. Exercise types include single-choice and multiple-choice questions, arithmetic problems, string questions, and combinations thereof (cloze). Output formats include standalone files (PDF, HTML, Docx, ODT, ...), Moodle XML, QTI 1.2, QTI 2.1, Blackboard, Canvas, OpenOlat, ILIAS, TestVision, Particify, ARSnova, Kahoot!, Grasple, and TCExam. In addition to fully customizable PDF exams, a standardized PDF format (NOPS) is provided that can be printed, scanned, and automatically evaluated.
Author: Achim Zeileis [aut, cre] , Bettina Gruen [aut] , Friedrich Leisch [aut] , Nikolaus Umlauf [aut] , Niels Smits [aut] , Mirko Birbaumer [ctb], Dominik Ernst [ctb] , Patrik Keller [ctb], Reto Stauffer [ctb] , Kenji Sato [ctb] , Florian Wickelmaier [ctb] [...truncated...]
Maintainer: Achim Zeileis <Achim.Zeileis@R-project.org>

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Package arrow updated to version 22.0.0.1 with previous version 22.0.0 dated 2025-10-29

Title: Integration to 'Apache' 'Arrow'
Description: 'Apache' 'Arrow' <https://arrow.apache.org/> is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. This package provides an interface to the 'Arrow C++' library.
Author: Neal Richardson [aut], Ian Cook [aut], Nic Crane [aut], Dewey Dunnington [aut] , Romain Francois [aut] , Jonathan Keane [aut, cre], Bryce Mecum [aut], Dragoș Moldovan-Gruenfeld [aut], Jeroen Ooms [aut], Jacob Wujciak-Jens [aut], Javier Luraschi [ctb], [...truncated...]
Maintainer: Jonathan Keane <jkeane@gmail.com>

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Package survdnn updated to version 0.7.0 with previous version 0.6.3 dated 2025-10-30

Title: Deep Neural Networks for Survival Analysis using 'torch'
Description: Provides deep learning models for right-censored survival data using the 'torch' backend. Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox, and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation, hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score, and integrated Brier score. For methodological details, see Kvamme et al. (2019) <https://www.jmlr.org/papers/v20/18-424.html>.
Author: Imad EL BADISY [aut, cre]
Maintainer: Imad EL BADISY <elbadisyimad@gmail.com>

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Package minimaxALT updated to version 1.0.3 with previous version 1.0.2 dated 2025-10-10

Title: Generate Optimal Designs of Accelerated Life Test using PSO-Based Algorithm
Description: A computationally efficient solution for generating optimal experimental designs in Accelerated Life Testing (ALT). Leveraging a Particle Swarm Optimization (PSO)-based hybrid algorithm, the package identifies optimal test plans that minimize estimation variance under specified failure models and stress profiles. For more detailed, see Lee et al. (2025), Optimal Robust Strategies for Accelerated Life Tests and Fatigue Testing of Polymer Composite Materials <doi:10.1214/25-AOAS2075>, and Hoang (2025), Model-Robust Minimax Design of Accelerated Life Tests via PSO-based Hybrid Algorithm, Master' Thesis, Unpublished.
Author: Hoai-Linh Hoang [aut, cre], I-Chen Lee [aut], Ping-Yang Chen [aut], Ray-Bing Chen [aut], Weng Kee Wong [aut]
Maintainer: Hoai-Linh Hoang <hoailinh.hoang17@gmail.com>

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Package loo updated to version 2.9.0 with previous version 2.8.0 dated 2024-07-03

Title: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models
Description: Efficient approximate leave-one-out cross-validation (LOO) for Bayesian models fit using Markov chain Monte Carlo, as described in Vehtari, Gelman, and Gabry (2017) <doi:10.1007/s11222-016-9696-4>. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, we also obtain approximate standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions.
Author: Aki Vehtari [aut], Jonah Gabry [cre, aut], Mans Magnusson [aut], Yuling Yao [aut], Paul-Christian Buerkner [aut], Topi Paananen [aut], Andrew Gelman [aut], Ben Goodrich [ctb], Juho Piironen [ctb], Bruno Nicenboim [ctb], Leevi Lindgren [ctb], Visruth S [...truncated...]
Maintainer: Jonah Gabry <jgabry@gmail.com>

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Package link2GI updated to version 0.7-2 with previous version 0.6-2 dated 2024-10-28

Title: Linking Geographic Information Systems, Remote Sensing and Other Command Line Tools
Description: Functions and tools for using open GIS and remote sensing command-line interfaces in a reproducible environment.
Author: Chris Reudenbach [cre, aut], Tim Appelhans [ctb]
Maintainer: Chris Reudenbach <reudenbach@uni-marburg.de>

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Package twang updated to version 2.6.2 with previous version 2.6.1 dated 2024-07-22

Title: Toolkit for Weighting and Analysis of Nonequivalent Groups
Description: Provides functions for propensity score estimating and weighting, nonresponse weighting, and diagnosis of the weights.
Author: Matthew Cefalu [aut], Greg Ridgeway [aut], Dan McCaffrey [aut], Andrew Morral [aut], Beth Ann Griffin [aut], Lane Burgette [aut, cre]
Maintainer: Lane Burgette <burgette@rand.org>

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Package tutorial.helpers updated to version 0.6.1 with previous version 0.6.0 dated 2025-09-11

Title: Helper Functions for Creating Tutorials
Description: Helper functions for creating, editing, and testing tutorials created with the 'learnr' package. Provides a simple method for allowing students to download their answers to tutorial questions. For examples of its use, see the 'r4ds.tutorials' package.
Author: David Kane [aut, cre, cph]
Maintainer: David Kane <dave.kane@gmail.com>

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Package plumber updated to version 1.3.2 with previous version 1.3.1 dated 2025-12-13

Title: An API Generator for R
Description: Gives the ability to automatically generate and serve an HTTP API from R functions using the annotations in the R documentation around your functions.
Author: Barret Schloerke [cre, aut] , Jeff Allen [aut, ccp], Bruno Tremblay [ctb], Frans van Dunne [ctb], Sebastiaan Vandewoude [ctb], Posit Software, PBC [cph, fnd]
Maintainer: Barret Schloerke <barret@posit.co>

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Package HelpersMG updated to version 2025.12.22 with previous version 6.6 dated 2025-06-16

Title: Tools for Various R Functions Helpers
Description: Contains miscellaneous functions useful for managing 'NetCDF' files (see <https://en.wikipedia.org/wiki/NetCDF>), get moon phase and time for sun rise and fall, tide level, analyse and reconstruct periodic time series of temperature with irregular sinusoidal pattern, show scales and wind rose in plot with change of color of text, Metropolis-Hastings algorithm for Bayesian MCMC analysis, plot graphs or boxplot with error bars, search files in disk by there names or their content, read the contents of all files from a folder at one time.
Author: Marc Girondot [aut, cre]
Maintainer: Marc Girondot <marc.girondot@gmail.com>

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Package fMRItools updated to version 0.7.2 with previous version 0.6.0 dated 2025-05-20

Title: Routines for Common fMRI Processing Tasks
Description: Supports fMRI (functional magnetic resonance imaging) analysis tasks including reading in 'CIFTI', 'GIFTI' and 'NIFTI' data, temporal filtering, nuisance regression, and aCompCor (anatomical Components Correction) (Muschelli et al. (2014) <doi:10.1016/j.neuroimage.2014.03.028>).
Author: Amanda Mejia [aut, cre], Damon Pham [aut] , Mark Fiecas [ctb]
Maintainer: Amanda Mejia <mandy.mejia@gmail.com>

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Package CohortConstructor updated to version 0.6.1 with previous version 0.6.0 dated 2025-10-31

Title: Build and Manipulate Study Cohorts Using a Common Data Model
Description: Create and manipulate study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model.
Author: Edward Burn [aut, cre] , Marti Catala [aut] , Nuria Mercade-Besora [aut] , Marta Alcalde-Herraiz [aut] , Mike Du [aut] , Yuchen Guo [aut] , Xihang Chen [aut] , Kim Lopez-Guell [aut] , Elin Rowlands [aut]
Maintainer: Edward Burn <edward.burn@ndorms.ox.ac.uk>

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Package BAS updated to version 2.0.2 with previous version 2.0.0 dated 2025-12-16

Title: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling
Description: Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors from Li and Clyde (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049> for details on the sampling algorithms. Uniform prior [...truncated...]
Author: Merlise Clyde [aut, cre, cph] , Michael Littman [ctb], Joyee Ghosh [ctb], Yingbo Li [ctb], Betsy Bersson [ctb], Don van de Bergh [ctb], Quanli Wang [ctb]
Maintainer: Merlise Clyde <clyde@duke.edu>

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 src/lm_amcmc.c             |   13 ++++
 src/lm_amcmc_growable.c    |   18 ++++--
 src/lm_deterministic.c     |   10 +++
 src/lm_mcmc.c              |   14 ++++
 src/lm_mcmc_grow.c         |   10 +++
 src/lm_mcmcbas.c           |   14 ++++
 src/lm_sampleworep-grow.c  |   11 +++
 src/lm_sampleworep.c       |   11 +++
 src/model_probabilities.c  |    7 +-
 20 files changed, 263 insertions(+), 110 deletions(-)

More information about BAS at CRAN
Permanent link

Mon, 22 Dec 2025

Package hmde updated to version 1.3.1 with previous version 1.3 dated 2025-11-26

Title: Hierarchical Methods for Differential Equations
Description: Wrapper for 'Stan' that offers a number of in-built models to implement a hierarchical Bayesian longitudinal model for repeat observation data. Model choice selects the differential equation that is fit to the observations. Single and multi-individual models are available. O'Brien et al. (2024) <doi:10.1111/2041-210X.14463>.
Author: Daniel Falster [aut, ctb] , Tess O'Brien [aut, cre, cph] , Fonti Kar [ctb] , David Warton [aut, ctb]
Maintainer: Tess O'Brien <tess_obrien@fastmail.com>

Diff between hmde versions 1.3 dated 2025-11-26 and 1.3.1 dated 2025-12-22

 DESCRIPTION                           |    6 -
 MD5                                   |   14 +-
 NEWS.md                               |    6 -
 inst/doc/canham.html                  |    2 
 inst/doc/constant-growth.html         |  180 +++++++++++++++++-----------------
 inst/doc/here_be_dragons.html         |   24 ++--
 inst/doc/hmde_for_mathematicians.html |    4 
 inst/doc/von-bertalanffy.html         |   79 +++++++-------
 8 files changed, 155 insertions(+), 160 deletions(-)

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Package crossmap updated to version 0.4.3 with previous version 0.4.2 dated 2025-04-25

Title: Apply Functions to All Combinations of List Elements
Description: Provides an extension to the 'purrr' family of mapping functions to apply a function to each combination of elements in a list of inputs. Also includes functions for automatically detecting output type in mapping functions, finding every combination of elements of lists or rows of data frames, and applying multiple models to multiple subsets of a dataset.
Author: Alexander Rossell Hayes [aut, cre, cph]
Maintainer: Alexander Rossell Hayes <alexander@rossellhayes.com>

Diff between crossmap versions 0.4.2 dated 2025-04-25 and 0.4.3 dated 2025-12-22

 DESCRIPTION             |   18 +++++++++---------
 MD5                     |   10 +++++-----
 NEWS.md                 |    4 ++++
 R/errors.R              |    7 +++----
 man/cross_fit_robust.Rd |    2 +-
 man/crossmap-package.Rd |    2 +-
 6 files changed, 23 insertions(+), 20 deletions(-)

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Package CAESAR.Suite updated to version 0.3.0 with previous version 0.2.3 dated 2025-11-07

Title: CAESAR: a Cross-Technology and Cross-Resolution Framework for Spatial Omics Annotation
Description: Biotechnology in spatial omics has advanced rapidly over the past few years, enhancing both throughput and resolution. However, existing annotation pipelines in spatial omics predominantly rely on clustering methods, lacking the flexibility to integrate extensive annotated information from single-cell RNA sequencing (scRNA-seq) due to discrepancies in spatial resolutions, species, or modalities. Here we introduce the CAESAR suite, an open-source software package that provides image-based spatial co-embedding of locations and genomic features. It uniquely transfers labels from scRNA-seq reference, enabling the annotation of spatial omics datasets across different technologies, resolutions, species, and modalities, based on the conserved relationship between signature genes and cells/locations at an appropriate level of granularity. Notably, CAESAR enriches location-level pathways, allowing for the detection of gradual biological pathway activation within spatially defined domain types. [...truncated...]
Author: Xiao Zhang [aut, cre], Wei Liu [aut], Jin Liu [aut]
Maintainer: Xiao Zhang <zhangxiao1994@cuhk.edu.cn>

Diff between CAESAR.Suite versions 0.2.3 dated 2025-11-07 and 0.3.0 dated 2025-12-22

 DESCRIPTION                 |    6 +++---
 MD5                         |   24 ++++++++++++------------
 R/caesar.R                  |    6 +++---
 R/caesar_annotation.R       |    8 ++++----
 R/caesar_enrichscore.R      |    2 +-
 R/caesar_image.R            |    2 +-
 R/ruv.R                     |    4 ++--
 R/utilities.r               |    1 +
 README.md                   |    1 +
 inst/doc/STMOB.html         |    6 +++---
 inst/doc/XeniumBC.html      |    6 +++---
 man/annotation_mat.Rd       |    2 +-
 man/cellembedding_matrix.Rd |    2 +-
 13 files changed, 36 insertions(+), 34 deletions(-)

More information about CAESAR.Suite at CRAN
Permanent link

Package dataquieR updated to version 2.8.2 with previous version 2.5.1 dated 2025-03-05

Title: Data Quality in Epidemiological Research
Description: Data quality assessments guided by a 'data quality framework introduced by Schmidt and colleagues, 2021' <doi:10.1186/s12874-021-01252-7> target the data quality dimensions integrity, completeness, consistency, and accuracy. The scope of applicable functions rests on the availability of extensive metadata which can be provided in spreadsheet tables. Either standardized (e.g. as 'html5' reports) or individually tailored reports can be generated. For an introduction into the specification of corresponding metadata, please refer to the 'package website' <https://dataquality.qihs.uni-greifswald.de/VIN_Annotation_of_Metadata.html>.
Author: University Medicine Greifswald [cph], Elisa Kasbohm [aut] , Elena Salogni [aut] , Joany Marino [aut] , Adrian Richter [aut] , Carsten Oliver Schmidt [aut] , Stephan Struckmann [aut, cre] , German Research Foundation [fnd], National Research Data Inf [...truncated...]
Maintainer: Stephan Struckmann <stephan.struckmann@uni-greifswald.de>

Diff between dataquieR versions 2.5.1 dated 2025-03-05 and 2.8.2 dated 2025-12-22

 dataquieR-2.5.1/dataquieR/R/util_anytime_installed.R                                                                 |only
 dataquieR-2.5.1/dataquieR/man/LONG_LABEL_EXCEPTION.Rd                                                                |only
 dataquieR-2.5.1/dataquieR/man/N_RULES.Rd                                                                             |only
 dataquieR-2.5.1/dataquieR/man/UNIVARIATE_OUTLIER_CHECKTYPE.Rd                                                        |only
 dataquieR-2.5.1/dataquieR/man/cash-.dataquieR_result.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/dataquieR.progress_fkt.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/dataquieR.progress_msg_fkt.Rd                                                          |only
 dataquieR-2.5.1/dataquieR/man/dot-.indicator_or_descriptor.Rd                                                        |only
 dataquieR-2.5.1/dataquieR/man/dot-.manual.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/dot-access_dq_rs2-set.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/dot-access_dq_rs2.Rd                                                                   |only
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 dataquieR-2.5.1/dataquieR/man/figures                                                                                |only
 dataquieR-2.5.1/dataquieR/man/menu_env.Rd                                                                            |only
 dataquieR-2.5.1/dataquieR/man/meta_data_env_criteria.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/meta_data_env_group_vars.Rd                                                            |only
 dataquieR-2.5.1/dataquieR/man/meta_data_env_id_vars.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/meta_data_env_n_rules.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/meta_data_env_time_vars.Rd                                                             |only
 dataquieR-2.5.1/dataquieR/man/sub-.dataquieR_result.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/sub-sub-.dataquieR_result.Rd                                                           |only
 dataquieR-2.5.1/dataquieR/man/util_3SD.Rd                                                                            |only
 dataquieR-2.5.1/dataquieR/man/util_abbreviate.Rd                                                                     |only
 dataquieR-2.5.1/dataquieR/man/util_abbreviate_unique.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/util_acc_loess_bin.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_acc_loess_continuous.Rd                                                           |only
 dataquieR-2.5.1/dataquieR/man/util_acc_varcomp.Rd                                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_adjust_data_type.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_adjust_geom_text_for_plotly.Rd                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_alias2caption.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_all_ind_functions.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/util_all_intro_vars_for_rv.Rd                                                          |only
 dataquieR-2.5.1/dataquieR/man/util_all_is_integer.Rd                                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_anytime_installed.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/util_app_cd.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_app_con_contradictions_redcap.Rd                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_app_dc.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_app_dl.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_app_ed.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_app_hl.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_app_iac.Rd                                                                        |only
 dataquieR-2.5.1/dataquieR/man/util_app_iav.Rd                                                                        |only
 dataquieR-2.5.1/dataquieR/man/util_app_im.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_app_loess.Rd                                                                      |only
 dataquieR-2.5.1/dataquieR/man/util_app_mar.Rd                                                                        |only
 dataquieR-2.5.1/dataquieR/man/util_app_mol.Rd                                                                        |only
 dataquieR-2.5.1/dataquieR/man/util_app_ol.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_app_sl.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_app_sm.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_app_sos.Rd                                                                        |only
 dataquieR-2.5.1/dataquieR/man/util_app_vc.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_as_cat.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_as_integer_cat.Rd                                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_as_numeric.Rd                                                                     |only
 dataquieR-2.5.1/dataquieR/man/util_as_plotly_from_res.Rd                                                             |only
 dataquieR-2.5.1/dataquieR/man/util_as_valid_missing_codes.Rd                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_assign_levlabs.Rd                                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_attach_attr.Rd                                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_bQuote.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_backtickQuote.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_bar_plot.Rd                                                                       |only
 dataquieR-2.5.1/dataquieR/man/util_cast_off.Rd                                                                       |only
 dataquieR-2.5.1/dataquieR/man/util_check_data_type.Rd                                                                |only
 dataquieR-2.5.1/dataquieR/man/util_check_group_levels.Rd                                                             |only
 dataquieR-2.5.1/dataquieR/man/util_check_one_unique_value.Rd                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_cll_nm2fkt_nm.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_col2rgb.Rd                                                                        |only
 dataquieR-2.5.1/dataquieR/man/util_col_description.Rd                                                                |only
 dataquieR-2.5.1/dataquieR/man/util_collapse_msgs.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_combine_list_report_summaries.Rd                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_combine_res.Rd                                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_combine_value_label_tables.Rd                                                     |only
 dataquieR-2.5.1/dataquieR/man/util_compare_meta_with_study.Rd                                                        |only
 dataquieR-2.5.1/dataquieR/man/util_compress_ggplots_in_res.Rd                                                        |only
 dataquieR-2.5.1/dataquieR/man/util_compute_SE_skewness.Rd                                                            |only
 dataquieR-2.5.1/dataquieR/man/util_compute_kurtosis.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_compute_skewness.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_condition_constructor_factory.Rd                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_condition_from_try_error.Rd                                                       |only
 dataquieR-2.5.1/dataquieR/man/util_conversion_stable.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/util_coord_flip.Rd                                                                     |only
 dataquieR-2.5.1/dataquieR/man/util_copy_all_deps.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_correct_variable_use.Rd                                                           |only
 dataquieR-2.5.1/dataquieR/man/util_count_expected_observations.Rd                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_create_page_file.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_create_report_by_overview.Rd                                                      |only
 dataquieR-2.5.1/dataquieR/man/util_dashboard_table.Rd                                                                |only
 dataquieR-2.5.1/dataquieR/man/util_data_type_conversion.Rd                                                           |only
 dataquieR-2.5.1/dataquieR/man/util_deparse1.Rd                                                                       |only
 dataquieR-2.5.1/dataquieR/man/util_detect_cores.Rd                                                                   |only
 dataquieR-2.5.1/dataquieR/man/util_df_escape.Rd                                                                      |only
 dataquieR-2.5.1/dataquieR/man/util_dichotomize.Rd                                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_dist_selection.Rd                                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_ds1_eval_env.Rd                                                                   |only
 dataquieR-2.5.1/dataquieR/man/util_empty.Rd                                                                          |only
 dataquieR-2.5.1/dataquieR/man/util_ensure_character.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_ensure_in.Rd                                                                      |only
 dataquieR-2.5.1/dataquieR/man/util_ensure_label.Rd                                                                   |only
 dataquieR-2.5.1/dataquieR/man/util_ensure_suggested.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_error.Rd                                                                          |only
 dataquieR-2.5.1/dataquieR/man/util_eval_rule.Rd                                                                      |only
 dataquieR-2.5.1/dataquieR/man/util_eval_to_dataquieR_result.Rd                                                       |only
 dataquieR-2.5.1/dataquieR/man/util_evaluate_calls.Rd                                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_expect_data_frame.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/util_expect_scalar.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_extract_all_ids.Rd                                                                |only
 dataquieR-2.5.1/dataquieR/man/util_extract_indicator_metrics.Rd                                                      |only
 dataquieR-2.5.1/dataquieR/man/util_extract_matches.Rd                                                                |only
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 dataquieR-2.5.1/dataquieR/man/util_finalize_sizing_hints.Rd                                                          |only
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 dataquieR-2.5.1/dataquieR/man/util_find_free_missing_code.Rd                                                         |only
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 dataquieR-2.5.1/dataquieR/man/util_find_var_by_meta.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_first_row_to_colnames.Rd                                                          |only
 dataquieR-2.5.1/dataquieR/man/util_fix_merge_dups.Rd                                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_fix_rstudio_bugs.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_fix_sizing_hints.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_fix_storr_object.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_float_index_menu.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_formattable.Rd                                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_function_description.Rd                                                           |only
 dataquieR-2.5.1/dataquieR/man/util_generate_anchor_link.Rd                                                           |only
 dataquieR-2.5.1/dataquieR/man/util_generate_anchor_tag.Rd                                                            |only
 dataquieR-2.5.1/dataquieR/man/util_generate_calls.Rd                                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_generate_calls_for_function.Rd                                                    |only
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 dataquieR-2.5.1/dataquieR/man/util_get_category_for_result.Rd                                                        |only
 dataquieR-2.5.1/dataquieR/man/util_get_code_list.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_get_colors.Rd                                                                     |only
 dataquieR-2.5.1/dataquieR/man/util_get_concept_info.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_get_encoding.Rd                                                                   |only
 dataquieR-2.5.1/dataquieR/man/util_get_fg_color.Rd                                                                   |only
 dataquieR-2.5.1/dataquieR/man/util_get_hovertext.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_get_labels_grading_class.Rd                                                       |only
 dataquieR-2.5.1/dataquieR/man/util_get_message_for_result.Rd                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_get_redcap_rule_env.Rd                                                            |only
 dataquieR-2.5.1/dataquieR/man/util_get_rule_sets.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_get_ruleset_formats.Rd                                                            |only
 dataquieR-2.5.1/dataquieR/man/util_get_storr_att_namespace.Rd                                                        |only
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 dataquieR-2.5.1/dataquieR/man/util_get_storr_summ_namespace.Rd                                                       |only
 dataquieR-2.5.1/dataquieR/man/util_get_thresholds.Rd                                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_get_var_att_names_of_level.Rd                                                     |only
 dataquieR-2.5.1/dataquieR/man/util_get_vars_in_segment.Rd                                                            |only
 dataquieR-2.5.1/dataquieR/man/util_get_voc_tab.Rd                                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_gg_var_label.Rd                                                                   |only
 dataquieR-2.5.1/dataquieR/man/util_has_no_group_vars.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/util_heatmap_1th.Rd                                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_hide_file_windows.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/util_histogram.Rd                                                                      |only
 dataquieR-2.5.1/dataquieR/man/util_html_attr_quote_escape.Rd                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_html_table.Rd                                                                     |only
 dataquieR-2.5.1/dataquieR/man/util_hubert.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_iframe_it_if_needed.Rd                                                            |only
 dataquieR-2.5.1/dataquieR/man/util_init_respum_tab.Rd                                                                |only
 dataquieR-2.5.1/dataquieR/man/util_int_breaks_rounded.Rd                                                             |only
 dataquieR-2.5.1/dataquieR/man/util_interpret_limits.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_is_integer.Rd                                                                     |only
 dataquieR-2.5.1/dataquieR/man/util_is_na_0_empty_or_false.Rd                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_is_numeric_in.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_is_svg_object.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_is_try_error.Rd                                                                   |only
 dataquieR-2.5.1/dataquieR/man/util_is_valid_missing_codes.Rd                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_load_manual.Rd                                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_looks_like_missing.Rd                                                             |only
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 dataquieR-2.5.1/dataquieR/man/util_map_all.Rd                                                                        |only
 dataquieR-2.5.1/dataquieR/man/util_map_by_largest_prefix.Rd                                                          |only
 dataquieR-2.5.1/dataquieR/man/util_map_labels.Rd                                                                     |only
 dataquieR-2.5.1/dataquieR/man/util_margins_bin.Rd                                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_margins_lm.Rd                                                                     |only
 dataquieR-2.5.1/dataquieR/man/util_margins_nom.Rd                                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_margins_ord.Rd                                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_margins_poi.Rd                                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_match_arg.Rd                                                                      |only
 dataquieR-2.5.1/dataquieR/man/util_merge_data_frame_list.Rd                                                          |only
 dataquieR-2.5.1/dataquieR/man/util_message.Rd                                                                        |only
 dataquieR-2.5.1/dataquieR/man/util_no_value_labels.Rd                                                                |only
 dataquieR-2.5.1/dataquieR/man/util_normalize_clt.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_normalize_cross_item.Rd                                                           |only
 dataquieR-2.5.1/dataquieR/man/util_normalize_value_labels.Rd                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_observation_expected.Rd                                                           |only
 dataquieR-2.5.1/dataquieR/man/util_observations_in_subgroups.Rd                                                      |only
 dataquieR-2.5.1/dataquieR/man/util_online_ref.Rd                                                                     |only
 dataquieR-2.5.1/dataquieR/man/util_optimize_histogram_bins.Rd                                                        |only
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 dataquieR-2.5.1/dataquieR/man/util_order_by_order.Rd                                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_par_pmap.Rd                                                                       |only
 dataquieR-2.5.1/dataquieR/man/util_parse_assignments.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/util_parse_interval.Rd                                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_parse_redcap_rule.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/util_paste0_with_na.Rd                                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_paste_with_na.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_plot2svg_object.Rd                                                                |only
 dataquieR-2.5.1/dataquieR/man/util_plot_categorical_vars.Rd                                                          |only
 dataquieR-2.5.1/dataquieR/man/util_plot_figure_no_plotly.Rd                                                          |only
 dataquieR-2.5.1/dataquieR/man/util_plot_figure_plotly.Rd                                                             |only
 dataquieR-2.5.1/dataquieR/man/util_plot_svg_to_uri.Rd                                                                |only
 dataquieR-2.5.1/dataquieR/man/util_plotly2svg_object.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/util_prep_location_check.Rd                                                            |only
 dataquieR-2.5.1/dataquieR/man/util_prep_proportion_check.Rd                                                          |only
 dataquieR-2.5.1/dataquieR/man/util_pretty_print.Rd                                                                   |only
 dataquieR-2.5.1/dataquieR/man/util_pretty_vector_string.Rd                                                           |only
 dataquieR-2.5.1/dataquieR/man/util_rbind.Rd                                                                          |only
 dataquieR-2.5.1/dataquieR/man/util_really_rstudio.Rd                                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_recode.Rd                                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_referred_vars.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_remove_empty_rows.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/util_remove_na_records.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/util_render_table_dataquieR_summary.Rd                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_replace_codes_by_NA.Rd                                                            |only
 dataquieR-2.5.1/dataquieR/man/util_replace_hard_limit_violations.Rd                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_rio_import.Rd                                                                     |only
 dataquieR-2.5.1/dataquieR/man/util_rio_import_list.Rd                                                                |only
 dataquieR-2.5.1/dataquieR/man/util_round_to_decimal_places.Rd                                                        |only
 dataquieR-2.5.1/dataquieR/man/util_set_dQuoteString.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_set_sQuoteString.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_set_size.Rd                                                                       |only
 dataquieR-2.5.1/dataquieR/man/util_setup_rstudio_job.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/util_sigmagap.Rd                                                                       |only
 dataquieR-2.5.1/dataquieR/man/util_sort_by_order.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_split_val_tab.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_standardise_ordinal_codes.Rd                                                      |only
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 dataquieR-2.5.1/dataquieR/man/util_stop_if_not.Rd                                                                    |only
 dataquieR-2.5.1/dataquieR/man/util_storr_factory.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_storr_object.Rd                                                                   |only
 dataquieR-2.5.1/dataquieR/man/util_string_is_not_categorical.Rd                                                      |only
 dataquieR-2.5.1/dataquieR/man/util_study_var2factor.Rd                                                               |only
 dataquieR-2.5.1/dataquieR/man/util_sub_string_left_from_..Rd                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_sub_string_right_from_..Rd                                                        |only
 dataquieR-2.5.1/dataquieR/man/util_suppress_output.Rd                                                                |only
 dataquieR-2.5.1/dataquieR/man/util_suppress_warnings.Rd                                                              |only
 dataquieR-2.5.1/dataquieR/man/util_table_of_vct.Rd                                                                   |only
 dataquieR-2.5.1/dataquieR/man/util_table_rotator.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_translate.Rd                                                                      |only
 dataquieR-2.5.1/dataquieR/man/util_tukey.Rd                                                                          |only
 dataquieR-2.5.1/dataquieR/man/util_undisclose.Rd                                                                     |only
 dataquieR-2.5.1/dataquieR/man/util_unit2baseunit.Rd                                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_user_hint.Rd                                                                      |only
 dataquieR-2.5.1/dataquieR/man/util_validate_known_meta.Rd                                                            |only
 dataquieR-2.5.1/dataquieR/man/util_validate_missing_lists.Rd                                                         |only
 dataquieR-2.5.1/dataquieR/man/util_validate_report_summary_table.Rd                                                  |only
 dataquieR-2.5.1/dataquieR/man/util_varcomp_robust.Rd                                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_variable_references.Rd                                                            |only
 dataquieR-2.5.1/dataquieR/man/util_verify_encoding.Rd                                                                |only
 dataquieR-2.5.1/dataquieR/man/util_verify_names.Rd                                                                   |only
 dataquieR-2.5.1/dataquieR/man/util_view_file.Rd                                                                      |only
 dataquieR-2.5.1/dataquieR/man/util_warn_unordered.Rd                                                                 |only
 dataquieR-2.5.1/dataquieR/man/util_warning.Rd                                                                        |only
 dataquieR-2.5.1/dataquieR/tests/testthat/_snaps/R4.3                                                                 |only
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 dataquieR-2.8.2/dataquieR/DESCRIPTION                                                                                |   52 
 dataquieR-2.8.2/dataquieR/MD5                                                                                        | 1930 +--
 dataquieR-2.8.2/dataquieR/NAMESPACE                                                                                  |   51 
 dataquieR-2.8.2/dataquieR/NEWS.md                                                                                    |   84 
 dataquieR-2.8.2/dataquieR/R/000_exception_classes.R                                                                  |    2 
 dataquieR-2.8.2/dataquieR/R/000_globs.R                                                                              |  551 
 dataquieR-2.8.2/dataquieR/R/000_options.R                                                                            |  516 
 dataquieR-2.8.2/dataquieR/R/acc_cat_distributions.R                                                                  |   92 
 dataquieR-2.8.2/dataquieR/R/acc_distributions.R                                                                      |  215 
 dataquieR-2.8.2/dataquieR/R/acc_distributions_ecdf.R                                                                 |   53 
 dataquieR-2.8.2/dataquieR/R/acc_loess.R                                                                              |    8 
 dataquieR-2.8.2/dataquieR/R/acc_mahalanobis.R                                                                        |only
 dataquieR-2.8.2/dataquieR/R/acc_margins.R                                                                            |  205 
 dataquieR-2.8.2/dataquieR/R/acc_multivariate_outlier.R                                                               |   57 
 dataquieR-2.8.2/dataquieR/R/acc_shape_or_scale.R                                                                     |   13 
 dataquieR-2.8.2/dataquieR/R/acc_univariate_outlier.R                                                                 |   18 
 dataquieR-2.8.2/dataquieR/R/com_qualified_item_missingness.R                                                         |  507 
 dataquieR-2.8.2/dataquieR/R/com_qualified_segment_missingness.R                                                      |  404 
 dataquieR-2.8.2/dataquieR/R/con_contradictions.R                                                                     |   22 
 dataquieR-2.8.2/dataquieR/R/con_contradictions_redcap.R                                                              |  275 
 dataquieR-2.8.2/dataquieR/R/con_inadmissible_categorical.R                                                           |    2 
 dataquieR-2.8.2/dataquieR/R/con_limit_deviations.R                                                                   | 1715 ++-
 dataquieR-2.8.2/dataquieR/R/dataquieR_resultset2.R                                                                   |  216 
 dataquieR-2.8.2/dataquieR/R/des_scatterplot_matrix.R                                                                 |   98 
 dataquieR-2.8.2/dataquieR/R/des_summary.R                                                                            | 2327 +---
 dataquieR-2.8.2/dataquieR/R/des_summary_categorical.R                                                                |only
 dataquieR-2.8.2/dataquieR/R/des_summary_continuous.R                                                                 |only
 dataquieR-2.8.2/dataquieR/R/dq_report2.R                                                                             |  226 
 dataquieR-2.8.2/dataquieR/R/dq_report_by.R                                                                           |  158 
 dataquieR-2.8.2/dataquieR/R/droplevels.ReportSummaryTable.R                                                          |only
 dataquieR-2.8.2/dataquieR/R/get_internal_api.R                                                                       |    2 
 dataquieR-2.8.2/dataquieR/R/html_dependency_jspdf.R                                                                  |only
 dataquieR-2.8.2/dataquieR/R/int_all_datastructure_dataframe.R                                                        |  109 
 dataquieR-2.8.2/dataquieR/R/int_all_datastructure_segment.R                                                          |  133 
 dataquieR-2.8.2/dataquieR/R/int_datatype_matrix.R                                                                    |    9 
 dataquieR-2.8.2/dataquieR/R/int_duplicate_content.R                                                                  |    8 
 dataquieR-2.8.2/dataquieR/R/int_duplicate_ids.R                                                                      |    8 
 dataquieR-2.8.2/dataquieR/R/int_encoding_errors.R                                                                    |    5 
 dataquieR-2.8.2/dataquieR/R/int_sts_element_segment.R                                                                |    7 
 dataquieR-2.8.2/dataquieR/R/int_unexp_records_set.R                                                                  |    8 
 dataquieR-2.8.2/dataquieR/R/menu_env.R                                                                               |   28 
 dataquieR-2.8.2/dataquieR/R/meta_data_env.R                                                                          |  110 
 dataquieR-2.8.2/dataquieR/R/plot.dataquieR_summary.R                                                                 |   42 
 dataquieR-2.8.2/dataquieR/R/prep_acc_distributions_with_ecdf.R                                                       |   80 
 dataquieR-2.8.2/dataquieR/R/prep_add_cause_label_df.R                                                                |    4 
 dataquieR-2.8.2/dataquieR/R/prep_add_computed_variables.R                                                            |  110 
 dataquieR-2.8.2/dataquieR/R/prep_add_missing_codes.R                                                                 |    2 
 dataquieR-2.8.2/dataquieR/R/prep_apply_coding.R                                                                      |    2 
 dataquieR-2.8.2/dataquieR/R/prep_check_meta_data_segment.R                                                           |    6 
 dataquieR-2.8.2/dataquieR/R/prep_create_meta_data_file.R                                                             |   15 
 dataquieR-2.8.2/dataquieR/R/prep_datatype_from_data.R                                                                |   13 
 dataquieR-2.8.2/dataquieR/R/prep_deparse_assignments.R                                                               |    3 
 dataquieR-2.8.2/dataquieR/R/prep_dq_data_type_of.R                                                                   |   37 
 dataquieR-2.8.2/dataquieR/R/prep_expand_codes.R                                                                      |    4 
 dataquieR-2.8.2/dataquieR/R/prep_extract_cause_label_df.R                                                            |   33 
 dataquieR-2.8.2/dataquieR/R/prep_extract_summary.R                                                                   |    2 
 dataquieR-2.8.2/dataquieR/R/prep_extract_summary.dataquieR_result.R                                                  |    2 
 dataquieR-2.8.2/dataquieR/R/prep_extract_summary.dataquieR_resultset2.R                                              |    2 
 dataquieR-2.8.2/dataquieR/R/prep_fix_meta_id_dups.R                                                                  |only
 dataquieR-2.8.2/dataquieR/R/prep_get_data_frame.R                                                                    |   21 
 dataquieR-2.8.2/dataquieR/R/prep_get_labels.R                                                                        |   33 
 dataquieR-2.8.2/dataquieR/R/prep_get_study_data_segment.R                                                            |   10 
 dataquieR-2.8.2/dataquieR/R/prep_load_folder_with_metadata.R                                                         |   55 
 dataquieR-2.8.2/dataquieR/R/prep_map_labels.R                                                                        |    2 
 dataquieR-2.8.2/dataquieR/R/prep_meta_data_v1_to_item_level_meta_data.R                                              |   37 
 dataquieR-2.8.2/dataquieR/R/prep_prepare_dataframes.R                                                                |  231 
 dataquieR-2.8.2/dataquieR/R/prep_render_pie_chart_from_summaryclasses_ggplot2.R                                      |   82 
 dataquieR-2.8.2/dataquieR/R/prep_render_pie_chart_from_summaryclasses_plotly.R                                       |  156 
 dataquieR-2.8.2/dataquieR/R/prep_robust_guess_data_type.R                                                            |    5 
 dataquieR-2.8.2/dataquieR/R/prep_scalelevel_from_data_and_metadata.R                                                 |    3 
 dataquieR-2.8.2/dataquieR/R/prep_study2meta.R                                                                        |   24 
 dataquieR-2.8.2/dataquieR/R/prep_summary_to_classes.R                                                                |    9 
 dataquieR-2.8.2/dataquieR/R/print.ReportSummaryTable.R                                                               |   95 
 dataquieR-2.8.2/dataquieR/R/print.dataquieR_result.R                                                                 |   91 
 dataquieR-2.8.2/dataquieR/R/print.dataquieR_resultset2.R                                                             |  810 +
 dataquieR-2.8.2/dataquieR/R/print.dataquieR_summary.R                                                                |   15 
 dataquieR-2.8.2/dataquieR/R/print.interval.R                                                                         |   19 
 dataquieR-2.8.2/dataquieR/R/pro_applicability_matrix.R                                                               |   50 
 dataquieR-2.8.2/dataquieR/R/reflection.R                                                                             |    6 
 dataquieR-2.8.2/dataquieR/R/summary.dataquieR_resultset2.R                                                           |    6 
 dataquieR-2.8.2/dataquieR/R/util_3SD.R                                                                               |    2 
 dataquieR-2.8.2/dataquieR/R/util_abbreviate.R                                                                        |    2 
 dataquieR-2.8.2/dataquieR/R/util_acc_loess_bin.R                                                                     |  124 
 dataquieR-2.8.2/dataquieR/R/util_acc_loess_continuous.R                                                              |  173 
 dataquieR-2.8.2/dataquieR/R/util_acc_varcomp.R                                                                       |    2 
 dataquieR-2.8.2/dataquieR/R/util_add_computed_internals.R                                                            |only
 dataquieR-2.8.2/dataquieR/R/util_adjust_data_type.R                                                                  |   44 
 dataquieR-2.8.2/dataquieR/R/util_adjust_geom_text_for_plotly.R                                                       |   21 
 dataquieR-2.8.2/dataquieR/R/util_alias2caption.R                                                                     |    4 
 dataquieR-2.8.2/dataquieR/R/util_align_colnames_case.R                                                               |only
 dataquieR-2.8.2/dataquieR/R/util_all_ind_functions.R                                                                 |    2 
 dataquieR-2.8.2/dataquieR/R/util_all_intro_vars_for_rv.R                                                             |    2 
 dataquieR-2.8.2/dataquieR/R/util_all_is_integer.R                                                                    |    4 
 dataquieR-2.8.2/dataquieR/R/util_amend_missing_metadata.R                                                            |only
 dataquieR-2.8.2/dataquieR/R/util_app_cd.R                                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_con_contradictions_redcap.R                                                     |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_dc.R                                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_dl.R                                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_ed.R                                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_hl.R                                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_iac.R                                                                           |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_iav.R                                                                           |    4 
 dataquieR-2.8.2/dataquieR/R/util_app_im.R                                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_loess.R                                                                         |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_mar.R                                                                           |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_mol.R                                                                           |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_ol.R                                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_sl.R                                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_sm.R                                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_sos.R                                                                           |    2 
 dataquieR-2.8.2/dataquieR/R/util_app_vc.R                                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_as_cat.R                                                                            |    4 
 dataquieR-2.8.2/dataquieR/R/util_as_character.R                                                                      |only
 dataquieR-2.8.2/dataquieR/R/util_as_numeric.R                                                                        |    2 
 dataquieR-2.8.2/dataquieR/R/util_as_numeric_with_unit.R                                                              |only
 dataquieR-2.8.2/dataquieR/R/util_as_valid_missing_codes.R                                                            |   13 
 dataquieR-2.8.2/dataquieR/R/util_assign_levlabs.R                                                                    |    2 
 dataquieR-2.8.2/dataquieR/R/util_attach_attr.R                                                                       |    2 
 dataquieR-2.8.2/dataquieR/R/util_avail_ram.R                                                                         |only
 dataquieR-2.8.2/dataquieR/R/util_bQuote.R                                                                            |    4 
 dataquieR-2.8.2/dataquieR/R/util_backtickQuote.R                                                                     |    4 
 dataquieR-2.8.2/dataquieR/R/util_bar_plot.R                                                                          |   86 
 dataquieR-2.8.2/dataquieR/R/util_cast_off.R                                                                          |   14 
 dataquieR-2.8.2/dataquieR/R/util_check_data_type.R                                                                   |   28 
 dataquieR-2.8.2/dataquieR/R/util_check_group_levels.R                                                                |    2 
 dataquieR-2.8.2/dataquieR/R/util_check_one_unique_value.R                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_cll_nm2fkt_nm.R                                                                     |   20 
 dataquieR-2.8.2/dataquieR/R/util_col2rgb.R                                                                           |    2 
 dataquieR-2.8.2/dataquieR/R/util_col_description.R                                                                   |    2 
 dataquieR-2.8.2/dataquieR/R/util_collapse_msgs.R                                                                     |    2 
 dataquieR-2.8.2/dataquieR/R/util_combine_list_report_summaries.R                                                     |    1 
 dataquieR-2.8.2/dataquieR/R/util_combine_res.R                                                                       |    7 
 dataquieR-2.8.2/dataquieR/R/util_compare_meta_with_study.R                                                           |   13 
 dataquieR-2.8.2/dataquieR/R/util_compress.R                                                                          |only
 dataquieR-2.8.2/dataquieR/R/util_compress_ggplots_in_res.R                                                           |   15 
 dataquieR-2.8.2/dataquieR/R/util_condition_constructor_factory.R                                                     |   37 
 dataquieR-2.8.2/dataquieR/R/util_conversion_stable.R                                                                 |   13 
 dataquieR-2.8.2/dataquieR/R/util_coord_flip.R                                                                        |   89 
 dataquieR-2.8.2/dataquieR/R/util_copy_all_deps.R                                                                     |   17 
 dataquieR-2.8.2/dataquieR/R/util_correct_variable_use.R                                                              |   18 
 dataquieR-2.8.2/dataquieR/R/util_count_expected_observations.R                                                       |    2 
 dataquieR-2.8.2/dataquieR/R/util_create_lean_ggplot.R                                                                |only
 dataquieR-2.8.2/dataquieR/R/util_create_page_file.R                                                                  |   18 
 dataquieR-2.8.2/dataquieR/R/util_create_report_by_overview.R                                                         |   29 
 dataquieR-2.8.2/dataquieR/R/util_dashboard_table.R                                                                   |    2 
 dataquieR-2.8.2/dataquieR/R/util_data_type_conversion.R                                                              |   18 
 dataquieR-2.8.2/dataquieR/R/util_deparse1.R                                                                          |    2 
 dataquieR-2.8.2/dataquieR/R/util_des_functions_env.R                                                                 |only
 dataquieR-2.8.2/dataquieR/R/util_detect_cores.R                                                                      |   18 
 dataquieR-2.8.2/dataquieR/R/util_df_escape.R                                                                         |    2 
 dataquieR-2.8.2/dataquieR/R/util_dichotomize.R                                                                       |    2 
 dataquieR-2.8.2/dataquieR/R/util_disjunct_var_sets.R                                                                 |only
 dataquieR-2.8.2/dataquieR/R/util_dist_selection.R                                                                    |    2 
 dataquieR-2.8.2/dataquieR/R/util_ds1_eval_env.R                                                                      |    2 
 dataquieR-2.8.2/dataquieR/R/util_duplicated_inclding_first.R                                                         |only
 dataquieR-2.8.2/dataquieR/R/util_empty.R                                                                             |    2 
 dataquieR-2.8.2/dataquieR/R/util_ensure_character.R                                                                  |    4 
 dataquieR-2.8.2/dataquieR/R/util_ensure_in.R                                                                         |    2 
 dataquieR-2.8.2/dataquieR/R/util_ensure_label.R                                                                      |   87 
 dataquieR-2.8.2/dataquieR/R/util_ensure_suggested.R                                                                  |    2 
 dataquieR-2.8.2/dataquieR/R/util_error.R                                                                             |    2 
 dataquieR-2.8.2/dataquieR/R/util_eval_rule.R                                                                         |   11 
 dataquieR-2.8.2/dataquieR/R/util_eval_to_dataquieR_result.R                                                          |  290 
 dataquieR-2.8.2/dataquieR/R/util_evaluate_calls.R                                                                    |  365 
 dataquieR-2.8.2/dataquieR/R/util_expect_data_frame.R                                                                 |    2 
 dataquieR-2.8.2/dataquieR/R/util_expect_scalar.R                                                                     |    2 
 dataquieR-2.8.2/dataquieR/R/util_extract_all_ids.R                                                                   |    2 
 dataquieR-2.8.2/dataquieR/R/util_extract_indicator_metrics.R                                                         |    2 
 dataquieR-2.8.2/dataquieR/R/util_extract_matches.R                                                                   |    2 
 dataquieR-2.8.2/dataquieR/R/util_extract_named_groups.R                                                              |only
 dataquieR-2.8.2/dataquieR/R/util_filter_missing_list_table_for_rv.R                                                  |    2 
 dataquieR-2.8.2/dataquieR/R/util_filter_names_by_regexps.R                                                           |    2 
 dataquieR-2.8.2/dataquieR/R/util_filter_repsum.R                                                                     |only
 dataquieR-2.8.2/dataquieR/R/util_finalize_sizing_hints.R                                                             |    4 
 dataquieR-2.8.2/dataquieR/R/util_find_external_functions_in_stacktrace.R                                             |    2 
 dataquieR-2.8.2/dataquieR/R/util_find_first_externally_called_functions_in_stacktrace.R                              |    2 
 dataquieR-2.8.2/dataquieR/R/util_find_free_missing_code.R                                                            |   14 
 dataquieR-2.8.2/dataquieR/R/util_find_indicator_function_in_callers.R                                                |    2 
 dataquieR-2.8.2/dataquieR/R/util_find_var_by_meta.R                                                                  |    2 
 dataquieR-2.8.2/dataquieR/R/util_fix_merge_dups.R                                                                    |    5 
 dataquieR-2.8.2/dataquieR/R/util_fix_rstudio_bugs.R                                                                  |    2 
 dataquieR-2.8.2/dataquieR/R/util_float_index_menu.R                                                                  |    2 
 dataquieR-2.8.2/dataquieR/R/util_formattable.R                                                                       |    4 
 dataquieR-2.8.2/dataquieR/R/util_free_varname.R                                                                      |only
 dataquieR-2.8.2/dataquieR/R/util_function_description.R                                                              |    2 
 dataquieR-2.8.2/dataquieR/R/util_generate_anchor_tag.R                                                               |    4 
 dataquieR-2.8.2/dataquieR/R/util_generate_calls.R                                                                    |   14 
 dataquieR-2.8.2/dataquieR/R/util_generate_calls_for_function.R                                                       |    2 
 dataquieR-2.8.2/dataquieR/R/util_generate_pages_from_report.R                                                        |  649 +
 dataquieR-2.8.2/dataquieR/R/util_generate_table_indicators_descriptors.R                                             |    2 
 dataquieR-2.8.2/dataquieR/R/util_get_category_for_result.R                                                           |    4 
 dataquieR-2.8.2/dataquieR/R/util_get_code_list.R                                                                     |   21 
 dataquieR-2.8.2/dataquieR/R/util_get_colors.R                                                                        |    2 
 dataquieR-2.8.2/dataquieR/R/util_get_combined_code_lists.R                                                           |    4 
 dataquieR-2.8.2/dataquieR/R/util_get_concept_info.R                                                                  |    4 
 dataquieR-2.8.2/dataquieR/R/util_get_dataquieR_result.R                                                              |    8 
 dataquieR-2.8.2/dataquieR/R/util_get_encoding.R                                                                      |    1 
 dataquieR-2.8.2/dataquieR/R/util_get_fg_color.R                                                                      |    2 
 dataquieR-2.8.2/dataquieR/R/util_get_hovertext.R                                                                     |    2 
 dataquieR-2.8.2/dataquieR/R/util_get_labels_grading_class.R                                                          |    2 
 dataquieR-2.8.2/dataquieR/R/util_get_message_for_result.R                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_get_redcap_rule_env.R                                                               |  530 
 dataquieR-2.8.2/dataquieR/R/util_get_rule_sets.R                                                                     |    9 
 dataquieR-2.8.2/dataquieR/R/util_get_ruleset_formats.R                                                               |    6 
 dataquieR-2.8.2/dataquieR/R/util_get_thresholds.R                                                                    |    2 
 dataquieR-2.8.2/dataquieR/R/util_get_var_att_names_of_level.R                                                        |    2 
 dataquieR-2.8.2/dataquieR/R/util_get_vars_in_segment.R                                                               |    2 
 dataquieR-2.8.2/dataquieR/R/util_get_voc_tab.R                                                                       |    2 
 dataquieR-2.8.2/dataquieR/R/util_gg_get.R                                                                            |only
 dataquieR-2.8.2/dataquieR/R/util_gg_var_label.R                                                                      |   54 
 dataquieR-2.8.2/dataquieR/R/util_ggplot_text.R                                                                       |only
 dataquieR-2.8.2/dataquieR/R/util_handle_complex_data_types.R                                                         |only
 dataquieR-2.8.2/dataquieR/R/util_has_no_group_vars.R                                                                 |    1 
 dataquieR-2.8.2/dataquieR/R/util_heatmap_1th.R                                                                       |    4 
 dataquieR-2.8.2/dataquieR/R/util_hide_file_windows.R                                                                 |    2 
 dataquieR-2.8.2/dataquieR/R/util_histogram.R                                                                         |  110 
 dataquieR-2.8.2/dataquieR/R/util_html_attr_quote_escape.R                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_html_for_dims.R                                                                     |  314 
 dataquieR-2.8.2/dataquieR/R/util_html_for_var.R                                                                      |  111 
 dataquieR-2.8.2/dataquieR/R/util_html_table.R                                                                        |   35 
 dataquieR-2.8.2/dataquieR/R/util_hubert.R                                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_iframe_it_if_needed.R                                                               |  282 
 dataquieR-2.8.2/dataquieR/R/util_init_respum_tab.R                                                                   |    2 
 dataquieR-2.8.2/dataquieR/R/util_int_breaks_rounded.R                                                                |    2 
 dataquieR-2.8.2/dataquieR/R/util_int_unexp_records_set_segment.R                                                     |    5 
 dataquieR-2.8.2/dataquieR/R/util_interpret_limits.R                                                                  |   90 
 dataquieR-2.8.2/dataquieR/R/util_is_integer.R                                                                        |    2 
 dataquieR-2.8.2/dataquieR/R/util_is_na_0_empty_or_false.R                                                            |   32 
 dataquieR-2.8.2/dataquieR/R/util_is_numeric_in.R                                                                     |    6 
 dataquieR-2.8.2/dataquieR/R/util_is_time_only.R                                                                      |only
 dataquieR-2.8.2/dataquieR/R/util_is_try_error.R                                                                      |    4 
 dataquieR-2.8.2/dataquieR/R/util_is_valid_missing_codes.R                                                            |    8 
 dataquieR-2.8.2/dataquieR/R/util_isolate_function.R                                                                  |only
 dataquieR-2.8.2/dataquieR/R/util_lazy_ggplot.R                                                                       |only
 dataquieR-2.8.2/dataquieR/R/util_looks_like_missing.R                                                                |    2 
 dataquieR-2.8.2/dataquieR/R/util_make_data_slot_from_table_slot.R                                                    |  103 
 dataquieR-2.8.2/dataquieR/R/util_map_all.R                                                                           |   24 
 dataquieR-2.8.2/dataquieR/R/util_map_by_largest_prefix.R                                                             |    2 
 dataquieR-2.8.2/dataquieR/R/util_map_labels.R                                                                        |    2 
 dataquieR-2.8.2/dataquieR/R/util_map_to_other_metrics.R                                                              |only
 dataquieR-2.8.2/dataquieR/R/util_margins_bin.R                                                                       |  220 
 dataquieR-2.8.2/dataquieR/R/util_margins_lm.R                                                                        |  232 
 dataquieR-2.8.2/dataquieR/R/util_margins_nom.R                                                                       |   35 
 dataquieR-2.8.2/dataquieR/R/util_margins_ord.R                                                                       |   47 
 dataquieR-2.8.2/dataquieR/R/util_margins_poi.R                                                                       |  188 
 dataquieR-2.8.2/dataquieR/R/util_match_arg.R                                                                         |    2 
 dataquieR-2.8.2/dataquieR/R/util_merge_data_frame_list.R                                                             |    2 
 dataquieR-2.8.2/dataquieR/R/util_message.R                                                                           |    2 
 dataquieR-2.8.2/dataquieR/R/util_no_value_labels.R                                                                   |    2 
 dataquieR-2.8.2/dataquieR/R/util_normalize_clt.R                                                                     |    2 
 dataquieR-2.8.2/dataquieR/R/util_normalize_cross_item.R                                                              |   54 
 dataquieR-2.8.2/dataquieR/R/util_normalize_time_only_columns.R                                                       |only
 dataquieR-2.8.2/dataquieR/R/util_normalize_value_labels.R                                                            |    4 
 dataquieR-2.8.2/dataquieR/R/util_observation_expected.R                                                              |    2 
 dataquieR-2.8.2/dataquieR/R/util_observations_in_subgroups.R                                                         |    4 
 dataquieR-2.8.2/dataquieR/R/util_online_ref.R                                                                        |    2 
 dataquieR-2.8.2/dataquieR/R/util_optimize_histogram_bins.R                                                           |   33 
 dataquieR-2.8.2/dataquieR/R/util_optimize_sequence_across_time_var.R                                                 |    8 
 dataquieR-2.8.2/dataquieR/R/util_order_of_indicator_metrics.R                                                        |only
 dataquieR-2.8.2/dataquieR/R/util_pairs_matrix.R                                                                      |only
 dataquieR-2.8.2/dataquieR/R/util_par_lapply_lb.R                                                                     |only
 dataquieR-2.8.2/dataquieR/R/util_par_pmap.R                                                                          |    2 
 dataquieR-2.8.2/dataquieR/R/util_parallel_classic.R                                                                  |   12 
 dataquieR-2.8.2/dataquieR/R/util_parallel_futures.R                                                                  |   14 
 dataquieR-2.8.2/dataquieR/R/util_parse_assignments.R                                                                 |   12 
 dataquieR-2.8.2/dataquieR/R/util_parse_date.R                                                                        |only
 dataquieR-2.8.2/dataquieR/R/util_parse_interval.R                                                                    |    2 
 dataquieR-2.8.2/dataquieR/R/util_parse_redcap_rule.R                                                                 |  205 
 dataquieR-2.8.2/dataquieR/R/util_paste_with_na.R                                                                     |    4 
 dataquieR-2.8.2/dataquieR/R/util_plot2svg_object.R                                                                   |   58 
 dataquieR-2.8.2/dataquieR/R/util_plot_categorical_vars.R                                                             |   22 
 dataquieR-2.8.2/dataquieR/R/util_plot_figure_no_plotly.R                                                             |    2 
 dataquieR-2.8.2/dataquieR/R/util_plot_figure_plotly.R                                                                |  165 
 dataquieR-2.8.2/dataquieR/R/util_plot_svg_to_uri.R                                                                   |    2 
 dataquieR-2.8.2/dataquieR/R/util_plotly_font_size.R                                                                  |    4 
 dataquieR-2.8.2/dataquieR/R/util_plotly_text.R                                                                       |only
 dataquieR-2.8.2/dataquieR/R/util_prep_location_check.R                                                               |    3 
 dataquieR-2.8.2/dataquieR/R/util_prep_proportion_check.R                                                             |    2 
 dataquieR-2.8.2/dataquieR/R/util_pretty_print.R                                                                      |  314 
 dataquieR-2.8.2/dataquieR/R/util_pretty_vector_string.R                                                              |    2 
 dataquieR-2.8.2/dataquieR/R/util_queue_cluster.R                                                                     |    2 
 dataquieR-2.8.2/dataquieR/R/util_rbind.R                                                                             |    2 
 dataquieR-2.8.2/dataquieR/R/util_readr_cols_from_metadata.R                                                          |only
 dataquieR-2.8.2/dataquieR/R/util_really_rstudio.R                                                                    |    2 
 dataquieR-2.8.2/dataquieR/R/util_recode.R                                                                            |    2 
 dataquieR-2.8.2/dataquieR/R/util_referred_vars.R                                                                     |   75 
 dataquieR-2.8.2/dataquieR/R/util_remove_dataquieR_result_class.R                                                     |    7 
 dataquieR-2.8.2/dataquieR/R/util_remove_empty_rows.R                                                                 |    2 
 dataquieR-2.8.2/dataquieR/R/util_remove_na_records.R                                                                 |    2 
 dataquieR-2.8.2/dataquieR/R/util_render_table_dataquieR_summary.R                                                    |   25 
 dataquieR-2.8.2/dataquieR/R/util_replace_codes_by_na.R                                                               |    8 
 dataquieR-2.8.2/dataquieR/R/util_replace_hard_limit_violations.R                                                     |    5 
 dataquieR-2.8.2/dataquieR/R/util_rio_import.R                                                                        |  175 
 dataquieR-2.8.2/dataquieR/R/util_round_to_decimal_places.R                                                           |    2 
 dataquieR-2.8.2/dataquieR/R/util_s3_register.R                                                                       |only
 dataquieR-2.8.2/dataquieR/R/util_seg_table.R                                                                         |    2 
 dataquieR-2.8.2/dataquieR/R/util_set_doublequotes.R                                                                  |    2 
 dataquieR-2.8.2/dataquieR/R/util_set_singlequotes.R                                                                  |    2 
 dataquieR-2.8.2/dataquieR/R/util_set_size.R                                                                          |    7 
 dataquieR-2.8.2/dataquieR/R/util_setup_dashboard.R                                                                   |only
 dataquieR-2.8.2/dataquieR/R/util_setup_rstudio_job.R                                                                 |   90 
 dataquieR-2.8.2/dataquieR/R/util_sigmagap.R                                                                          |    2 
 dataquieR-2.8.2/dataquieR/R/util_sort_by_order.R                                                                     |   24 
 dataquieR-2.8.2/dataquieR/R/util_split_val_tab.R                                                                     |    2 
 dataquieR-2.8.2/dataquieR/R/util_standardise_ordinal_codes.R                                                         |    2 
 dataquieR-2.8.2/dataquieR/R/util_startsWith_prefix._or_equals_prefix.R                                               |    2 
 dataquieR-2.8.2/dataquieR/R/util_stop_if_not.R                                                                       |    2 
 dataquieR-2.8.2/dataquieR/R/util_storr_factory.R                                                                     |   37 
 dataquieR-2.8.2/dataquieR/R/util_string_is_not_categorical.R                                                         |    2 
 dataquieR-2.8.2/dataquieR/R/util_study_var2factor.R                                                                  |    2 
 dataquieR-2.8.2/dataquieR/R/util_suppress_output.R                                                                   |   20 
 dataquieR-2.8.2/dataquieR/R/util_suppress_warnings.R                                                                 |    2 
 dataquieR-2.8.2/dataquieR/R/util_table_of_vct.R                                                                      |    2 
 dataquieR-2.8.2/dataquieR/R/util_table_rotator.R                                                                     |    2 
 dataquieR-2.8.2/dataquieR/R/util_tail_file.R                                                                         |only
 dataquieR-2.8.2/dataquieR/R/util_translate.R                                                                         |    4 
 dataquieR-2.8.2/dataquieR/R/util_try_with_trace.R                                                                    |only
 dataquieR-2.8.2/dataquieR/R/util_tukey.R                                                                             |    2 
 dataquieR-2.8.2/dataquieR/R/util_undisclose.R                                                                        |   43 
 dataquieR-2.8.2/dataquieR/R/util_unit2baseunit.R                                                                     |    4 
 dataquieR-2.8.2/dataquieR/R/util_user_hint.R                                                                         |    2 
 dataquieR-2.8.2/dataquieR/R/util_validate_known_meta.R                                                               |   10 
 dataquieR-2.8.2/dataquieR/R/util_validate_missing_lists.R                                                            |   11 
 dataquieR-2.8.2/dataquieR/R/util_validate_report_summary_table.R                                                     |    2 
 dataquieR-2.8.2/dataquieR/R/util_varcomp_robust.R                                                                    |    1 
 dataquieR-2.8.2/dataquieR/R/util_variable_references.R                                                               |    2 
 dataquieR-2.8.2/dataquieR/R/util_verify_names.R                                                                      |    3 
 dataquieR-2.8.2/dataquieR/R/util_view_file.R                                                                         |    2 
 dataquieR-2.8.2/dataquieR/R/util_warn_unordered.R                                                                    |    2 
 dataquieR-2.8.2/dataquieR/R/util_warning.R                                                                           |    2 
 dataquieR-2.8.2/dataquieR/R/zzz-dq-lazy-s7.R                                                                         |only
 dataquieR-2.8.2/dataquieR/R/zzz_globs.R                                                                              |   21 
 dataquieR-2.8.2/dataquieR/README.md                                                                                  |   10 
 dataquieR-2.8.2/dataquieR/build/vignette.rds                                                                         |binary
 dataquieR-2.8.2/dataquieR/inst/CITATION                                                                              |    4 
 dataquieR-2.8.2/dataquieR/inst/WORDLIST                                                                              |   10 
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 dataquieR-2.8.2/dataquieR/inst/dqi.rds                                                                               |binary
 dataquieR-2.8.2/dataquieR/inst/grading_formats.xlsx                                                                  |binary
 dataquieR-2.8.2/dataquieR/inst/grading_rulesets.xlsx                                                                 |binary
 dataquieR-2.8.2/dataquieR/inst/hovertext.rds                                                                         |binary
 dataquieR-2.8.2/dataquieR/inst/implementations.rds                                                                   |binary
 dataquieR-2.8.2/dataquieR/inst/menu/script.js                                                                        |  577 +
 dataquieR-2.8.2/dataquieR/inst/menu/script_iframe.js                                                                 |   24 
 dataquieR-2.8.2/dataquieR/inst/menu/script_toplevel.js                                                               |  236 
 dataquieR-2.8.2/dataquieR/inst/menu/style_toplevel.css                                                               |   45 
 dataquieR-2.8.2/dataquieR/inst/mimetypes/media-types.RDS                                                             |binary
 dataquieR-2.8.2/dataquieR/inst/ssi.rds                                                                               |only
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 dataquieR-2.8.2/dataquieR/inst/templates/default/overview.html                                                       |    4 
 dataquieR-2.8.2/dataquieR/inst/templates/default/report.html                                                         |    5 
 dataquieR-2.8.2/dataquieR/inst/templates/default/single_indicator_with_menu.html                                     |    4 
 dataquieR-2.8.2/dataquieR/inst/translations.rds                                                                      |binary
 dataquieR-2.8.2/dataquieR/inst/voc.rds                                                                               |binary
 dataquieR-2.8.2/dataquieR/man/API_VERSION.Rd                                                                         |    2 
 dataquieR-2.8.2/dataquieR/man/ASSOCIATION_DIRECTION.Rd                                                               |   16 
 dataquieR-2.8.2/dataquieR/man/ASSOCIATION_FORM.Rd                                                                    |   16 
 dataquieR-2.8.2/dataquieR/man/ASSOCIATION_METRIC.Rd                                                                  |   16 
 dataquieR-2.8.2/dataquieR/man/ASSOCIATION_RANGE.Rd                                                                   |   16 
 dataquieR-2.8.2/dataquieR/man/CHECK_ID.Rd                                                                            |   16 
 dataquieR-2.8.2/dataquieR/man/CHECK_LABEL.Rd                                                                         |   16 
 dataquieR-2.8.2/dataquieR/man/COMPUTATION_RULE.Rd                                                                    |only
 dataquieR-2.8.2/dataquieR/man/COMPUTED_VARIABLE_ROLES.Rd                                                             |only
 dataquieR-2.8.2/dataquieR/man/CONTRADICTION_TERM.Rd                                                                  |   16 
 dataquieR-2.8.2/dataquieR/man/CONTRADICTION_TYPE.Rd                                                                  |   16 
 dataquieR-2.8.2/dataquieR/man/DATA_PREPARATION.Rd                                                                    |   19 
 dataquieR-2.8.2/dataquieR/man/DATA_TYPES.Rd                                                                          |    5 
 dataquieR-2.8.2/dataquieR/man/DATA_TYPES_OF_R_TYPE.Rd                                                                |    2 
 dataquieR-2.8.2/dataquieR/man/GOLDSTANDARD.Rd                                                                        |   16 
 dataquieR-2.8.2/dataquieR/man/IRV.Rd                                                                                 |only
 dataquieR-2.8.2/dataquieR/man/MAHALANOBIS_THRESHOLD.Rd                                                               |only
 dataquieR-2.8.2/dataquieR/man/MAXIMUM_LONG_STRING.Rd                                                                 |only
 dataquieR-2.8.2/dataquieR/man/MISS_RESP.Rd                                                                           |only
 dataquieR-2.8.2/dataquieR/man/MULTIVARIATE_OUTLIER_CHECK.Rd                                                          |   16 
 dataquieR-2.8.2/dataquieR/man/MULTIVARIATE_OUTLIER_CHECKTYPE.Rd                                                      |   16 
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 dataquieR-2.8.2/dataquieR/man/UNITS.Rd                                                                               |    1 
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 dataquieR-2.8.2/dataquieR/man/acc_loess.Rd                                                                           |    8 
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 dataquieR-2.8.2/dataquieR/man/acc_margins.Rd                                                                         |    3 
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 dataquieR-2.8.2/dataquieR/man/con_limit_deviations.Rd                                                                |    9 
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 dataquieR-2.8.2/dataquieR/man/dataquieR.Rd                                                                           |   59 
 dataquieR-2.8.2/dataquieR/man/dataquieR.VALUE_LABELS_htmlescaped.Rd                                                  |   33 
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 dataquieR-2.8.2/dataquieR/man/dataquieR.force_label_col.Rd                                                           |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.grading_formats.Rd                                                           |   33 
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 dataquieR-2.8.2/dataquieR/man/des_summary.Rd                                                                         |   16 
 dataquieR-2.8.2/dataquieR/man/des_summary_categorical.Rd                                                             |    2 
 dataquieR-2.8.2/dataquieR/man/des_summary_continuous.Rd                                                              |    9 
 dataquieR-2.8.2/dataquieR/man/dims.Rd                                                                                |    4 
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 dataquieR-2.8.2/dataquieR/man/dq_report2.Rd                                                                          |   56 
 dataquieR-2.8.2/dataquieR/man/dq_report_by.Rd                                                                        |   14 
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 dataquieR-2.8.2/dataquieR/man/int_duplicate_ids.Rd                                                                   |    8 
 dataquieR-2.8.2/dataquieR/man/int_unexp_records_set.Rd                                                               |    8 
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 dataquieR-2.8.2/dataquieR/man/meta_data_env.Rd                                                                       |    6 
 dataquieR-2.8.2/dataquieR/man/plot.dataquieR_summary.Rd                                                              |   15 
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 dataquieR-2.8.2/dataquieR/man/prep_add_missing_codes.Rd                                                              |    2 
 dataquieR-2.8.2/dataquieR/man/prep_combine_report_summaries.Rd                                                       |   14 
 dataquieR-2.8.2/dataquieR/man/prep_datatype_from_data.Rd                                                             |   10 
 dataquieR-2.8.2/dataquieR/man/prep_dq_data_type_of.Rd                                                                |    9 
 dataquieR-2.8.2/dataquieR/man/prep_extract_classes_by_functions.Rd                                                   |   14 
 dataquieR-2.8.2/dataquieR/man/prep_extract_summary.Rd                                                                |   16 
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 dataquieR-2.8.2/dataquieR/man/prep_get_labels.Rd                                                                     |    7 
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 dataquieR-2.8.2/dataquieR/man/prep_map_labels.Rd                                                                     |    2 
 dataquieR-2.8.2/dataquieR/man/prep_prepare_dataframes.Rd                                                             |    3 
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 dataquieR-2.8.2/dataquieR/man/prep_robust_guess_data_type.Rd                                                         |    3 
 dataquieR-2.8.2/dataquieR/man/prep_study2meta.Rd                                                                     |    7 
 dataquieR-2.8.2/dataquieR/man/prep_summary_to_classes.Rd                                                             |   14 
 dataquieR-2.8.2/dataquieR/man/prep_undisclose.Rd                                                                     |    6 
 dataquieR-2.8.2/dataquieR/man/print.ReportSummaryTable.Rd                                                            |    5 
 dataquieR-2.8.2/dataquieR/man/print.dataquieR_result.Rd                                                              |    4 
 dataquieR-2.8.2/dataquieR/man/print.dataquieR_resultset2.Rd                                                          |   15 
 dataquieR-2.8.2/dataquieR/man/print.dataquieR_summary.Rd                                                             |    5 
 dataquieR-2.8.2/dataquieR/man/print.master_result.Rd                                                                 |    4 
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 dataquieR-2.8.2/dataquieR/man/util_generate_pages_from_report.Rd                                                     |    6 
 dataquieR-2.8.2/dataquieR/man/util_html_for_dims.Rd                                                                  |    2 
 dataquieR-2.8.2/dataquieR/man/util_html_for_var.Rd                                                                   |   13 
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Package btw updated to version 1.1.0 with previous version 1.0.0 dated 2025-11-04

Title: A Toolkit for Connecting R and Large Language Models
Description: A complete toolkit for connecting 'R' environments with Large Language Models (LLMs). Provides utilities for describing 'R' objects, package documentation, and workspace state in plain text formats optimized for LLM consumption. Supports multiple workflows: interactive copy-paste to external chat interfaces, programmatic tool registration with 'ellmer' chat clients, batteries-included chat applications via 'shinychat', and exposure to external coding agents through the Model Context Protocol. Project configuration files enable stable, repeatable conversations with project-specific context and preferred LLM settings.
Author: Garrick Aden-Buie [aut, cre] , Simon Couch [aut] , Joe Cheng [aut], Posit Software, PBC [cph, fnd], Google [cph] , Microsoft [cph] , Jamie Perkins [cph]
Maintainer: Garrick Aden-Buie <garrick@adenbuie.com>

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Package fastrerandomize updated to version 0.3 with previous version 0.2 dated 2025-01-14

Title: Hardware-Accelerated Rerandomization for Improved Balance
Description: Provides hardware-accelerated tools for performing rerandomization and randomization testing in experimental research. Using a 'JAX' backend, the package enables exact rerandomization inference even for large experiments with hundreds of billions of possible randomizations. Key functionalities include generating pools of acceptable rerandomizations based on covariate balance, conducting exact randomization tests, and performing pre-analysis evaluations to determine optimal rerandomization acceptance thresholds. The package supports various hardware acceleration frameworks including 'CPU', 'CUDA', and 'METAL', making it versatile across accelerated computing environments. This allows researchers to efficiently implement stringent rerandomization designs and conduct valid inference even with large sample sizes. The package is partly based on Jerzak and Goldstein (2023) <doi:10.48550/arXiv.2310.00861>.
Author: Fucheng Warren Zhu [aut] , Aniket Sachin Kamat [aut] , Connor Jerzak [aut, cre] , Rebecca Goldstein [aut]
Maintainer: Connor Jerzak <connor.jerzak@gmail.com>

Diff between fastrerandomize versions 0.2 dated 2025-01-14 and 0.3 dated 2025-12-22

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Package embryogrowth updated to version 2025.12.22 with previous version 10.4 dated 2025-09-10

Title: Tools to Analyze the Thermal Reaction Norm of Embryo Growth
Description: Tools to analyze the embryo growth and the sexualisation thermal reaction norms. See <doi:10.7717/peerj.8451> for tsd functions; see <doi:10.1016/j.jtherbio.2014.08.005> for thermal reaction norm of embryo growth.
Author: Marc Girondot [aut, cre]
Maintainer: Marc Girondot <marc.girondot@gmail.com>

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Package BayesfMRI updated to version 0.11.0 with previous version 0.10.1 dated 2025-03-07

Title: Spatial Bayesian Methods for Task Functional MRI Studies
Description: Performs a spatial Bayesian general linear model (GLM) for task functional magnetic resonance imaging (fMRI) data on the cortical surface. Additional models include group analysis and inference to detect thresholded areas of activation. Includes direct support for the 'CIFTI' neuroimaging file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D. Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) <doi:10.1016/j.neuroimage.2022.118908>.
Author: Amanda Mejia [aut, cre], Damon Pham [ctb] , David Bolin [ctb], Yu Yue [ctb], Daniel Spencer [aut] , Sarah Ryan [ctb]
Maintainer: Amanda Mejia <mandy.mejia@gmail.com>

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Package StMoSim updated to version 3.2.0 with previous version 3.1.1 dated 2018-11-19

Title: Quantile-Quantile Plot with Several Gaussian Simulations
Description: Plots a QQ-Norm Plot with several Gaussian simulations.
Author: Matthias Salvisberg [aut, cre]
Maintainer: Matthias Salvisberg <matthias.salvisberg@gmail.com>

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Package rebus.datetimes updated to version 0.0-2.1 with previous version 0.0-2 dated 2022-11-03

Title: Date and Time Extensions for the 'rebus' Package
Description: Build regular expressions piece by piece using human readable code. This package contains date and time functionality, and is primarily intended to be used by package developers.
Author: Richard Cotton [aut, cre]
Maintainer: Richard Cotton <richierocks@gmail.com>

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Package Kendall updated to version 2.2.2 with previous version 2.2.1 dated 2022-03-20

Title: Kendall Rank Correlation and Mann-Kendall Trend Test
Description: Computes the Kendall rank correlation and Mann-Kendall trend test. See documentation for use of block bootstrap when there is autocorrelation.
Author: A.I. McLeod [aut, cre]
Maintainer: A.I. McLeod <aimcleod@uwo.ca>

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Package dySEM updated to version 1.4.1 with previous version 1.1.1 dated 2025-01-07

Title: Dyadic Structural Equation Modeling
Description: Scripting of structural equation models via 'lavaan' for Dyadic Data Analysis, and helper functions for supplemental calculations, tabling, and model visualization.
Author: John Sakaluk [aut, cre, cph] , Omar Camanto [aut] , Christopher Quinn-Nilas [ctb] , Merissa Prine [ctb], Robyn Kilshaw [ctb], Alexandra Fisher [ctb]
Maintainer: John Sakaluk <jksakaluk@gmail.com>

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Package matrixCorr updated to version 0.8.3 with previous version 0.7.2 dated 2025-12-03

Title: Collection of Correlation and Association Estimators
Description: Compute correlation and other association matrices from small to high-dimensional datasets with relative simple functions and sensible defaults. Includes options for shrinkage and robustness to improve results in noisy or high-dimensional settings (p >= n), plus convenient print/plot methods for inspection. Implemented with optimised C++ backends using BLAS/OpenMP and memory-aware symmetric updates. Works with base matrices and data frames, returning standard R objects via a consistent S3 interface. Useful across genomics, agriculture, and machine-learning workflows. Supports Pearson, Spearman, Kendall, distance correlation, partial correlation, and robust biweight mid-correlation; Bland–Altman analyses and Lin's concordance correlation coefficient (including repeated-measures extensions). Methods based on Ledoit and Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4>; Schäfer and Strimmer (2005) <doi:10.2202/1544-6115.1175>; Lin (1989) <doi:10.2307/2532051>.
Author: Thiago de Paula Oliveira [aut, cre]
Maintainer: Thiago de Paula Oliveira <thiago.paula.oliveira@gmail.com>

Diff between matrixCorr versions 0.7.2 dated 2025-12-03 and 0.8.3 dated 2025-12-22

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Package hdf5lib updated to version 2.0.0.4 with previous version 2.0.0.3 dated 2025-12-14

Title: Headers and Static Libraries for 'HDF5'
Description: 'HDF5' (Hierarchical Data Format 5) is a high-performance library and file format for storing and managing large, complex data. This package provides the static libraries and headers for the 'HDF5' 'C' library (release 2.0.0). It is intended for R package developers to use in the 'LinkingTo' field, which eliminates the need for users to install system-level 'HDF5' dependencies. This build is compiled with thread-safety enabled and supports dynamic loading of external compression filters. 'HDF5' is developed by 'The HDF Group' <https://www.hdfgroup.org/>.
Author: Daniel P. Smith [aut, cre] , The HDF Group [cph] , Jean-loup Mark [cph] , Gailly Adler [cph]
Maintainer: Daniel P. Smith <dansmith01@gmail.com>

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Package FGLMtrunc updated to version 0.2.0 with previous version 0.1.0 dated 2022-05-26

Title: Truncated Functional Generalized Linear Models
Description: An implementation of the methodologies described in Xi Liu, Afshin A. Divani, and Alexander Petersen (2022) <doi:10.1016/j.csda.2022.107421>, including truncated functional linear and truncated functional logistic regression models.
Author: Xi Liu [aut], Chau Tran [aut, cre], Alexander Petersen [aut]
Maintainer: Chau Tran <cbtr@ucdavis.edu>

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Package PenCoxFrail updated to version 2.0.1 with previous version 2.0.0 dated 2024-07-04

Title: Regularization in Cox Frailty Models
Description: Different regularization approaches for Cox Frailty Models by penalization methods are provided. see Groll et al. (2017) <doi:10.1111/biom.12637> for effects selection. See also Groll and Hohberg (2024) <doi:10.1002/bimj.202300020> for classical LASSO approach.
Author: Andreas Groll [aut, cre]
Maintainer: Andreas Groll <groll@statistik.tu-dortmund.de>

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Package BrokenAdaptiveRidge updated to version 1.0.2 with previous version 1.0.1 dated 2025-07-23

Title: Broken Adaptive Ridge Regression with Cyclops
Description: Approximates best-subset selection (L0) regression with an iteratively adaptive Ridge (L2) penalty for large-scale models. This package uses Cyclops for an efficient implementation and the iterative method is described in Kawaguchi et al (2020) <doi:10.1002/sim.8438> and Li et al (2021) <doi:10.1016/j.jspi.2020.12.001>.
Author: Marc A. Suchard [aut, cre], Eric Kawaguchi [aut], Ning Li [aut], Gang Li [aut], Observational Health Data Sciences and Informatics [cph]
Maintainer: Marc A. Suchard <msuchard@ucla.edu>

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Package trace updated to version 1.0.0 with previous version 0.6.0 dated 2025-03-14

Title: Tandem Repeat Analysis by Capillary Electrophoresis
Description: A pipeline for short tandem repeat instability analysis from fragment analysis data. Inputs of fsa files or peak tables, and a user supplied metadata data-frame. The package identifies ladders, calls peaks, identifies the modal peaks, calls repeats, then calculates repeat instability metrics (e.g. expansion index from Lee et al. (2010) <doi:10.1186/1752-0509-4-29>).
Author: Zachariah McLean [aut, cre, cph] , Kevin Correia [aut], Andrew Jiang [ctb]
Maintainer: Zachariah McLean <zachariah.louis.mclean@gmail.com>

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Package shapr readmission to version 1.0.7 with previous version 1.0.6 dated 2025-11-17

Title: Prediction Explanation with Dependence-Aware Shapley Values
Description: Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements methods which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. An accompanying 'Python' wrapper ('shaprpy') is available through PyPI.
Author: Martin Jullum [cre, aut] , Lars Henry Berge Olsen [aut] , Annabelle Redelmeier [aut], Jon Lachmann [aut] , Nikolai Sellereite [aut] , Anders Loeland [ctb], Jens Christian Wahl [ctb], Camilla Lingjaerde [ctb], Norsk Regnesentral [cph, fnd]
Maintainer: Martin Jullum <Martin.Jullum@nr.no>

This is a re-admission after prior archival of version 1.0.6 dated 2025-11-17

Diff between shapr versions 1.0.6 dated 2025-11-17 and 1.0.7 dated 2025-12-22

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More information about shapr at CRAN
Permanent link

Package sde updated to version 2.0.21 with previous version 2.0.18 dated 2022-08-09

Title: Simulation and Inference for Stochastic Differential Equations
Description: Description: Provides functions for simulation and inference for stochastic differential equations (SDEs). It accompanies the book "Simulation and Inference for Stochastic Differential Equations: With R Examples" (Iacus, 2008, Springer; ISBN: 978-0-387-75838-1).
Author: Stefano Maria Iacus [aut, cre]
Maintainer: Stefano Maria Iacus <smiacus@gmail.com>

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Package paths updated to version 0.1.2 with previous version 0.1.1 dated 2021-06-18

Title: An Imputation Approach to Estimating Path-Specific Causal Effects
Description: In causal mediation analysis with multiple causally ordered mediators, a set of path-specific effects are identified under standard ignorability assumptions. This package implements an imputation approach to estimating these effects along with a set of bias formulas for conducting sensitivity analysis (Zhou and Yamamoto <doi:10.31235/osf.io/2rx6p>). It contains two main functions: paths() for estimating path-specific effects and sens() for conducting sensitivity analysis. Estimation uncertainty is quantified using the nonparametric bootstrap.
Author: Minh Trinh [aut], Teppei Yamamoto [aut], Xiang Zhou [cre]
Maintainer: Xiang Zhou <xiang_zhou@fas.harvard.edu>

Diff between paths versions 0.1.1 dated 2021-06-18 and 0.1.2 dated 2025-12-22

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Package pathfindR updated to version 2.7.0 with previous version 2.6.0 dated 2025-09-20

Title: Enrichment Analysis Utilizing Active Subnetworks
Description: Enrichment analysis enables researchers to uncover mechanisms underlying a phenotype. However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. 'pathfindR' is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. 'pathfindR' also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in 'pathfindR' are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. 'pathfindR [...truncated...]
Author: Ege Ulgen [cre, cph] , Ozan Ozisik [aut]
Maintainer: Ege Ulgen <egeulgen@gmail.com>

Diff between pathfindR versions 2.6.0 dated 2025-09-20 and 2.7.0 dated 2025-12-22

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Package LOMAR updated to version 0.5.1 with previous version 0.5.0 dated 2024-09-26

Title: Localization Microscopy Data Analysis
Description: Read, register and compare point sets from single molecule localization microscopy.
Author: Jean-Karim Heriche [cre, aut]
Maintainer: Jean-Karim Heriche <heriche@embl.de>

Diff between LOMAR versions 0.5.0 dated 2024-09-26 and 0.5.1 dated 2025-12-22

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Package ggseg updated to version 1.6.8 with previous version 1.6.7 dated 2025-11-14

Title: Plotting Tool for Brain Atlases
Description: Contains 'ggplot2' geom for plotting brain atlases using simple features. The largest component of the package is the data for the two built-in atlases. Mowinckel & Vidal-Piñeiro (2020) <doi:10.1177/2515245920928009>.
Author: Athanasia Mo Mowinckel [aut, cre] , Didac Vidal-Pineiro [aut] , Ramiro Magno [aut]
Maintainer: Athanasia Mo Mowinckel <a.m.mowinckel@psykologi.uio.no>

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Package ClusterR updated to version 1.3.6 with previous version 1.3.5 dated 2025-10-18

Title: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering
Description: Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, <doi:10.18637/jss.v001.i04>; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, <doi:10.1145/1772690.1772862>; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, <doi:10.21105/joss.00026>; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, <doi:10.1126/science.1136800>.
Author: Lampros Mouselimis [aut, cre] , Conrad Sanderson [cph] , Ryan Curtin [cph] , Siddharth Agrawal [cph] ), Brendan Frey [cph] ), Delbert Dueck [cph] , Vitalie Spinu [ctb] , Frederiek - Maarten Kerckhof [ctb]
Maintainer: Lampros Mouselimis <mouselimislampros@gmail.com>

Diff between ClusterR versions 1.3.5 dated 2025-10-18 and 1.3.6 dated 2025-12-22

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Package rsurveycto updated to version 0.2.4 with previous version 0.2.2 dated 2025-06-18

Title: Interact with Data on 'SurveyCTO'
Description: 'SurveyCTO' is a platform for mobile data collection in offline settings. The 'rsurveycto' R package uses the 'SurveyCTO' REST API <https://docs.surveycto.com/05-exporting-and-publishing-data/05-api-access/01.api-access.html> to read datasets and forms from a 'SurveyCTO' server into R as 'data.table's and to download file attachments. The package also has limited support to write datasets to a server.
Author: Jake Hughey [aut, cre], Robert On [aut]
Maintainer: Jake Hughey <jake@agency.fund>

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Package regressoR updated to version 4.0.7 with previous version 4.0.4 dated 2025-05-28

Title: Regression Data Analysis System
Description: Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines.
Author: Oldemar Rodriguez [aut, cre], Andres Navarro D. [ctb, prg], Diego Jimenez A. [ctb, prg], Ariel Arroyo S. [ctb, prg], Joseline Quiros M. [ctb, prg], Alejandro Vargas V. [ctb, prg], Jennifer Lobo V. [ctb, prg]
Maintainer: Oldemar Rodriguez <oldemar.rodriguez@ucr.ac.cr>

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Package predictoR updated to version 4.1.6 with previous version 4.1.5 dated 2025-05-28

Title: Predictive Data Analysis System
Description: Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.
Author: Oldemar Rodriguez [aut, cre], Diego Jimenez [ctb, prg], Andres Navarro [ctb, prg], Jennifer Lobo [ctb, prg]
Maintainer: Oldemar Rodriguez <oldemar.rodriguez@ucr.ac.cr>

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Package T4transport updated to version 0.1.7 with previous version 0.1.5 dated 2025-11-13

Title: Tools for Computational Optimal Transport
Description: Transport theory has seen much success in many fields of statistics and machine learning. We provide a variety of algorithms to compute Wasserstein distance, barycenter, and others. See Peyré and Cuturi (2019) <doi:10.1561/2200000073> for the general exposition to the study of computational optimal transport.
Author: Kisung You [aut, cre]
Maintainer: Kisung You <kisung.you@outlook.com>

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Package plotfunctions updated to version 1.5 with previous version 1.4 dated 2020-04-28

Title: Various Functions to Facilitate Visualization of Data and Analysis
Description: When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.
Author: Jacolien van Rij [aut, cre]
Maintainer: Jacolien van Rij <j.c.van.rij@rug.nl>

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Package motherduck updated to version 0.2.1 with previous version 0.2.0 dated 2025-12-02

Title: Utilities for Managing a 'Motherduck' Database
Description: Provides helper functions, metadata utilities, and workflows for administering and managing databases on the 'Motherduck' cloud platform. Some features require a 'Motherduck' account (<https://motherduck.com/>).
Author: Alejandro Hagan [aut, cre]
Maintainer: Alejandro Hagan <alejandro.hagan@outlook.com>

Diff between motherduck versions 0.2.0 dated 2025-12-02 and 0.2.1 dated 2025-12-22

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Package hySpc.testthat readmission to version 0.2.2 with previous version 0.2.1 dated 2020-06-24

Title: Unit Test Add-on for 'testthat'
Description: Enhance package 'testthat' by allowing tests to be attached to the function/object they test. This allows to keep functional and unit test code together.
Author: Claudia Beleites [aut, cre], Erick Oduniyi [aut]
Maintainer: Claudia Beleites <Claudia.Beleites@chemometrix.gmbh>

This is a re-admission after prior archival of version 0.2.1 dated 2020-06-24

Diff between hySpc.testthat versions 0.2.1 dated 2020-06-24 and 0.2.2 dated 2025-12-22

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Package centerline updated to version 0.2.5 with previous version 0.2.4 dated 2025-09-09

Title: Extract Centerline from Closed Polygons
Description: Generates skeletons of closed 2D polygons using Voronoi diagrams. It provides methods for 'sf', 'terra', and 'geos' objects to compute polygon centerlines based on the generated skeletons. Voronoi, G. (1908) <doi:10.1515/crll.1908.134.198>.
Author: Anatoly Tsyplenkov [aut, cre, cph]
Maintainer: Anatoly Tsyplenkov <atsyplenkov@fastmail.com>

Diff between centerline versions 0.2.4 dated 2025-09-09 and 0.2.5 dated 2025-12-22

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Package mtdesign updated to version 0.1.4 with previous version 0.1.3 dated 2025-08-23

Title: Mander and Thompson Designs
Description: Implements Mander & Thompson's (2010) <doi:10.1016/j.cct.2010.07.008> methods for two-stage designs optimal under the alternative hypothesis for phase II [cancer] trials. Also provides an implementation of Simon's (1989) <doi:10.1016/0197-2456(89)90015-9> original methodology and allows exploration of the operating characteristics of sub-optimal designs.
Author: John Kirkpatrick [aut, cre]
Maintainer: John Kirkpatrick <mtdesign@puzzledface.net>

Diff between mtdesign versions 0.1.3 dated 2025-08-23 and 0.1.4 dated 2025-12-22

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Package epiworldR updated to version 0.11.0.0 with previous version 0.10.0.0 dated 2025-11-14

Title: Fast Agent-Based Epi Models
Description: A flexible framework for Agent-Based Models (ABM), the 'epiworldR' package provides methods for prototyping disease outbreaks and transmission models using a 'C++' backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents' features, providing great complexity for the model dynamics. Furthermore, 'epiworldR' is ideal for simulation studies featuring large populations.
Author: George Vega Yon [aut, cre] , Derek Meyer [aut] , Andrew Pulsipher [aut] , Susan Holmes [rev] , Abinash Satapathy [rev] , Carinogurjao [rev], Centers for Disease Control and Prevention [fnd]
Maintainer: George Vega Yon <g.vegayon@gmail.com>

Diff between epiworldR versions 0.10.0.0 dated 2025-11-14 and 0.11.0.0 dated 2025-12-22

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Permanent link

Package BigDataStatMeth updated to version 1.0.3 with previous version 1.0.2 dated 2025-11-29

Title: Tools and Infrastructure for Developing 'Scalable' 'HDF5'-Based Methods
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 1.0.2 dated 2025-11-29 and 1.0.3 dated 2025-12-22

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Permanent link

Package ReVAMP updated to version 1.0.1 with previous version 1.0.0 dated 2025-12-19

Title: Interface to 'Vamp' Audio Analysis Plugins
Description: Provides an interface to the 'Vamp' audio analysis plugin system <https://www.vamp-plugins.org/> developed by Queen Mary University of London's Centre for Digital Music. Enables loading and running 'Vamp' plugins for various audio analysis tasks including tempo detection, onset detection, spectral analysis, and audio feature extraction. Supports mono and stereo audio with automatic channel adaptation and domain conversion.
Author: Ed Baker [aut, cre] , Authors of vamp-sdk [cph] , Mark Borgerding [cph]
Maintainer: Ed Baker <ed@ebaker.me.uk>

Diff between ReVAMP versions 1.0.0 dated 2025-12-19 and 1.0.1 dated 2025-12-22

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Package modsem updated to version 1.0.15 with previous version 1.0.14 dated 2025-11-18

Title: Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)
Description: Estimation of interaction (i.e., moderation) effects between latent variables in structural equation models (SEM). The supported methods are: The constrained approach (Algina & Moulder, 2001). The unconstrained approach (Marsh et al., 2004). The residual centering approach (Little et al., 2006). The double centering approach (Lin et al., 2010). The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000). The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) The constrained- unconstrained, residual- and double centering- approaches are estimated via 'lavaan' (Rosseel, 2012), whilst the LMS- and QML- approaches are estimated via 'modsem' it self. Alternatively model can be estimated via 'Mplus' (Muthén & Muthén, 1998-2017). References: Algina, J., & Moulder, B. C. (2001). <doi:10.1207/S15328007SEM0801_3>. "A note on estimating the Jöreskog-Yang model for latent variable interaction using 'LISREL' 8.3." Klein, A., & Moosb [...truncated...]
Author: Kjell Solem Slupphaug [aut, cre] , Mehmet Mehmetoglu [ctb] , Matthias Mittner [ctb]
Maintainer: Kjell Solem Slupphaug <slupphaugkjell@gmail.com>

Diff between modsem versions 1.0.14 dated 2025-11-18 and 1.0.15 dated 2025-12-22

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Package GBOP2 updated to version 0.1.4 with previous version 0.1.3 dated 2025-04-22

Title: Generalized Bayesian Optimal Phase II Design (G-BOP2)
Description: Provides functions for implementing the Generalized Bayesian Optimal Phase II (G-BOP2) design using various Particle Swarm Optimization (PSO) algorithms, including: - PSO-Default, based on Kennedy and Eberhart (1995) <doi:10.1109/ICNN.1995.488968>, "Particle Swarm Optimization"; - PSO-Quantum, based on Sun, Xu, and Feng (2004) <doi:10.1109/ICCIS.2004.1460396>, "A Global Search Strategy of Quantum-Behaved Particle Swarm Optimization"; - PSO-Dexp, based on Stehlík et al. (2024) <doi:10.1016/j.asoc.2024.111913>, "A Double Exponential Particle Swarm Optimization with Non-Uniform Variates as Stochastic Tuning and Guaranteed Convergence to a Global Optimum with Sample Applications to Finding Optimal Exact Designs in Biostatistics"; - and PSO-GO.
Author: Xinying Fang [aut], Wanni Lei [aut, cre], Shouhao Zhou [aut], Ping-Yang Chen [aut], Ray-Bing Chen [aut], Weng Kee Wong [aut], J. Jack Lee [aut]
Maintainer: Wanni Lei <wanni.lei17@gmail.com>

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Package fastml updated to version 0.7.5 with previous version 0.7.0 dated 2025-10-29

Title: Guarded Resampling Workflows for Safe and Automated Machine Learning in R
Description: Provides a guarded resampling workflow for training and evaluating machine‑learning models. When the guarded resampling path is used, preprocessing and model fitting are re‑estimated within each resampling split to reduce leakage risk. Supports multiple resampling schemes, integrates with established engines in the 'tidymodels' ecosystem, and aims to improve evaluation reliability by coordinating preprocessing, fitting, and evaluation within supported workflows. Offers a lightweight AutoML‑style workflow by automating model training, resampling, and tuning across multiple algorithms, while keeping evaluation design explicit and user‑controlled.
Author: Selcuk Korkmaz [aut, cre] , Dincer Goksuluk [aut] , Eda Karaismailoglu [aut]
Maintainer: Selcuk Korkmaz <selcukorkmaz@gmail.com>

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Package ergm updated to version 4.11.0 with previous version 4.10.1 dated 2025-08-27

Title: Fit, Simulate and Diagnose Exponential-Family Models for Networks
Description: An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis. See Hunter, Handcock, Butts, Goodreau, and Morris (2008) <doi:10.18637/jss.v024.i03> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.
Author: Mark S. Handcock [aut], David R. Hunter [aut], Carter T. Butts [aut], Steven M. Goodreau [aut], Pavel N. Krivitsky [aut, cre] , Martina Morris [aut], Li Wang [ctb], Kirk Li [ctb], Skye Bender-deMoll [ctb], Chad Klumb [ctb], Michal Bojanowski [ctb] , [...truncated...]
Maintainer: Pavel N. Krivitsky <pavel@statnet.org>

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 ergm-4.11.0/ergm/MD5                                                                              |  336 ++++----
 ergm-4.11.0/ergm/NAMESPACE                                                                        |    8 
 ergm-4.11.0/ergm/R/InitErgmConstraint.R                                                           |    2 
 ergm-4.11.0/ergm/R/InitErgmConstraint.operator.R                                                  |  120 ++
 ergm-4.11.0/ergm/R/InitErgmProposal.R                                                             |   17 
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 ergm-4.11.0/ergm/R/control.ergm.R                                                                 |   35 
 ergm-4.11.0/ergm/R/data.R                                                                         |  412 ++++------
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 ergm-4.11.0/ergm/R/ergm.CD.fixed.R                                                                |    9 
 ergm-4.11.0/ergm/R/ergm.MCMLE.R                                                                   |   20 
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 ergm-4.11.0/ergm/R/ergm.bridge.R                                                                  |   15 
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 ergm-4.11.0/ergm/R/ergm.getMCMCsample.R                                                           |   37 
 ergm-4.11.0/ergm/R/ergm.logitreg.R                                                                |    2 
 ergm-4.11.0/ergm/R/ergm.mple.R                                                                    |   23 
 ergm-4.11.0/ergm/R/ergm.san.R                                                                     |   10 
 ergm-4.11.0/ergm/R/ergm.utility.R                                                                 |   24 
 ergm-4.11.0/ergm/R/ergm_estfun.R                                                                  |    2 
 ergm-4.11.0/ergm/R/ergm_model.R                                                                   |   22 
 ergm-4.11.0/ergm/R/ergm_model.utils.R                                                             |    2 
 ergm-4.11.0/ergm/R/ergm_mplecov.R                                                                 |   10 
 ergm-4.11.0/ergm/R/ergm_proposal.R                                                                |   12 
 ergm-4.11.0/ergm/R/get.node.attr.R                                                                |   46 -
 ergm-4.11.0/ergm/R/godfather.R                                                                    |   50 -
 ergm-4.11.0/ergm/R/gof.ergm.R                                                                     |  248 ++++--
 ergm-4.11.0/ergm/R/logLik.ergm.R                                                                  |    5 
 ergm-4.11.0/ergm/R/mcmc.diagnostics.ergm.R                                                        |    8 
 ergm-4.11.0/ergm/R/nonidentifiability.R                                                           |   90 ++
 ergm-4.11.0/ergm/R/simulate.ergm.R                                                                |    5 
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 ergm-4.11.0/ergm/R/zzz.R                                                                          |    6 
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 ergm-4.11.0/ergm/data/datalist                                                                    |    2 
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 ergm-4.11.0/ergm/data/sampson.RData                                                               |binary
 ergm-4.11.0/ergm/inst/CITATION                                                                    |    4 
 ergm-4.11.0/ergm/inst/NEWS.Rd                                                                     |  125 +++
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 ergm-4.11.0/ergm/man/as.network.numeric.Rd                                                        |    5 
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 ergm-4.11.0/ergm/man/cohab.Rd                                                                     |   10 
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 ergm-4.11.0/ergm/man/simulate.ergm.Rd                                                             |    5 
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 ergm-4.11.0/ergm/src/changestats_auxnet.c                                                         |   10 
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 ergm-4.11.0/ergm/src/changestats_operator.c                                                       |   26 
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 ergm-4.11.0/ergm/src/wtchangestats_auxnet.c                                                       |    8 
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 ergm-4.11.0/ergm/tests/testthat/test-bridge-target.stats.R                                        |    3 
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 ergm-4.11.0/ergm/tests/testthat/test-constrain-dind.R                                             |    8 
 ergm-4.11.0/ergm/tests/testthat/test-constraints.R                                                |   32 
 ergm-4.11.0/ergm/tests/testthat/test-ergm-term-doc.R                                              |    6 
 ergm-4.11.0/ergm/tests/testthat/test-gof.R                                                        |   27 
 ergm-4.11.0/ergm/tests/testthat/test-nonident-test.R                                              |    4 
 ergm-4.11.0/ergm/tests/testthat/test-parallel.R                                                   |    1 
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More information about ergm at CRAN
Permanent link

Package orgutils updated to version 0.5-3 with previous version 0.5-2 dated 2025-12-15

Title: Helper Functions for Org Files
Description: Helper functions for Org files (<https://orgmode.org/>): a generic function 'toOrg' for transforming R objects into Org markup (most useful for data frames; there are also methods for Dates/POSIXt) and a function to read Org tables into data frames.
Author: Enrico Schumann [aut, cre]
Maintainer: Enrico Schumann <es@enricoschumann.net>

Diff between orgutils versions 0.5-2 dated 2025-12-15 and 0.5-3 dated 2025-12-22

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More information about orgutils at CRAN
Permanent link

Package keras3 updated to version 1.5.0 with previous version 1.4.0 dated 2025-05-04

Title: R Interface to 'Keras'
Description: Interface to 'Keras' <https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.
Author: Tomasz Kalinowski [aut, cph, cre], Daniel Falbel [ctb, cph], JJ Allaire [aut, cph], Francois Chollet [aut, cph], Posit Software, PBC [cph, fnd], Google [cph, fnd], Yuan Tang [ctb, cph] , Wouter Van Der Bijl [ctb, cph], Martin Studer [ctb, cph], Sigri [...truncated...]
Maintainer: Tomasz Kalinowski <tomasz@posit.co>

Diff between keras3 versions 1.4.0 dated 2025-05-04 and 1.5.0 dated 2025-12-22

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Package driveR updated to version 0.5.0 with previous version 0.4.1 dated 2023-08-19

Title: Prioritizing Cancer Driver Genes Using Genomics Data
Description: Cancer genomes contain large numbers of somatic alterations but few genes drive tumor development. Identifying cancer driver genes is critical for precision oncology. Most of current approaches either identify driver genes based on mutational recurrence or using estimated scores predicting the functional consequences of mutations. 'driveR' is a tool for personalized or batch analysis of genomic data for driver gene prioritization by combining genomic information and prior biological knowledge. As features, 'driveR' uses coding impact metaprediction scores, non-coding impact scores, somatic copy number alteration scores, hotspot gene/double-hit gene condition, 'phenolyzer' gene scores and memberships to cancer-related KEGG pathways. It uses these features to estimate cancer-type-specific probability for each gene of being a cancer driver using the related task of a multi-task learning classification model. The method is described in detail in Ulgen E, Sezerman OU. 2021. driveR: driveR: [...truncated...]
Author: Ege Ulgen [aut, cre, cph]
Maintainer: Ege Ulgen <egeulgen@gmail.com>

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Package matchingMarkets updated to version 1.0-5 with previous version 1.0-4 dated 2023-08-08

Title: Analysis of Stable Matchings
Description: Implements structural estimators to estimate preferences and correct for the sample selection bias of observed outcomes in matching markets. This includes one-sided matching of agents into groups (Klein, 2015) <doi:10.17863/CAM.5812> as well as two-sided matching of students to schools (Klein et al., 2024) <doi:10.1016/j.geb.2024.07.003>. The package also contains algorithms to find stable matchings in the three most common matching problems: the stable roommates problem (Irving, 1985) <doi:10.1016/0196-6774(85)90033-1>, the college admissions problem (Gale and Shapley, 1962) <doi:10.2307/2312726>, and the house allocation problem (Shapley and Scarf, 1974) <doi:10.1016/0304-4068(74)90033-0>.
Author: Thilo Klein [aut, cre, cph], Robert Aue [ctb], Fahiem Bacchus [cph], Sven Giegerich [ctb], Matthias Hericks [ctb], Alexander Sauer [ctb], Niklas Sorensson [cph]
Maintainer: Thilo Klein <thilo@klein.uk>

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Package jmotif updated to version 1.2.1 with previous version 1.2.0 dated 2025-10-23

Title: Time Series Analysis Toolkit Based on Symbolic Aggregate Discretization, i.e. SAX
Description: Implements time series z-normalization, SAX, HOT-SAX, VSM, SAX-VSM, RePair, and RRA algorithms facilitating time series motif (i.e., recurrent pattern), discord (i.e., anomaly), and characteristic pattern discovery along with interpretable time series classification.
Author: Pavel Senin [aut, cre]
Maintainer: Pavel Senin <seninp@gmail.com>

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Package gooseR updated to version 0.1.1 with previous version 0.1.0 dated 2025-12-17

Title: R Integration for 'Goose' AI
Description: Seamless integration between R and 'Goose' AI capabilities including memory management, visualization enhancements, and workflow automation. Save R objects to 'Goose' memory, apply Block branding to visualizations, and manage data science project workflows. For more information about 'Goose' AI, see <https://github.com/block/goose>.
Author: Brandon Theriault [aut, cre]
Maintainer: Brandon Theriault <btheriault@block.xyz>

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Package DFD updated to version 0.4.0 with previous version 0.3.0 dated 2025-02-11

Title: Extract Drugs from Differential Expression Data from LINCS Database
Description: Get Drug information from given differential expression profile. The package search for the bioactive compounds from reference databases such as LINCS containing the genome-wide gene expression signature (GES) from tens of thousands of drug and genetic perturbations (Subramanian et al. (2017) <DOI:10.1016/j.cell.2017.10.049>).
Author: Mohamed Soudy [aut, cre]
Maintainer: Mohamed Soudy <MohmedSoudy2009@gmail.com>

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Package rqlm updated to version 4.2-2 with previous version 4.2-1 dated 2025-12-15

Title: Modified Poisson Regression for Binary Outcome and Related Methods Involving Target Trial Emulations
Description: Modified Poisson, logistic and least-squares regression analyses for binary outcomes of Zou (2004) <doi:10.1093/aje/kwh090>, Noma (2025)<Forthcoming>, and Cheung (2007) <doi:10.1093/aje/kwm223> have been standard multivariate analysis methods to estimate risk ratio and risk difference in clinical and epidemiological studies. This R package involves an easy-to-handle function to implement these analyses by simple commands. Missing data analysis tools (multiple imputation) are also involved. In addition, recent studies have shown the ordinary robust variance estimator possibly has serious bias under small or moderate sample size situations for these methods. This package also provides computational tools to calculate alternative accurate confidence intervals. Also, standard computational tools for target trial emulation are included.
Author: Hisashi Noma [aut, cre]
Maintainer: Hisashi Noma <noma@ism.ac.jp>

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Package RMaCzek readmission to version 1.6.1 with previous version 1.6.0 dated 2024-06-23

Title: Czekanowski's Diagrams
Description: Allows for production of Czekanowski's Diagrams with clusters. See K. Bartoszek, A. Vasterlund (2020) <doi:10.2478/bile-2020-0008> and K. Bartoszek, Y. Luo (2023) <doi:10.14708/ma.v51i2.7259>. The suggested 'FuzzyDBScan' package (which allows for fuzzy clustering) can be obtained from <https://github.com/henrifnk/FuzzyDBScan/> (or from CRAN's Archive <https://cran.r-project.org/src/contrib/Archive/FuzzyDBScan/>).
Author: Albin Vasterlund [aut], Krzysztof Bartoszek [cre, aut, ths], Ying Luo [aut], Piotr Jaskulski [ctb]
Maintainer: Krzysztof Bartoszek <krzbar@protonmail.ch>

This is a re-admission after prior archival of version 1.6.0 dated 2024-06-23

Diff between RMaCzek versions 1.6.0 dated 2024-06-23 and 1.6.1 dated 2025-12-22

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Package ELYP updated to version 0.7-6 with previous version 0.7-5 dated 2018-08-19

Title: Empirical Likelihood Analysis for the Cox Model and Yang-Prentice (2005) Model
Description: Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) model. Empirical likelihood tests within a Cox model, for parameters defined via both baseline hazard function and regression parameters.
Author: Mai Zhou [aut, cre]
Maintainer: Mai Zhou <maizhou@gmail.com>

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Package biomod2 updated to version 4.3-4-3 with previous version 4.2-6-2 dated 2025-01-24

Title: Ensemble Platform for Species Distribution Modeling
Description: Functions for species distribution modeling, calibration and evaluation, ensemble of models, ensemble forecasting and visualization. The package permits to run consistently up to 10 single models on a presence/absences (resp presences/pseudo-absences) dataset and to combine them in ensemble models and ensemble projections. Some bench of other evaluation and visualisation tools are also available within the package.
Author: Maya Gueguen [aut, cre], Helene Blancheteau [aut], Remi Lemaire-Patin [aut], Wilfried Thuiller [aut]
Maintainer: Maya Gueguen <maya.gueguen@univ-grenoble-alpes.fr>

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Package tind readmission to version 0.2.3 with previous version 0.2.2 dated 2025-11-07

Title: A Common Representation of Time Indices of Different Types
Description: Provides an easy-to-use tind class representing time indices of different types (years, quarters, months, ISO 8601 weeks, dates, time of day, date-time, and arbitrary integer/numeric indices). Includes an extensive collection of functions for calendrical computations (including business applications), index conversions, index parsing, and other operations. Auxiliary classes representing time differences and time intervals (with set operations and index matching functionality) are also provided. All routines have been optimised for speed in order to facilitate computations on large datasets. More details regarding calendars in general and calendrical algorithms can be found in "Calendar FAQ" by Claus Tøndering <https://www.tondering.dk/claus/calendar.html>.
Author: Grzegorz Klima [aut, cre, cph]
Maintainer: Grzegorz Klima <dever@post.pl>

This is a re-admission after prior archival of version 0.2.2 dated 2025-11-07

Diff between tind versions 0.2.2 dated 2025-11-07 and 0.2.3 dated 2025-12-22

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Package PANACEA updated to version 1.1.0 with previous version 1.0.1 dated 2023-08-19

Title: Personalized Network-Based Anti-Cancer Therapy Evaluation
Description: Identification of the most appropriate pharmacotherapy for each patient based on genomic alterations is a major challenge in personalized oncology. 'PANACEA' is a collection of personalized anti-cancer drug prioritization approaches utilizing network methods. The methods utilize personalized "driverness" scores from 'driveR' to rank drugs, mapping these onto a protein-protein interaction network. The "distance-based" method scores each drug based on these scores and distances between drugs and genes to rank given drugs. The "RWR" method propagates these scores via a random-walk with restart framework to rank the drugs. The methods are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2023. PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology. Bioinformatics <doi:10.1093/bioinformatics/btad022>.
Author: Ege Ulgen [aut, cre, cph]
Maintainer: Ege Ulgen <egeulgen@gmail.com>

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Package BLA updated to version 1.0.2 with previous version 1.0.1 dated 2024-05-28

Title: Boundary Line Analysis
Description: Fits boundary line models to datasets as proposed by Webb (1972) <doi:10.1080/00221589.1972.11514472> and makes statistical inferences about their parameters. Provides additional tools for testing datasets for evidence of boundary presence and selecting initial starting values for model optimization prior to fitting the boundary line models. It also includes tools for conducting post-hoc analyses such as predicting boundary values and identifying the most limiting factor (Miti, Milne, Giller, Lark (2024) <doi:10.1016/j.fcr.2024.109365>). This ensures a comprehensive analysis for datasets that exhibit upper boundary structures.
Author: Chawezi Miti [cre, aut, cph] , Richard M Lark [aut] , Alice E Milne [aut] , Ken E Giller [aut] , Victor O Sadras [ctb], University of Nottingham/Rothamsted Research [fnd]
Maintainer: Chawezi Miti <mitichawezi14@gmail.com>

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Package texreg updated to version 1.39.5 with previous version 1.39.4 dated 2024-07-24

Title: Conversion of R Regression Output to LaTeX or HTML Tables
Description: Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. Details can be found in Leifeld (2013), JStatSoft <doi:10.18637/jss.v055.i08>.)
Author: Philip Leifeld [aut, cre], Claudia Zucca [ctb]
Maintainer: Philip Leifeld <philip.leifeld@manchester.ac.uk>

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Package pak updated to version 0.9.2 with previous version 0.9.1 dated 2025-12-01

Title: Another Approach to Package Installation
Description: The goal of 'pak' is to make package installation faster and more reliable. In particular, it performs all HTTP operations in parallel, so metadata resolution and package downloads are fast. Metadata and package files are cached on the local disk as well. 'pak' has a dependency solver, so it finds version conflicts before performing the installation. This version of 'pak' supports CRAN, 'Bioconductor' and 'GitHub' packages as well.
Author: Gabor Csardi [aut, cre], Jim Hester [aut], Posit Software, PBC [cph, fnd] , Winston Chang [ctb] , Ascent Digital Services [cph, fnd] , Hadley Wickham [ctb, cph] , Jeroen Ooms [ctb] , Maelle Salmon [ctb] , Duncan Temple Lang [ctb] , Lloyd Hilaiel [cph [...truncated...]
Maintainer: Gabor Csardi <csardi.gabor@gmail.com>

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Package ggstats updated to version 0.12.0 with previous version 0.11.0 dated 2025-09-15

Title: Extension to 'ggplot2' for Plotting Stats
Description: Provides new statistics, new geometries and new positions for 'ggplot2' and a suite of functions to facilitate the creation of statistical plots.
Author: Joseph Larmarange [aut, cre]
Maintainer: Joseph Larmarange <joseph@larmarange.net>

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Package ggseqlogo updated to version 0.2.2 with previous version 0.2 dated 2024-02-08

Title: A 'ggplot2' Extension for Drawing Publication-Ready Sequence Logos
Description: The extensive range of functions provided by this package makes it possible to draw highly versatile sequence logos. Features include, but not limited to, modifying colour schemes and fonts used to draw the logo, generating multiple logo plots, and aiding the visualisation with annotations. Sequence logos can easily be combined with other plots 'ggplot2' plots.
Author: Omar Wagih [aut, cre]
Maintainer: Omar Wagih <omarwagih@gmail.com>

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Package dialrjars updated to version 9.0.21 with previous version 9.0.14 dated 2025-09-16

Title: Required 'libphonenumber' jars for the 'dialr' Package
Description: Collects 'libphonenumber' jars required for the 'dialr' package.
Author: Danny Smith [aut, cre], Google Inc. [ctb, cph] , Vonage Holdings Corp. [ctb, cph] , Ian Galpin [ctb, cph] , Ben Gertzfield [ctb, cph]
Maintainer: Danny Smith <danny@gorcha.org>

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Sun, 21 Dec 2025

Package MEGB updated to version 0.2 with previous version 0.1 dated 2025-01-29

Title: Gradient Boosting for Longitudinal Data
Description: Gradient boosting is a powerful statistical learning method known for its ability to model complex relationships between predictors and outcomes while performing inherent variable selection. However, traditional gradient boosting methods lack flexibility in handling longitudinal data where within-subject correlations play a critical role. In this package, we propose a novel approach Mixed Effect Gradient Boosting ('MEGB'), designed specifically for high-dimensional longitudinal data. 'MEGB' incorporates a flexible semi-parametric model that embeds random effects within the gradient boosting framework, allowing it to account for within-individual covariance over time. Additionally, the method efficiently handles scenarios where the number of predictors greatly exceeds the number of observations (p>>n) making it particularly suitable for genomics data and other large-scale biomedical studies.
Author: Oyebayo Ridwan Olaniran [aut, cre], Saidat Fehintola Olaniran [aut]
Maintainer: Oyebayo Ridwan Olaniran <olaniran.or@unilorin.edu.ng>

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Package itraxR updated to version 1.13.2 with previous version 1.12.2 dated 2024-05-30

Title: Itrax Data Analysis Tools
Description: Parse, trim, join, visualise and analyse data from Itrax sediment core multi-parameter scanners manufactured by Cox Analytical Systems, Sweden. Functions are provided for parsing XRF-peak area files, line-scan optical images, and radiographic images, alongside accompanying metadata. A variety of data wrangling tasks like trimming, joining and reducing XRF-peak area data are simplified. Multivariate methods are implemented with appropriate data transformation.
Author: Thomas Bishop [aut, cre]
Maintainer: Thomas Bishop <tombishopemail@gmail.com>

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Package brfinance updated to version 0.6.0 with previous version 0.2.2 dated 2025-10-19

Title: Simplified Access to Brazilian Financial and Macroeconomic Data
Description: It offers simplified access to Brazilian macroeconomic and financial indicators selected from official sources, such as the 'IBGE' (Brazilian Institute of Geography and Statistics) via the 'SIDRA' API and the 'Central Bank of Brazil' via the 'SGS' API. It allows users to quickly retrieve and visualize data series such as the unemployment rate and the Selic interest rate. This package was developed for data access and visualization purposes, without generating forecasts or statistical results. For more information, see the official APIs: <https://sidra.ibge.gov.br/> and <https://dadosabertos.bcb.gov.br/dataset/>.
Author: Joao Paulo dos Santos Pereira Barbosa [aut, cre]
Maintainer: Joao Paulo dos Santos Pereira Barbosa <joao.31582129@gmail.com>

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Package spicy updated to version 0.4.1 with previous version 0.3.0 dated 2025-10-22

Title: Descriptive Statistics and Data Management Tools
Description: Extracts and summarizes metadata from data frames, including variable names, labels, types, and missing values. Computes compact descriptive statistics, frequency tables, and cross-tabulations to assist with efficient data exploration. Includes an interactive and exportable codebook generator for documenting variable metadata. Facilitates the identification of missing data patterns and structural issues in datasets. Designed to streamline initial data management and exploratory analysis workflows within 'R'.
Author: Amal Tawfik [aut, cre, cph]
Maintainer: Amal Tawfik <amal.tawfik@hesav.ch>

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Package vein updated to version 1.6.0 with previous version 1.5.0 dated 2025-10-04

Title: Vehicular Emissions Inventories
Description: Elaboration of vehicular emissions inventories, consisting in four stages, pre-processing activity data, preparing emissions factors, estimating the emissions and post-processing of emissions in maps and databases. More details in Ibarra-Espinosa et al (2018) <doi:10.5194/gmd-11-2209-2018>. Before using VEIN you need to know the vehicular composition of your study area, in other words, the combination of of type of vehicles, size and fuel of the fleet. Then, it is recommended to start with the project to download a template to create a structure of directories and scripts.
Author: Sergio Ibarra-Espinosa [aut, cre] , Daniel Schuch [ctb] , Joao Bazzo [ctb] , Mario Gavidia-Calderon [ctb] , Karl Ropkins [ctb]
Maintainer: Sergio Ibarra-Espinosa <zergioibarra@gmail.com>

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Package protein8k updated to version 0.0.2 with previous version 0.0.1 dated 2021-08-16

Title: Perform Analysis and Create Visualizations of Proteins
Description: Read Protein Data Bank (PDB) files, performs its analysis, and presents the result using different visualization types including 3D. The package also has additional capability for handling Virus Report data from the National Center for Biotechnology Information (NCBI) database. Nature Structural Biology 10, 980 (2003) <doi:10.1038/nsb1203-980>. US National Library of Medicine (2021) <https://www.ncbi.nlm.nih.gov/datasets/docs/reference-docs/data-reports/virus/>.
Author: Simon Liles [aut, cre]
Maintainer: Simon Liles <simon@quantknot.com>

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Package APRScenario updated to version 0.0.3.1 with previous version 0.0.3.0 dated 2025-07-24

Title: Structural Scenario Analysis for Bayesian Structural Vector Autoregression Models
Description: Implements the scenario analysis proposed by Antolin-Diaz, Petrella and Rubio-Ramirez (2021) "Structural scenario analysis with SVARs" <doi:10.1016/j.jmoneco.2020.06.001>.
Author: Giovanni Lombardo [aut, cre]
Maintainer: Giovanni Lombardo <giannilmbd@gmail.com>

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Package SuperLearner updated to version 2.0-40 with previous version 2.0-29 dated 2024-02-20

Title: Super Learner Prediction
Description: Implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.
Author: Eric Polley [aut, cre], Erin LeDell [aut], Chris Kennedy [aut], Sam Lendle [ctb], Mark van der Laan [aut, ths]
Maintainer: Eric Polley <epolley@uchicago.edu>

Diff between SuperLearner versions 2.0-29 dated 2024-02-20 and 2.0-40 dated 2025-12-21

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Package NCA updated to version 4.0.5 with previous version 4.0.4 dated 2025-11-14

Title: Necessary Condition Analysis
Description: Performs a Necessary Condition Analysis (NCA). (Dul, J. 2016. Necessary Condition Analysis (NCA). ''Logic and Methodology of 'Necessary but not Sufficient' causality." Organizational Research Methods 19(1), 10-52) <doi:10.1177/1094428115584005>. NCA identifies necessary (but not sufficient) conditions in datasets, where x causes (e.g. precedes) y. Instead of drawing a regression line ''through the middle of the data'' in an xy-plot, NCA draws the ceiling line. The ceiling line y = f(x) separates the area with observations from the area without observations. (Nearly) all observations are below the ceiling line: y <= f(x). The empty zone is in the upper left hand corner of the xy-plot (with the convention that the x-axis is ''horizontal'' and the y-axis is ''vertical'' and that values increase ''upwards'' and ''to the right''). The ceiling line is a (piecewise) linear non-decreasing line: a linear step function or a straight line. It indicates which level of x (e.g. an effort or [...truncated...]
Author: Jan Dul [aut], Govert Buijs [cre]
Maintainer: Govert Buijs <buijs@rsm.nl>

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Package galamm updated to version 0.4.0 with previous version 0.3.0 dated 2025-11-26

Title: Generalized Additive Latent and Mixed Models
Description: Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. (2023) <doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and Skrondal (2004)'s unifying framework for multilevel latent variable modeling <doi:10.1007/BF02295939>. Efficient computation is done using sparse matrix methods, Laplace approximation, and automatic differentiation. The framework includes generalized multilevel models with heteroscedastic residuals, mixed response types, factor loadings, smoothing splines, crossed random effects, and combinations thereof. Syntax for model formulation is close to 'lme4' (Bates et al. (2015) <doi:10.18637/jss.v067.i01>) and 'PLmixed' (Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>).
Author: Oeystein Soerensen [aut, cre] , Douglas Bates [ctb], Ben Bolker [ctb], Martin Maechler [ctb], Allan Leal [ctb], Fabian Scheipl [ctb], Steven Walker [ctb], Simon Wood [ctb]
Maintainer: Oeystein Soerensen <oystein.sorensen@psykologi.uio.no>

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Package EMAS updated to version 0.2.4 with previous version 0.2.3 dated 2025-09-04

Title: Epigenome-Wide Mediation Analysis Study
Description: DNA methylation is essential for human, and environment can change the DNA methylation and affect body status. Epigenome-Wide Mediation Analysis Study (EMAS) can find potential mediator CpG sites between exposure (x) and outcome (y) in epigenome-wide. For more information on the methods we used, please see the following references: Tingley, D. (2014) <doi:10.18637/jss.v059.i05>, Turner, S. D. (2018) <doi:10.21105/joss.00731>, Rosseel, D. (2012) <doi:10.18637/jss.v048.i02>.
Author: Xiuquan Nie [aut, cph, cre], Mengyi Wang [ctb, rev]
Maintainer: Xiuquan Nie <niexiuquan1995@foxmail.com>

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Package audubon updated to version 0.6.1 with previous version 0.5.2 dated 2024-04-26

Title: Japanese Text Processing Tools
Description: A collection of Japanese text processing tools for filling Japanese iteration marks, Japanese character type conversions, segmentation by phrase, and text normalization which is based on rules for the 'Sudachi' morphological analyzer and the 'NEologd' (Neologism dictionary for 'MeCab'). These features are specific to Japanese and are not implemented in 'ICU' (International Components for Unicode).
Author: Akiru Kato [cre, aut], Koki Takahashi [cph] , Shuhei Iitsuka [cph] , Taku Kudo [cph]
Maintainer: Akiru Kato <paithiov909@gmail.com>

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Package apcf updated to version 0.3.3 with previous version 0.3.2 dated 2025-02-28

Title: Adapted Pair Correlation Function
Description: The adapted pair correlation function transfers the concept of the pair correlation function from point patterns to patterns of objects of finite size and irregular shape (e.g. lakes within a country). The pair correlation function describes the spatial distribution of objects, e.g. random, aggregated or regularly spaced. This is a reimplementation of the method suggested by Nuske et al. (2009) <doi:10.1016/j.foreco.2009.09.050> using the library 'GEOS' <doi:10.5281/zenodo.11396894>.
Author: Robert Nuske [aut, cre]
Maintainer: Robert Nuske <robert.nuske@mailbox.org>

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Package WData updated to version 0.1.1 with previous version 0.1.0 dated 2025-08-18

Title: Statistical Inference for Weighted Data
Description: Analyzes and models data subject to sampling biases. Provides functions to estimate the density and cumulative distribution functions from biased samples of continuous distributions. Includes the estimators proposed by Bhattacharyya et al. (1988) <doi:10.1080/03610928808829825> and Jones (1991) <doi:10.2307/2337020> for density, and by Cox (2005, ISBN:052184939X) and Bose and Dutta (2022) <doi:10.1007/s00184-021-00824-3> for distribution, with different bandwidth selectors. Also includes a real length-biased dataset on shrub width from Muttlak (1988) <https://www.proquest.com/openview/3dd74592e623cdbcfa6176e85bd3d390/1?cbl=18750&diss=y&pq-origsite=gscholar>.
Author: Sanchez Martinez Noelia [cre, aut] , Borrajo Garcia Maria Isabel [aut] , Conde Amboage Mercedes [aut]
Maintainer: Sanchez Martinez Noelia <noelia.sanchez.martinez@rai.usc.es>

Diff between WData versions 0.1.0 dated 2025-08-18 and 0.1.1 dated 2025-12-21

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Package lavaan updated to version 0.6-21 with previous version 0.6-20 dated 2025-09-21

Title: Latent Variable Analysis
Description: Fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models.
Author: Yves Rosseel [aut, cre] , Terrence D. Jorgensen [aut] , Luc De Wilde [aut], Daniel Oberski [ctb], Jarrett Byrnes [ctb], Leonard Vanbrabant [ctb], Victoria Savalei [ctb], Ed Merkle [ctb], Michael Hallquist [ctb], Mijke Rhemtulla [ctb], Myrsini Katsika [...truncated...]
Maintainer: Yves Rosseel <Yves.Rosseel@UGent.be>

Diff between lavaan versions 0.6-20 dated 2025-09-21 and 0.6-21 dated 2025-12-21

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More information about lavaan at CRAN
Permanent link

Package bartXViz updated to version 1.0.9 with previous version 1.0.8 dated 2025-07-28

Title: Visualization of BART and BARP using SHAP
Description: Complex machine learning models are often difficult to interpret. Shapley values serve as a powerful tool to understand and explain why a model makes a particular prediction. This package computes variable contributions using permutation-based Shapley values for Bayesian Additive Regression Trees (BART) and its extension with Post-Stratification (BARP). The permutation-based SHAP method proposed by Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> is grounded in data obtained via MCMC sampling. Similar to the BART model introduced by Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>, this package leverages Bayesian posterior samples generated during model estimation, allowing variable contributions to be computed without requiring additional sampling. The BART model is designed to work with the following R packages: 'BART' <doi:10.18637/jss.v097.i01>, 'bartMachine' <doi:10.18637/jss.v070.i04>, and 'dbarts' <https://CRAN.R-project.org/ [...truncated...]
Author: Dong-eun Lee [aut, cre], Eun-Kyung Lee [aut]
Maintainer: Dong-eun Lee <ldongeun.leel@gmail.com>

Diff between bartXViz versions 1.0.8 dated 2025-07-28 and 1.0.9 dated 2025-12-21

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Package vapour updated to version 0.15.0 with previous version 0.13.0 dated 2025-11-18

Title: Access to the 'Geospatial Data Abstraction Library' ('GDAL')
Description: Provides low-level access to 'GDAL' functionality. 'GDAL' is the 'Geospatial Data Abstraction Library' a translator for raster and vector geospatial data formats that presents a single raster abstract data model and single vector abstract data model to the calling application for all supported formats <https://gdal.org/>. This package is focussed on providing exactly and only what GDAL does, to enable developing further tools.
Author: Michael Sumner [aut, cre] , Simon Wotherspoon [ctb] , Mark Padgham [ctb] ), Edzer Pebesma [ctb] , Roger Bivand [ctb] , Jim Hester [ctb, cph] , Timothy Keitt [ctb] , Jeroen Ooms [ctb] , Dale Maschette [ctb] , Joseph Stachelek [ctb], Even Rouault [ctb] [...truncated...]
Maintainer: Michael Sumner <mdsumner@gmail.com>

Diff between vapour versions 0.13.0 dated 2025-11-18 and 0.15.0 dated 2025-12-21

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More information about vapour at CRAN
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Package tinytable updated to version 0.15.2 with previous version 0.15.1 dated 2025-11-02

Title: Simple and Configurable Tables in 'HTML', 'LaTeX', 'Markdown', 'Word', 'PNG', 'PDF', and 'Typst' Formats
Description: Create highly customized tables with this simple and dependency-free package. Data frames can be converted to 'HTML', 'LaTeX', 'Markdown', 'Word', 'PNG', 'PDF', or 'Typst' tables. The user interface is minimalist and easy to learn. The syntax is concise. 'HTML' tables can be customized using the flexible 'Bootstrap' framework, and 'LaTeX' code with the 'tabularray' package.
Author: Vincent Arel-Bundock [aut, cre]
Maintainer: Vincent Arel-Bundock <vincent.arel-bundock@umontreal.ca>

Diff between tinytable versions 0.15.1 dated 2025-11-02 and 0.15.2 dated 2025-12-21

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 R/tabulator_data.R                                                             |    6 
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More information about tinytable at CRAN
Permanent link

Package supportR updated to version 1.6.0 with previous version 1.5.0 dated 2025-06-03

Title: Support Functions for Wrangling and Visualization
Description: Suite of helper functions for data wrangling and visualization. The only theme for these functions is that they tend towards simple, short, and narrowly-scoped. These functions are built for tasks that often recur but are not large enough in scope to warrant an ecosystem of interdependent functions.
Author: Nicholas J Lyon [aut, cre, cph]
Maintainer: Nicholas J Lyon <njlyon@alumni.iastate.edu>

Diff between supportR versions 1.5.0 dated 2025-06-03 and 1.6.0 dated 2025-12-21

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More information about supportR at CRAN
Permanent link

Package rdwd updated to version 1.9.8 with previous version 1.9.3 dated 2025-08-18

Title: Select and Download Climate Data from 'DWD' (German Weather Service)
Description: Handle climate data from the 'DWD' ('Deutscher Wetterdienst', see <https://www.dwd.de/EN/climate_environment/cdc/cdc_node_en.html> for more information). Choose observational time series from meteorological stations with 'selectDWD()'. Find raster data from radar and interpolation according to <https://brry.github.io/rdwd/raster-data.html>. Download (multiple) data sets with progress bars and no re-downloads through 'dataDWD()'. Read both tabular observational data and binary gridded datasets with 'readDWD()'.
Author: Berry Boessenkool [aut, cre]
Maintainer: Berry Boessenkool <berry-b@gmx.de>

Diff between rdwd versions 1.9.3 dated 2025-08-18 and 1.9.8 dated 2025-12-21

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More information about rdwd at CRAN
Permanent link

Package INLAspacetime updated to version 0.1.13 with previous version 0.1.12 dated 2025-03-14

Title: Spatial and Spatio-Temporal Models using 'INLA'
Description: Prepare objects to implement models over spatial and spacetime domains with the 'INLA' package (<https://www.r-inla.org>). These objects contain data to for the 'cgeneric' interface in 'INLA', enabling fast parallel computations. We implemented the spatial barrier model, see Bakka et. al. (2019) <doi:10.1016/j.spasta.2019.01.002>, and some of the spatio-temporal models proposed in Lindgren et. al. (2024) <https://raco.cat/index.php/SORT/article/view/428665>. Details are provided in the available vignettes and from the URL bellow.
Author: Elias Teixeira Krainski [cre, aut, cph] , Finn Lindgren [aut] , Haavard Rue [aut]
Maintainer: Elias Teixeira Krainski <eliaskrainski@gmail.com>

Diff between INLAspacetime versions 0.1.12 dated 2025-03-14 and 0.1.13 dated 2025-12-21

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More information about INLAspacetime at CRAN
Permanent link

Package EDISON updated to version 1.1.2 with previous version 1.1.1 dated 2016-03-30

Title: Network Reconstruction and Changepoint Detection
Description: Package EDISON (Estimation of Directed Interactions from Sequences Of Non-homogeneous gene expression) runs an MCMC simulation to reconstruct networks from time series data, using a non-homogeneous, time-varying dynamic Bayesian network. Networks segments and changepoints are inferred concurrently, and information sharing priors provide a reduction of the inference uncertainty.
Author: Frank Dondelinger [aut, cre], Sophie Lebre [aut]
Maintainer: Frank Dondelinger <fdondelinger.work@gmail.com>

Diff between EDISON versions 1.1.1 dated 2016-03-30 and 1.1.2 dated 2025-12-21

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 NAMESPACE                                   |  130 ++---
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 127 files changed, 8132 insertions(+), 8088 deletions(-)

More information about EDISON at CRAN
Permanent link

Sat, 20 Dec 2025

Package CausalQueries updated to version 1.4.4 with previous version 1.4.3 dated 2025-07-22

Title: Make, Update, and Query Binary Causal Models
Description: Users can declare causal models over binary nodes, update beliefs about causal types given data, and calculate arbitrary queries. Updating is implemented in 'stan'. See Humphreys and Jacobs, 2023, Integrated Inferences (<DOI: 10.1017/9781316718636>) and Pearl, 2009 Causality (<DOI:10.1017/CBO9780511803161>).
Author: Clara Bicalho [ctb], Jasper Cooper [ctb], Macartan Humphreys [aut] , Till Tietz [aut, cre] , Alan Jacobs [aut], Merlin Heidemanns [ctb], Lily Medina [aut] , Julio Solis [ctb], Georgiy Syunyaev [aut] , Moritz Marbach [ctb]
Maintainer: Till Tietz <ttietz2014@gmail.com>

Diff between CausalQueries versions 1.4.3 dated 2025-07-22 and 1.4.4 dated 2025-12-20

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Package mixedBayes updated to version 0.1.12 with previous version 0.1.11 dated 2025-08-20

Title: Bayesian Longitudinal Regularized Quantile Mixed Model
Description: With high-dimensional omics features, repeated measure ANOVA leads to longitudinal gene-environment interaction studies that have intra-cluster correlations, outlying observations and structured sparsity arising from the ANOVA design. In this package, we have developed robust sparse Bayesian mixed effect models tailored for the above studies (Fan et al. (2025) <doi:10.1093/jrsssc/qlaf027>). An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in 'C++'. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.
Author: Kun Fan [aut, cre], Shejuty Devnath [aut], Cen Wu [aut]
Maintainer: Kun Fan <kfan@ksu.edu>

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Package tinyVAST updated to version 1.4.0 with previous version 1.3.0 dated 2025-09-13

Title: Multivariate Spatio-Temporal Models using Structural Equations
Description: Fits a wide variety of multivariate spatio-temporal models with simultaneous and lagged interactions among variables (including vector autoregressive spatio-temporal ('VAST') dynamics) for areal, continuous, or network spatial domains. It includes time-variable, space-variable, and space-time-variable interactions using dynamic structural equation models ('DSEM') as expressive interface, and the 'mgcv' package to specify splines via the formula interface. See Thorson et al. (2025) <doi:10.1111/geb.70035> for more details.
Author: James T. Thorson [aut, cre] , Sean C. Anderson [aut]
Maintainer: James T. Thorson <James.Thorson@noaa.gov>

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Package mmcmcBayes updated to version 0.2.0 with previous version 0.1.0 dated 2025-11-24

Title: Multistage MCMC Method for Detecting DMRs
Description: Implements differential methylation region (DMR) detection using a multistage Markov chain Monte Carlo (MCMC) algorithm based on the alpha-skew generalized normal (ASGN) distribution. Version 0.2.0 removes the Anderson-Darling test stage, improves computational efficiency of the core ASGN and multistage MCMC routines, and adds convenience functions for summarizing and visualizing detected DMRs. The methodology is based on Yang (2025) <https://www.proquest.com/docview/3218878972>.
Author: Zhexuan Yang [aut, cre], Duchwan Ryu [aut], Feng Luan [aut]
Maintainer: Zhexuan Yang <zky5198@psu.edu>

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Package isopam updated to version 3.3 with previous version 3.2 dated 2025-01-16

Title: Clustering of Sites with Species Data
Description: Clustering algorithm developed for use with plot inventories of species. It groups plots by subsets of diagnostic species rather than overall species composition. There is an unsupervised and a supervised mode, the latter accepting suggestions for species with greater weight and cluster medoids.
Author: Sebastian Schmidtlein [aut, cre] , Jason Collison [aut], Robin Pfannendoerfer [aut], Lubomir Tichy [ctb]
Maintainer: Sebastian Schmidtlein <schmidtlein@kit.edu>

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Package futureverse updated to version 0.1.1 with previous version 0.1.0 dated 2024-06-07

Title: Install 'Futureverse' in One Go
Description: The 'Futureverse' is a set of packages for parallel and distributed process with the 'future' package at its core, cf. Bengtsson (2021) <doi:10.32614/RJ-2021-048>. This package is designed to make it easy to install common 'Futureverse' packages in a single step. This package is intended for end-users, interactive use, and R scripts. Packages must not list it as a dependency - instead, explicitly declare each 'Futureverse' package as a dependency as needed.
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

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Package DOVE updated to version 1.11 with previous version 1.10 dated 2023-12-13

Title: Durability of Vaccine Efficacy
Description: Implements maximum likelihood methods for evaluating the durability of vaccine efficacy in a randomized, placebo-controlled clinical trial with staggered enrollment of participants and potential crossover of placebo recipients before the end of the trial. Lin, D. Y., Zeng, D., and Gilbert, P. B. (2021) <doi:10.1093/cid/ciab226> and Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) <doi:10.1093/cid/ciab630>.
Author: Danyu Lin [aut], Yu Gu [aut], Donglin Zeng [aut], Shannon T. Holloway [aut, cre]
Maintainer: Shannon T. Holloway <shannon.t.holloway@gmail.com>

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Package tidypopgen updated to version 0.4.1 with previous version 0.4.0 dated 2025-10-24

Title: Tidy Population Genetics
Description: We provide a tidy grammar of population genetics, facilitating the manipulation and analysis of data on biallelic single nucleotide polymorphisms (SNPs). 'tidypopgen' scales to very large genetic datasets by storing genotypes on disk, and performing operations on them in chunks, without ever loading all data in memory. The full functionalities of the package are described in Carter et al. (2025) <doi:10.1111/2041-210x.70204>.
Author: Evie Carter [aut], Eirlys Tysall [aut], Andrea Manica [aut, cre, cph] , Chang Christopher [ctb] ), Shaun Purcell [ctb] ), Bengtsson Henrik [ctb] )
Maintainer: Andrea Manica <am315@cam.ac.uk>

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Package Rsomoclu updated to version 1.7.7 with previous version 1.7.6 dated 2022-09-26

Title: Somoclu
Description: Somoclu is a massively parallel implementation of self-organizing maps. It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal . Details refer to (Peter Wittek, et al (2017)) <doi:10.18637/jss.v078.i09>.
Author: Peter Wittek [aut], Shichao Gao [cre]
Maintainer: Shichao Gao <xgdgsc@gmail.com>

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Package PupilPre readmission to version 0.6.3 with previous version 0.6.2 dated 2020-03-10

Title: Preprocessing Pupil Size Data
Description: Pupillometric data collected using SR Research Eyelink eye trackers requires significant preprocessing. This package contains functions for preparing pupil dilation data for visualization and statistical analysis. Specifically, it provides a pipeline of functions which aid in data validation, the removal of blinks/artifacts, downsampling, and baselining, among others. Additionally, plotting functions for creating grand average and conditional average plots are provided. See the vignette for samples of the functionality. The package is designed for handling data collected with SR Research Eyelink eye trackers using Sample Reports created in SR Research Data Viewer.
Author: Aki-Juhani Kyroelaeinen [aut, cre], Vincent Porretta [aut], Jacolien van Rij [ctb], Juhani Jaervikivi [ctb]
Maintainer: Aki-Juhani Kyroelaeinen <akkyro@gmail.com>

This is a re-admission after prior archival of version 0.6.2 dated 2020-03-10

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Package phylospatial readmission to version 1.2.0 with previous version 1.1.1 dated 2025-05-02

Title: Spatial Phylogenetic Analysis
Description: Analyze spatial phylogenetic diversity patterns. Use your data on an evolutionary tree and geographic distributions of the terminal taxa to compute diversity and endemism metrics, test significance with null model randomization, analyze community turnover and biotic regionalization, and perform spatial conservation prioritizations. All functions support quantitative community data in addition to binary data.
Author: Matthew Kling [aut, cre, cph]
Maintainer: Matthew Kling <mattkling@berkeley.edu>

This is a re-admission after prior archival of version 1.1.1 dated 2025-05-02

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Package npANCOVA updated to version 0.1.1 with previous version 0.1.0 dated 2025-11-09

Title: Nonparametric ANCOVA Methods
Description: Nonparametric methods for analysis of covariance (ANCOVA) are distribution-free and provide a flexible statistical framework for situations where the assumptions of parametric ANCOVA are violated or when the response variable is ordinal. This package implements several well-known nonparametric ANCOVA procedures, including Quade, Puri and Sen, McSweeney and Porter, Burnett and Barr, Hettmansperger and McKean, Shirley, and Puri-Sen-Harwell-Serlin. The package provides user-friendly functions to apply these methods in practice. These methods are described in Olejnik et al. (1985) <doi:10.1177/0193841X8500900104> and Harwell et al. (1988) <doi:10.1037/0033-2909.104.2.268>.
Author: Mina Jahangiri [aut, cre] , Ali Taghavi Rad [aut] , Anoshirvan Kazemnejad [aut] , Keith Goldfeld [ctb] , Shayan Mostafaei [ctb]
Maintainer: Mina Jahangiri <minajahangiri984@gmail.com>

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Package knitr updated to version 1.51 with previous version 1.50 dated 2025-03-16

Title: A General-Purpose Package for Dynamic Report Generation in R
Description: Provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques.
Author: Yihui Xie [aut, cre] , Abhraneel Sarma [ctb], Adam Vogt [ctb], Alastair Andrew [ctb], Alex Zvoleff [ctb], Amar Al-Zubaidi [ctb], Andre Simon [ctb] , Aron Atkins [ctb], Aaron Wolen [ctb], Ashley Manton [ctb], Atsushi Yasumoto [ctb] , Ben Baumer [ctb], [...truncated...]
Maintainer: Yihui Xie <xie@yihui.name>

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Package junco readmission to version 0.1.2 with previous version 0.1.1 dated 2025-07-11

Title: Create Common Tables and Listings Used in Clinical Trials
Description: Structure and formatting requirements for clinical trial table and listing outputs vary between pharmaceutical companies. 'junco' provides additional tooling for use alongside the 'rtables', 'rlistings' and 'tern' packages when creating table and listing outputs. While motivated by the specifics of Johnson and Johnson Clinical and Statistical Programming's table and listing shells, 'junco' provides functionality that is general and reusable. Major features include a) alternative and extended statistical analyses beyond what 'tern' supports for use in standard safety and efficacy tables, b) a robust production-grade Rich Text Format (RTF) exporter for both tables and listings, c) structural support for spanning column headers and risk difference columns in tables, and d) robust font-aware automatic column width algorithms for both listings and tables.
Author: Gabriel Becker [cre, aut] , Ilse Augustyns [aut], Paul Jenkins [aut], Daniel Hofstaedter [aut], Joseph Kovach [aut], David Munoz Tord [aut], Daniel Sabanes Bove [aut], Ezequiel Anokian [ctb], Renfei Mao [ctb], Mrinal Das [ctb], Wojciech Wojciak [ctb] [...truncated...]
Maintainer: Gabriel Becker <gabembecker@gmail.com>

This is a re-admission after prior archival of version 0.1.1 dated 2025-07-11

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Package AzureAuth updated to version 1.3.4 with previous version 1.3.3 dated 2021-09-13

Title: Authentication Services for Azure Active Directory
Description: Provides Azure Active Directory (AAD) authentication functionality for R users of Microsoft's 'Azure' cloud <https://azure.microsoft.com/en-us>. Use this package to obtain 'OAuth' 2.0 tokens for services including Azure Resource Manager, Azure Storage and others. It supports both AAD v1.0 and v2.0, as well as multiple authentication methods, including device code and resource owner grant. Tokens are cached in a user-specific directory obtained using the 'rappdirs' package. The interface is based on the 'OAuth' framework in the 'httr' package, but customised and streamlined for Azure. Part of the 'AzureR' family of packages.
Author: Hong Ooi [aut, cre], Tyler Littlefield [ctb], httr development team [ctb] , Scott Holden [ctb] , Chris Stone [ctb] , Microsoft [cph]
Maintainer: Hong Ooi <hongooi73@gmail.com>

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Package wbids updated to version 1.1.3 with previous version 1.1.2 dated 2025-11-27

Title: Seamless Access to World Bank International Debt Statistics (IDS)
Description: Access and analyze the World Bank's International Debt Statistics (IDS) <https://www.worldbank.org/en/programs/debt-statistics/ids>. IDS provides creditor-debtor relationships between countries, regions, and institutions. 'wbids' enables users to download, process and work with IDS series across multiple geographies, counterparts, and time periods.
Author: Teal Emery [aut, cre], Teal Insights [cph], Christoph Scheuch [aut] , Christopher Smith [ctb]
Maintainer: Teal Emery <lte@tealinsights.com>

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Package emuR updated to version 2.6.0 with previous version 2.5.2 dated 2025-09-02

Title: Main Package of the EMU Speech Database Management System
Description: Provide the EMU Speech Database Management System (EMU-SDMS) with database management, data extraction, data preparation and data visualization facilities. See <https://ips-lmu.github.io/The-EMU-SDMS-Manual/> for more details.
Author: Markus Jochim [aut, cre] , Raphael Winkelmann [aut], Klaus Jaensch [aut, ctb], Steve Cassidy [aut, ctb], Jonathan Harrington [aut, ctb]
Maintainer: Markus Jochim <markusjochim@phonetik.uni-muenchen.de>

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Package ciftiTools updated to version 0.18.0 with previous version 0.17.4 dated 2025-01-23

Title: Tools for Reading, Writing, Viewing and Manipulating CIFTI Files
Description: CIFTI files contain brain imaging data in "grayordinates," which represent the gray matter as cortical surface vertices (left and right) and subcortical voxels (cerebellum, basal ganglia, and other deep gray matter). 'ciftiTools' provides a unified environment for reading, writing, visualizing and manipulating CIFTI-format data. It supports the "dscalar," "dlabel," and "dtseries" intents. Grayordinate data is read in as a "xifti" object, which is structured for convenient access to the data and metadata, and includes support for surface geometry files to enable spatially-dependent functionality such as static or interactive visualizations and smoothing.
Author: Amanda Mejia [aut, cre], Damon Pham [aut] , John Muschelli [ctb]
Maintainer: Amanda Mejia <mandy.mejia@gmail.com>

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Fri, 19 Dec 2025

Package scpoisson updated to version 0.0.2 with previous version 0.0.1 dated 2022-08-17

Title: Single Cell Poisson Probability Paradigm
Description: Useful to visualize the Poissoneity (an independent Poisson statistical framework, where each RNA measurement for each cell comes from its own independent Poisson distribution) of Unique Molecular Identifier (UMI) based single cell RNA sequencing (scRNA-seq) data, and explore cell clustering based on model departure as a novel data representation.
Author: Yue Pan [aut, cre], Justin Landis [aut] , Dirk Dittmer [aut], James S. Marron [aut], Di Wu [aut]
Maintainer: Yue Pan <yuep027@gmail.com>

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Package spatialRF updated to version 1.1.5 with previous version 1.1.4 dated 2022-08-19

Title: Easy Spatial Modeling with Random Forest
Description: Automatic generation and selection of spatial predictors for Random Forest models fitted to spatially structured data. Spatial predictors are constructed from a distance matrix among training samples using Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 <DOI:10.1016/j.ecolmodel.2006.02.015>) or the RFsp approach (Hengl et al. <DOI:10.7717/peerj.5518>). These predictors are used alongside user-supplied explanatory variables in Random Forest models. The package provides functions for model fitting, multicollinearity reduction, interaction identification, hyperparameter tuning, evaluation via spatial cross-validation, and result visualization using partial dependence and interaction plots. Model fitting relies on the 'ranger' package (Wright and Ziegler 2017 <DOI:10.18637/jss.v077.i01>).
Author: Blas M. Benito [aut, cre, cph]
Maintainer: Blas M. Benito <blasbenito@gmail.com>

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Package ergmito readmission to version 0.3-2 with previous version 0.3-1 dated 2023-06-14

Title: Exponential Random Graph Models for Small Networks
Description: Simulation and estimation of Exponential Random Graph Models (ERGMs) for small networks using exact statistics as shown in Vega Yon et al. (2020) <DOI:10.1016/j.socnet.2020.07.005>. As a difference from the 'ergm' package, 'ergmito' circumvents using Markov-Chain Maximum Likelihood Estimator (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs for small networks. As exhaustive enumeration is computationally feasible for small networks, this R package takes advantage of this and provides tools for calculating likelihood functions, and other relevant functions, directly, meaning that in many cases both estimation and simulation of ERGMs for small networks can be faster and more accurate than simulation-based algorithms.
Author: George Vega Yon [cre, aut] , Kayla de la Haye [ths] , Army Research Laboratory and the U.S. Army Research Office [fnd]
Maintainer: George Vega Yon <g.vegayon@gmail.com>

This is a re-admission after prior archival of version 0.3-1 dated 2023-06-14

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Package refdb readmission to version 0.1.2 with previous version 0.1.1 dated 2022-09-22

Title: A DNA Reference Library Manager
Description: Reference database manager offering a set of functions to import, organize, clean, filter, audit and export reference genetic data. Provide functions to download sequence data from NCBI GenBank <https://www.ncbi.nlm.nih.gov/genbank/>. Designed as an environment for semi-automatic and assisted construction of reference databases and to improve standardization and repeatability in barcoding and metabarcoding studies.
Author: Francois Keck [aut, cre, cph]
Maintainer: Francois Keck <francois.keck@gmail.com>

This is a re-admission after prior archival of version 0.1.1 dated 2022-09-22

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Package plot3logit readmission to version 3.2.0 with previous version 3.1.4 dated 2023-12-10

Title: Ternary Plots for Trinomial Regression Models
Description: An implementation of the ternary plot for interpreting regression coefficients of trinomial regression models, as proposed in Santi, Dickson and Espa (2019) <doi:10.1080/00031305.2018.1442368>. Ternary plots can be drawn using either 'ggtern' package (based on 'ggplot2') or 'Ternary' package (based on standard graphics). The package and its features are illustrated in Santi, Dickson, Espa and Giuliani (2022) <doi:10.18637/jss.v103.c01>.
Author: Flavio Santi [cre, aut] , Maria Michela Dickson [aut] , Giuseppe Espa [aut] , Diego Giuliani [aut]
Maintainer: Flavio Santi <flavio.santi@unitn.it>

This is a re-admission after prior archival of version 3.1.4 dated 2023-12-10

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Package ganttify updated to version 0.1.8 with previous version 0.1.7 dated 2025-12-04

Title: Create Interactive Gantt Charts with Work Breakdown Structure
Description: Create Primavera-style interactive Gantt charts with Work Breakdown Structure (WBS) hierarchy and activities. Features include color-coded WBS items, indented labels, scrollable views for large projects, dynamic date formatting, and the ability to dim past activities. Built on top of 'plotly' for interactive visualizations.
Author: Ahmed Aredah [aut, cre]
Maintainer: Ahmed Aredah <Ahmed.Aredah@gmail.com>

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Package ethiodate updated to version 0.3.0 with previous version 0.2.0 dated 2025-05-28

Title: Working with Ethiopian Dates
Description: A robust and efficient solution for working with Ethiopian dates. It can seamlessly convert to and from Gregorian dates. It is designed to be compatible with the 'tidyverse' data workflow, including plotting with 'ggplot2'. It ensures lightning-fast computations by integrating high-performance 'C++' code through 'Rcpp' package.
Author: Gutama Girja Urago [aut, cre, cph]
Maintainer: Gutama Girja Urago <girjagutama@gmail.com>

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Package mindr readmission to version 1.4.1 with previous version 1.3.2 dated 2021-11-22

Title: Generate Mind Maps
Description: Convert Markdown ('.md') or R Markdown ('.Rmd') texts, R scripts, directory structures, and other hierarchical structured documents into mind map widgets or 'Freemind' codes or 'Mermaid' mind map codes, and vice versa. 'Freemind' mind map ('.mm') files can be opened by or imported to common mind map software such as 'Freemind' (<https://freemind.sourceforge.io/wiki/index.php/Main_Page>). 'Mermaid' mind map codes (<https://mermaid.js.org/>) can be directly embedded in documents.
Author: Peng Zhao [aut, cre]
Maintainer: Peng Zhao <pengzhao20@outlook.com>

This is a re-admission after prior archival of version 1.3.2 dated 2021-11-22

Diff between mindr versions 1.3.2 dated 2021-11-22 and 1.4.1 dated 2025-12-19

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More information about mindr at CRAN
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Package interactionRCS updated to version 0.1.2 with previous version 0.1.1 dated 2023-11-21

Title: Calculate Estimates in Models with Interaction
Description: A tool to calculate and plot estimates from models in which an interaction between the main predictor and a continuous covariate has been specified. Methods used in the package refer to Harrell Jr FE (2015, ISBN:9783319330396); Durrleman S, Simon R. (1989) <doi:10.1002/sim.4780080504>; Greenland S. (1995) <doi:10.1097/00001648-199507000-00005>.
Author: Giorgio Melloni [aut, cre] , Andrea Bellavia [aut] , Hong Xiong [aut]
Maintainer: Giorgio Melloni <melloni.giorgio@gmail.com>

Diff between interactionRCS versions 0.1.1 dated 2023-11-21 and 0.1.2 dated 2025-12-19

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Package ursa updated to version 3.11.5 with previous version 3.11.4 dated 2025-02-20

Title: Non-Interactive Spatial Tools for Raster Processing and Visualization
Description: S3 classes and methods for manipulation with georeferenced raster data: reading/writing, processing, multi-panel visualization.
Author: Nikita Platonov [aut, cre]
Maintainer: Nikita Platonov <platonov@sev-in.ru>

Diff between ursa versions 3.11.4 dated 2025-02-20 and 3.11.5 dated 2025-12-19

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Package datareportR updated to version 0.1.2 with previous version 0.1.1 dated 2025-10-20

Title: Fast Data Summary Reports
Description: Generates an RMarkdown data report with two components: a summary of an input dataset and a diff of the dataset relative to an old version.
Author: Bryant Cong [aut, cre], Alex Gordon [aut]
Maintainer: Bryant Cong <bryant.bcp@gmail.com>

Diff between datareportR versions 0.1.1 dated 2025-10-20 and 0.1.2 dated 2025-12-19

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Package TrialSimulator updated to version 1.7.0 with previous version 1.3.0 dated 2025-09-26

Title: Clinical Trial Simulator
Description: Simulate phase II and/or phase III clinical trials. It supports various types of endpoints and adaptive strategies. Tools for carrying out graphical testing procedure and combination test under group sequential design are also provided.
Author: Han Zhang [cre, aut]
Maintainer: Han Zhang <zhangh.ustc@gmail.com>

Diff between TrialSimulator versions 1.3.0 dated 2025-09-26 and 1.7.0 dated 2025-12-19

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Package Rediscover readmission to version 0.3.3 with previous version 0.3.2 dated 2023-04-14

Title: Identify Mutually Exclusive Mutations
Description: An optimized method for identifying mutually exclusive genomic events. Its main contribution is a statistical analysis based on the Poisson-Binomial distribution that takes into account that some samples are more mutated than others. See [Canisius, Sander, John WM Martens, and Lodewyk FA Wessels. (2016) "A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence." Genome biology 17.1 : 1-17. <doi:10.1186/s13059-016-1114-x>]. The mutations matrices are sparse matrices. The method developed takes advantage of the advantages of this type of matrix to save time and computing resources.
Author: Juan A. Ferrer-Bonsoms [aut, cre], Laura Jareno [aut], Angel Rubio [aut]
Maintainer: Juan A. Ferrer-Bonsoms <jafhernandez@tecnun.es>

This is a re-admission after prior archival of version 0.3.2 dated 2023-04-14

Diff between Rediscover versions 0.3.2 dated 2023-04-14 and 0.3.3 dated 2025-12-19

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Package matRiks updated to version 0.1.5 with previous version 0.1.4 dated 2025-12-19

Title: Generates Raven-Like Matrices According to Rules
Description: Generates Raven like matrices according to different rules and the response list associated to the matrix. The package can generate matrices composed of 4 or 9 cells, along with a response list of 11 elements (the correct response + 10 incorrect responses). The matrices can be generated according to both logical rules (i.e., the relationships between the elements in the matrix are manipulated to create the matrix) and visual-spatial rules (i.e., the visual or spatial characteristics of the elements are manipulated to generate the matrix). The graphical elements of this package are based on the 'DescTools' package. This package has been developed within the PRIN2020 Project (Prot. 20209WKCLL) titled "Computerized, Adaptive and Personalized Assessment of Executive Functions and Fluid Intelligence" and founded by the Italian Ministry of Education and Research.
Author: Andrea Brancaccio [aut, ctb, cph, cre], Ottavia M. Epifania [aut, ctb, com], Debora de Chiusole [ctb]
Maintainer: Andrea Brancaccio <andreabrancaccio01@gmail.com>

Diff between matRiks versions 0.1.4 dated 2025-12-19 and 0.1.5 dated 2025-12-19

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Package evsim updated to version 1.7.1 with previous version 1.7.0 dated 2025-10-08

Title: Electric Vehicle Charging Sessions Simulation
Description: Simulation of Electric Vehicles charging sessions using Gaussian models, together with time-series power demand calculations.
Author: Marc Canigueral [aut, cre, cph]
Maintainer: Marc Canigueral <marccanyigueral@gmail.com>

Diff between evsim versions 1.7.0 dated 2025-10-08 and 1.7.1 dated 2025-12-19

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Package SkeletalVis updated to version 0.1.2 with previous version 0.1.1 dated 2025-05-06

Title: Exploration and Visualisation of Skeletal Transcriptomics Data
Description: Allows search and visualisation of a collection of uniformly processed skeletal transcriptomic datasets. Includes methods to identify datasets where genes of interest are differentially expressed and find datasets with a similar gene expression pattern to a query dataset Soul J, Hardingham TE, Boot-Handford RP, Schwartz JM (2019) <doi:10.1093/bioinformatics/bty947>.
Author: Jamie Soul [aut, cre, cph]
Maintainer: Jamie Soul <jamie.soul@liverpool.ac.uk>

Diff between SkeletalVis versions 0.1.1 dated 2025-05-06 and 0.1.2 dated 2025-12-19

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More information about SkeletalVis at CRAN
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Package robslopes updated to version 1.1.4 with previous version 1.1.3 dated 2023-04-27

Title: Fast Algorithms for Robust Slopes
Description: Fast algorithms for the Theil-Sen estimator, Siegel's repeated median slope estimator, and Passing-Bablok regression. The implementation is based on algorithms by Dillencourt et. al (1992) <doi:10.1142/S0218195992000020> and Matousek et. al (1998) <doi:10.1007/PL00009190>. The implementations are detailed in Raymaekers (2023) <doi:10.32614/RJ-2023-012> and Raymaekers J., Dufey F. (2022) <doi:10.48550/arXiv.2202.08060>. All algorithms run in quasilinear time.
Author: Jakob Raymaekers [aut, cre]
Maintainer: Jakob Raymaekers <jakob.raymaekers@uantwerpen.be>

Diff between robslopes versions 1.1.3 dated 2023-04-27 and 1.1.4 dated 2025-12-19

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More information about robslopes at CRAN
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Package tsforecast updated to version 1.2.1 with previous version 1.2.0 dated 2025-12-15

Title: Time Series Forecasting Functions
Description: Fundamental time series forecasting models such as autoregressive integrated moving average (ARIMA), exponential smoothing, and simple moving average are included. For ARIMA models, the output follows the traditional parameterisation by Box and Jenkins (1970, ISBN: 0816210942, 9780816210947). Furthermore, there are functions for detailed time series exploration and decomposition, respectively. All data and result visualisations are generated by 'ggplot2' instead of conventional R graphical output. For more details regarding the theoretical background of the models see Hyndman, R.J. and Athanasopoulos, G. (2021) <https://otexts.com/fpp3/>.
Author: Ka Yui Karl Wu [aut, cre]
Maintainer: Ka Yui Karl Wu <karlwuky@suss.edu.sg>

Diff between tsforecast versions 1.2.0 dated 2025-12-15 and 1.2.1 dated 2025-12-19

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Package ream updated to version 1.0-10 with previous version 1.0-9 dated 2025-12-18

Title: Density, Distribution, and Sampling Functions for Evidence Accumulation Models
Description: Calculate the probability density functions (PDFs) for two threshold evidence accumulation models (EAMs). These are defined using the following Stochastic Differential Equation (SDE), dx(t) = v(x(t),t)*dt+D(x(t),t)*dW, where x(t) is the accumulated evidence at time t, v(x(t),t) is the drift rate, D(x(t),t) is the noise scale, and W is the standard Wiener process. The boundary conditions of this process are the upper and lower decision thresholds, represented by b_u(t) and b_l(t), respectively. Upper threshold b_u(t) > 0, while lower threshold b_l(t) < 0. The initial condition of this process x(0) = z where b_l(t) < z < b_u(t). We represent this as the relative start point w = z/(b_u(0)-b_l(0)), defined as a ratio of the initial threshold location. This package generates the PDF using the same approach as the 'python' package it is based upon, 'PyBEAM' by Murrow and Holmes (2023) <doi:10.3758/s13428-023-02162-w>. First, it converts the SDE model into the forwards Fokke [...truncated...]
Author: Raphael Hartmann [aut, cre] , Matthew Murrow [aut]
Maintainer: Raphael Hartmann <raphael.hartmann@protonmail.com>

Diff between ream versions 1.0-9 dated 2025-12-18 and 1.0-10 dated 2025-12-19

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Package KINSIMU updated to version 0.1.3 with previous version 0.1.2-2 dated 2024-06-13

Title: Panel Evaluation in Forensic Kinship Analysis
Description: Evaluate specific panels in different aspects: i) Simulation tools related to pedigree researches; ii) calculation for systemic effectiveness indicators, such as probability of exclusion (PE).
Author: Guanju Ma [aut, cre], Shujin Li [ctb]
Maintainer: Guanju Ma <guanjuma@hebmu.edu.cn>

Diff between KINSIMU versions 0.1.2-2 dated 2024-06-13 and 0.1.3 dated 2025-12-19

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Package grates updated to version 1.7.1 with previous version 1.7.0 dated 2025-11-21

Title: Grouped Date Classes
Description: Provides a coherent interface and implementation for creating grouped date classes.
Author: Tim Taylor [aut, cre]
Maintainer: Tim Taylor <tim.taylor@hiddenelephants.co.uk>

Diff between grates versions 1.7.0 dated 2025-11-21 and 1.7.1 dated 2025-12-19

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Package fUnitRoots updated to version 4052.82 with previous version 4040.81 dated 2024-05-15

Title: Rmetrics - Modelling Trends and Unit Roots
Description: Provides four addons for analyzing trends and unit roots in financial time series: (i) functions for the density and probability of the augmented Dickey-Fuller Test, (ii) functions for the density and probability of MacKinnon's unit root test statistics, (iii) reimplementations for the ADF and MacKinnon Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's unit root test suite.
Author: Diethelm Wuertz [aut] , Tobias Setz [aut], Yohan Chalabi [aut], Georgi N. Boshnakov [cre]
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>

Diff between fUnitRoots versions 4040.81 dated 2024-05-15 and 4052.82 dated 2025-12-19

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Package clinpubr updated to version 1.1.1 with previous version 1.1.0 dated 2025-10-30

Title: Clinical Publication
Description: Accelerate the process from clinical data to medical publication, including clinical data cleaning, significant result screening, and the generation of publish-ready tables and figures.
Author: Yue Niu [aut, cre, cph] , Keyun Wang [aut]
Maintainer: Yue Niu <niuyuesam@163.com>

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Package CICI updated to version 0.9.7 with previous version 0.9.6 dated 2025-07-21

Title: Causal Inference with Continuous (Multiple Time Point) Interventions
Description: Estimation of counterfactual outcomes for multiple values of continuous interventions at different time points, and plotting of causal dose-response curves. Details are given in Schomaker, McIlleron, Denti, Diaz (2024) <doi:10.48550/arXiv.2305.06645>.
Author: Michael Schomaker [aut, cre], Leo Fuhrhop [ctb], Han Bao [ctb]
Maintainer: Michael Schomaker <michael.schomaker@stat.uni-muenchen.de>

Diff between CICI versions 0.9.6 dated 2025-07-21 and 0.9.7 dated 2025-12-19

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Package guideR updated to version 0.8.1 with previous version 0.8.0 dated 2025-12-10

Title: Miscellaneous Statistical Functions Used in 'guide-R'
Description: Companion package for the manual 'guide-R : Guide pour l’analyse de données d’enquêtes avec R' available at <https://larmarange.github.io/guide-R/>. 'guideR' implements miscellaneous functions introduced in 'guide-R' to facilitate statistical analysis and manipulation of survey data.
Author: Joseph Larmarange [aut, cre]
Maintainer: Joseph Larmarange <joseph@larmarange.net>

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Package ganDataModel updated to version 2.0.1 with previous version 1.1.7 dated 2024-07-21

Title: Build a Metric Subspaces Data Model for a Data Source
Description: Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analyzed for different levels. For each level metric subspaces with density values above a level are determined. The obtained set of metric subspaces and the trained neural network are assembled into a data model. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package 'ganGenerativeData' <https://cran.r-project.org/package=ganGenerativeData>.
Author: Werner Mueller [aut, cre]
Maintainer: Werner Mueller <werner.mueller5@chello.at>

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Package rtpcr updated to version 2.1.0 with previous version 2.0.5 dated 2025-12-12

Title: qPCR Data Analysis
Description: Amplification efficiency estimation, statistical analysis, and graphical representation of quantitative real-time PCR (qPCR) data using one or more specified reference genes is handled by 'rtpcr' package. By accounting for amplification efficiency values, 'rtpcr' was developed using a general calculation method described by Ganger et al. (2017) <doi:10.1186/s12859-017-1949-5> and Taylor et al. (2019) <doi:10.1016/j.tibtech.2018.12.002>, covering both the Livak and Pfaffl methods. Based on the experimental conditions, the functions of the 'rtpcr' package use t-test (for experiments with a two-level factor), analysis of variance (ANOVA), analysis of covariance (ANCOVA) or analysis of repeated measure data to analyse the relative expression (Delta Delta Ct method or Delta Ct method). The functions further provide standard errors and confidence intervals for means, apply statistical mean comparisons and present significance.
Author: Ghader Mirzaghaderi [aut, cre, cph]
Maintainer: Ghader Mirzaghaderi <gh.mirzaghaderi@uok.ac.ir>

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Package RPEGLMEN readmission to version 1.1.4 with previous version 1.1.3 dated 2025-03-30

Title: Gamma and Exponential Generalized Linear Models with Elastic Net Penalty
Description: Implements the fast iterative shrinkage-thresholding algorithm (FISTA) algorithm to fit a Gamma distribution with an elastic net penalty as described in Chen, Arakvin and Martin (2018) <doi:10.48550/arXiv.1804.07780>. An implementation for the case of the exponential distribution is also available, with details available in Chen and Martin (2018) <doi:10.2139/ssrn.3085672>.
Author: Anthony Christidis [aut, cre], Xin Chen [aut], Daniel Hanson [aut]
Maintainer: Anthony Christidis <anthony.christidis@stat.ubc.ca>

This is a re-admission after prior archival of version 1.1.3 dated 2025-03-30

Diff between RPEGLMEN versions 1.1.3 dated 2025-03-30 and 1.1.4 dated 2025-12-19

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Package ReliaShiny readmission to version 0.4.1 with previous version 0.4 dated 2025-11-11

Title: A 'Shiny' App for Reliability Analysis
Description: An interactive web application for reliability analysis using the 'shiny' <https://shiny.posit.co/> framework. The app provides an easy-to-use interface for performing reliability analysis using 'WeibullR' <https://cran.r-project.org/package=WeibullR> and 'ReliaGrowR' <https://cran.r-project.org/package=ReliaGrowR>.
Author: Paul Govan [aut, cre, cph]
Maintainer: Paul Govan <paul.govan2@gmail.com>

This is a re-admission after prior archival of version 0.4 dated 2025-11-11

Diff between ReliaShiny versions 0.4 dated 2025-11-11 and 0.4.1 dated 2025-12-19

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Package PupillometryR readmission to version 0.0.7 with previous version 0.0.6 dated 2025-11-10

Title: A Unified Pipeline for Pupillometry Data
Description: Provides a unified pipeline to clean, prepare, plot, and run basic analyses on pupillometry experiments.
Author: Samuel Forbes [aut, cre], David Robinson [ctb]
Maintainer: Samuel Forbes <samuel.h.forbes@gmail.com>

This is a re-admission after prior archival of version 0.0.6 dated 2025-11-10

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Package knitrdata readmission to version 0.6.2 with previous version 0.6.1 dated 2020-12-08

Title: Data Language Engine for 'knitr' / 'rmarkdown'
Description: Implements a data language engine for incorporating data directly in 'rmarkdown' documents so that they can be made completely standalone.
Author: David M. Kaplan [aut, cre, cph]
Maintainer: David M. Kaplan <dmkaplan2000@gmail.com>

This is a re-admission after prior archival of version 0.6.1 dated 2020-12-08

Diff between knitrdata versions 0.6.1 dated 2020-12-08 and 0.6.2 dated 2025-12-19

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Package INLAtools updated to version 0.0.6 with previous version 0.0.5 dated 2025-11-20

Title: Functionalities for the 'INLA' Package
Description: Contain code to work with a C struct, in short cgeneric, to define a Gaussian Markov random (GMRF) model. The cgeneric contain code to specify GMRF elements such as the graph and the precision matrix, and also the initial and prior for its parameters, useful for model inference. It can be accessed from a C program and is the recommended way to implement new GMRF models in the 'INLA' package (<https://www.r-inla.org>). The 'INLAtools' implement functions to evaluate each one of the model specifications from R. The implemented functionalities leverage the use of 'cgeneric' models and provide a way to debug the code as well to work with the prior for the model parameters and to sample from it. A very useful functionality is the Kronecker product method that creates a new model from multiple cgeneric models. It also works with the rgeneric, the R version of the cgeneric intended to easy try implementation of new GMRF models. The Kronecker between two cgeneric models was used in Sterr [...truncated...]
Author: Elias Teixeira Krainski [cre, aut, cph] , Finn Lindgren [aut] , Haavard Rue’ [aut]
Maintainer: Elias Teixeira Krainski <eliaskrainski@gmail.com>

Diff between INLAtools versions 0.0.5 dated 2025-11-20 and 0.0.6 dated 2025-12-19

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Package hyreg2 updated to version 1.1.0 with previous version 1.0.0 dated 2025-10-06

Title: Estimate Latent Classes on a Mixture of Continuous and Dichotomous Data
Description: EQ-5D value set estimation can be done using the hybrid model likelihood as described by Oppe and van Hout (2010) <doi:10.1002/hec.3560> and Ramos-Goñi et al. (2017) <doi:10.1097/MLR.0000000000000283>. The package is based on 'flexmix' and among others contains an M-step-driver as described by Leisch (2004) <doi:10.18637/jss.v011.i08>. Users can estimate latent classes and address preference heterogeneity. Both uncensored and censored data are supported. Furthermore, heteroscedasticity can be taken into account. It is possible to control for different covariates on the continuous and dichotomous parts of the data and start values can differ between the expected latent classes.
Author: Svenja Elkenkamp [aut, cre], Kim Rand [aut], John Grosser [aut], EuroQol [fnd]
Maintainer: Svenja Elkenkamp <svenja.elkenkamp@uni-bielefeld.de>

Diff between hyreg2 versions 1.0.0 dated 2025-10-06 and 1.1.0 dated 2025-12-19

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Package flint updated to version 0.1.4 with previous version 0.1.3 dated 2025-12-18

Title: Fast Library for Number Theory
Description: An R interface to 'FLINT' <https://flintlib.org/>, a C library for number theory. 'FLINT' extends GNU 'MPFR' <https://www.mpfr.org/> and GNU 'MP' <https://gmplib.org/> with support for operations on standard rings (the integers, the integers modulo n, finite fields, the rational, p-adic, real, and complex numbers) as well as matrices and polynomials over rings. 'FLINT' implements midpoint-radius interval arithmetic, also known as ball arithmetic, in the real and complex numbers, enabling computation in arbitrary precision with rigorous propagation of rounding and other errors; see Johansson (2017) <doi:10.1109/TC.2017.2690633>. Finally, 'FLINT' provides ball arithmetic implementations of many special mathematical functions, with high coverage of reference works such as the NIST Digital Library of Mathematical Functions <https://dlmf.nist.gov/>. The R interface defines S4 classes, generic functions, and methods for representation and basic operations as [...truncated...]
Author: Mikael Jagan [aut, cre] , Martin Maechler [ctb]
Maintainer: Mikael Jagan <jaganmn2@gmail.com>

Diff between flint versions 0.1.3 dated 2025-12-18 and 0.1.4 dated 2025-12-19

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Package abn updated to version 3.1.13 with previous version 3.1.12 dated 2025-11-25

Title: Modelling Multivariate Data with Additive Bayesian Networks
Description: The 'abn' R package facilitates Bayesian network analysis, a probabilistic graphical model that derives from empirical data a directed acyclic graph (DAG). This DAG describes the dependency structure between random variables. The R package 'abn' provides routines to help determine optimal Bayesian network models for a given data set. These models are used to identify statistical dependencies in messy, complex data. Their additive formulation is equivalent to multivariate generalised linear modelling, including mixed models with independent and identically distributed (iid) random effects. The core functionality of the 'abn' package revolves around model selection, also known as structure discovery. It supports both exact and heuristic structure learning algorithms and does not restrict the data distribution of parent-child combinations, providing flexibility in model creation and analysis. The 'abn' package uses Laplace approximations for metric estimation and includes wrappers to the [...truncated...]
Author: Matteo Delucchi [aut, cre] , Reinhard Furrer [aut] , Gilles Kratzer [aut] , Fraser Iain Lewis [aut] , Jonas I. Liechti [ctb] , Marta Pittavino [ctb] , Kalina Cherneva [ctb]
Maintainer: Matteo Delucchi <matteo.delucchi@math.uzh.ch>

Diff between abn versions 3.1.12 dated 2025-11-25 and 3.1.13 dated 2025-12-19

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Package tidyr updated to version 1.3.2 with previous version 1.3.1 dated 2024-01-24

Title: Tidy Messy Data
Description: Tools to help to create tidy data, where each column is a variable, each row is an observation, and each cell contains a single value. 'tidyr' contains tools for changing the shape (pivoting) and hierarchy (nesting and 'unnesting') of a dataset, turning deeply nested lists into rectangular data frames ('rectangling'), and extracting values out of string columns. It also includes tools for working with missing values (both implicit and explicit).
Author: Hadley Wickham [aut, cre], Davis Vaughan [aut], Maximilian Girlich [aut], Kevin Ushey [ctb], Posit Software, PBC [cph, fnd]
Maintainer: Hadley Wickham <hadley@posit.co>

Diff between tidyr versions 1.3.1 dated 2024-01-24 and 1.3.2 dated 2025-12-19

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More information about tidyr at CRAN
Permanent link

Package matRiks updated to version 0.1.4 with previous version 0.1.3 dated 2024-02-16

Title: Generates Raven-Like Matrices According to Rules
Description: Generates Raven like matrices according to different rules and the response list associated to the matrix. The package can generate matrices composed of 4 or 9 cells, along with a response list of 11 elements (the correct response + 10 incorrect responses). The matrices can be generated according to both logical rules (i.e., the relationships between the elements in the matrix are manipulated to create the matrix) and visual-spatial rules (i.e., the visual or spatial characteristics of the elements are manipulated to generate the matrix). The graphical elements of this package are based on the 'DescTools' package. This package has been developed within the PRIN2020 Project (Prot. 20209WKCLL) titled "Computerized, Adaptive and Personalized Assessment of Executive Functions and Fluid Intelligence" and founded by the Italian Ministry of Education and Research.
Author: Andrea Brancaccio [aut, ctb, cph, cre], Ottavia M. Epifania [aut, ctb, com], Debora de Chiusole [ctb]
Maintainer: Andrea Brancaccio <andreabrancaccio01@gmail.com>

Diff between matRiks versions 0.1.3 dated 2024-02-16 and 0.1.4 dated 2025-12-19

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Package journalR updated to version 0.6.0 with previous version 0.2.1 dated 2025-12-14

Title: Formatting Tools for Scientific Journal Writing
Description: Scientific journal numeric formatting policies implemented in code. Emphasis on formatting mean/upper/lower sets of values to pasteable text for journal submission. For example c(2e6, 1e6, 3e6) becomes "2.00 million (1.00--3.00)". Lancet and Nature have built-in styles for rounding and punctuation marks. Users may extend journal styles arbitrarily. Four metrics are supported; proportions, percentage points, counts and rates. Magnitudes for all metrics are discovered automatically.
Author: Sam Byrne [aut, cre, cph]
Maintainer: Sam Byrne <ssbyrne@uw.edu>

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Package geos updated to version 0.2.5 with previous version 0.2.4 dated 2023-11-30

Title: Open Source Geometry Engine ('GEOS') R API
Description: Provides an R API to the Open Source Geometry Engine ('GEOS') library (<https://libgeos.org/>) and a vector format with which to efficiently store 'GEOS' geometries. High-performance functions to extract information from, calculate relationships between, and transform geometries are provided. Finally, facilities to import and export geometry vectors to other spatial formats are provided.
Author: Dewey Dunnington [aut, cre] , Edzer Pebesma [aut]
Maintainer: Dewey Dunnington <dewey@fishandwhistle.net>

Diff between geos versions 0.2.4 dated 2023-11-30 and 0.2.5 dated 2025-12-19

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Permanent link

Package Rbeast updated to version 1.0.2 with previous version 1.0.1 dated 2024-08-30

Title: Bayesian Change-Point Detection and Time Series Decomposition
Description: BEAST is a Bayesian estimator of abrupt change, seasonality, and trend for decomposing univariate time series and 1D sequential data. Interpretation of time series depends on model choice; different models can yield contrasting or contradicting estimates of patterns, trends, and mechanisms. BEAST alleviates this by abandoning the single-best-model paradigm and instead using Bayesian model averaging over many competing decompositions. It detects and characterizes abrupt changes (changepoints, breakpoints, structural breaks, joinpoints), cyclic or seasonal variation, and nonlinear trends. BEAST not only detects when changes occur but also quantifies how likely the changes are true. It estimates not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is generically applicable to any real-valued time series, such as those from remote sensing, economics, climate science, ecology, hydrology, and other environmental and biological systems. Example applications include iden [...truncated...]
Author: Tongxi Hu [aut], Yang Li [aut], Xuesong Zhang [aut], Kaiguang Zhao [aut, cre], Jack Dongarra [ctb], Cleve Moler [ctb]
Maintainer: Kaiguang Zhao <zhao.1423@osu.edu>

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More information about Rbeast at CRAN
Permanent link

Package citation updated to version 0.12.2 with previous version 0.8.2 dated 2023-03-30

Title: Software Citation Tools
Description: A collection of functions to extract citation information from 'R' packages and to deal with files in 'citation file format' (<https://citation-file-format.github.io/>), extending the functionality already provided by the citation() function in the 'utils' package.
Author: Jan Philipp Dietrich [aut, cre] , Waldir Leoncio [aut]
Maintainer: Jan Philipp Dietrich <dietrich@pik-potsdam.de>

Diff between citation versions 0.8.2 dated 2023-03-30 and 0.12.2 dated 2025-12-19

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Package tidytlg updated to version 0.11.0 with previous version 0.10.0 dated 2025-07-02

Title: Create TLGs using the 'tidyverse'
Description: Generate tables, listings, and graphs (TLG) using 'tidyverse'. Tables can be created functionally, using a standard TLG process, or by specifying table and column metadata to create generic analysis summaries. The 'envsetup' package can also be leveraged to create environments for table creation.
Author: Nicholas Masel [aut], Steven Haesendonckx [aut], Pelagia Alexandra Papadopoulou [aut], Sheng-Wei Wang [aut], Eli Miller [aut] , Nathan Kosiba [aut] , Aidan Ceney [aut] , Janssen R&D [cph, fnd], David Hugh-Jones [cph] , Konrad Pagacz [aut, cre]
Maintainer: Konrad Pagacz <kpagacz@its.jnj.com>

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Package testit updated to version 0.15 with previous version 0.14 dated 2025-12-06

Title: A Simple Package for Testing R Packages
Description: Provides two convenience functions assert() and test_pkg() to facilitate testing R packages.
Author: Yihui Xie [aut, cre] , Tomas Kalibera [ctb], Steven Mortimer [ctb]
Maintainer: Yihui Xie <xie@yihui.name>

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Package surveydown updated to version 1.0.1 with previous version 0.14.0 dated 2025-11-03

Title: Markdown-Based Programmable Surveys Using 'Quarto' and 'shiny'
Description: Generate programmable surveys using markdown and R code chunks. Surveys are composed of two files: a survey.qmd 'Quarto' file defining the survey content (pages, questions, etc), and an app.R file defining a 'shiny' app with global settings (libraries, database configuration, etc.) and server configuration options (e.g., conditional skipping / display, etc.). Survey data collected from respondents is stored in a 'PostgreSQL' database. Features include controls for conditional skip logic (skip to a page based on an answer to a question), conditional display logic (display a question based on an answer to a question), a customizable progress bar, and a wide variety of question types, including multiple choice (single choice and multiple choices), select, text, numeric, multiple choice buttons, text area, and dates. Because the surveys render into a 'shiny' app, designers can also leverage the reactive capabilities of 'shiny' to create dynamic and interactive surveys.
Author: John Paul Helveston [aut, cre, cph] , Pingfan Hu [aut, cph] , Bogdan Bunea [aut, cph] , Stefan Munnes [ctb]
Maintainer: John Paul Helveston <john.helveston@gmail.com>

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Package RLumShiny updated to version 0.2.6 with previous version 0.2.5 dated 2025-07-12

Title: 'Shiny' Applications for the R Package 'Luminescence'
Description: A collection of 'shiny' applications for the R package 'Luminescence'. These mainly, but not exclusively, include applications for plotting chronometric data from e.g. luminescence or radiocarbon dating. It further provides access to bootstraps tooltip and popover functionality and contains the 'jscolor.js' library with a custom 'shiny' output binding.
Author: Christoph Burow [aut, cre] , Urs Tilmann Wolpert [aut], Sebastian Kreutzer [aut] , Marco Colombo [aut] , R Luminescence Package Team [ctb], Jan Odvarko [cph] , AnalytixWare [cph] , RStudio [cph]
Maintainer: Christoph Burow <christoph.burow@gmx.net>

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Package dbscan updated to version 1.2.4 with previous version 1.2.3 dated 2025-08-20

Title: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms
Description: A fast reimplementation of several density-based algorithms of the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based spatial clustering of applications with noise) and HDBSCAN (hierarchical DBSCAN), the ordering algorithm OPTICS (ordering points to identify the clustering structure), shared nearest neighbor clustering, and the outlier detection algorithms LOF (local outlier factor) and GLOSH (global-local outlier score from hierarchies). The implementations use the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. An R interface to fast kNN and fixed-radius NN search is also provided. Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.
Author: Michael Hahsler [aut, cre, cph] , Matthew Piekenbrock [aut, cph], Sunil Arya [ctb, cph], David Mount [ctb, cph], Claudia Malzer [ctb]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>

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Package TPLSr updated to version 1.0.5 with previous version 1.0.4 dated 2022-06-10

Title: Thresholded Partial Least Squares Model for Neuroimaging Data
Description: Uses thresholded partial least squares algorithm to create a regression or classification model. For more information, see Lee, Bradlow, and Kable <doi:10.1016/j.crmeth.2022.100227>.
Author: Sangil Lee [aut, cre]
Maintainer: Sangil Lee <sangillee3rd@gmail.com>

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Package contoso updated to version 1.2.2 with previous version 1.2.1 dated 2025-11-24

Title: Dataset of the 'Contoso' Company
Description: A collection of synthetic datasets simulating sales transactions from a fictional company. The dataset includes various related tables that contain essential business and operational data, useful for analyzing sales performance and other business insights. Key tables included in the package are: - "sales": Contains data on individual sales transactions, including order details, pricing, quantities, and customer information. - "customer": Stores customer-specific details such as demographics, geographic location, occupation, and birthday. - "store": Provides information about stores, including location, size, status, and operational dates. - "orders": Contains details about customer orders, including order and delivery dates, store, and customer data. - "product": Contains data on products, including attributes such as product name, category, price, cost, and weight. - "date": A time-based table that includes date-related attributes like year, month, quarter, day, and working day indica [...truncated...]
Author: Alejandro Hagan [aut, cre]
Maintainer: Alejandro Hagan <alejandro.hagan@outlook.com>

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Package Buddle updated to version 2.0.2 with previous version 2.0.1 dated 2020-02-13

Title: A Deep Learning for Statistical Classification and Regression Analysis with Random Effects
Description: Statistical classification and regression have been popular among various fields and stayed in the limelight of scientists of those fields. Examples of the fields include clinical trials where the statistical classification of patients is indispensable to predict the clinical courses of diseases. Considering the negative impact of diseases on performing daily tasks, correctly classifying patients based on the clinical information is vital in that we need to identify patients of the high-risk group to develop a severe state and arrange medical treatment for them at an opportune moment. Deep learning - a part of artificial intelligence - has gained much attention, and research on it burgeons during past decades: see, e.g, Kazemi and Mirroshandel (2018) <DOI:10.1016/j.artmed.2017.12.001>. It is a veritable technique which was originally designed for the classification, and hence, the Buddle package can provide sublime solutions to various challenging classification and regression pr [...truncated...]
Author: Jiwoong Kim [aut, cre]
Maintainer: Jiwoong Kim <jwboys26@gmail.com>

Diff between Buddle versions 2.0.1 dated 2020-02-13 and 2.0.2 dated 2025-12-19

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Thu, 18 Dec 2025

Package sglg updated to version 0.2.5 with previous version 0.2.4 dated 2025-12-09

Title: Fitting Semi-Parametric Generalized log-Gamma Regression Models
Description: Set of tools to fit a linear multiple or semi-parametric regression models with the possibility of non-informative random right or left censoring. Under this setup, the localization parameter of the response variable distribution is modeled by using linear multiple regression or semi-parametric functions, whose non-parametric components may be approximated by natural cubic spline or P-splines. The supported distribution for the model error is a generalized log-gamma distribution which includes the generalized extreme value and standard normal distributions as important special cases. Inference is based on likelihood, penalized likelihood and bootstrap methods. Lastly, some numerical and graphical devices for diagnostic of the fitted models are offered.
Author: Carlos Alberto Cardozo Delgado [aut, cre, cph] , Gilberto Paula [aut], Luis Vanegas [aut]
Maintainer: Carlos Alberto Cardozo Delgado <cardozorpackages@gmail.com>

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Package httping updated to version 0.3.0 with previous version 0.2.0 dated 2018-01-02

Title: 'Ping' 'URLs' to Time 'Requests'
Description: A suite of functions to ping 'URLs' and to time 'HTTP' 'requests'. Designed to work with 'httr'.
Author: Scott Chamberlain [aut, cre]
Maintainer: Scott Chamberlain <myrmecocystus@gmail.com>

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Package catregs updated to version 1.3 with previous version 1.2.4 dated 2025-11-06

Title: Post-Estimation Functions for Generalized Linear Mixed Models
Description: Several functions for working with mixed effects regression models for limited dependent variables. The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518).
Author: David Melamed [aut, cre]
Maintainer: David Melamed <dmmelamed@gmail.com>

Diff between catregs versions 1.2.4 dated 2025-11-06 and 1.3 dated 2025-12-18

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Package tagtools updated to version 0.3.0 with previous version 0.2.0 dated 2024-06-28

Title: Work with Data from High-Resolution Biologging Tags
Description: High-resolution movement-sensor tags typically include accelerometers to measure body posture and sudden movements or changes in speed, magnetometers to measure direction of travel, and pressure sensors to measure dive depth in aquatic or marine animals. The sensors in these tags usually sample many times per second. Some tags include sensors for speed, turning rate (gyroscopes), and sound. This package provides software tools to facilitate calibration, processing, and analysis of such data. Tools are provided for: data import/export; calibration (from raw data to calibrated data in scientific units); visualization (for example, multi-panel time-series plots); data processing (such as event detection, calculation of derived metrics like jerk and dynamic acceleration, dive detection, and dive parameter calculation); and statistical analysis (for example, track reconstruction, a rotation test, and Mahalanobis distance analysis).
Author: Stacy DeRuiter [aut, cre, cph] , Mark Johnson [aut, cph], David Sweeney [aut], Ye Joo McNamara-Oh [aut], Samuel Fynewever [aut], Racheal Tejevbo [aut], Tiago Marques [aut], Yuqian Wang [aut], Su Ogedegbe [aut]
Maintainer: Stacy DeRuiter <stacy.deruiter@calvin.edu>

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Package GofKmt updated to version 2.3.1 with previous version 2.2.0 dated 2020-10-20

Title: Khmaladze Martingale Transformation Goodness-of-Fit Test
Description: Consider a goodness-of-fit (GOF) problem of testing whether a random sample comes from one sample location-scale model where location and scale parameters are unknown. It is well known that Khmaladze martingale transformation method proposed by Khmaladze (1981) <doi:10.1137/1126027> provides asymptotic distribution free test for the GOF problem. This package provides test statistic and critical value of GOF test for normal, Cauchy, and logistic distributions. This package used the main algorithm proposed by Kim (2020) <doi:10.1007/s00180-020-00971-7> and tests for other distributions will be available at the later version.
Author: Jiwoong Kim [aut, cre]
Maintainer: Jiwoong Kim <jwboys26@gmail.com>

Diff between GofKmt versions 2.2.0 dated 2020-10-20 and 2.3.1 dated 2025-12-18

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Package hdMTD updated to version 0.1.4 with previous version 0.1.3 dated 2025-11-01

Title: Inference for High-Dimensional Mixture Transition Distribution Models
Description: Estimates parameters in Mixture Transition Distribution (MTD) models, a class of high-order Markov chains. The set of relevant pasts (lags) is selected using either the Bayesian Information Criterion or the Forward Stepwise and Cut algorithms. Other model parameters (e.g. transition probabilities and oscillations) can be estimated via maximum likelihood estimation or the Expectation-Maximization algorithm. Additionally, 'hdMTD' includes a perfect sampling algorithm that generates samples of an MTD model from its invariant distribution. For theory, see Ost & Takahashi (2023) <http://jmlr.org/papers/v24/22-0266.html>.
Author: Maiara Gripp [aut, cre], Guilherme Ost [ths], Giulio Iacobelli [ths]
Maintainer: Maiara Gripp <maiara@dme.ufrj.br>

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Package zen4R updated to version 0.10.4 with previous version 0.10.3 dated 2025-11-24

Title: Interface to 'Zenodo' REST API
Description: Provides an Interface to 'Zenodo' (<https://zenodo.org>) REST API, including management of depositions, attribution of DOIs by 'Zenodo' and upload and download of files.
Author: Emmanuel Blondel [aut, cre] , Julien Barde [ctb] , Stephen Eglen [ctb] , Hans Van Calster [ctb] , Floris Vanderhaeghe [ctb] , Jemma Stachelek [ctb] , Collin Schwantes [ctb] , Nicholas Tierney [ctb]
Maintainer: Emmanuel Blondel <emmanuel.blondel1@gmail.com>

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Package dang updated to version 0.0.17 with previous version 0.0.16 dated 2023-12-02

Title: 'Dang' Associated New Goodies
Description: A collection of utility functions.
Author: Dirk Eddelbuettel [aut, cre] , Petr Pikal [ctb] ), David Hinds [ctb] ), Brodie Gaslam [ctb] '), Kevin Denny [ctb] , Kabira Namit [ctb] , R Core [ctb] , Joshua Ulrich [ctb] , Jordan Mark Barbone [ctb] , Charlie Gao [ctb] , Andrew Uhl [ctb] , Dewey Dun [...truncated...]
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package biogrowth updated to version 1.0.8 with previous version 1.0.6 dated 2025-03-17

Title: Modelling of Population Growth
Description: Modelling of population growth under static and dynamic environmental conditions. Includes functions for model fitting and making prediction under isothermal and dynamic conditions. The methods (algorithms & models) are based on predictive microbiology (See Perez-Rodriguez and Valero (2012, ISBN:978-1-4614-5519-6)).
Author: Alberto Garre [aut, cre] , Jeroen Koomen [aut], Heidy den Besten [aut], Marcel Zwietering [aut]
Maintainer: Alberto Garre <garre.alberto@gmail.com>

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Package jpinfect updated to version 0.1.9 with previous version 0.1.8 dated 2025-11-25

Title: Acquiring and Processing Data from Japan Institute for Health Security
Description: Download and post process the infectious disease case data from Japan Institute for Health Security. Also the package included ready-to-analyse datasets. See the data source website for further details <https://id-info.jihs.go.jp/>.
Author: Tomonori Hoshi [aut, cre], Erina Ishigaki [aut], Satoshi Kaneko [aut]
Maintainer: Tomonori Hoshi <tomonori.hoshi.japan@gmail.com>

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Package psre updated to version 0.4 with previous version 0.3 dated 2025-12-04

Title: Presenting Statistical Results Effectively
Description: Includes functions and data used in the book "Presenting Statistical Results Effectively", Andersen and Armstrong (2022, ISBN: 978-1446269800). Several functions aid in data visualization - creating compact letter displays for simple slopes, kernel density estimates with normal density overlay. Other functions aid in post-model evaluation heatmap fit statistics for binary predictors, several variable importance measures, compact letter displays and simple-slope calculation. Finally, the package makes available the example datasets used in the book.
Author: Dave Armstrong [aut, cre], Robert Andersen [aut], Justin Esarey [cph], John Fox [cph], Michael Friendly [cph], Adrian Bowman [cph], Adelchi Azzalini [cph], Dewey Michael [cph]
Maintainer: Dave Armstrong <davearmstrong.ps@gmail.com>

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Package mixture updated to version 2.2.0 with previous version 2.1.2 dated 2025-05-06

Title: Mixture Models for Clustering and Classification
Description: An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.
Author: Nik Pocuca [aut] , Ryan P. Browne [aut] , Paul D. McNicholas [aut, cre] , Alexa A. Sochaniwsky [aut]
Maintainer: Paul D. McNicholas <mcnicholas@math.mcmaster.ca>

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Package metap updated to version 1.13 with previous version 1.12 dated 2025-03-15

Title: Meta-Analysis of Significance Values
Description: The canonical way to perform meta-analysis involves using effect sizes. When they are not available this package provides a number of methods for meta-analysis of significance values including the methods of Edgington, Fisher, Lancaster, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results; and routines for graphical display.
Author: Michael Dewey [aut, cre]
Maintainer: Michael Dewey <lists@dewey.myzen.co.uk>

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Package litedown updated to version 0.9 with previous version 0.8 dated 2025-11-02

Title: A Lightweight Version of R Markdown
Description: Render R Markdown to Markdown (without using 'knitr'), and Markdown to lightweight HTML or 'LaTeX' documents with the 'commonmark' package (instead of 'Pandoc'). Some missing Markdown features in 'commonmark' are also supported, such as raw HTML or 'LaTeX' blocks, 'LaTeX' math, superscripts, subscripts, footnotes, element attributes, and appendices, but not all 'Pandoc' Markdown features are (or will be) supported. With additional JavaScript and CSS, you can also create HTML slides and articles. This package can be viewed as a trimmed-down version of R Markdown and 'knitr'. It does not aim at rich Markdown features or a large variety of output formats (the primary formats are HTML and 'LaTeX'). Book and website projects of multiple input documents are also supported.
Author: Yihui Xie [aut, cre] , Tim Taylor [ctb]
Maintainer: Yihui Xie <xie@yihui.name>

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Package gbutils updated to version 0.5.1 with previous version 0.5 dated 2022-05-27

Title: Utilities for Simulation, Plots, Quantile Functions and Programming
Description: Plot density and distribution functions with automatic selection of suitable regions. Numerically invert (compute quantiles) distribution functions. Simulate real and complex numbers from distributions of their magnitude and arguments. Optionally, the magnitudes and/or arguments may be fixed in almost arbitrary ways. Create polynomials from roots given in Cartesian or polar form. Small programming utilities: check if an object is identical to NA, count positional arguments in a call, set intersection of more than two sets, check if an argument is unnamed, compute the graph of S4 classes in packages.
Author: Georgi N. Boshnakov [aut, cre]
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>

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Package kstMatrix updated to version 2.2-0 with previous version 2.1-0 dated 2025-11-21

Title: Basic Functions in Knowledge Space Theory Using Matrix Representation
Description: Knowledge space theory by Doignon and Falmagne (1999) <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical framework, which proposes mathematical formalisms to operationalize knowledge structures in a particular domain. The 'kstMatrix' package provides basic functionalities to generate, handle, and manipulate knowledge structures and knowledge spaces. Opposed to the 'kst' package, 'kstMatrix' uses matrix representations for knowledge structures. Furthermore, 'kstMatrix' contains several knowledge spaces developed by the research group around Cornelia Dowling through querying experts.
Author: Cord Hockemeyer [aut, cre], Peter Steiner [aut], Wai Wong [aut]
Maintainer: Cord Hockemeyer <cord.hockemeyer@uni-graz.at>

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Package fImport updated to version 4052.89 with previous version 4041.88 dated 2024-09-20

Title: Rmetrics - Importing Economic and Financial Data
Description: Provides a collection of utility functions to download and manage data sets from the Internet or from other sources.
Author: Diethelm Wuertz [aut] , Tobias Setz [aut], Yohan Chalabi [aut], Georgi N. Boshnakov [cre, aut]
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>

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Package exams.forge.data updated to version 0.1.3 with previous version 0.1.2 dated 2025-09-11

Title: Sample and Precomputed Data for Use with 'exams.forge'
Description: Provides a small collection of datasets supporting Pearson correlation and linear regression analysis. It includes the precomputed dataset 'sos100', with integer values summing to zero and squared sum equal to 100. For other values of 'n' and user-defined parameters, the 'sos()' function from the 'exams.forge' package can be used to generate datasets on the fly. In addition, the package contains around 500 german R Markdown exercises that illustrate the usage of 'exams.forge' commands.
Author: Sigbert Klinke [aut, cre]
Maintainer: Sigbert Klinke <sigbert@hu-berlin.de>

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Package EasyABC updated to version 1.6 with previous version 1.5.2 dated 2023-01-05

Title: Efficient Approximate Bayesian Computation Sampling Schemes
Description: Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.
Author: Franck Jabot [aut], Nicolas Dumoulin [aut, cre], Thierry Faure [aut], Carlo Albert. [aut]
Maintainer: Nicolas Dumoulin <nicolas.dumoulin@inrae.fr>

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Package EvaluateCore readmission to version 0.1.4 with previous version 0.1.3 dated 2022-07-03

Title: Quality Evaluation of Core Collections
Description: Implements various quality evaluation statistics to assess the value of plant germplasm core collections using qualitative and quantitative phenotypic trait data according to Odong et al. (2015) <doi:10.1007/s00122-012-1971-y>.
Author: J. Aravind [aut, cre] , Vikender Kaur [aut] , Dhammaprakash Pandhari Wankhede [aut] , Joghee Nanjundan [aut] , ICAR-NBGPR [cph]
Maintainer: J. Aravind <j.aravind@icar.org.in>

This is a re-admission after prior archival of version 0.1.3 dated 2022-07-03

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 68 files changed, 2173 insertions(+), 1558 deletions(-)

More information about EvaluateCore at CRAN
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Package e1071 updated to version 1.7-17 with previous version 1.7-16 dated 2024-09-16

Title: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien
Description: Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, generalized k-nearest neighbour ...
Author: David Meyer [aut, cre] , Evgenia Dimitriadou [aut, cph], Kurt Hornik [aut] , Andreas Weingessel [aut], Friedrich Leisch [aut], Chih-Chung Chang [ctb, cph] , Chih-Chen Lin [ctb, cph]
Maintainer: David Meyer <David.Meyer@R-project.org>

Diff between e1071 versions 1.7-16 dated 2024-09-16 and 1.7-17 dated 2025-12-18

 DESCRIPTION                |   10 +++++-----
 MD5                        |   23 ++++++++++++-----------
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 build/vignette.rds         |binary
 inst/NEWS.Rd               |    5 +++++
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 inst/doc/svmdoc.Rnw        |   32 +++++++++++++++++++++++---------
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 inst/doc/svminternals.pdf  |binary
 man/bclust.Rd              |    3 ++-
 vignettes/svmdoc.Rnw       |   32 +++++++++++++++++++++++---------
 vignettes/svminternals.Rnw |    2 +-
 13 files changed, 79 insertions(+), 44 deletions(-)

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Package routr updated to version 2.0.0 with previous version 1.1.0 dated 2025-11-18

Title: A Simple Router for HTTP and WebSocket Requests
Description: In order to make sure that web request ends up in the correct handler function a router is often used. 'routr' is a package implementing a simple but powerful routing functionality for R based servers. It is a fully functional 'fiery' plugin, but can also be used with other 'httpuv' based servers.
Author: Thomas Lin Pedersen [cre, aut]
Maintainer: Thomas Lin Pedersen <thomasp85@gmail.com>

Diff between routr versions 1.1.0 dated 2025-11-18 and 2.0.0 dated 2025-12-18

 DESCRIPTION                               |   10 
 MD5                                       |   32 +--
 NAMESPACE                                 |    3 
 NEWS.md                                   |   10 
 R/openapi_route.R                         |    6 
 R/otel.R                                  |   32 ---
 R/redirector.R                            |   22 --
 R/route.R                                 |  314 ++++++++++++++----------------
 R/routestack.R                            |   38 ++-
 man/Route-class.Rd                        |   83 +++++--
 tests/testthat/_snaps/redirector.md       |    4 
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 tests/testthat/test-openapi_route.R       |   12 -
 tests/testthat/test-redirector.R          |    3 
 tests/testthat/test-route.R               |   16 -
 tests/testthat/test-shared_secret_route.R |    2 
 tests/testthat/test-tidy_api.R            |    4 
 17 files changed, 304 insertions(+), 301 deletions(-)

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