Sun, 04 Jan 2026

Package bigMap (with last version 2.3.1) was removed from CRAN

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

2020-06-30 2.3.1
2019-03-01 2.1.0
2019-02-01 2.0.0

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Package tipitaka (with last version 0.1.2) was removed from CRAN

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

2021-03-30 0.1.2
2020-11-24 0.1.1
2020-09-22 0.1.0

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Package hBayesDM (with last version 1.2.1) was removed from CRAN

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

2022-09-23 1.2.1
2022-09-10 1.2.0
2021-05-03 1.1.1
2019-11-13 1.0.2
2019-09-01 1.0.1
2019-08-30 1.0.0
2019-02-12 0.7.2
2019-01-21 0.7.1
2018-12-13 0.7.0
2018-09-11 0.6.0
2018-01-03 0.5.0
2017-05-23 0.4.0
2017-01-22 0.3.0
2016-12-28 0.2.3.3
2016-12-21 0.2.3.2
2016-09-30 0.2.3.1
2016-07-17 0.2.3
2016-04-03 0.2.1
2016-03-25 0.2.0

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Package SeqDetect (with last version 1.0.7) was removed from CRAN

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2020-03-02 1.0.7
2020-02-21 1.0.6
2020-02-20 1.0.5

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Tue, 30 Dec 2025

New package sensortowerR with initial version 0.9.4
Package: sensortowerR
Title: Interface to 'Sensor Tower' Mobile App Intelligence API
Version: 0.9.4
Description: Interface to the 'Sensor Tower' API <https://app.sensortower.com/api/docs/app_analysis> for mobile app analytics and market intelligence. Provides functions to retrieve app metadata, publisher information, download and revenue estimates, active user metrics, category rankings, and market trends. The package includes data processing utilities to clean and aggregate metrics across platforms, automatic app name resolution, and tools for generating professional analytics dashboards. Supports both iOS and Android app ecosystems with unified data structures for cross-platform analysis.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: dplyr, glue, httr, httr2, jsonlite, lubridate, openssl, purrr, rlang, stats, stringr, tibble, tidyr, utils
Suggests: gt, gtExtras, knitr, pkgbuild, rcmdcheck, rmarkdown, rhub, testthat (>= 3.0.0)
VignetteBuilder: knitr
Author: Phillip Black [aut, cre]
Maintainer: Phillip Black <pblack@gameeconomistconsulting.com>
Depends: R (>= 4.1.0)
NeedsCompilation: no
Packaged: 2025-12-19 15:23:46 UTC; phillip
Repository: CRAN
Date/Publication: 2025-12-30 19:20:02 UTC

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New package plavaan with initial version 0.0.1
Package: plavaan
Title: Penalized Estimation for Latent Variable Models with 'lavaan'
Version: 0.0.1
Description: Extends the popular 'lavaan' package by adding penalized estimation capabilities. It supports penalty on individual parameters as well as the difference between parameters.
License: MIT + file LICENSE
Encoding: UTF-8
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
Imports: lavaan, numDeriv
URL: https://marklhc.github.io/plavaan/, https://github.com/marklhc/plavaan
BugReports: https://github.com/marklhc/plavaan/issues
NeedsCompilation: no
Packaged: 2025-12-19 06:30:15 UTC; marklai
Author: Hok Chio Lai [aut, cre]
Maintainer: Hok Chio (Mark) Lai <marklhc@gmail.com>
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2025-12-30 19:00:02 UTC

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New package DRsurvCRT with initial version 0.0.1
Package: DRsurvCRT
Title: Doubly-Robust Estimation for Survival Outcomes in Cluster-Randomized Trials
Version: 0.0.1
Description: Cluster-randomized trials (CRTs) assign treatment to groups rather than individuals, so valid analyses must distinguish cluster-level and individual-level effects and define estimands within a potential-outcomes framework. This package supports right-censored survival outcomes for both single-state (binary) and multi-state settings. For single-state outcomes, it provides estimands based on stage-specific survival contrasts (SPCE) and restricted mean survival time (RMST). For multi-state outcomes, it provides SPCE as well as a generalized win-based restricted mean time-in-favor estimand (RMT-IF). The package implements doubly robust estimators that accommodate covariate-dependent censoring and remain consistent if either the outcome model or the censoring model is correctly specified. Users can choose marginal Cox or gamma-frailty Cox working models for nuisance estimation, and inference is supported via leave-one-cluster-out jackknife variance and confidence interval estimation. Method [...truncated...]
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: Rcpp, frailtyEM, survival, ggplot2, pracma, abind
LinkingTo: Rcpp, RcppArmadillo
Depends: R (>= 3.5)
NeedsCompilation: yes
Packaged: 2025-12-19 03:14:10 UTC; fancy
Author: Xi Fang [aut, cre], Fan Li [aut]
Maintainer: Xi Fang <x.fang@yale.edu>
Repository: CRAN
Date/Publication: 2025-12-30 19:00:06 UTC

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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

 DESCRIPTION           |    6 +++---
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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

Diff between extraDistr versions 1.10.0 dated 2023-11-30 and 1.10.0.1 dated 2025-12-29

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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 heuristicsmineR (with last version 0.3.0) was removed from CRAN

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

2023-04-04 0.3.0
2022-07-21 0.2.7
2022-07-14 0.2.6
2021-10-11 0.2.5

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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>

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Fri, 26 Dec 2025

Package Rcpp11 (with last version 3.1.2.0.1) was removed from CRAN

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2020-10-14 3.1.2.0.1
2014-11-10 3.1.2.0
2014-07-22 3.1.1
2014-04-16 3.1.0.1
2014-04-11 3.1.0

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Thu, 25 Dec 2025

Package triangulr (with last version 1.2.1) was removed from CRAN

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2021-05-27 1.2.1
2021-05-16 1.2.0
2020-11-20 1.1.0
2020-09-28 1.0.0

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Package unine (with last version 0.2.0) was removed from CRAN

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2019-04-13 0.2.0
2019-03-29 0.1.0

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Package survival.svb (with last version 0.0-2) was removed from CRAN

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2022-01-17 0.0-2
2021-12-15 0.0-1

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Package sparkwarc (with last version 0.1.6) was removed from CRAN

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2022-01-11 0.1.6
2020-12-15 0.1.5
2017-01-13 0.1.1
2017-01-10 0.1.0

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Package sift (with last version 0.1.0) was removed from CRAN

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2021-07-05 0.1.0

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Package spFW (with last version 0.1.0) was removed from CRAN

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2022-03-29 0.1.0

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Package sprintr (with last version 0.9.0) was removed from CRAN

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2019-08-24 0.9.0

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Package strider (with last version 1.3) was removed from CRAN

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2020-06-03 1.3
2019-01-26 1.2
2018-01-10 1.1
2017-11-20 1.0

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Package qris (with last version 1.1.1) was removed from CRAN

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2024-03-05 1.1.1
2022-08-29 1.0.0

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Package sdpdth (with last version 0.2) was removed from CRAN

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2021-03-22 0.2

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Package samurais (with last version 0.1.0) was removed from CRAN

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2019-07-28 0.1.0

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Package rtk (with last version 0.2.6.1) was removed from CRAN

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2020-06-13 0.2.6.1
2020-01-13 0.2.5.8
2018-12-08 0.2.5.7
2018-02-11 0.2.5.4
2017-07-30 0.2.5.3
2017-07-16 0.2.5.2
2017-04-19 0.2.5.1
2017-03-22 0.2.5
2017-02-22 0.2.4.1
2016-08-10 0.2.2

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Package sparseHessianFD (with last version 0.3.3.7) was removed from CRAN

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2022-10-19 0.3.3.7
2022-08-16 0.3.3.6
2021-09-24 0.3.3.5
2019-03-05 0.3.3.4
2018-03-27 0.3.3.3
2017-11-25 0.3.3.2
2017-10-07 0.3.3.1
2017-04-19 0.3.3
2016-12-26 0.3.2
2016-03-15 0.3.0
2015-02-04 0.2.0
2013-11-06 0.1.1
2012-11-26 0.1.0

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Package surbayes (with last version 0.1.2) was removed from CRAN

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2020-08-26 0.1.2
2020-05-15 0.1.1
2020-05-06 0.1.0

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Package slasso (with last version 1.0.0) was removed from CRAN

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2021-10-15 1.0.0

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Package pgsc (with last version 1.0.0) was removed from CRAN

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2018-10-28 1.0.0

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2021-06-23 1.1.0-0
2020-08-06 0.1.0

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

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2022-05-27 1.1.0
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Package mined (with last version 1.0-3) was removed from CRAN

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2022-06-26 1.0-3

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Package ldsr (with last version 0.0.2) was removed from CRAN

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2020-05-04 0.0.2

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2019-10-09 0.1.0

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Package motmot (with last version 2.1.3) was removed from CRAN

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2019-11-25 2.1.3
2019-06-09 2.1.2

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Package penPHcure (with last version 1.0.2) was removed from CRAN

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2019-12-03 1.0.2
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Package imptree (with last version 0.5.1) was removed from CRAN

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2018-08-17 0.5.1

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2022-01-29 2.1.0
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Package latentgraph (with last version 1.1) was removed from CRAN

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2020-12-10 1.1
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Package intcensROC (with last version 0.1.3) was removed from CRAN

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2021-06-15 0.1.3
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Package infinitefactor (with last version 1.0) was removed from CRAN

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2020-04-03 1.0

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Package imbalance (with last version 1.0.2.1) was removed from CRAN

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2020-04-07 1.0.2.1
2019-12-18 1.0.2
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2021-12-20 1.6.1
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Package hilbertSimilarity (with last version 0.4.3) was removed from CRAN

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2019-11-11 0.4.3
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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>

Diff between data.table versions 1.17.8 dated 2025-07-10 and 1.18.0 dated 2025-12-24

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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>

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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>

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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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 fMRItools-0.7.2/fMRItools/man/cor_mat.Rd             |    4 
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 fMRItools-0.7.2/fMRItools/tests/testthat/test-misc.R |    6 
 14 files changed, 151 insertions(+), 84 deletions(-)

More information about fMRItools at CRAN
Permanent link

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>

Diff between CohortConstructor versions 0.6.0 dated 2025-10-31 and 0.6.1 dated 2025-12-23

 DESCRIPTION                                 |   20 
 MD5                                         |  197 +--
 NAMESPACE                                   |    1 
 R/addIndex.R                                |    6 
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 R/copyCohorts.R                             |    2 
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 README.md                                   |   61 
 build/vignette.rds                          |binary
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 101 files changed, 685 insertions(+), 3787 deletions(-)

More information about CohortConstructor at CRAN
Permanent link

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>

Diff between BAS versions 2.0.0 dated 2025-12-16 and 2.0.2 dated 2025-12-23

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 NEWS.md                    |   13 ++++
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 man/tr.power.prior.Rd      |   10 +--
 src/glm_deterministic.c    |   14 ++++
 src/glm_mcmc.c             |   10 +++
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 src/glm_mcmcbas.c          |    8 ++
 src/glm_sampleworep.c      |   12 ++++
 src/glm_sampleworep_grow.c |   13 ++++
 src/lm_amcmc.c             |   13 ++++
 src/lm_amcmc_growable.c    |   18 ++++--
 src/lm_deterministic.c     |   10 +++
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 src/model_probabilities.c  |    7 +-
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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 -
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 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(-)

More information about hmde at CRAN
Permanent link

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 +++++-----
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 R/errors.R              |    7 +++----
 man/cross_fit_robust.Rd |    2 +-
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More information about crossmap at CRAN
Permanent link

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 ++++++++++++------------
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 man/annotation_mat.Rd       |    2 +-
 man/cellembedding_matrix.Rd |    2 +-
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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

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 dataquieR-2.5.1/dataquieR/man/figures                                                                                |only
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 dataquieR-2.5.1/dataquieR/man/util_3SD.Rd                                                                            |only
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 dataquieR-2.5.1/dataquieR/man/util_app_cd.Rd                                                                         |only
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 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
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 dataquieR-2.5.1/dataquieR/man/util_app_sm.Rd                                                                         |only
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 dataquieR-2.5.1/dataquieR/man/util_app_vc.Rd                                                                         |only
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 dataquieR-2.5.1/dataquieR/man/util_filter_names_by_regexps.Rd                                                        |only
 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
 dataquieR-2.5.1/dataquieR/man/util_find_indicator_function_in_callers.Rd                                             |only
 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
 dataquieR-2.5.1/dataquieR/man/util_generate_table_indicators_descriptors.Rd                                          |only
 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
 dataquieR-2.5.1/dataquieR/man/util_get_storr_object_from_report.Rd                                                   |only
 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
 dataquieR-2.5.1/dataquieR/man/util_make_data_slot_from_table_slot.Rd                                                 |only
 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
 dataquieR-2.5.1/dataquieR/man/util_optimize_sequence_across_time_var.Rd                                              |only
 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
 dataquieR-2.5.1/dataquieR/man/util_startsWith_prefix._or_equals_prefix.Rd                                            |only
 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 
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 dataquieR-2.8.2/dataquieR/R/util_translate.R                                                                         |    4 
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 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
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 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 
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 dataquieR-2.8.2/dataquieR/inst/voc.rds                                                                               |binary
 dataquieR-2.8.2/dataquieR/man/API_VERSION.Rd                                                                         |    2 
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 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 
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 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
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 dataquieR-2.8.2/dataquieR/man/MISS_RESP.Rd                                                                           |only
 dataquieR-2.8.2/dataquieR/man/MULTIVARIATE_OUTLIER_CHECK.Rd                                                          |   16 
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 dataquieR-2.8.2/dataquieR/man/REL_VAL.Rd                                                                             |   16 
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 dataquieR-2.8.2/dataquieR/man/SCALE_ACRONYM.Rd                                                                       |only
 dataquieR-2.8.2/dataquieR/man/SCALE_NAME.Rd                                                                          |only
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 dataquieR-2.8.2/dataquieR/man/UNITS.Rd                                                                               |    1 
 dataquieR-2.8.2/dataquieR/man/UNIT_IS_COUNT.Rd                                                                       |    1 
 dataquieR-2.8.2/dataquieR/man/UNIT_PREFIXES.Rd                                                                       |    1 
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 dataquieR-2.8.2/dataquieR/man/UNIT_SOURCES.Rd                                                                        |    1 
 dataquieR-2.8.2/dataquieR/man/VARIABLE_LIST.Rd                                                                       |   16 
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 dataquieR-2.8.2/dataquieR/man/WELL_KNOWN_META_VARIABLE_NAMES.Rd                                                      |   15 
 dataquieR-2.8.2/dataquieR/man/acc_cat_distributions.Rd                                                               |   19 
 dataquieR-2.8.2/dataquieR/man/acc_loess.Rd                                                                           |    8 
 dataquieR-2.8.2/dataquieR/man/acc_mahalanobis.Rd                                                                     |only
 dataquieR-2.8.2/dataquieR/man/acc_margins.Rd                                                                         |    3 
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 dataquieR-2.8.2/dataquieR/man/com_qualified_item_missingness.Rd                                                      |   46 
 dataquieR-2.8.2/dataquieR/man/con_inadmissible_vocabulary.Rd                                                         |    2 
 dataquieR-2.8.2/dataquieR/man/con_limit_deviations.Rd                                                                |    9 
 dataquieR-2.8.2/dataquieR/man/dataquieR.CONDITIONS_LEVEL_TRHESHOLD.Rd                                                |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.CONDITIONS_WITH_STACKTRACE.Rd                                                |   35 
 dataquieR-2.8.2/dataquieR/man/dataquieR.ELEMENT_MISSMATCH_CHECKTYPE.Rd                                               |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.ERRORS_WITH_CALLER.Rd                                                        |   35 
 dataquieR-2.8.2/dataquieR/man/dataquieR.GAM_for_LOESS.Rd                                                             |   33 
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 dataquieR-2.8.2/dataquieR/man/dataquieR.MAX_LABEL_LEN.Rd                                                             |   35 
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 dataquieR-2.8.2/dataquieR/man/dataquieR.MAX_VALUE_LABEL_LEN.Rd                                                       |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.MESSAGES_WITH_CALLER.Rd                                                      |   35 
 dataquieR-2.8.2/dataquieR/man/dataquieR.MULTIVARIATE_OUTLIER_CHECK.Rd                                                |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.Rd                                                                           |   59 
 dataquieR-2.8.2/dataquieR/man/dataquieR.VALUE_LABELS_htmlescaped.Rd                                                  |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.WARNINGS_WITH_CALLER.Rd                                                      |   35 
 dataquieR-2.8.2/dataquieR/man/dataquieR.acc_loess.exclude_constant_subgroups.Rd                                      |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.acc_loess.mark_time_points.Rd                                                |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.acc_loess.min_bw.Rd                                                          |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.acc_loess.min_proportion.Rd                                                  |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.acc_loess.plot_format.Rd                                                     |   33 
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 dataquieR-2.8.2/dataquieR/man/dataquieR.acc_margins_num.Rd                                                           |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.acc_margins_sort.Rd                                                          |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.acc_multivariate_outlier.scale.Rd                                            |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.col_con_con_empirical.Rd                                                     |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.col_con_con_logical.Rd                                                       |   33 
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 dataquieR-2.8.2/dataquieR/man/dataquieR.debug.Rd                                                                     |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.des_summary_hard_lim_remove.Rd                                               |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.dontwrapresults.Rd                                                           |   33 
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 dataquieR-2.8.2/dataquieR/man/dataquieR.dt_adjust.Rd                                                                 |only
 dataquieR-2.8.2/dataquieR/man/dataquieR.fix_column_type_on_read.Rd                                                   |   35 
 dataquieR-2.8.2/dataquieR/man/dataquieR.flip_mode.Rd                                                                 |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.force_item_specific_missing_codes.Rd                                         |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.force_label_col.Rd                                                           |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.grading_formats.Rd                                                           |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.grading_rulesets.Rd                                                          |   33 
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 dataquieR-2.8.2/dataquieR/man/dataquieR.guess_missing_codes.Rd                                                       |   33 
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 dataquieR-2.8.2/dataquieR/man/dataquieR.non_disclosure.Rd                                                            |   33 
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 dataquieR-2.8.2/dataquieR/man/dataquieR.testdebug.Rd                                                                 |   33 
 dataquieR-2.8.2/dataquieR/man/dataquieR.traceback.Rd                                                                 |only
 dataquieR-2.8.2/dataquieR/man/dataquieR.type_adjust_parallel.Rd                                                      |only
 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/dot-variable_arg_roles.Rd                                                              |    6 
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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_content.Rd                                                               |    8 
 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_cross.Rd                                                                     |   16 
 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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More information about dataquieR at CRAN
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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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More information about btw at CRAN
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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>

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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>

Diff between embryogrowth versions 10.4 dated 2025-09-10 and 2025.12.22 dated 2025-12-22

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More information about embryogrowth at CRAN
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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>

Diff between BayesfMRI versions 0.10.1 dated 2025-03-07 and 0.11.0 dated 2025-12-22

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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>

Diff between StMoSim versions 3.1.1 dated 2018-11-19 and 3.2.0 dated 2025-12-22

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New package sitrep with initial version 0.2.3
Package: sitrep
Title: Report Templates and Helper Functions for Applied Epidemiology
Version: 0.2.3
Description: A meta-package that loads the complete sitrep ecosystem for applied epidemiology analysis. This package provides report templates and automatically loads companion packages, including 'epitabulate' (for epidemiological tables), 'epidict' (for data dictionaries), 'epikit' (for epidemiological utilities), and 'apyramid' (for age-sex pyramids). Simply load 'sitrep' to access all functions from the ecosystem.
License: GPL-3
URL: https://github.com/R4EPI/sitrep/, https://r4epi.github.io/sitrep/
BugReports: https://github.com/R4EPI/sitrep/issues/
Depends: R (>= 3.2)
Imports: apyramid (>= 0.1.0), epidict (>= 0.1.0), epikit, epitabulate (>= 0.1.0), utils
Suggests: anthro, binom, broom, clipr, covr, dplyr (>= 0.8.0), flextable, forcats, ggalluvial, ggplot2 (>= 3.0.0), ggspatial, glue, gtsummary (>= 2.0.0), here, janitor, knitr, labelled, lubridate, matchmaker, pacman, parsedate, patchwork, purrr, rio, rlang (>= 0.4.0), rmarkdown, scales, sessioninfo, sf, slider, srvyr, stats, stringr, summarytools, survey, testthat (>= 2.1.0), tibble, tidyr (>= 1.0.0), tidyselect, tsibble, vdiffr
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2025-12-18 06:54:23 UTC; spina
Author: Alexander Spina [aut, cre] , Zhian N. Kamvar [aut] , Dirk Schumacher [aut], Kate Doyle [aut], Chris Jarvis [aut], Lukas Richter [aut], Paula Blomquist [aut], Annick Lenglet [ctb], Neale Batra [ctb], Applied Epi Incorporated [cph], Medecins Sans Front [...truncated...]
Maintainer: Alexander Spina <aspina@appliedepi.org>
Repository: CRAN
Date/Publication: 2025-12-22 19:40:02 UTC

More information about sitrep at CRAN
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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>

Diff between rebus.datetimes versions 0.0-2 dated 2022-11-03 and 0.0-2.1 dated 2025-12-22

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More information about rebus.datetimes at CRAN
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New package pairwiseLLM with initial version 1.1.0
Package: pairwiseLLM
Title: Pairwise Comparison Tools for Large Language Model-Based Writing Evaluation
Version: 1.1.0
Description: Provides a unified framework for generating, submitting, and analyzing pairwise comparisons of writing quality using large language models (LLMs). The package supports live and/or batch evaluation workflows across multiple providers ('OpenAI', 'Anthropic', 'Google Gemini', 'Together AI', and locally-hosted 'Ollama' models), includes bias-tested prompt templates and a flexible template registry, and offers tools for constructing forward and reversed comparison sets to analyze consistency and positional bias. Results can be modeled using Bradley–Terry (1952) <doi:10.2307/2334029> or Elo rating methods to derive writing quality scores. For information on the method of pairwise comparisons, see Thurstone (1927) <doi:10.1037/h0070288> and Heldsinger & Humphry (2010) <doi:10.1007/BF03216919>. For information on Elo ratings, see Clark et al. (2018) <doi:10.1371/journal.pone.0190393>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: curl, dplyr, httr2, jsonlite, rlang, stats, tibble, tidyselect, tools, utils
Suggests: BradleyTerry2, EloChoice, knitr, mockery, purrr, readr, rmarkdown, sirt, stringr, testthat (>= 3.0.0), tidyr, withr
URL: https://github.com/shmercer/pairwiseLLM, https://shmercer.github.io/pairwiseLLM/
BugReports: https://github.com/shmercer/pairwiseLLM/issues
Depends: R (>= 4.1)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-12-18 02:11:26 UTC; shmer
Author: Sterett H. Mercer [aut, cre]
Maintainer: Sterett H. Mercer <sterett.mercer@ubc.ca>
Repository: CRAN
Date/Publication: 2025-12-22 19:10:02 UTC

More information about pairwiseLLM at CRAN
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New package meetupr with initial version 0.3.1
Package: meetupr
Title: Access Meetup Data
Version: 0.3.1
Description: Provides programmatic access to the 'Meetup' 'GraphQL' API (<https://www.meetup.com/graphql/>), enabling users to retrieve information about groups, events, and members from 'Meetup' (<https://www.meetup.com/>). Supports authentication via 'OAuth2' and includes functions for common queries and data manipulation tasks.
License: MIT + file LICENSE
Depends: R (>= 4.2)
Imports: countrycode, cli, clipr, dplyr, fs, glue, httr2, jsonlite, lifecycle, purrr, rlang, rlist, S7, tools, stats, utils, withr, rstudioapi
Suggests: covr, cyphr, ggplot2, ggwordcloud, httpuv, jose, knitr, openssl, rmarkdown, sodium, testthat, tidyr, tidytext, vcr
VignetteBuilder: knitr
Encoding: UTF-8
URL: https://rladies.org/meetupr/
BugReports: https://github.com/rladies/meetupr/issues
NeedsCompilation: no
Packaged: 2025-12-18 15:45:12 UTC; athanasm
Author: Athanasia Mo Mowinckel [aut, cre] , Erin LeDell [aut], Olga Mierzwa-Sulima [aut], Lucy D'Agostino McGowan [aut], Claudia Vitolo [aut], Gabriela De Queiroz [ctb], Michael Beigelmacher [ctb], Augustina Ragwitz [ctb], Greg Sutcliffe [ctb], Rick Pack [ct [...truncated...]
Maintainer: Athanasia Mo Mowinckel <a.m.mowinckel@psykologi.uio.no>
Repository: CRAN
Date/Publication: 2025-12-22 19:50:02 UTC

More information about meetupr at CRAN
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New package MBRM with initial version 0.1.1
Package: MBRM
Title: Mixed Regression Models with Generalized Log-Gamma Random Effects
Version: 0.1.1
Author: Lizandra C. Fabio [aut], Vanessa Barros [aut], Cristian Lobos [aut], Jalmar M. F. Carrasco [aut, cre]
Maintainer: Jalmar M. F. Carrasco <carrasco.jalmar@ufba.br>
Description: Multivariate distribution derived from a Bernoulli mixed model under a marginal approach, incorporating a non-normal random intercept whose distribution is assumed to follow a generalized log-gamma (GLG) specification under a particular parameter setting. Estimation is performed by maximizing the log-likelihood using numerical optimization techniques (Lizandra C. Fabio, Vanessa Barros, Cristian Lobos, Jalmar M. F. Carrasco, Marginal multivariate approach: A novel strategy for handling correlated binary outcomes, 2025, under submission).
License: GPL-3
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp
Imports: Rcpp, stats, Formula, tibble, dplyr, ggplot2
Depends: R (>= 3.5)
NeedsCompilation: yes
Packaged: 2025-12-18 12:42:04 UTC; carra
Repository: CRAN
Date/Publication: 2025-12-22 19:50:07 UTC

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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>

Diff between Kendall versions 2.2.1 dated 2022-03-20 and 2.2.2 dated 2025-12-22

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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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 dySEM-1.4.1/dySEM/man/scriptINULL.Rd                           |   12 
 dySEM-1.4.1/dySEM/man/scriptISAT.Rd                            |   12 
 dySEM-1.4.1/dySEM/man/scriptMIM.Rd                             |   14 
 dySEM-1.4.1/dySEM/man/scriptUni.Rd                             |only
 dySEM-1.4.1/dySEM/tests/testthat/test-getDyReliability.R       |    2 
 dySEM-1.4.1/dySEM/tests/testthat/test-outHelpers.R             |only
 dySEM-1.4.1/dySEM/tests/testthat/test-outputConstraintTab.R    |only
 dySEM-1.4.1/dySEM/tests/testthat/test-outputInvarCompTab.R     |only
 dySEM-1.4.1/dySEM/tests/testthat/test-outputModel-fileIO.R     |only
 dySEM-1.4.1/dySEM/tests/testthat/test-outputParamFig.R         |only
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 dySEM-1.4.1/dySEM/tests/testthat/test-outputUniConstructComp.R |only
 dySEM-1.4.1/dySEM/tests/testthat/test-scrapeVarCross.R         |  607 +++
 dySEM-1.4.1/dySEM/tests/testthat/test-scriptAPIM.R             |  565 ++-
 dySEM-1.4.1/dySEM/tests/testthat/test-scriptBiDy.R             |  292 +
 dySEM-1.4.1/dySEM/tests/testthat/test-scriptBifac.R            |only
 dySEM-1.4.1/dySEM/tests/testthat/test-scriptCFA.R              | 1541 ++++++--
 dySEM-1.4.1/dySEM/tests/testthat/test-scriptCFM.R              |  366 ++
 dySEM-1.4.1/dySEM/tests/testthat/test-scriptCor.R              |only
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 94 files changed, 7389 insertions(+), 2979 deletions(-)

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New package BootstrapTests with initial version 0.1.0
Package: BootstrapTests
Title: Bootstrap-Based Hypothesis Testing using Different Resampling Schemes
Version: 0.1.0
Description: Perform bootstrap-based hypothesis testing procedures on three statistical problems. In particular, it covers independence testing, testing the slope in a linear regression setting, and goodness-of-fit testing, following (Derumigny, Galanis, Schipper and Van der Vaart, 2025) <doi:10.48550/arXiv.2512.10546>.
License: GPL-3
URL: https://github.com/AlexisDerumigny/BootstrapTests
BugReports: https://github.com/AlexisDerumigny/BootstrapTests/issues
Imports: pbapply (>= 1.7.2), stats (>= 4.4.0)
Suggests: testthat (>= 3.0.0)
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2025-12-18 16:06:00 UTC; wiegerschipper
Author: Alexis Derumigny [aut] , Miltiadis Galanis [aut], Wieger Schipper [aut, cre] , Aad van der Vaart [aut]
Maintainer: Wieger Schipper <W.R.Schipper@tudelft.nl>
Repository: CRAN
Date/Publication: 2025-12-22 20:00:11 UTC

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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

 DESCRIPTION                                 |    9 
 MD5                                         |   83 +-
 NAMESPACE                                   |    6 
 R/biweight_mid_corr.R                       |  186 +++---
 R/bland_altman.R                            |  128 ++--
 R/bland_altman_repeated.R                   |   76 ++
 R/ccc_repeated.R                            |  865 ++++++++++++++--------------
 R/concordance_corr.R                        |  129 ++--
 R/distance_corr.R                           |   65 +-
 R/kendall_corr.R                            |  140 ++--
 R/partial_correlation.R                     |  132 ++--
 R/pearson_corr.R                            |   51 -
 R/schafer_corr.R                            |   51 -
 R/spearman_rho.R                            |   28 
 R/validate_corr_input.R                     |   12 
 R/view_corr_shiny.R                         |only
 man/biweight_mid_corr.Rd                    |   12 
 man/ccc.Rd                                  |    5 
 man/ccc_lmm_reml.Rd                         |    9 
 man/ccc_pairwise_u_stat.Rd                  |    5 
 man/distance_corr.Rd                        |    5 
 man/kendall_tau.Rd                          |    5 
 man/partial_correlation.Rd                  |    9 
 man/pearson_corr.Rd                         |    5 
 man/schafer_corr.Rd                         |    7 
 man/spearman_rho.Rd                         |    4 
 man/view_corr_shiny.Rd                      |only
 src/biweight_mid_corr.cpp                   |  198 ++++--
 src/ccc_repeated.cpp                        |    4 
 src/concordance_corr.cpp                    |   25 
 src/distance_correlation.cpp                |   57 -
 src/kendall_corr.cpp                        |   13 
 src/matrixCorr_detail.h                     |    9 
 src/partial_correlation.cpp                 |   28 
 tests/testthat/test_biweight_mid_corr.R     |   35 -
 tests/testthat/test_bland_altman.R          |   10 
 tests/testthat/test_bland_altman_repeated.R |   36 +
 tests/testthat/test_distance_corr.R         |   20 
 tests/testthat/test_kendall.R               |   45 -
 tests/testthat/test_partial_correlation.R   |only
 tests/testthat/test_pearson_corr.R          |   22 
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 tests/testthat/test_spearman_rho.R          |   22 
 tests/testthat/test_view_corr_shiny.R       |only
 tests/testthat/tests_partial_correlation.R  |    2 
 45 files changed, 1539 insertions(+), 1014 deletions(-)

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New package manureshed with initial version 0.1.0
Package: manureshed
Title: Spatiotemporal Nutrient Balance Analysis Across Agricultural and Municipal Systems
Version: 0.1.0
Maintainer: Olatunde D. Akanbi <olatunde.akanbi@case.edu>
Description: A comprehensive framework for analyzing agricultural nutrient balances across multiple spatial scales (county, 'HUC8', 'HUC2') with integration of wastewater treatment plant ('WWTP') effluent loads for both nitrogen and phosphorus. Supports classification of spatial units as nutrient sources, sinks, or balanced areas based on agricultural surplus and deficit calculations. Includes visualization tools, spatial transition probability analysis, and nutrient flow network mapping. Built-in datasets include agricultural nutrient balance data from the Nutrient Use Geographic Information System ('NuGIS'; The Fertilizer Institute and Plant Nutrition Canada, 1987-2016) <https://nugis.tfi.org/tabular_data/> and U.S. Environmental Protection Agency ('EPA') wastewater discharge data from the 'ECHO' Discharge Monitoring Report ('DMR') Loading Tool (2007-2016) <https://echo.epa.gov/trends/loading-tool/water-pollution-search>. Data are downloaded on demand from the Open Science Framework ( [...truncated...]
License: MIT + file LICENSE
URL: https://osf.io/g39xa/
Encoding: UTF-8
Depends: R (>= 4.0.0)
Imports: dplyr (>= 1.0.0), sf (>= 1.0.0), ggplot2 (>= 3.3.0), tidyr (>= 1.1.0), jsonlite (>= 1.7.0), rlang (>= 0.4.0), magrittr, scales (>= 1.1.0), igraph (>= 1.2.0), tigris (>= 1.5.0), stats, utils, tools
Suggests: nhdplusTools (>= 0.5.0), RColorBrewer (>= 1.1.0), cowplot (>= 1.1.0), ggpubr (>= 0.4.0), viridis (>= 0.6.0), testthat (>= 3.0.0), knitr, progress, rmarkdown
SystemRequirements: GDAL (>= 2.0.1), GEOS (>= 3.4.0), PROJ (>= 4.8.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-12-17 18:15:16 UTC; oda10
Author: Olatunde D. Akanbi [aut, cre, cph] , Vibha Mandayam [aut] , Atharva Gupta [aut] , K. Colton Flynn [aut] , Jeffrey Yarus [aut] , Erika I. Barcelos [aut, cph] , Roger H. French [aut, cph]
Repository: CRAN
Date/Publication: 2025-12-22 18:10:02 UTC

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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>

Diff between hdf5lib versions 2.0.0.3 dated 2025-12-14 and 2.0.0.4 dated 2025-12-22

 DESCRIPTION           |    6 +++---
 MD5                   |    8 ++++----
 NEWS.md               |    6 ++++++
 configure             |    7 +++++--
 src/zlib-1.3.1.tar.gz |binary
 5 files changed, 18 insertions(+), 9 deletions(-)

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New package FMCensSkewReg with initial version 0.1.1
Package: FMCensSkewReg
Title: Finite Mixture of Censored Regression Models with Skewed Distributions
Version: 0.1.1
Author: Jiwon Park [aut, cre], Victor Hugo Lachos Davila [aut], Dipak Dey [aut]
Maintainer: Jiwon Park <pcjylove87@gmail.com>
Description: Provides an implementation of finite mixture regression models for censored data under four distributional families: Normal (FM-NCR), Student t (FM-TCR), skew-Normal (FM-SNCR), and skew-t (FM-STCR). The package enables flexible modeling of skewness and heavy tails often observed in real-world data, while explicitly accounting for censoring. Functions are included for parameter estimation via the Expectation-Maximization (EM) algorithm, computation of standard errors, and model comparison criteria such as the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the Efficient Determination Criterion (EDC). The underlying methodology is described in Park et al. (2024) <doi:10.1007/s00180-024-01459-4>.
License: MIT + file LICENSE
URL: https://github.com/JiwonPark41/FMCensSkewReg
BugReports: https://github.com/JiwonPark41/FMCensSkewReg/issues
Depends: R (>= 3.6.0)
Encoding: UTF-8
Imports: stats, mvtnorm, MomTrunc, mnormt, sn, truncdist, mixsmsn
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
NeedsCompilation: no
Packaged: 2025-12-17 14:21:11 UTC; jiwonpark
Repository: CRAN
Date/Publication: 2025-12-22 18:10:09 UTC

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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>

Diff between FGLMtrunc versions 0.1.0 dated 2022-05-26 and 0.2.0 dated 2025-12-22

 DESCRIPTION             |   12 -
 MD5                     |   12 -
 build/vignette.rds      |binary
 inst/doc/FGLMtrunc.R    |   34 ++---
 inst/doc/FGLMtrunc.html |  294 ++++++++++++++++++++++++------------------------
 src/Makevars            |    1 
 src/Makevars.win        |    1 
 7 files changed, 180 insertions(+), 174 deletions(-)

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New package AsianOption with initial version 0.1.0
Package: AsianOption
Title: Asian Option Pricing with Price Impact
Version: 0.1.0
Date: 2025-12-04
Maintainer: Priyanshu Tiwari <tiwari.priyanshu.iitk@gmail.com>
Description: Implements binomial tree pricing for geometric and arithmetic Asian options incorporating market price impact from hedging activities. Uses the Cox-Ross-Rubinstein (CRR) model with the replicating portfolio method. Provides exact pricing for geometric Asian options and bounds for arithmetic Asian options based on Jensen's inequality. The price impact mechanism models how hedging volumes affect stock prices, leading to modified risk-neutral probabilities. Based on the methodology described in Tiwari and Majumdar (2025) <doi:10.48550/arXiv.2512.07154>.
License: GPL (>= 3)
URL: https://github.com/plato-12/AsianOption
BugReports: https://github.com/plato-12/AsianOption/issues
Encoding: UTF-8
Depends: R (>= 4.0.0)
Imports: Rcpp (>= 1.0.0)
LinkingTo: Rcpp
Suggests: testthat (>= 3.0.0), covr
NeedsCompilation: yes
Packaged: 2025-12-17 20:11:49 UTC; priyanshutiwari
Author: Priyanshu Tiwari [aut, cre] , Sourav Majumdar [ctb]
Repository: CRAN
Date/Publication: 2025-12-22 18:20:02 UTC

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New package weightedsurv with initial version 0.1.0
Package: weightedsurv
Title: Survival Analysis with Subject-Specific (Case Weights) and Time-Dependent Weighting
Version: 0.1.0
Description: Provides survival analysis functions with support for time-dependent and subject-specific (e.g., propensity score) weighting. Implements weighted estimation for Cox models, Kaplan-Meier survival curves, and treatment differences with point-wise and simultaneous confidence bands. Includes restricted mean survival time (RMST) comparisons evaluated across all potential truncation times with both point-wise and simultaneous confidence bands. See Cole, S. R. & Hernán, M. A. (2004) <doi:10.1016/j.cmpb.2003.10.004> for methodological background.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: ggplot2, graphics, rlang, stats, survival, utils
Suggests: adjustedCurves, data.table, DiagrammeR, dplyr, gt, knitr, pammtools, rmarkdown
URL: https://github.com/larry-leon/weightedsurv
BugReports: https://github.com/larry-leon/weightedsurv/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-12-16 22:36:01 UTC; larryleon
Author: Larry Leon [aut, cre]
Maintainer: Larry Leon <larry.leon.05@post.harvard.edu>
Repository: CRAN
Date/Publication: 2025-12-22 17:10:02 UTC

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New package TemporalForest with initial version 0.1.4
Package: TemporalForest
Title: Network-Guided Temporal Forests for Feature Selection in High-Dimensional Longitudinal Data
Version: 0.1.4
Description: Implements the Temporal Forest algorithm for feature selection in high-dimensional longitudinal data. The method combines time-aware network construction via weighted gene co-expression network analysis (WGCNA), module-based feature screening, and stability selection using tree-based models. This package provides tools for reproducible longitudinal analysis, closely following the methodology described in Shao, Moore, and Ramirez (2025) <https://github.com/SisiShao/TemporalForest>.
License: MIT + file LICENSE
URL: https://github.com/SisiShao/TemporalForest
BugReports: https://github.com/SisiShao/TemporalForest/issues
Encoding: UTF-8
Language: en-US
Imports: WGCNA, dynamicTreeCut, flashClust, glmertree, partykit, stats, graphics, grDevices,
Suggests: knitr, rmarkdown, igraph, Matrix, MASS
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-12-17 08:48:16 UTC; sisishao
Author: Sisi Shao [aut, cre] , Jason H. Moore [aut] , Christina M. Ramirez [aut]
Maintainer: Sisi Shao <sisishao@g.ucla.edu>
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2025-12-22 17:30:02 UTC

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New package RegEnRF with initial version 1.0.0
Package: RegEnRF
Title: Regression-Enhanced Random Forests
Version: 1.0.0
Description: A novel generalized Random Forest method, that can improve on RFs by borrowing the strength of penalized parametric regression. Based on Zhang et al. (2019) <doi:10.48550/arXiv.1904.10416>.
License: MIT + file LICENSE
BugReports: https://github.com/umbe1987/regenrf/issues
Imports: glmnet, randomForest
Suggests: testthat (>= 3.0.0)
Encoding: UTF-8
URL: https://github.com/umbe1987/regenrf
NeedsCompilation: no
Packaged: 2025-12-17 13:12:29 UTC; minorum
Author: Umberto Minora [aut, cre, cph]
Maintainer: Umberto Minora <umbertofilippo@tiscali.it>
Repository: CRAN
Date/Publication: 2025-12-22 18:00:08 UTC

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New package PlateVision with initial version 0.1.0
Package: PlateVision
Title: Automated qPCR Analysis and Visual Quality Control
Version: 0.1.0
Description: Directly pipes raw quantitative PCR (qPCR) machine outputs into downstream analyses using the comparative Ct (Delta-Delta Ct) method described by Livak and Schmittgen (2001) <doi:10.1006/meth.2001.1262>. Streamlines the workflow from 'Excel' export to publication-ready plots. Integrates unique visual quality control by reconstructing 96-well plate heatmaps, allowing users to instantly detect pipetting errors, edge effects, and outliers. Key features include automated error propagation, laboratory master mix calculations, and generation of bar charts and volcano plots.
License: GPL-3
Encoding: UTF-8
Imports: dplyr, readxl, ggplot2, stringr, plotly
Suggests: testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2025-12-17 10:46:00 UTC; shubhamlove
Author: Shubham Love [aut, cre]
Maintainer: Shubham Love <shubhamlove2101@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-22 17:50:07 UTC

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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>

Diff between PenCoxFrail versions 2.0.0 dated 2024-07-04 and 2.0.1 dated 2025-12-22

 DESCRIPTION            |   21 +++++++++++++--------
 MD5                    |   16 ++++++++--------
 demo/pencoxfrail-pbc.r |    4 ++--
 man/coxFL.Rd           |    4 ++--
 man/coxlasso.rd        |    4 ++--
 man/coxridge.rd        |    4 ++--
 man/pencoxfrail.rd     |   15 ++++++++-------
 src/IntegrMulti.cpp    |    1 -
 src/Makevars.win       |    1 -
 9 files changed, 37 insertions(+), 33 deletions(-)

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New package pipr with initial version 1.4.0
Package: pipr
Title: Client for the Poverty and Inequality Platform ('PIP') API
Version: 1.4.0
Description: An interface to compute poverty and inequality indicators for more than 160 countries and regions from the World Bank's database of household surveys, through the Poverty and Inequality Portal (PIP).
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
URL: https://worldbank.github.io/pipr/, https://github.com/worldbank/pipr
BugReports: https://github.com/worldbank/pipr/issues
Suggests: covr, testthat (>= 3.0.0), spelling, knitr, rmarkdown, markdown, callr, ggplot2, tidyr, ggthemes, forcats, scales, dplyr, readr
Language: en-US
Imports: arrow, attempt, curl, jsonlite, tibble, purrr, cli, rlang, utils, httr2, stringr, vroom
Depends: R (>= 4.1.0)
Date: 2025-12-17
NeedsCompilation: no
Packaged: 2025-12-17 12:14:03 UTC; wb384996
Author: Tony Fujs [aut], Aleksander Eilertsen [aut], Ronak Shah [aut], R.Andres Castaneda [aut, cre], Giorgia Cecchinato [aut], World Bank [cph]
Maintainer: R.Andres Castaneda <acastanedaa@worldbank.org>
Repository: CRAN
Date/Publication: 2025-12-22 18:00:01 UTC

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New package oCELLoc with initial version 1.0.0
Package: oCELLoc
Title: Predicts Suitable Cell Types in Spatial Transcriptomics and scRNA-seq Data
Version: 1.0.0
Description: Picks the suitable cell types in spatial and scRNA-seq data using shrinkage methods. The package includes curated reference gene expression profiles for human and mouse cell types, facilitating immediate application to common spatial transcriptomics or scRNA datasets. Additionally, users can input custom reference data to support tissue- or experiment-specific analyses.
License: MIT + file LICENSE
URL: https://doi.org/10.64898/2025.12.11.693812
Encoding: UTF-8
Imports: glmnet, utils, stats, ggplot2, rlang, reshape2,
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
LazyData: true
Depends: R (>= 3.5.0)
NeedsCompilation: no
Packaged: 2025-12-17 06:14:28 UTC; vlaruks
Author: Afeefa Zainab [aut, cre] , Vladyslav Honcharuk [aut] , Alexis Vandenbon [aut]
Maintainer: Afeefa Zainab <afeeffazainab@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-22 17:20:02 UTC

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New package enrichit with initial version 0.0.8
Package: enrichit
Title: 'C++' Implementations of Functional Enrichment Analysis
Version: 0.0.8
Description: Fast implementations of functional enrichment analysis methods using 'C++' via 'Rcpp'. Currently provides Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). The multilevel GSEA algorithm is derived from the 'fgsea' package. Methods are described in Subramanian et al. (2005) <doi:10.1073/pnas.0506580102> and Korotkevich et al. (2021) <doi:10.1101/060012>.
License: Artistic-2.0
Depends: R (>= 3.5.0)
Imports: methods, Rcpp (>= 1.0.10), stats, yulab.utils (> 0.2.1)
LinkingTo: Rcpp
Suggests: AnnotationDbi, clusterProfiler, DOSE, gson, qvalue, quarto, testthat
Encoding: UTF-8
VignetteBuilder: quarto
NeedsCompilation: yes
Packaged: 2025-12-17 01:10:20 UTC; HUAWEI
Author: Guangchuang Yu [aut, cre]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-22 17:10:09 UTC

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New package contourforest with initial version 0.1.0
Package: contourforest
Title: Contour-Enhanced Forest Plots for Meta-Analysis
Version: 0.1.0
Description: Provides functions to create contour-enhanced forest plots for continuous and binary outcomes in meta-analysis, including options for prediction intervals, customized colors, and study labeling. Based on 'metafor' and 'ggplot2'.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: ggplot2, dplyr, metafor, stringr, grid
Depends: R (>= 4.0.0)
NeedsCompilation: no
Packaged: 2025-12-17 11:35:48 UTC; DR. Umar Hussain
Author: Umar Hussain [aut, cre]
Maintainer: Umar Hussain <drumarhussain@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-22 17:50:02 UTC

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New package citsr with initial version 0.1.3
Package: citsr
Title: Controlled Interrupted Time Series Analysis and Visualization
Version: 0.1.3
Description: Implements controlled interrupted time series (CITS) analysis for evaluating interventions in comparative time-series data. The package provides tools for preparing panel time-series datasets, fitting models using generalized least squares (GLS) with optional autoregressive–moving-average (ARMA) error structures, and computing fitted values and robust standard errors using cluster-robust variance estimators (CR2). Visualization functions enable clear presentation of estimated effects and counterfactual trajectories following interventions. Background on methods for causal inference in interrupted time series can be found in Linden and Adams (2011) <doi:10.1111/j.1365-2753.2010.01504.x> and Lopez Bernal, Cummins, and Gasparrini (2018) <doi:10.1093/ije/dyy135>.
License: MIT + file LICENSE
Imports: dplyr, nlme, clubSandwich, ggplot2, stats, AICcmodavg
Suggests: knitr, rmarkdown, lmtest, testthat (>= 3.0.0)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-12-17 07:50:07 UTC; Gu
Author: Hanmin Gu [aut, cre]
Maintainer: Hanmin Gu <ghm21zzang@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-22 17:20:07 UTC

More information about citsr at CRAN
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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>

Diff between BrokenAdaptiveRidge versions 1.0.1 dated 2025-07-23 and 1.0.2 dated 2025-12-22

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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>

Diff between trace versions 0.6.0 dated 2025-03-14 and 1.0.0 dated 2025-12-22

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More information about trace at CRAN
Permanent link

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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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>

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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>

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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>

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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>

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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 psc (with last version 2.0.0) was removed from CRAN

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

2025-11-13 2.0.0
2025-05-29 1.3.0
2025-04-10 1.2.0
2024-12-12 1.1.0
2024-11-20 1.0.0

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Package leanpubr (with last version 0.4.0) was removed from CRAN

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

2025-04-01 0.4.0
2018-08-16 0.3.1

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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>

Diff between plotfunctions versions 1.4 dated 2020-04-28 and 1.5 dated 2025-12-22

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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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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>

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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>

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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>

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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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