Fri, 21 Aug 2026

Package worldbank updated to version 0.10.0 with previous version 0.9.1 dated 2026-06-29

Title: Client for the 'World Bank' APIs
Description: Download and search data from the 'World Bank' APIs, including the 'Indicators' API, the 'Poverty and Inequality Platform (PIP)' API, the 'Finances One' API, and the 'Projects' API. See <https://datahelpdesk.worldbank.org/knowledgebase/articles/889386-developer-information-overview> for further details.
Author: Maximilian Muecke [aut, cre]
Maintainer: Maximilian Muecke <muecke.maximilian@gmail.com>

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Package vitae updated to version 0.7.0 with previous version 0.6.0 dated 2024-06-12

Title: Curriculum Vitae for R Markdown
Description: Provides templates and functions to simplify the production and maintenance of curriculum vitae.
Author: Mitchell O'Hara-Wild [aut, cre] , Rob Hyndman [aut] , Yihui Xie [ctb] , Albert Krewinkel [cph] , JooYoung Seo [ctb] , Isabelle Greco [ctb]
Maintainer: Mitchell O'Hara-Wild <mail@mitchelloharawild.com>

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Package ulrb updated to version 0.1.9 with previous version 0.1.8 dated 2025-07-07

Title: Unsupervised Learning Based Definition of Microbial Rare Biosphere
Description: A tool to define the rare biosphere. 'ulrb' solves the problem of the definition of rarity by replacing arbitrary thresholds with an unsupervised machine learning algorithm (partitioning around medoids, or k-medoids). This algorithm works for any type of microbiome data, provided there is an abundance table. This method also works for non-microbiome data.
Author: Francisco Pascoal [aut, cre] , Paula Branco [aut] , Luis Torgo [aut] , Rodrigo Costa [aut] , Catarina Magalhaes [aut]
Maintainer: Francisco Pascoal <fpascoal1996@gmail.com>

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Package tinysnapshot updated to version 0.3.0 with previous version 0.2.0 dated 2025-07-18

Title: Snapshots for Unit Tests using the 'tinytest' Framework
Description: Snapshots for unit tests using the 'tinytest' framework for R. Includes expectations to test base R and 'ggplot2' plots as well as console output from print().
Author: Vincent Arel-Bundock [aut, cre]
Maintainer: Vincent Arel-Bundock <vincent.arel-bundock@umontreal.ca>

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Package tinycodet updated to version 0.7.1 with previous version 0.7.0 dated 2026-06-08

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

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Package summata updated to version 0.12.0 with previous version 0.11.5 dated 2026-05-07

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

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Package scanr updated to version 0.1.1 with previous version 0.1.0 dated 2026-08-20

Title: Sequential Change-Point Detection via Nonparametric Inference
Description: Detects change points in long univariate time series using the SCAN framework. The implementation uses a native Rust backend exposed to R via 'extendr'.
Author: Ashoka Prabashwara [aut, cre], Patricia Menendez [aut], Liam Hodgkinson [aut], Stuart Lee [aut]
Maintainer: Ashoka Prabashwara <smashoka123@gmail.com>

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Package matchednull updated to version 0.2.1 with previous version 0.1.0 dated 2026-07-21

Title: Matched-Null Tests for Cluster-Count Claims
Description: Builds matched nulls for cluster-count claims: synthetic twins of a dataset that preserve every marginal distribution and the full correlation matrix while containing no cluster structure by construction. A reported number of clusters or "types" can then be tested against what the data's own margins and covariance already produce, using any clustering pipeline. A t-copula option adds tail dependence to the null, so that an apparent excess of clusters can be checked against a heavier-tailed alternative before it is read as evidence of types. Implements the matched-null procedure of Meng (2026) "Types Without Taxa" <doi:10.17605/OSF.IO/2EKCG>.
Author: Miura Meng [aut, cre]
Maintainer: Miura Meng <haomeng797@gmail.com>

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Package marp readmission to version 0.1.1 with previous version 0.1.0 dated 2022-08-11

Title: Model-Averaged Renewal Process
Description: To implement a model-averaging approach with different renewal models, with a primary focus on forecasting large earthquakes. Based on six renewal models (i.e., Poisson, Gamma, Log-Logistics, Weibull, Log-Normal and BPT), model-averaged point estimates are calculated using AIC weights. Additionally, both percentile and studentized bootstrapped model-averaged confidence intervals are constructed. In comparison, point and interval estimation from the individual or "best" model (determined via model selection) can be retrieved.
Author: Jie Kang [aut], Chris Scott [aut], Vanessa Huang [aut], Veronica Tsou [aut, cre], Albert Savary [ctb]
Maintainer: Veronica Tsou <wantungtsou@gmail.com>

This is a re-admission after prior archival of version 0.1.0 dated 2022-08-11

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Package jellyfisher updated to version 1.1.2 with previous version 1.1.1 dated 2025-11-26

Title: Visualize Spatiotemporal Tumor Evolution with Jellyfish Plots
Description: Generates interactive Jellyfish plots to visualize spatiotemporal tumor evolution by integrating sample and phylogenetic trees into a unified plot. This approach provides an intuitive way to analyze tumor heterogeneity and evolution over time and across anatomical locations. The Jellyfish plot visualization design was first introduced by Lahtinen, Lavikka, et al. (2023, <doi:10.1016/j.ccell.2023.04.017>). This package also supports visualizing ClonEvol results, a tool developed by Dang, et al. (2017, <doi:10.1093/annonc/mdx517>), for analyzing clonal evolution from multi-sample sequencing data. The 'clonevol' package is not available on CRAN but can be installed from its GitHub repository (<https://github.com/hdng/clonevol>).
Author: Kari Lavikka [cph, aut, cre]
Maintainer: Kari Lavikka <kari@karilavikka.fi>

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Package inDAGO updated to version 1.0.4 with previous version 1.0.3 dated 2025-10-21

Title: A GUI for Dual and Bulk RNA-Sequencing Analysis
Description: A 'shiny' app that supports both dual and bulk RNA-seq, with the dual RNA-seq functionality offering the flexibility to perform either a sequential approach (where reads are mapped separately to each genome) or a combined approach (where reads are aligned to a single merged genome). The user-friendly interface automates the analysis process, providing step-by-step guidance, making it easy for users to navigate between different analysis steps, and download intermediate results and publication-ready plots.
Author: Carmine Fruggiero [aut, cre], Gaetano Aufiero [aut]
Maintainer: Carmine Fruggiero <fruggierocarmine3@gmail.com>

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Package implicitMeasures readmission to version 1.0.0 with previous version 0.3.1 dated 2025-08-22

Title: Compute Scores for Different Implicit Measures
Description: A tool for computing the scores for the Implicit Association Test (IAT; Greenwald, McGhee & Schwartz (1998) <doi:10.1037/0022-3514.74.6.1464>) and the Single Category-IAT (SC-IAT: Karpinski & Steinman (2006) <doi:10.1037/0022-3514.91.1.16>). Functions for preparing the data (both for the IAT and the SC-IAT), plotting the results, and obtaining a table with the scores of implicit measures descriptive statistics are provided.
Author: Ottavia M. Epifania [aut, cre], Pasquale Anselmi [ctb], Egidio Robusto [ctb]
Maintainer: Ottavia M. Epifania <otta.epifania@gmail.com>

This is a re-admission after prior archival of version 0.3.1 dated 2025-08-22

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Package ggRandomForests updated to version 3.5.2 with previous version 3.5.0 dated 2026-08-04

Title: Visually Exploring Random Forests
Description: Graphic elements for exploring Random Forests using the 'randomForest' or 'randomForestSRC' package for survival, regression and classification forests and 'ggplot2' package plotting. Implements visualizations of the methods described in Breiman (2001) <doi:10.1023/A:1010933404324> and Ishwaran, Kogalur, Blackstone, and Lauer (2008) <doi:10.1214/08-AOAS169>.
Author: John Ehrlinger [aut, cre]
Maintainer: John Ehrlinger <john.ehrlinger@gmail.com>

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Package fable.bayesRecon updated to version 0.2.0 with previous version 0.1.0 dated 2026-05-28

Title: Bayesian Reconciliation in the 'fable' Framework
Description: Implements the 'bayesRecon' probabilistic reconciliation methods within the 'fable' framework for hierarchical time series forecasting. Bayesian reconciliation (bayesRecon) methods are accessed via the 'reconcile' verb, following 'fable' conventions. For methodological background, see Corani et al. (2021) <doi:10.1007/978-3-030-67664-3_13>, Zambon et al. (2024a) <doi:10.1007/s11222-023-10343-y>, Zambon et al. (2024b) <https://proceedings.mlr.press/v244/zambon24a.html>, and Carrara et al. (2026) <doi:10.1016/j.ijforecast.2026.07.003>.
Author: Dario Azzimonti [aut, cre, cph] , Stefano Damato [aut] , Lorenzo Zambon [aut] , Chiara Carrara [aut] , Giorgio Corani [aut]
Maintainer: Dario Azzimonti <dario.azzimonti@gmail.com>

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Package dyadicMarkov updated to version 0.1.2 with previous version 0.1.1 dated 2026-06-21

Title: Pattern Estimation and Identification for Dyadic Sequences Using Transition Matrices in R
Description: Provides methods for analyzing categorical dyadic sequences using transition matrices within the Longitudinal Actor-Partner Interdependence Model and Markov-chain framework. The package supports empirical transition counts, maximum likelihood estimation of transition probabilities, and identification of univariate and bivariate patterns of interaction in dyadic sequences.
Author: Mattia Boellenruecher [aut, cre, cph] , Megane Bollenruecher [aut] , Jean-Philippe Antonietti [aut]
Maintainer: Mattia Boellenruecher <mboellenruec@student.ethz.ch>

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Package cpge updated to version 1.0.2 with previous version 1.0.1 dated 2026-08-08

Title: Interactive Clustered Graph for French Scientific Preparatory Classes
Description: To help French students from scientific preparatory classes for the Grandes Ecoles (CPGE) in their choice of field of study and career options, this package provides an interactive tool and data visualization of a graph clustered by different competitive exams and sectors of activity for French selective engineering schools and selective higher education institutions like Ecoles Normales Superieures (ENS) or specialized university programs (magisteres). Besides, there are two drop-down menus to select on the graph many fields or more than 200 engineering schools or ENS or magisteres. It gives the opportunity to expand, collapse clusters of selective exams interactively too. For more information, see the demonstration video: <https://valerierobert-maths.re/index.php/maths-en-cpge/>. The data was collected via the official French website: <https://www.scei-concours.fr/statistiques.html>.
Author: Valerie Robert [aut, cre]
Maintainer: Valerie Robert <valerie.robert.math@gmail.com>

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Package bsvarSIGNs updated to version 3.0 with previous version 2.0 dated 2025-01-29

Title: Bayesian SVARs with Sign, Zero, and Narrative Restrictions
Description: Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) <doi:10.1162/REST_a_00483> extended by the COVID-specific heteroskedasticity proposed by Lenza, Primiceri (2022) <doi:10.1002/jae.2895>. The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010) <doi:10.1111/j.1467-937X.2009.00578.x>, while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018) <doi:10.3982/ECTA14468>. Furthermore, our toolset provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolín-Díaz and Rubio-Ramírez (201 [...truncated...]
Author: Xiaolei Wang [aut, cre] , Tomasz Wozniak [aut] , Fei Shang [ctb]
Maintainer: Xiaolei Wang <adamwang15@gmail.com>

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Package broadcast updated to version 0.1.9.6 with previous version 0.1.9.5 dated 2026-08-20

Title: Broadcasted Array Operations Like 'NumPy'
Description: Implements efficient 'NumPy'-like broadcasted operations for atomic and recursive arrays. In the context of operations involving 2 (or more) arrays, “broadcasting” (AKA singleton expansion) refers to efficiently recycling array dimensions, without making copies. Besides linking to 'Rcpp', 'broadcast' does not use any external libraries in any way; 'broadcast' was essentially made from scratch and can be installed out-of-the-box. The implementations available in 'broadcast' include, but are not limited to, the following. 1) Broadcasted element-wise operations on any 2 arrays; they support a large set of relational, arithmetic, Boolean, string, and bit-wise operations. 2) A faster, more memory efficient, and broadcasted abind-like function, for binding arrays along an arbitrary dimension. 3) Broadcasted ifelse-like and apply-like functions. 4) Casting functions, that cast subset-groups of an array to a new dimension, cast nested lists to dimensional lists, and vice-versa. 5) A few linear [...truncated...]
Author: Tony Wilkes [aut, cre, cph]
Maintainer: Tony Wilkes <tonywilkes.nl@gmail.com>

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Package Statamarkdown updated to version 1.0.0 with previous version 0.9.7 dated 2026-07-19

Title: 'Stata' Markdown
Description: Settings and functions to extend the 'knitr' 'Stata' engine.
Author: Doug Hemken [aut] ), Tom Palmer [aut, cre] , Philipp Lepert [ctb]
Maintainer: Tom Palmer <remlapmot@hotmail.com>

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Package spOccupancy updated to version 0.8.1 with previous version 0.8.0 dated 2024-12-14

Title: Single-Species, Multi-Species, and Integrated Spatial Occupancy Models
Description: Fits single-species, multi-species, and integrated non-spatial and spatial occupancy models using Markov Chain Monte Carlo (MCMC). Models are fit using Polya-Gamma data augmentation detailed in Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>. Spatial models are fit using either Gaussian processes or Nearest Neighbor Gaussian Processes (NNGP) for large spatial datasets. Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2022) <doi:10.18637/jss.v103.i05>. Provides functionality for data integration of multiple single-species occupancy data sets using a joint likelihood framework. Details on data integration are given in Miller, Pacifici, Sanderlin, and Reich (2019) <doi:10.1111/2041-210X.13110>. Details on single-species and multi-species models are found in MacKenzie, Nichols, Lachman, Droege, Royle, and Langtimm (2002) <doi:10.1890/0012-9658(2 [...truncated...]
Author: Jeffrey Doser [aut, cre], Andrew Finley [aut], Marc Kery [ctb]
Maintainer: Jeffrey Doser <jwdoser@ncsu.edu>

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Package sov updated to version 2.0.0 with previous version 1.0.3 dated 2026-04-04

Title: Calculate vs-SOVs and SOVs for Assemblies with D-Dimensional Voting
Description: Calculates vote-specific and traditional Shapley-Owen power indices (vs-SOVs and SOVs) for spatial voting games in one to four dimensions. Evaluates voter influence through an a posteriori analysis of relative preferences. Supports weighted voting and various voting thresholds. Compatible with ideal point estimates from NOMINATE, Optimal Classification, and 'MCMCpack'. The method builds on Bibina and Dougherty (2025) <doi:10.2139/ssrn.6324519>.
Author: Keith Dougherty [aut], Emma Bibina [aut, cre]
Maintainer: Emma Bibina <emma.bibina@uga.edu>

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Package smaa updated to version 0.3-4 with previous version 0.3-3 dated 2024-09-06

Title: Stochastic Multi-Criteria Acceptability Analysis
Description: Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA) family of Multiple Criteria Decision Analysis (MCDA) methods. Tervonen, T. and Figueira, J. R. (2008) <doi:10.1002/mcda.407>.
Author: Gert van Valkenhoef [aut, cre, cph]
Maintainer: Gert van Valkenhoef <gert@gertvv.nl>

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Package SimplexRegression updated to version 0.1.6 with previous version 0.1.5 dated 2026-07-19

Title: Simplex Regression Models with Parametric or Fixed Mean Link Functions
Description: Fits and analyzes simplex regression models with either fixed or parametric mean link functions. Implements the simplex probability density function, cumulative distribution function, quantile function, random number generation, and variance evaluation. Offers several fixed and parametric link functions for the mean submodel, tools for residual analysis and diagnostic plotting, hypothesis testing procedures, and influence measures such as Cook's distance and leverage (hat values). Includes the Scout Score (SS) criterion for model selection, enabling comprehensive inference and diagnostic analysis within the simplex regression framework. For more details see Barndorff-Nielsen and Jorgensen (1991) <doi:10.1016/0047-259X(91)90008-P> and Justino and Cribari-Neto (2026) <doi:10.1016/j.apm.2025.116713>.
Author: Maria Eduarda da Cruz Justino [aut, cre] , Francisco Cribari-Neto [ctb, ths]
Maintainer: Maria Eduarda da Cruz Justino <eueduardacruz@gmail.com>

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Package shinyglass updated to version 0.2.0 with previous version 0.1.1 dated 2026-08-02

Title: Liquid Glass Design Themes for 'shiny' Applications
Description: Provides drop-in Liquid Glass themes for 'shiny'. Call glass_theme() and pass the result as theme = to fluidPage(), navbarPage(), or any 'bslib'-aware page function to get translucent surfaces, backdrop blur, and system typography on 'Bootstrap' components. Includes light and dark presets with runtime switching and an OS-following 'auto' mode, an iOS-style intensity control from Ultra Clear to Tinted (glass_intensity_slider()), and options for accent color, blur, corner radius, and motion or tint behavior.
Author: Eric Anderson [aut, cre, cph]
Maintainer: Eric Anderson <eric.ray.anderson@gmail.com>

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Package rtpcr updated to version 2.1.9 with previous version 2.1.8 dated 2026-05-21

Title: qPCR Data Analysis
Description: Tools for qPCR data analysis using Delta Ct and Delta Delta Ct methods, including t-test, Wilcoxon-test, ANOVA models, and publication-ready visualizations. The package supports multiple target, and multiple reference genes, and uses a calculation framework adopted from Ganger et al. (2017) <doi:10.1186/s12859-017-1949-5> and Taylor et al. (2019) <doi:10.1016/j.tibtech.2018.12.002>, covering both the Livak and Pfaffl methods.
Author: Ghader Mirzaghaderi [aut, cre, cph]
Maintainer: Ghader Mirzaghaderi <mirzaghaderi@gmail.com>

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Package RTMBdist updated to version 1.0.6 with previous version 1.0.5 dated 2026-07-22

Title: Distributions Compatible with Automatic Differentiation by 'RTMB'
Description: Extends the functionality of the 'RTMB' <https://kaskr.r-universe.dev/RTMB> package by providing a collection of non-standard probability distributions compatible with automatic differentiation (AD). While 'RTMB' enables flexible and efficient modelling, including random effects, its built-in support is limited to standard distributions. The package adds additional AD-compatible distributions, broadening the range of models that can be implemented and estimated using 'RTMB'. Automatic differentiation and Laplace approximation are described in Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>.
Author: Jan-Ole Fischer [aut, cre]
Maintainer: Jan-Ole Fischer <jan-ole.fischer@mailbox.org>

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Package rPDBapi updated to version 3.0.2 with previous version 3.0.1 dated 2026-03-07

Title: A Comprehensive Interface for Accessing the Protein Data Bank
Description: Provides an R interface to the 'RCSB' Protein Data Bank ('PDB') Search and Data APIs (<https://www.rcsb.org/>). Supports full-text, attribute, sequence, motif, structure, and chemical searches; retrieval of entry-, assembly-, polymer-entity-, and chemical-component-level metadata; and conversion of API responses into analysis-ready tables and typed R objects for reproducible structural bioinformatics workflows.
Author: Selcuk Korkmaz [aut, cre] , Bilge Eren Yamasan [aut]
Maintainer: Selcuk Korkmaz <selcukorkmaz@gmail.com>

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Package RESI updated to version 1.5.1 with previous version 1.4.2 dated 2026-07-24

Title: Robust Effect Size Index (RESI) Estimation
Description: Summarize model output using a robust effect size index. The index is introduced in Vandekar, Tao, & Blume (2020, <doi:10.1007/s11336-020-09698-2>). Software paper available at <doi:10.18637/jss.v112.i03>.
Author: Megan Jones [aut], Kaidi Kang [aut], Simon Vandekar [aut, cre], Gina Yu [ctb], Xinyu Zhang [ctb]
Maintainer: Simon Vandekar <simon.vandekar@vumc.org>

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Package ReportSubtotal updated to version 0.2.1 with previous version 0.1.2 dated 2024-12-02

Title: Adds Subtotals to Data Reports
Description: Adds subtotal rows / sections (a la the 'SAS' 'Proc Tabulate' All option) to a Group By output by running a series of Group By functions with partial sets of the same variables and combining the results with the original. Can be used to add comprehensive information to a data report or to quickly aggregate Group By outputs used to gain a greater understanding of data.
Author: Yoni Aboody [aut, cre, cph]
Maintainer: Yoni Aboody <yoniaboody@gmail.com>

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Package ReDaMoR updated to version 1.0.1 with previous version 1.0.0 dated 2026-05-19

Title: Relational Data Modeler
Description: The aim of this package is to manipulate relational data models in R. It provides functions to create, modify and export data models in json format. It also allows importing models created with 'MySQL Workbench' (<https://www.mysql.com/products/workbench/>). These functions are accessible through a graphical user interface made with 'shiny'. Constraints such as types, keys, uniqueness and mandatory fields are automatically checked and corrected when editing a model. Finally, real data can be confronted to a model to check their compatibility.
Author: Patrice Godard [aut, cre, cph], Kai Lin [ctb]
Maintainer: Patrice Godard <patrice.godard@gmail.com>

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Package RColetum updated to version 1.1.0 with previous version 1.0.0 dated 2026-04-16

Title: Access your Coletum's Data from API
Description: Get your data (forms, structures, answers) from Coletum <https://coletum.com> to handle and analyse.
Author: Andre Smaniotto [aut, cre], Marcelo Magnani [aut], Rodrigo Sant'Ana [aut], GeoSapiens [cph, fnd]
Maintainer: Andre Smaniotto <smaniotto@geosapiens.com.br>

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Package ramps updated to version 0.6.19 with previous version 0.6.18 dated 2023-03-13

Title: Bayesian Geostatistical Modeling with RAMPS
Description: Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples. Package performance is tuned for large spatial datasets.
Author: Brian J Smith [aut, cre], Jun Yan [aut], Mary Kathryn Cowles [aut]
Maintainer: Brian J Smith <brian-j-smith@uiowa.edu>

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Package qol updated to version 1.3.4 with previous version 1.3.3 dated 2026-07-16

Title: Powerful 'SAS' Inspired Concepts for more Efficient Bigger Outputs
Description: The main goal is to make descriptive evaluations easier to create bigger and more complex outputs in less time with less code. Introducing format containers with multilabels <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/p06ciqes4eaqo6n0zyqtz9p21nfb.htm>, a more powerful summarise which is capable to output every possible combination of the provided grouping variables in one go <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/p0jvbbqkt0gs2cn1lo4zndbqs1pe.htm>, tabulation functions which can create any table in different styles <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/n1ql5xnu0k3kdtn11gwa5hc7u435.htm> and other more readable functions. The code is optimized to work fast even with datasets of over a million observations.
Author: Tim Siebenmorgen [aut, cre, cph]
Maintainer: Tim Siebenmorgen <qol_package@proton.me>

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Package PubMatrixR updated to version 1.0.1 with previous version 1.0.0 dated 2026-03-12

Title: PubMed Pairwise Co-Occurrence Matrix Construction and Visualization
Description: Queries the 'NCBI' (National Center for Biotechnology Information) Entrez 'E-utilities' API to count pairwise co-occurrences between two sets of terms in 'PubMed' or 'PubMed Central'. It returns a matrix-like data frame of publication counts and can export hyperlink-enabled results in CSV or ODS format. The package also provides heatmap helpers for exploratory visualization of overlap patterns. Based on the method described in Becker et al. (2003) "PubMatrix: a tool for multiplex literature mining" <doi:10.1186/1471-2105-4-61>.
Author: Tyler Laird [aut], Enrique Toledo [aut, cre]
Maintainer: Enrique Toledo <enriquetoledo@gmail.com>

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Package psrwe updated to version 3.2-2 with previous version 3.2-1 dated 2026-02-18

Title: PS-Integrated Methods for Incorporating Real-World Evidence in Clinical Studies
Description: High-quality real-world data can be transformed into scientific real-world evidence for regulatory and healthcare decision-making using proven analytical methods and techniques. For example, propensity score (PS) methodology can be applied to select a subset of real-world data containing patients that are similar to those in the current clinical study in terms of baseline covariates, and to stratify the selected patients together with those in the current study into more homogeneous strata. Then, statistical methods such as the power prior approach or composite likelihood approach can be applied in each stratum to draw inference for the parameters of interest. This package provides functions that implement the PS-integrated real-world evidence analysis methods such as Wang et al. (2019) <doi:10.1080/10543406.2019.1657133>, Wang et al. (2020) <doi:10.1080/10543406.2019.1684309>, and Chen et al. (2020) <doi:10.1080/10543406.2020.1730877>.
Author: Chenguang Wang [aut], Trustees of Columbia University [cph] , Wei-Chen Chen [aut, cre]
Maintainer: Wei-Chen Chen <wccsnow@gmail.com>

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Package PhysMove updated to version 1.2.5 with previous version 1.2.4 dated 2026-08-04

Title: Quantifying Animal Movement and Space-Use Patterns with Statistical Physics
Description: Provides tools to analyse animal movement and space-use patterns from telemetry data using methods derived from statistical physics. Methods span displacement-based approaches, distribution fitting, space-use metrics (including the influence of correlations on space-use), network-based community detection, and measures of entropy and predictability. The package enables characterisation of these patterns across spatial and temporal scales, including variation within and among individuals (inter- and intraspecific analyses). Outputs include interpretable metrics and visualisations to support ecological analysis and the investigation of fundamental movement processes. For applications of these methods in ecological studies see Rodríguez et al. (2017) <doi:10.1038/s41598-017-00165-0> and Sequeira et al. (2018) <doi:10.1073/pnas.1716137115>.
Author: Hannah J. Calich [aut, cre, cph] , Jorge Rodriguez [aut] , Victor Eguiluz [aut] , Ana M. M. Sequeira [aut]
Maintainer: Hannah J. Calich <hannah.calich@gmail.com>

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Package parafac4microbiome updated to version 1.3.3 with previous version 1.3.2 dated 2025-07-31

Title: Parallel Factor Analysis Modelling of Longitudinal Microbiome Data
Description: Creation and selection of PARAllel FACtor Analysis (PARAFAC) models of longitudinal microbiome data. You can import your own data with our import functions or use one of the example datasets to create your own PARAFAC models. Selection of the optimal number of components can be done using assessModelQuality() and assessModelStability(). The selected model can then be plotted using plotPARAFACmodel(). The Parallel Factor Analysis method was originally described by Caroll and Chang (1970) <doi:10.1007/BF02310791> and Harshman (1970) <https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf>.
Author: Geert Roelof van der Ploeg [aut, cre] , Johan Westerhuis [ctb] , Anna Heintz-Buschart [ctb] , Age Smilde [ctb] , University of Amsterdam [cph, fnd]
Maintainer: Geert Roelof van der Ploeg <roel@simula.no>

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Package orgutils updated to version 0.5-4 with previous version 0.5-3 dated 2025-12-22

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

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Package Orangutan updated to version 2.2.0 with previous version 2.1.0 dated 2026-03-31

Title: Automated Analysis of Phenotypic Data
Description: Provides functions to analyze and visualize meristic, mensural, and categorical phenotypic data in a comparative framework. The package implements an automated pipeline that summarizes traits, identifies diagnostic variables among groups, performs multivariate and univariate statistical analyses, and produces publication-ready graphics. Earlier implementation are described in Torres (2025) <doi:10.64898/2025.12.18.695244> (v1.0.0) and Torres (2026) <doi:10.1002/ece3.73111> (v2.0.0).
Author: Javier Torres [aut, cre]
Maintainer: Javier Torres <metalofis@gmail.com>

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Package NPLStoolbox updated to version 1.1.1 with previous version 1.1.0 dated 2025-07-31

Title: N-Way Partial Least Squares Modelling of Multi-Way Data
Description: Creation and selection of N-way Partial Least Squares (NPLS) models. Selection of the optimal number of components can be done using ncrossreg(). NPLS was originally described by Rasmus Bro, see <doi:10.1002/%28SICI%291099-128X%28199601%2910%3A1%3C47%3A%3AAID-CEM400%3E3.0.CO%3B2-C>.
Author: Geert Roelof van der Ploeg [aut, cre] , Johan Westerhuis [ctb] , Anna Heintz-Buschart [ctb] , Age Smilde [ctb] , University of Amsterdam [cph, fnd]
Maintainer: Geert Roelof van der Ploeg <roel@simula.no>

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Package NormData updated to version 1.2 with previous version 1.1 dated 2024-04-12

Title: Derivation of Regression-Based Normative Data
Description: Normative data are often used to estimate the relative position of a raw test score in the population. This package allows for deriving regression-based normative data. It includes functions that enable the fitting of regression models for the mean and residual (or variance) structures, test the model assumptions, derive the normative data in the form of normative tables or automatic scoring sheets, and estimate confidence intervals for the norms. This package accompanies the book Van der Elst, W. (2024). Regression-based normative data for psychological assessment. A hands-on approach using R. Springer Nature.
Author: Wim Van der Elst [aut, cre]
Maintainer: Wim Van der Elst <Wim.vanderelst@gmail.com>

Diff between NormData versions 1.1 dated 2024-04-12 and 1.2 dated 2026-08-21

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Package gtregression updated to version 1.1.0 with previous version 1.0.0 dated 2025-08-18

Title: Tools for Creating Publication-Ready Regression Tables
Description: Simplifies regression modeling in R by integrating multiple modeling and summarization tools into a cohesive, user-friendly interface. Designed to be accessible for researchers, particularly those in Low- and Middle-Income Countries (LMIC). Built upon widely accepted statistical methods, including logistic regression (Hosmer et al. 2013, ISBN:9781118548429), log-binomial regression (Spiegelman and Hertzmark 2005 <doi:10.1093/aje/kwi188>), Firth penalized logistic regression (Firth 1993 <doi:10.1093/biomet/80.1.27>), Poisson and robust Poisson regression (Zou 2004 <doi:10.1093/aje/kwh090>), negative binomial regression (Hilbe 2011, ISBN:9780521179515), Cox proportional hazards regression, parametric survival regression, causal mediation analysis, and linear regression (Kutner et al. 2005, ISBN:9780071122214). Leverages multiple dependencies to ensure high-quality output and generate reproducible, publication-ready tables in alignment with best practices in epidemiology [...truncated...]
Author: Rubeshkumar Polani [aut, cre] , Salin K Eliyas [aut] , Manikandanesan Sakthivel [aut] , Mogan Kaviprawin [aut] , Yuvaraj Krishnamoorthy [aut] , Marie Gilbert Majella [aut]
Maintainer: Rubeshkumar Polani <rubesh@thinkdenominator.com>

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Package altmeta updated to version 4.4 with previous version 4.3.1 dated 2026-04-30

Title: Alternative Meta-Analysis Methods
Description: Provides alternative statistical methods for meta-analysis, including: - bivariate generalized linear mixed models for synthesizing odds ratios, relative risks, and risk differences (Chu et al., 2012 <doi:10.1177/0962280210393712>) - tests and measures for between-study heterogeneity (Lin et al., 2017 <doi:10.1111/biom.12543>; Wang et al., 2022 <doi:10.1002/sim.9261>; Yu et al., 2025 <doi:10.1186/s12874-025-02719-7>); - measures, tests, and visualization tools for publication bias, small-study effects, or related bias (Lin and Chu, 2018 <doi:10.1111/biom.12817>; Lin, 2019 <doi:10.1002/jrsm.1340>; Lin, 2020 <doi:10.1177/0962280220910172>; Shi et al., 2020 <doi:10.1002/jrsm.1415>); - meta-analysis of combining standardized mean differences and odds ratios (Jing et al., 2023 <doi:10.1080/10543406.2022.2105345>); - meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc. (Reitsma et al., 2005 <doi:10.1016 [...truncated...]
Author: Lifeng Lin [aut, cre] , Yaqi Jing [ctb], Kristine J. Rosenberger [ctb], Linyu Shi [ctb], Yipeng Wang [ctb], Xing Xing [ctb] , Zhiyuan Yu [ctb], Haitao Chu [aut]
Maintainer: Lifeng Lin <lifenglin@arizona.edu>

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Package bigtabulate (with last version 1.1.9) was removed from CRAN

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

2022-04-11 1.1.9
2016-02-18 1.1.5
2015-10-22 1.1.4
2013-11-05 1.1.2
2013-04-01 1.1.1
2012-09-17 1.1.0
2010-07-24 1.0.13
2010-06-09 1.0.11
2010-05-10 1.0.10
2010-05-06 1.0.8

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Package nmw updated to version 0.5.1 with previous version 0.3.1 dated 2026-06-09

Title: Understanding Nonlinear Mixed Effects Modeling for Population Pharmacokinetics
Description: This shows how 'NONMEM' (Beal SL, Sheiner LB, Boeckmann AJ, Bauer RJ. NONMEM 7.5 Users Guides. Icon plc, 2020) software works. 'NONMEM' classical estimation methods such as 'First Order (FO) approximation', 'First Order Conditional Estimation (FOCE)', and 'Laplacian approximation' are explained. Functions are also provided for post-run processing of NONMEM output files, generating PDF diagnostic reports including objective function value analysis, parameter estimates, prediction and residual diagnostics, empirical Bayes estimate (EBE) analysis, input data summary, and individual pharmacokinetic parameter distributions. Helper utilities for building NONMEM-ready datasets from SDTM-style source tables are also included.
Author: Kyun-Seop Bae [aut, cre]
Maintainer: Kyun-Seop Bae <k@acr.kr>

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Package modsem updated to version 1.0.22 with previous version 1.0.21 dated 2026-07-02

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 mlr3misc updated to version 0.23.0 with previous version 0.22.0 dated 2026-06-10

Title: Helper Functions for 'mlr3'
Description: Frequently used helper functions and assertions used in 'mlr3' and its companion packages. Comes with helper functions for functional programming, for printing, to work with 'data.table', as well as some generally useful 'R6' classes. This package also supersedes the package 'BBmisc'.
Author: Marc Becker [cre, aut] , Michel Lang [aut] , Patrick Schratz [aut]
Maintainer: Marc Becker <marcbecker@posteo.de>

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Package mlr3inferr updated to version 0.2.2 with previous version 0.2.1 dated 2025-11-26

Title: Inference on the Generalization Error
Description: Confidence interval and resampling methods for inference on the generalization error.
Author: Sebastian Fischer [cre, aut] , Hannah Schulz-Kuempel [aut]
Maintainer: Sebastian Fischer <sebf.fischer@gmail.com>

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Package mfrmr updated to version 0.2.3 with previous version 0.2.2 dated 2026-07-27

Title: Estimation and Diagnostics for Many-Facet Measurement Models
Description: Native R implementation of many-facet ordered-response measurement models with arbitrary facet counts, rating-scale and partial-credit parameterizations, a bounded generalized partial-credit extension, and both marginal and joint maximum likelihood estimation. The package provides a fit / diagnose / report pipeline covering anchoring, linking, bias and differential-functioning screening, and publication-oriented reporting summaries, with reproducibility manifests for replay. See 'Andrich' (1978) <doi:10.1007/BF02293814>, 'Masters' (1982) <doi:10.1007/BF02296272>, and 'Muraki' (1992) <doi:10.1177/014662169201600206> for the underlying ordered-response models.
Author: Ryuya Komuro [aut, cre, cph]
Maintainer: Ryuya Komuro <ryuya.komuro.c4@tohoku.ac.jp>

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Package locaR updated to version 0.3.0 with previous version 0.2.0 dated 2026-02-23

Title: A Set of Tools for Sound Localization
Description: A set of functions and tools to conduct acoustic source localization, as well as organize and check localization data and results. The localization functions implement the modified steered response power algorithm described by Cobos et al. (2011) <doi:10.1109/LSP.2010.2091502>.
Author: Richard Hedley [cre, aut] , Marcus Becker [aut], Tim Huang [aut]
Maintainer: Richard Hedley <rwhedley@gmail.com>

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Package localIV updated to version 0.3.2 with previous version 0.3.1 dated 2020-06-26

Title: Estimation of Marginal Treatment Effects using Local Instrumental Variables
Description: In the generalized Roy model, the marginal treatment effect (MTE) can be used as a building block for constructing conventional causal parameters such as the average treatment effect (ATE) and the average treatment effect on the treated (ATT). Given a treatment selection equation and an outcome equation, the function mte() estimates the MTE via the semiparametric local instrumental variables method or the normal selection model. The function mte_at() evaluates MTE at different values of the latent resistance u with a given X = x, and the function mte_tilde_at() evaluates MTE projected onto the estimated propensity score. The function ace() estimates population-level average causal effects such as ATE, ATT, or the marginal policy relevant treatment effect.
Author: Xiang Zhou [aut, cre]
Maintainer: Xiang Zhou <xiang_zhou@fas.harvard.edu>

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Package linf updated to version 0.2.0 with previous version 0.1.0 dated 2026-08-05

Title: L-Infinity Normalization and Dominant Community State Types
Description: Implements L-infinity normalization for compositional matrices, assigns samples to dominant features, constructs truncated and hierarchically refined dominant community state types, and computes representative landmark profiles. The methods are described in the accompanying publication <doi:10.48550/arXiv.2503.21543>. Bundled vaginal and gut microbiome data support reproducible demonstrations of the package interface; phenotype fields in the stratified gut subset are illustrative and are not suitable for population-level inference.
Author: Pawel Gajer [aut, cre]
Maintainer: Pawel Gajer <pgajer@gmail.com>

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Package lavinteract updated to version 0.5.3 with previous version 0.5.1 dated 2026-04-28

Title: Post-Estimation Utilities for 'lavaan' Fitted Models
Description: Companion toolbox for structural equation models fitted with 'lavaan'. Provides post-estimation diagnostics and graphics that operate directly on a fitted object using its estimates and covariance, and refits auxiliary models when needed. The package relies on 'lavaan' (Rosseel, 2012) <doi:10.18637/jss.v048.i02>.
Author: Giuseppe Corbelli [aut, cre]
Maintainer: Giuseppe Corbelli <giuseppe.corbelli@uninettunouniversity.net>

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Package IssueTrackeR updated to version 1.5.0 with previous version 1.4.1 dated 2026-07-31

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

Diff between IssueTrackeR versions 1.4.1 dated 2026-07-31 and 1.5.0 dated 2026-08-21

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Package IsoplotR updated to version 7.0 with previous version 6.8 dated 2025-10-27

Title: Statistical Toolbox for Radiometric Geochronology
Description: Plots U-Pb data on Wetherill and Tera-Wasserburg concordia diagrams. Calculates concordia and discordia ages. Performs linear regression of measurements with correlated errors using 'York', 'Titterington', 'Ludwig' and Omnivariant Generalised Least-Squares ('OGLS') approaches. Generates Kernel Density Estimates (KDEs) and Cumulative Age Distributions (CADs). Produces Multidimensional Scaling (MDS) configurations and Shepard plots of multi-sample detrital datasets using the Kolmogorov-Smirnov distance as a dissimilarity measure. Calculates 40Ar/39Ar ages, isochrons, and age spectra. Computes weighted means accounting for overdispersion. Calculates U-Th-He (single grain and central) ages, logratio plots and ternary diagrams. Processes fission track data using the external detector method and LA-ICP-MS, calculates central ages and plots fission track and other data on radial (a.k.a. 'Galbraith') plots. Constructs total Pb-U, Pb-Pb, Th-Pb, K-Ca, Re-Os, Sm-Nd, Lu-Hf, Rb-Sr and 230Th-U isoch [...truncated...]
Author: Pieter Vermeesch [aut, cre]
Maintainer: Pieter Vermeesch <p.vermeesch@ucl.ac.uk>

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Package isocountry updated to version 0.7.0 with previous version 0.6.1 dated 2026-06-29

Title: ISO 3166-1 Country Codes
Description: ISO 3166-1 country codes and ISO 4217 currency codes provided by the International Organization for Standardization.
Author: Maximilian Muecke [aut, cre]
Maintainer: Maximilian Muecke <muecke.maximilian@gmail.com>

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Package icarm updated to version 0.3.0 with previous version 0.2.0 dated 2026-07-09

Title: Interpretable Contextual-Accountable and Responsible Machine Learning
Description: A general-purpose framework for Interpretable Contextual-Accountable and Responsible Machine Learning (ICARM) that works with any clean tabular data across any application domain including healthcare, finance, social science, business, and education. Automatically detects whether a prediction task is binary classification, multi-class classification, or regression from the target variable type. Provides a unified entry point icarm_fit() supporting both interpretable learners (Classification and Regression Trees (CART), logistic regression, linear regression, Generalized Additive Models (GAM)) and extended learners (random forest, 'XGBoost', Support Vector Machines (SVM)) with consistent interfaces for global and local model explanation including approximate SHapley Additive exPlanations (SHAP) values and Partial Dependence Profiles (PDPs), learning curve diagnostics, group-level fairness auditing across protected attributes, probability calibration, threshold analysis, multi-model comp [...truncated...]
Author: Olushina Olawale Awe [aut, cre], Ludwigsburg University of Education [fnd]
Maintainer: Olushina Olawale Awe <olawaleawe@gmail.com>

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Package huito updated to version 0.2.7 with previous version 0.2.6 dated 2025-10-18

Title: Reproducible and Flexible Label Design
Description: An open-source R package to deploys reproducible and flexible labels using layers. The 'huito' package is part of the 'inkaverse' project for developing different procedures and tools used in plant science and experimental designs. Learn more about the 'inkaverse' project at <https://inkaverse.com/>.
Author: Flavio Lozano-Isla [aut, cre] , Victor-Hugo Baldera-Chaponan [aut] , Inkaverse [cph]
Maintainer: Flavio Lozano-Isla <flozanoisla@gmail.com>

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Package hmetad updated to version 0.2.0 with previous version 0.1.2 dated 2026-05-15

Title: Fit the Meta-D' Model of Confidence Ratings Using 'brms'
Description: Implementation of Bayesian regressions over the meta-d' model of psychological data from two alternative forced choice tasks with ordinal confidence ratings. For more information, see Maniscalco & Lau (2012) <doi:10.1016/j.concog.2011.09.021>. The package is a front-end to the 'brms' package, which facilitates a wide range of regression designs, as well as tools for efficiently extracting posterior estimates, plotting, and significance testing.
Author: Kevin O'Neill [aut, cre, cph] , Stephen Fleming [aut, cph]
Maintainer: Kevin O'Neill <kevin.o'neill@ucl.ac.uk>

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Package healthyAddress updated to version 0.5.2 with previous version 0.5.1 dated 2025-11-11

Title: Convert Addresses to Standard Inputs
Description: Efficient tools for parsing and standardizing Australian addresses from textual data. It utilizes optimized algorithms to accurately identify and extract components of addresses, such as street names, types, and postcodes, especially for large batched data in contexts where sending addresses to internet services may be slow or inappropriate. The core functionality is built on fast string processing techniques to handle variations in address formats and abbreviations commonly found in Australian address data. Designed for data scientists, urban planners, and logistics analysts, the package facilitates the cleaning and normalization of address information, supporting better data integration and analysis in urban studies, geography, and related fields.
Author: Hugh Parsonage [aut, cre]
Maintainer: Hugh Parsonage <hugh.parsonage@gmail.com>

Diff between healthyAddress versions 0.5.1 dated 2025-11-11 and 0.5.2 dated 2026-08-21

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More information about healthyAddress at CRAN
Permanent link

Package healthatlas updated to version 0.2.3 with previous version 0.2.2 dated 2025-07-22

Title: Explore and Import 'Metopio' Health Atlas Data and Spatial Layers
Description: Allows for painless use of the 'Metopio' health atlas APIs <https://metopio.com/health-atlas> to explore and import data. 'Metopio' health atlases store open public health data. See what topics (or indicators) are available among specific populations, periods, and geographic layers. Download relevant data along with geographic boundaries or point datasets. Spatial datasets are returned as 'sf' objects.
Author: Ryan Zomorrodi [aut, cre, cph]
Maintainer: Ryan Zomorrodi <rzomor2@uic.edu>

Diff between healthatlas versions 0.2.2 dated 2025-07-22 and 0.2.3 dated 2026-08-21

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Package grip updated to version 0.1.3 with previous version 0.1.2 dated 2026-08-05

Title: Graph Drawing with Intelligent Placement (GRIP)
Description: Implements GRIP multiscale graph layout with a unified choice between hop-count and geometry-aware edge-length graph metrics in 2D and 3D. Provides layout scoring, candidate comparison, multiscale trace diagnostics, synthetic graph families, and advanced experimental geodesic-KK utilities for weighted-layout evaluation and polish. Based on Gajer and Kobourov (2002) <doi:10.7155/jgaa.00052> and Gajer, Goodrich and Kobourov (2004) <doi:10.1016/j.comgeo.2004.03.014>.
Author: Pawel Gajer [aut, cre]
Maintainer: Pawel Gajer <pgajer@gmail.com>

Diff between grip versions 0.1.2 dated 2026-08-05 and 0.1.3 dated 2026-08-21

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More information about grip at CRAN
Permanent link

Package gp3tools updated to version 2.3.0 with previous version 2.0.1 dated 2026-07-14

Title: Import, Inspect, Analyse, and Report Gazepoint GP3 Exports
Description: Tools for importing, inspecting, cleaning, summarising, modelling, and reporting Gazepoint GP3 and Gazepoint Analysis CSV exports. The package supports offline workflows for all-gaze, fixation, pupil, area-of-interest, transition, time-course, quality-audit, and manuscript-reporting analyses. The package methodology is described in the peer-reviewed software paper <doi:10.3390/jemr19040076>.
Author: Stefanos Balaskas [aut, cre]
Maintainer: Stefanos Balaskas <s.balaskas@ac.upatras.gr>

Diff between gp3tools versions 2.0.1 dated 2026-07-14 and 2.3.0 dated 2026-08-21

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Package GLSME updated to version 1.0.6 with previous version 1.0.5 dated 2019-09-15

Title: Generalized Least Squares with Measurement Error
Description: Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.
Author: Krzysztof Bartoszek [cre, aut, ths]
Maintainer: Krzysztof Bartoszek <krzbar@protonmail.ch>

Diff between GLSME versions 1.0.5 dated 2019-09-15 and 1.0.6 dated 2026-08-21

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Package gemtc updated to version 1.1-2 with previous version 1.1-1 dated 2026-03-26

Title: Network Meta-Analysis Using Bayesian Methods
Description: Network meta-analyses (mixed treatment comparisons) in the Bayesian framework using JAGS. Includes methods to assess heterogeneity and inconsistency, and a number of standard visualizations. van Valkenhoef et al. (2012) <doi:10.1002/jrsm.1054>; van Valkenhoef et al. (2015) <doi:10.1002/jrsm.1167>.
Author: Gert van Valkenhoef [aut, cre], Joel Kuiper [aut]
Maintainer: Gert van Valkenhoef <gert@gertvv.nl>

Diff between gemtc versions 1.1-1 dated 2026-03-26 and 1.1-2 dated 2026-08-21

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Package datanugget updated to version 1.5.0 with previous version 1.4.0 dated 2025-12-04

Title: Create, Optimize, and Refine Data Nuggets
Description: Creating, optimizing and refining data nuggets. Data nuggets reduce a large dataset into a small collection of nuggets of data, each containing a center (location), weight (importance), and scale (variability) parameter. Data nugget centers are selected based on a space-filling maximum-entropy scheme. Data nugget weights are created by counting the number observations closest to a given data nugget center. We then say the data nugget 'contains' these observations and the data nugget center is recalculated as the mean of these observations. Data nugget scales are created by calculating the trace of the covariance matrix of the observations contained within a data nugget divided by the dimension of the dataset. The optimal number of data nuggets is determined data-driven based on the relative second-order differences of propensity score indices. Data nuggets are refined by 'splitting' data nuggets which have high scales or elongated shapes (defined as the ratio of the two largest eigenva [...truncated...]
Author: Rituparna Dey [aut, cre], Yajie Duan [aut], Traymon Beavers [aut], Javier Cabrera [aut], Ge Cheng [aut], Kunting Qi [aut], Mariusz Lubomirski [aut]
Maintainer: Rituparna Dey <rituparnadey525@gmail.com>

Diff between datanugget versions 1.4.0 dated 2025-12-04 and 1.5.0 dated 2026-08-21

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Package CopulaSCR updated to version 1.0.2 with previous version 1.0.1 dated 2026-06-12

Title: Analysis of Semi-Competing Risks Data Using Copula-Based Models
Description: Simulate and analyze Semi-competing Risks Data using copula-based models. The Semi-competing Risks Data consist of a terminal event time and single or multiple intermediate event times. The marginal survival functions of these event times are estimated without parametric assumptions. The association parameters measuring dependency among these event times involving the copula model are yielded from solving a concordance estimating equations or maximizing a pseudo-likelihood function. Details can be found in the article by Tonghui Yu and Liming Xiang (2026) <doi:10.1093/biomtc/ujag087>.
Author: Tonghui Yu [aut, cre], Binhui Zhang [aut]
Maintainer: Tonghui Yu <tonghui_yu@126.com>

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Package Compositional updated to version 8.3 with previous version 8.2 dated 2026-05-23

Title: Compositional Data Analysis
Description: Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. We further include functions for percentages (or proportions). The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include: a) Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data--based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. <doi:10.48550/arXiv.1106.1451>. b) Tsagris M. (2014). "The k--NN algorithm for compositional data: a revised approach with and without zero values present". Journal of Data Science, 12(3): 519--534. <doi:10.6339/JDS.201407_12(3).0008>. c) Tsagris M. (2015). "A novel, divergence based, regression for compositional data". Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. <doi:10.48550/arXiv. [...truncated...]
Author: Michail Tsagris [aut, cre], Giorgos Athineou [aut], Abdulaziz Alenazi [ctb], Christos Adam [ctb]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>

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Package admiralonco updated to version 1.5.0 with previous version 1.4.1 dated 2026-05-21

Title: Oncology Extension Package for ADaM in 'R' Asset Library
Description: Programming oncology specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team (2021), <https://www.cdisc.org/standards/foundational/adam>). The package is an extension package of the 'admiral' package.
Author: Stefan Bundfuss [aut, cre], Amit Jain [aut], Vinh Nguyen [aut], Olga Starostecka [aut], Kiran Peddamudium [aut], Tomoyuki Namai [aut], Ross Farrugia [aut], Yirong Cao [ctb], F. Hoffmann-La Roche AG [cph, fnd], GlaxoSmithKline LLC [cph, fnd], Bristol [...truncated...]
Maintainer: Stefan Bundfuss <stefan.bundfuss@external.roche.com>

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Package ggvariant updated to version 0.2.0 with previous version 0.1.0 dated 2026-02-27

Title: Tidy, 'ggplot2'-Native Visualization for Genomic Variants
Description: A simple, opinionated toolkit for visualizing genomic variant data using a 'ggplot2'-native grammar. Accepts VCF files or plain data frames and produces lollipop plots, consequence summaries, mutational spectrum charts, and cohort-level comparisons as standard 'ggplot2' objects. Designed for both wet-lab biologists and experienced bioinformaticians.
Author: Joash Joshua Ayo [aut, cre]
Maintainer: Joash Joshua Ayo <joashjoshua789@gmail.com>

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Package ggExametrika updated to version 1.1.2 with previous version 1.1.1 dated 2026-06-15

Title: Visualization of 'exametrika' Output Using 'ggplot2'
Description: Provides 'ggplot2'-based visualization functions for output objects from the 'exametrika' package, which implements test data engineering methods described in Shojima (2022, ISBN:978-981-16-9547-1). Supports a wide range of psychometric models including Item Response Theory, Latent Class Analysis, Latent Rank Analysis, Biclustering (binary, ordinal, and nominal), Bayesian Network Models, and related network models. All plot functions return 'ggplot2' objects that can be further customized by the user.
Author: Koji Kosugi [aut, cre] , Daichi Kamimura [aut]
Maintainer: Koji Kosugi <kosugitti@gmail.com>

Diff between ggExametrika versions 1.1.1 dated 2026-06-15 and 1.1.2 dated 2026-08-21

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Package gfunctions updated to version 1.2 with previous version 1.1 dated 2025-07-23

Title: G-Functions
Description: Modified versions of the lag() and summary() functions: glag() and gsummary(). The prefix 'g' is a reminder of who to blame if things do not work as they should.
Author: Genaro Sucarrat [aut, cre]
Maintainer: Genaro Sucarrat <gsucarrat@gmail.com>

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Package GeoModels updated to version 2.2.8 with previous version 2.2.7 dated 2026-07-29

Title: Procedures for Gaussian and Non Gaussian Geostatistical (Large) Data Analysis
Description: Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial and Poisson. Se [...truncated...]
Author: Moreno Bevilacqua [aut, cre, cph], Victor Morales-Onate [ctb], Francisco Cuevas-Pacheco [ctb], Christian Caamano-Carrillo [ctb]
Maintainer: Moreno Bevilacqua <moreno.bevilacqua89@gmail.com>

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More information about GeoModels at CRAN
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Package ExactTree updated to version 0.1.2 with previous version 0.1.1 dated 2026-08-07

Title: Exact Tree
Description: Grows optimally global trees based on the algorithm defined in the thesis by van Os, B.J. (2001, ISBN:9789090144795). It is possible to obtain both classification and regression trees depending on the measurement level of the outcome variable. The algorithm is based on the dynamic programming principle and guarantees that the resulting tree is optimal with respect to the chosen impurity measure. The package also includes a function to visualize the resulting trees, a function that summarizes the tree with its splitting information and leaf information, and a predict function that provides estimates for a new dataset given a model fit.
Author: Juan Claramunt Gonzalez [aut, cre, cph], Bart Jan van Os [aut], Elise Dusseldorp [aut]
Maintainer: Juan Claramunt Gonzalez <j.claramunt.gonzalez@fsw.leidenuniv.nl>

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More information about ExactTree at CRAN
Permanent link

Package envar updated to version 0.1.1 with previous version 0.1.0 dated 2026-07-31

Title: Download and Process Environmental Variables
Description: Provides a unified interface to download, harmonise and extract a wide range of environmental and socio-economic variables from established open data web services (such as 'WorldClim' <https://www.worldclim.org/>, 'CHELSA' <https://chelsa-climate.org/> and 'Bio-ORACLE' <https://www.bio-oracle.org/>, among others) for use in macroecology and biogeography. The package handles spatial subsetting to a study area, reprojection to a common coordinate reference system, and extraction of values at sampling points, so that predictors from heterogeneous sources can be assembled within a single reproducible workflow. Helper functions for collinearity checking and variable exploration are also included.
Author: Andrea Simoncini [aut, cre] , Michele Bertoncini [aut] , Andrea Cerofolini [aut] , Andrea Dalpasso [aut] , Mattia Falaschi [aut] , Gentile Francesco Ficetola [aut] , Elia Lo Parrino [aut]
Maintainer: Andrea Simoncini <simonciniandre@gmail.com>

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Permanent link

Package EE.Data updated to version 0.2.0 with previous version 0.1.1 dated 2026-04-01

Title: Objects for Predicting Energy Expenditure
Description: This is a data-only package containing model objects that predict human energy expenditure from wearable sensor data. Supported methods include the neural networks of Montoye et al. (2017) <doi:10.1080/1091367X.2017.1337638> and the models of Staudenmayer et al. (2015) <doi:10.1152/japplphysiol.00026.2015>, one a linear model and the other a random forest. The package is intended as a spoke for the hub-package 'accelEE', which brings together the above methods and others from packages such as 'Sojourn' and 'TwoRegression.'
Author: Paul R. Hibbing [aut, cre], Alexander H.K. Montoye [ctb], John Staudenmayer [ctb], Children's Mercy Kansas City [cph]
Maintainer: Paul R. Hibbing <paulhibbing@gmail.com>

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Package dyadMLM updated to version 0.2.0 with previous version 0.1.0 dated 2026-07-30

Title: Tools for Dyadic Multilevel Models
Description: Provides tools for dyadic multilevel modeling with linear and generalized linear mixed-effects models. It validates and prepares long-format cross-sectional and intensive longitudinal data, including ecological momentary assessment designs, for distinguishable and exchangeable dyads. It also supports datasets containing multiple observed dyad compositions. It constructs composition-aware, model-ready variables for Actor-Partner Interdependence Models (APIMs), Dyadic Score Models (DSMs), and Dyad-Individual Models (DIMs). Prepared data can be used with model engines such as 'glmmTMB' and 'brms' for Gaussian and non-Gaussian outcomes, including counts, proportions, and skewed continuous responses. Post-estimation tools compare compatible fitted models and back-transform exchangeable sum-and-difference random-effect covariance structures into member-level quantities. The APIM and DSM specifications and their relationships follow Iida et al. (2018) <doi:10.1177/0265407517725407>; the [...truncated...]
Author: Pascal Kueng [aut, cre, cph]
Maintainer: Pascal Kueng <kueng.pascal@gmail.com>

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Package diegr updated to version 0.3.1 with previous version 0.2.0 dated 2026-01-24

Title: Dynamic and Interactive EEG Graphics
Description: Allows to visualize high-density electroencephalography (HD-EEG) data through interactive plots and animations, enabling exploratory and communicative analysis of temporal-spatial brain signals. Funder: Masaryk University (Grant No. MUNI/A/1457/2023).
Author: Zdeňka Gerslova [aut, cre] , Stanislav Katina [rev] , Martin Lamos [ctb]
Maintainer: Zdeňka Gerslova <gerslovaz@math.muni.cz>

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Package CurricularComplexity updated to version 1.1.0 with previous version 1.0.3 dated 2026-07-08

Title: Toolkit for Analyzing Curricular Complexity
Description: Enables educational researchers and practitioners to calculate the curricular complexity of a plan of study, visualize its prerequisite structure at scale, and conduct customizable analyses. The original tool can be found at <https://curricularanalytics.org>. Additional functions to explore curriculum complexity from the literature are also included.
Author: David Reeping [aut, cre]
Maintainer: David Reeping <reepindp@ucmail.uc.edu>

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Package cuda.ml updated to version 0.4.0 with previous version 0.3.3 dated 2026-04-29

Title: R Interface for the RAPIDS cuML Suite of Libraries
Description: R interface for RAPIDS cuML (<https://github.com/NVIDIA/cuml>), a suite of GPU-accelerated machine learning libraries powered by CUDA (<https://en.wikipedia.org/wiki/CUDA>).
Author: Yitao Li [aut, cph] , Tomasz Kalinowski [aut, cre, cph], Daniel Falbel [aut, cph], RStudio [cph, fnd]
Maintainer: Tomasz Kalinowski <tomasz@posit.co>

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Package csranks updated to version 1.3.0 with previous version 1.2.3 dated 2024-09-12

Title: Statistical Tools for Ranks
Description: Account for uncertainty when working with ranks. Estimate standard errors consistently in linear regression with ranked variables. Construct confidence sets of various kinds for positions of populations in a ranking based on values of a certain feature and their estimation errors. Theory based on Mogstad, Romano, Shaikh, and Wilhelm (2023)<doi:10.1093/restud/rdad006> and Chetverikov and Wilhelm (2023) <doi:10.48550/arXiv.2310.15512>.
Author: Daniel Wilhelm [aut, cre], Pawel Morgen [aut]
Maintainer: Daniel Wilhelm <d.wilhelm@lmu.de>

Diff between csranks versions 1.2.3 dated 2024-09-12 and 1.3.0 dated 2026-08-21

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Package countSTAR updated to version 1.2.1 with previous version 1.2.0 dated 2026-04-03

Title: Flexible Modeling of Count Data
Description: For Bayesian and classical inference and prediction with count-valued data, Simultaneous Transformation and Rounding (STAR) Models provide a flexible, interpretable, and easy-to-use approach. STAR models the observed count data using a rounded continuous data model and incorporates a transformation for greater flexibility. Implicitly, STAR formalizes the commonly-applied yet incoherent procedure of (i) transforming count-valued data and subsequently (ii) modeling the transformed data using Gaussian models. STAR is well-defined for count-valued data, which is reflected in predictive accuracy, and is designed to account for zero-inflation, bounded or censored data, and over- or underdispersion. Importantly, STAR is easy to combine with existing MCMC or point estimation methods for continuous data, which allows seamless adaptation of continuous data models (such as linear regressions, additive models, BART, random forests, and gradient boosting machines) for count-valued data. The package [...truncated...]
Author: Brian King [aut, cre], Dan Kowal [aut]
Maintainer: Brian King <brianking387@gmail.com>

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Package corto updated to version 1.3.1 with previous version 1.2.4 dated 2023-12-06

Title: Inference of Gene Regulatory Networks
Description: We present 'corto' (Correlation Tool), a simple package to infer gene regulatory networks and visualize master regulators from gene expression data using DPI (Data Processing Inequality) and bootstrapping to recover edges. An initial step is performed to calculate all significant edges between a list of source nodes (centroids) and target genes. Then all triplets containing two centroids and one target are tested in a DPI step which removes edges. A bootstrapping process then calculates the robustness of the network, eventually re-adding edges previously removed by DPI. The algorithm has been optimized to run outside a computing cluster, using a fast correlation implementation. The package finally provides functions to calculate network enrichment analysis from RNA-Seq and ATAC-Seq signatures as described in the article by Giorgi lab (2020) <doi:10.1093/bioinformatics/btaa223>.
Author: Federico M. Giorgi [aut, cre], Daniele Mercatelli [ctb], Gonzalo Lopez-Garcia [ctb], Hugo Tovar [ctb], Hualin Wang [ctb]
Maintainer: Federico M. Giorgi <federico.giorgi@gmail.com>

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Package cofad updated to version 0.4.0 with previous version 0.3.3 dated 2025-05-15

Title: Contrast Analyses for Factorial Designs
Description: Contrast analysis for factorial designs provides an alternative to the traditional ANOVA approach, offering the distinct advantage of testing targeted hypotheses. The foundation of this package is primarily rooted in the works of Rosenthal, Rosnow, and Rubin (2000, ISBN: 978-0521659802) as well as Sedlmeier and Renkewitz (2018, ISBN: 978-3868943214).
Author: Johannes Titz [aut, cre], Markus Burkhardt [aut], Mirka Henninger [ctb], Simone Malejka [ctb]
Maintainer: Johannes Titz <johannes.titz@gmail.com>

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Package CLRtools updated to version 0.1.2 with previous version 0.1.1 dated 2026-03-16

Title: Diagnostic Tools for Logistic and Conditional Logistic Regression
Description: Provides tools for fitting, assessing, and comparing logistic and conditional logistic regression models. Includes residual diagnostics and goodness of fit measures for model development and evaluation in matched case control studies.
Author: Brenda Contla Hernandez [aut, cre], Matthieu Vignes [ctb] , Chris Compton [ctb]
Maintainer: Brenda Contla Hernandez <B.Hernandez@massey.ac.nz>

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Package civic.icarm updated to version 0.4.0 with previous version 0.3.0 dated 2026-06-22

Title: Interpretable Civic-Accountable and Responsible Machine Learning
Description: A general-purpose framework for Interpretable Civic-Accountable and Responsible Machine Learning (ICARM). Works with any clean tabular data and automatically detects whether a task is binary classification, multi-class classification, or regression from the target variable type. Provides a single unified entry point civic_fit() alongside tidy interfaces for global and local model explanations, group-level fairness auditing, probability calibration, multi-model comparison, threshold analysis, and reproducible audit trails. Designed to support the DataCitizen-Pro research agenda at Ludwigsburg University of Education: developing data literacy, statistical reasoning, and democratic judgment formation in civic and political teacher education. References: Biecek (2018) <doi:10.18637/jss.v085.i04>, Kuhn (2008) <doi:10.18637/jss.v028.i05>, Awe (2025) <https://github.com/Olawaleawe/civic.icarm>.
Author: Olushina Olawale Awe [aut, cre], Ludwigsburg University of Education [fnd]
Maintainer: Olushina Olawale Awe <olawaleawe@gmail.com>

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Package childfree updated to version 0.0.6 with previous version 0.0.5 dated 2026-02-26

Title: Access and Harmonize Childfree Demographic Data
Description: Reads demographic data from a variety of public data sources, extracting and harmonizing variables useful for the study of childfree individuals. The identification of childfree individuals and those with other family statuses uses Neal & Neal's (2024) "A Framework for Studying Adults who Neither have Nor Want Children" <doi:10.1177/10664807231198869>; A pre-print is available at <doi:10.31234/osf.io/fa89m>.
Author: Zachary Neal [aut, cre] , Jennifer Watling Neal [aut]
Maintainer: Zachary Neal <zpneal@msu.edu>

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Package cgmguru updated to version 1.3.0 with previous version 1.2.0 dated 2026-07-08

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

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Package canvasXpress updated to version 1.65.2 with previous version 1.59.5 dated 2026-01-14

Title: Visualization Package for CanvasXpress in R
Description: Enables creation of visualizations using the CanvasXpress framework in R. CanvasXpress is a standalone JavaScript library for reproducible research with complete tracking of data and end-user modifications stored in a single PNG image that can be played back. See <https://www.canvasxpress.org> for more information.
Author: Isaac Neuhaus [aut], Connie Brett [aut, cre]
Maintainer: Connie Brett <connie@aggregate-genius.com>

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Package BoundIRT updated to version 0.6.0 with previous version 0.5.0 dated 2026-05-05

Title: Fit Bounded Continuous Item Response Theory Models to Data
Description: Bounded continuous data are encountered in many areas of test application. Examples include visual analogue scales used in the measurement of personality, mood, depression, and quality of life; item response times from tests with item deadlines; confidence ratings; and pain intensity ratings. Using this package, item response theory (IRT) models suitable for bounded continuous item scores can be fitted to data within a Bayesian framework. The package draws on posterior sampling facilities provided by R-package 'rstan' (Stan Development Team, 2025)<https://mc-stan.org/>. Available models include the Beta IRT model by Noel and Dauvier (2007)<doi:10.1177/0146621605287691>, the continuous response model by Samejima (1973)<doi:10.1007/BF03372160>, the unbounded normal model by Mellenbergh (1994)<doi:10.1207/s15327906mbr2903_2>, and the Simplex IRT model by Flores et al. (2020)<doi:10.1007/978-3-030-43469-4_8>. All models can be fitted with or without zero-one i [...truncated...]
Author: Dylan Molenaar [aut, cre]
Maintainer: Dylan Molenaar <d.molenaar@uva.nl>

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Package binsreg updated to version 2.2 with previous version 2.1 dated 2026-05-22

Title: Binscatter Estimation and Inference
Description: Provides tools for statistical analysis using the binscatter methods developed by Cattaneo, Crump, Farrell and Feng (2024) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2024_AER.pdf>, Cattaneo, Crump, Farrell and Feng (2025) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2025_Stata.pdf> and Cattaneo, Crump, Farrell and Feng (2026) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2026_RESTAT.pdf>. Binscatter provides a flexible way of describing the relationship between two variables based on partitioning/binning of the independent variable of interest. binsreg(), binsqreg() and binsglm() implement binscatter least squares regression, quantile regression and generalized linear regression respectively, with particular focus on constructing binned scatter plots. They also implement robust (pointwise and uniform) inference of regression functions and derivatives thereof. binstest() implements hypothesis test [...truncated...]
Author: Matias D. Cattaneo [aut, cre], Richard K. Crump [aut], Max H. Farrell [aut], Yingjie Feng [aut]
Maintainer: Matias D. Cattaneo <matias.d.cattaneo@gmail.com>

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Package bayesQRsurvey updated to version 0.3.1 with previous version 0.3.0 dated 2026-07-07

Title: Bayesian Quantile Regression Models for Complex Survey Data Analysis
Description: Provides Bayesian quantile regression models for complex survey data under informative sampling using survey-weighted estimators. Both single- and multiple-output models are supported. To accelerate computation, all algorithms are implemented in 'C++' using 'Rcpp', 'RcppArmadillo', and 'RcppEigen', and are called from 'R'. See Nascimento and Gonçalves (2024) <doi:10.1093/jssam/smae015> and Nascimento and Gonçalves (2026) <doi:10.1093/jssam/smaf040>.
Author: Tomas Rodriguez Taborda [aut, cre], Johnatan Cardona Jimenez [aut], Marcus L. Nascimento [aut], Kelly Cristina Mota Goncalves [aut]
Maintainer: Tomas Rodriguez Taborda <torodriguezt@unal.edu.co>

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Package autotestR updated to version 1.2.16 with previous version 1.2.15 dated 2026-04-28

Title: Automated Functions for Basic Statistical Tests
Description: Provides simple and intuitive functions for basic statistical analyses. Methods include the t-test (Student 1908 <doi:10.1093/biomet/6.1.1>), the Mann-Whitney U test (Mann and Whitney 1947 <doi:10.1214/aoms/1177730491>), Pearson's correlation (Pearson 1895 <doi:10.1098/rspl.1895.0041>), and analysis of variance (Fisher 1925, <doi:10.1007/978-1-4612-4380-9_5>). Functions are compatible with 'ggplot2' and 'dplyr'.
Author: Luiz Garcia [aut, cre]
Maintainer: Luiz Garcia <luiz.cardoso@ufpr.br>

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Package asleep updated to version 0.3.0 with previous version 0.1.0 dated 2026-08-04

Title: Estimate Sleep from 'Accelerometry' Data
Description: Interfaces the 'asleep' python module <https://github.com/OxWearables/asleep> from Yuan (2024) <doi:10.1038/s41746-024-01148-y> to estimate sleep from 'accelerometry' data.
Author: John Muschelli [aut, cre]
Maintainer: John Muschelli <muschellij2@gmail.com>

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Thu, 20 Aug 2026

Package SynergyLMM updated to version 1.1.4 with previous version 1.1.3 dated 2026-05-12

Title: Statistical Framework for in Vivo Drug Combination Studies
Description: A framework for evaluating drug combination effects in preclinical in vivo studies. 'SynergyLMM' provides functions to analyze longitudinal tumor growth experiments using mixed-effects models, perform time-resolved analyses of synergy and antagonism, evaluate model diagnostics and performance, and assess both post-hoc and a priori statistical power. The calculation of drug combination synergy follows the statistical framework provided by Demidenko and Miller (2019, <doi:10.1371/journal.pone.0224137>). The implementation and analysis of linear mixed-effect models is based on the methods described by Pinheiro and Bates (2000, <doi:10.1007/b98882>), and Gałecki and Burzykowski (2013, <doi:10.1007/978-1-4614-3900-4>).
Author: Rafael Romero-Becerra [aut, cre] , Zhi Zhao [ctb], Tero Aittokallio [ctb]
Maintainer: Rafael Romero-Becerra <rafrombec@gmail.com>

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Package surveyframe updated to version 0.4.0 with previous version 0.3.4 dated 2026-07-24

Title: Survey Instrument Workflows
Description: Supports survey research workflows built around a typed instrument object (the sframe). Features include visual instrument design via a browser-based builder or 'Shiny' studio, export to a self-contained static HTML survey, an embeddable 'Shiny' module, SHA-256 integrity-checked serialisation to the '.sframe' format, multi-page survey rendering, branching logic, response quality checking, scale scoring, psychometric diagnostics, analysis-plan execution, model syntax planning, an interactive response dashboard, codebook generation, and reproducible HTML reporting. This release adds three capability themes: multi-criteria decision analysis (AHP, ANP, DEMATEL, TOPSIS, VIKOR, MOORA, SMART, WASPAS, PROMETHEE II, ELECTRE I), small-sample survey helpers, and text and open-ended response analysis (term and n-gram frequency, keyword in context, co-occurrence and co-occurrence networks, sentiment, document-feature matrices, and topic modelling via LDA or structural topic models).
Author: Mohammed Ali Sharafuddin [aut, cre]
Maintainer: Mohammed Ali Sharafuddin <mohammedali.page@gmail.com>

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Package RobustLPA updated to version 1.0.0 with previous version 0.1.0 dated 2026-07-05

Title: Robust Latent Profile Analysis
Description: Provides a comprehensive toolset for estimating Latent Profile Analysis (LPA) models that are robust to multivariate outliers and missing data. By integrating a high-performance 'C++' engine via 'RcppArmadillo', it reliably extracts latent profiles using both Expectation-Maximization (EM) and Markov Chain Monte Carlo (MCMC) Bayesian estimation. The EM engine implements a Full Information Maximum Likelihood (FIML) approach, Huber weighting, and LASSO regularization with k-fold cross-validation for optimal penalty tuning. The MCMC engine utilizes a Bayesian Lasso approach with Laplace priors, the same Huber down-weighting available in the EM engine, multiple chains (4 by default), and classic Gelman-Rubin/effective sample size convergence diagnostics. It supports multiple geometric variance-covariance models, along with functions for bootstrapped likelihood ratio tests (BLRT), BCH auxiliary variable analysis, and plotting. For methodological details on the Bootstrapped Likelihood Ratio T [...truncated...]
Author: Valerio Riccardo Aquila [aut, cre]
Maintainer: Valerio Riccardo Aquila <valerio_aquila@hotmail.it>

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Package RNifti updated to version 1.10.0 with previous version 1.9.0 dated 2026-01-13

Title: Fast R and C++ Access to NIfTI Images
Description: Provides very fast read and write access to images stored in the NIfTI-1, NIfTI-2 and ANALYZE-7.5 formats, with seamless synchronisation of in-memory image objects between compiled C and interpreted R code. Also provides a simple image viewer, and a C/C++ API that can be used by other packages. Not to be confused with 'RNiftyReg', which performs image registration and applies spatial transformations.
Author: Jon Clayden [cre, aut] , Bob Cox [aut], Mark Jenkinson [aut], Matt Hall [ctb], Rick Reynolds [ctb], Kate Fissell [ctb], Jean-loup Gailly [cph], Mark Adler [cph]
Maintainer: Jon Clayden <code@clayden.org>

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Package RCtest updated to version 1.1 with previous version 1.0 dated 2026-06-02

Title: Reality Check and Predictive Ability Tests for Forecast Evaluation
Description: Implements a comprehensive suite of statistical tests for evaluating the accuracy of forecasting models against a benchmark. The package is grounded in the reality check framework of White (2000) <doi:10.1111/1468-0262.00152>, extended by Hansen (2005) <doi:10.1198/073500105000000063> for Superior Predictive Ability (SPA), Giacomini & White (2006) <doi:10.1111/j.1468-0262.2006.00718.x> for Conditional Predictive Ability (CPA), and Corradi & Swanson (2006) <doi:10.1016/j.jeconom.2005.07.026> for predictive density evaluation via the Kullback-Leibler Information Criterion (KLIC) and ZP Quantile Loss test, the Continuous Ranked Probability Score (CRPS) (Gneiting & Raftery, 2007) <doi:10.1198/016214506000001437>, coverage tests (Kupiec, 1995) <doi:10.3905/jod.1995.407942>, HAC covariance estimation (Newey & West, 1987) <doi:10.2307/1913610>, and Moving Block Bootstrap resampling (Kunsch, 1989) <doi:10.1214/aos/1176347265>.
Author: Joanna Jedrzejewska [aut, cre] , Krzysztof Drachal [ctb]
Maintainer: Joanna Jedrzejewska <j.jedrzejewska3@uw.edu.pl>

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Package prova updated to version 2.3.0 with previous version 1.0.0 dated 2026-07-16

Title: Nonparametric Probabilistic-Statistical Variate Analysis
Description: Calculate posterior joint and conditional probabilities, probability distributions of population frequencies, information-theoretic measures, and expected utilities, by means of Bayesian nonparametrics. Data can be any combination of nominal, ordinal, continuous, censored, rounded types. Data imputation is automatic and done in a principled way. Markov-chain Monte Carlo calculations are automatically handled and do not require user supervision. Applications range from statistical estimation and probabilistic hypothesis testing to evidence-based inference and decision making, in a wide range of disciplines from astrophysics to medicine. For more details and examples see for instance Porta Mana & al. (2026) <doi:10.31219/osf.io/8nr56>, Dunson & Bhattacharya (2011) <doi:10.1093/acprof:oso/9780199694587.003.0005>, Lindley & Novick (1981) <doi:10.1214/aos/1176345331>, Bernardo & Smith (2000) <doi:10.1002/9780470316870>, Müller et al. (2015) <doi:10 [...truncated...]
Author: PierGianLuca Porta Mana [aut, cre, cph] , Aurora Grefsrud [ctb] , Hakon Mydland [ctb] , Maksim Ohvrill [ctb], Simen Hesthamar Hauge [ctb]
Maintainer: PierGianLuca Porta Mana <pgl@portamana.org>

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Package glmMisrep updated to version 0.1.2 with previous version 0.1.1 dated 2024-04-18

Title: Generalized Linear Models Adjusting for Misrepresentation
Description: Fit Generalized Linear Models to continuous and count outcomes, as well as estimate the prevalence of misrepresentation of an important binary predictor. Misrepresentation typically arises when there is an incentive for the binary factor to be misclassified in one direction (e.g., in insurance settings where policy holders may purposely deny a risk status in order to lower the insurance premium). This is accomplished by treating a subset of the response variable as resulting from a mixture distribution. Model parameters are estimated via the Expectation Maximization algorithm and standard errors of the estimates are obtained from closed forms of the Observed Fisher Information. For an introduction to the models and the misrepresentation framework, see Xia et. al., (2023) <https://variancejournal.org/article/73151-maximum-likelihood-approaches-to-misrepresentation-models-in-glm-ratemaking-model-comparisons>.
Author: Patrick Rafael [cre, aut], Xia Michelle [aut], Rexford Akakpo [aut]
Maintainer: Patrick Rafael <pbr2608@vt.edu>

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Package BIDistances updated to version 1.0.1 with previous version 1.0.0 dated 2026-08-19

Title: Bioinformatic Distances
Description: Provides a unified interface for computing, comparing, and examining distances, dissimilarities, divergences, and selected similarities for bioinformatics data. The core installation exposes 60 canonical named routes for numerical data and more than 90 when the suggested 'philentropy' backend is installed; aliases, user-defined functions, and mixed-data combinations are not included in these counts. Weighted Minkowski distances can be computed through 'parallelDist', an internal multicore implementation, or optional 'OpenCL' kernels, while the established weighted Euclidean GPU implementation is retained as the optimized p = 2 route. The package also supports theory-guided comparison of distance distributions for clustering, explicit mathematical property classifications, mixed-data constructions through 'manydist', and a specialized Gene Ontology-derived TF-IDF distance.
Author: Quirin Stier [aut, rev, ctb] , Michael Thrun [aut, cre] , Luca Brinkmann [ctb]
Maintainer: Michael Thrun <m.thrun@gmx.net>

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Package Argentum updated to version 2.1.0 with previous version 1.0.0 dated 2025-02-12

Title: Access Argentine WFS and WMS Geospatial Web Services
Description: Discovers and reads geospatial layers published by Argentine public organizations through the Open Geospatial Consortium standards Web Feature Service (WFS) and Web Map Service (WMS). Provides a cached catalogue of endpoints, capability parsing with version negotiation, paginated vector downloads returned as 'sf' objects, and raster map retrieval returned as 'terra' objects. For the underlying standards see <https://www.ogc.org/standards/wfs/> and <https://www.ogc.org/standards/wms/>.
Author: Thomas Artopoulos [aut, cre]
Maintainer: Thomas Artopoulos <thomas.artopoulos@gmail.com>

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

Title: A Comprehensive Collection of Agricultural and Agronomic Datasets
Description: Offers a rich and diverse collection of datasets focused on agriculture, agronomy, animal science, and related fields. The package includes experimental, observational, and field-trial data on crops such as rice, wheat, corn, soybean, cotton, coffee, avocado, and orange, as well as forestry species including bamboo, eucalyptus, and timber. Datasets cover plant breeding and genetics, factorial and randomized block experiments, herbicide and insecticide efficacy trials, pest and disease infestation, soil characteristics and land suitability, plant growth regulators, seed germination, and crop yield modeling. Additional datasets address animal science topics such as cattle insemination and conception, pig and broiler growth, lamb births, and toxicology studies on aquatic and non-target species. Data sources include peer-reviewed agronomic studies, uniformity and Latin square field trials, glasshouse experiments, and international agricultural surveys. Designed for agronomists, researchers [...truncated...]
Author: Renzo Caceres Rossi [aut, cre] , Prabhanjan Tattar [ctb] , A.P. Gore [ctb] , S.A. Paranjape [ctb] , M.B. Kulkarni [ctb] , Gabriel Danilo Shimizu [ctb] , Rodrigo Yudi Palhaci Marubayashi [ctb] , Leandro Simoes Azeredo Goncalves [ctb] , Al-Ahmadgaid B. [...truncated...]
Maintainer: Renzo Caceres Rossi <arenzocaceresrossi@gmail.com>

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Package TransHDM updated to version 1.1.3 with previous version 1.0.1 dated 2026-03-17

Title: High-Dimensional Mediation Analysis via Transfer Learning
Description: Provides a framework for high-dimensional mediation analysis using transfer learning. The main function TransHDM() integrates large-scale source data to improve the detection power of potential mediators in small-sample target studies. It addresses data heterogeneity via transfer regularization and debiased estimation while controlling the false discovery rate. The package also includes utilities for data generation (gen_simData_homo(), gen_simData_hetero()), baseline methods such as lasso() and dblasso(), sure independence screening via SIS(), and model diagnostics through source_detection(). The methodology is described in Pan et al. (2025) <doi:10.1093/bib/bbaf460>.
Author: Huer Gao [aut, cre, cph], Lulu Pan [aut, cph], Yongfu Yu [ctb, cph], Guoyou Qin [ctb, cph]
Maintainer: Huer Gao <26111020050@m.fudan.edu.cn>

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Package rjd3xjars updated to version 0.1.2 with previous version 0.1.1 dated 2026-07-08

Title: Interface to 'JDemetra+ 3.x' Time Series Analysis Software
Description: R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It provides functions allowing to decompose a time series, including high-frequency data with multiple periodicities.
Author: Jean Palate [aut], Tanguy Barthelemy [aut, cre, cph]
Maintainer: Tanguy Barthelemy <timeserieswithjdemetraandr@gmail.com>

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Package admiralneuro updated to version 0.3.0 with previous version 0.2.0 dated 2026-02-04

Title: Neuroscience Extension Package for ADaM in 'R' Asset Library
Description: Programming neuroscience specific Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>). This package extends the 'admiral' package.
Author: Jian Wang [aut] , Meilin Jiang [aut, cre] , Miles Almond [aut] , Xiao Chen [aut] , Fanny Gautier [aut] , Gayatri G. [aut], Leena Khatri [aut] , Edoardo Mancini [aut] , Eric Nantz [aut], Lina Patil [aut], Chris Pelentrides [aut], Katie Withycombe [aut [...truncated...]
Maintainer: Meilin Jiang <meilin.jiang@lilly.com>

Diff between admiralneuro versions 0.2.0 dated 2026-02-04 and 0.3.0 dated 2026-08-20

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New package ivdtools with initial version 0.1.2
Package: ivdtools
Title: Statistical Tools for Evaluation of in Vitro Diagnostic Reagents
Version: 0.1.2
Description: Provides statistical workflows used in the evaluation of in vitro diagnostic reagents. Facilities include method comparison and Bland-Altman analysis, receiver operating characteristic analysis, qualitative agreement, precision and variance-component analysis, reference intervals, stability studies, quality-control charts, curve fitting, analytical sensitivity, outlier and normality assessment, and sample-size calculations. For methodological details, see Bland and Altman (1986) <doi:10.1016/S0140-6736(86)90837-8>, Passing and Bablok (1983) <doi:10.1515/cclm.1983.21.11.709>, Linnet (1993) <doi:10.1093/clinchem/39.3.424>, Hanley and McNeil (1982) <doi:10.1148/radiology.143.1.7063747>, Horn et al. (1998) <doi:10.1093/clinchem/44.3.622>, Westgard et al. (1981) <doi:10.1093/clinchem/27.3.493>, and Lu et al. (2016) <doi:10.1515/ijb-2015-0039>.
License: MIT + file LICENSE
URL: https://github.com/hiox-tech/ivdtools
BugReports: https://github.com/hiox-tech/ivdtools/issues
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: ggplot2, ggrepel, minpack.lm, nloptr, nls2, nortest, rlang, stats, utils, VCA, VFP
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-08-19 14:34:51 UTC; zhangkai
Author: hiox-tech [cph, aut, cre]
Maintainer: hiox-tech <GeorgeBinDragon@outlook.com>
Repository: CRAN
Date/Publication: 2026-08-20 17:00:02 UTC

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New package dgraphs with initial version 0.1.0
Package: dgraphs
Title: Data-Derived Graph Construction Utilities
Version: 0.1.0
Description: Constructs data-derived graphs from numerical observations using mutual, shared-neighbor, intersection, geodesic, radius, adaptive-radius, and minimum-spanning-tree completion methods. Provides graph conversion, pruning, diagnostics, spectral embedding, endpoint detection, and path utilities. The implemented graph constructions include methods described by Jarvis and Patrick (1973) <doi:10.1109/T-C.1973.223640>, Brito et al. (1997) <doi:10.1016/S0167-7152(96)00213-1>, Berry and Sauer (2019) <doi:10.3934/fods.2019001>, and Gower and Ross (1969) <doi:10.2307/2346439>.
License: MIT + file LICENSE
Copyright: file inst/COPYRIGHTS
URL: https://github.com/pgajer/dgraphs
BugReports: https://github.com/pgajer/dgraphs/issues
Encoding: UTF-8
Language: en-US
SystemRequirements: C++17
Depends: R (>= 4.1.0)
Imports: FNN, igraph (>= 2.2.0), parallel
LinkingTo: Rcpp, RcppEigen
Suggests: Matrix, testthat (>= 3.0.0)
NeedsCompilation: yes
Packaged: 2026-08-06 07:57:36 UTC; pgajer
Author: Peter Gajer [aut, cre], Sunil Arya [ctb] , David M. Mount [ctb] , University of Maryland [cph] , Yixuan Qiu [ctb, cph] , Anna Araslanova [ctb, cph] , Gael Guennebaud [ctb, cph] , Jitse Niesen [ctb, cph] , Netherlands eScience Center [ctb, cph]
Maintainer: Peter Gajer <pgajer@gmail.com>
Repository: CRAN
Date/Publication: 2026-08-20 17:00:11 UTC

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New package underdisp with initial version 0.1.0
Package: underdisp
Title: Diagnostics and Models for Underdispersed Count Data
Version: 0.1.0
Description: Tools for detecting and modeling underdispersion in count data (conditional variance below the conditional mean), a phenomenon overlooked by the Poisson and negative binomial defaults. Provides a screening diagnostic that benchmarks at-risk dispersion against a zero-truncated Poisson; the continuous parameter binomial (CPB) regression and its zero-truncated variant, with an interpretable observation-specific bound and high-dimensional fixed-effects support; validated bootstrap (for coefficients) and profile-likelihood (for the dispersion parameter) inference; and quantities of interest including predicted probabilities and the implied ceiling. The likelihood is implemented in C++ for speed.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 4.0)
Imports: Rcpp, stats, MASS, VGAM, graphics, methods, numDeriv
LinkingTo: Rcpp
Suggests: sandwich, pscl, DHARMa, testthat (>= 3.0.0), knitr, rmarkdown, broom, modelsummary, texreg
VignetteBuilder: knitr
URL: https://github.com/bagozzib/underdisp
BugReports: https://github.com/bagozzib/underdisp/issues
NeedsCompilation: yes
Packaged: 2026-08-19 12:44:18 UTC; bagoz
Author: Benjamin E. Bagozzi [aut, cre]
Maintainer: Benjamin E. Bagozzi <bagozzib@udel.edu>
Repository: CRAN
Date/Publication: 2026-08-20 16:40:02 UTC

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New package scanr with initial version 0.1.0
Package: scanr
Title: Sequential Change-Point Detection via Nonparametric Inference
Version: 0.1.0
Description: Detects change points in long univariate time series using the SCAN framework. The implementation uses a native Rust backend exposed to R via 'extendr'.
License: GPL-3
Language: en-US
Encoding: UTF-8
SystemRequirements: Cargo (Rust's package manager), rustc, Quarto CLI
Imports: ggplot2, jsonlite, rlang
Suggests: knitr, quarto, testthat (>= 3.0.0)
VignetteBuilder: quarto
URL: https://github.com/Prabashoka/scanr, https://prabashoka.github.io/scanr-vignette/
BugReports: https://github.com/Prabashoka/scanr/issues
NeedsCompilation: yes
Packaged: 2026-08-19 14:18:45 UTC; aseelappumud
Author: Ashoka Prabashwara [aut, cre], Patricia Menendez [aut], Liam Hodgkinson [aut], Stuart Lee [aut]
Maintainer: Ashoka Prabashwara <smashoka123@gmail.com>
Repository: CRAN
Date/Publication: 2026-08-20 16:50:02 UTC

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Package bvpSolve readmission to version 1.4.4.2 with previous version 1.4.4.1 dated 2023-09-21

Title: Solvers for Boundary Value Problems of Differential Equations
Description: Functions that solve boundary value problems ('BVP') of systems of ordinary differential equations ('ODE') and differential algebraic equations ('DAE'). The functions provide an interface to the FORTRAN functions 'twpbvpC', 'colnew/colsys', and an R-implementation of the shooting method. Mazzia, F., J.R. Cash and K. Soetaert (2014), "Solving Boundary Value Problems in the Open Source Software R: Package bvpSolve" <doi:10.7494/OpMath.2014.34.2.387>.
Author: Karline Soetaert [aut] , Jeff Cash [aut], Francesca Mazzia [aut], Uri M. Ascher [ctb] , G. Bader [ctb] , J. Christiansen [ctb] , Robert R. Russell [ctb] , George Helffrich [cre, ctb]
Maintainer: George Helffrich <ghfbsd@gmail.com>

This is a re-admission after prior archival of version 1.4.4.1 dated 2023-09-21

Diff between bvpSolve versions 1.4.4.1 dated 2023-09-21 and 1.4.4.2 dated 2026-08-20

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New package weatherMRJD with initial version 0.1.1
Package: weatherMRJD
Title: Weather Analysis and Markov Regime Switching Jump Diffusion Models
Version: 0.1.1
Description: Provides statistical tools for analyzing weather patterns, temperature anomalies, and climate risk. Implements Markov regime-switching jump diffusion (MRJD) models to capture abrupt shifts, extreme weather events, and structural breaks in environmental time series data. Estimates model parameters using maximum likelihood estimation and offers utility functions for simulating regime-dependent stochastic processes. The regime-switching methodology is based on Hamilton (1989) "Analysis of Time Series Subject to Changes in Regime" <doi:10.2307/1912559>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: stats
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-08-19 16:35:46 UTC; Dr. O. J. Obulezi
Author: Okechukwu J. Obulezi [aut, cre]
Maintainer: Okechukwu J. Obulezi <oj.obulezi@unizik.edu.ng>
Repository: CRAN
Date/Publication: 2026-08-20 15:20:02 UTC

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New package robustrcp with initial version 0.1.0
Package: robustrcp
Title: Outlier-Robust Ratio-cum-Product Estimators of Finite Population Mean
Version: 0.1.0
Description: Implements robust ratio-cum-product estimators using auxiliary medians for estimating the population mean under simple random sampling without replacement (SRSWOR). Provides analytical optimal tuning parameters, bias, Mean Squared Error (MSE), and Percent Relative Efficiency (PRE) evaluations.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: stats
Depends: R (>= 3.5)
NeedsCompilation: no
Packaged: 2026-08-06 20:15:01 UTC; Dr. O. J. Obulezi
Author: Okechukwu J. Obulezi [aut, cre]
Maintainer: Okechukwu J. Obulezi <oj.obulezi@unizik.edu.ng>
Repository: CRAN
Date/Publication: 2026-08-20 15:30:02 UTC

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New package MLMES with initial version 0.1.2
Package: MLMES
Title: Model-Based Effect Sizes for Multilevel Models
Version: 0.1.2
Description: Computes model-based effect sizes for fixed-effect coefficients in multilevel (hierarchical) models. The coefficient effect sizes are standardized mean differences from zero (d) and unique variance-explained measures (squared semi-partial correlations, sr2). The package also reports variance components and level-specific and total R-squared values. It supports 2-level and 3-level linear and binary logistic models fitted with 'lme4' (Bates et al., 2015) <doi:10.18637/jss.v067.i01>, and 2-level Gaussian and Bernoulli models fitted with 'brms' (Bürkner, 2017) <doi:10.18637/jss.v080.i01>. Sanders, Konold, and Cheng (in press), "Model-based effect sizes for multilevel linear regression coefficients," Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, describe the 2-level linear-model methods.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: lme4 (>= 1.1.34), Matrix (>= 1.6-5), dplyr, stats
Suggests: brms, bayestestR, lmerTest (>= 3.1-0), testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2026-08-06 14:14:22 UTC; zigzag
Author: Yijun Cheng [aut, cre], Elizabeth A. Sanders [aut], Timothy R. Konold [aut], Zhigang Zhang [aut], Zixie Zheng [aut]
Maintainer: Yijun Cheng <chengxb@uw.edu>
Repository: CRAN
Date/Publication: 2026-08-20 15:52:04 UTC

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New package caverify with initial version 0.1.2
Package: caverify
Title: Fast Strength-t Coverage Verification for Covering Arrays
Version: 0.1.2
Description: Verifies that an array covers every t-way interaction, the certificate check for covering arrays, using compiled C code with optional 'OpenMP' threading. Missing values are treated as wildcard ("flexible") entries that count as every symbol. Designed to be easy to embed in other packages: a single C file with a registered .Call entry point and one R wrapper.
License: MIT + file LICENSE
URL: https://github.com/chrispbsmolen/ca-tools
BugReports: https://github.com/chrispbsmolen/ca-tools/issues
Encoding: UTF-8
Language: en-US
Imports: parallel
NeedsCompilation: yes
Packaged: 2026-08-19 16:02:20 UTC; root
Author: Christopher Smolen [aut, cre]
Maintainer: Christopher Smolen <chrispbsmolen@gmail.com>
Repository: CRAN
Date/Publication: 2026-08-20 15:02:05 UTC

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Package xegaDfGene updated to version 1.0.0.10 with previous version 1.0.0.9 dated 2026-02-16

Title: Gene Operations for Real-Coded Genes
Description: Representation-dependent gene-level operations for genetic and evolutionary algorithms with real-coded genes used in the R-package 'xega' <https://CRAN.R-project.org/package=xega> are collected in this package. The common feature of the gene operations is that all of them are useful for derivation-free optimization algorithms. At the moment the package implements initialization, mutation, crossover, and replication operations for differential evolution as described in Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) <doi:10.1007/3-540-31306-0>. In addition, several (more recent) methods for determining the scale factor are provided. For 'xega''s architecture, see Geyer-Schulz, A. (2025) <doi:10.5445/IR/1000187255>.
Author: Andreas Geyer-Schulz [aut, cre]
Maintainer: Andreas Geyer-Schulz <Andreas.Geyer-Schulz@kit.edu>

Diff between xegaDfGene versions 1.0.0.9 dated 2026-02-16 and 1.0.0.10 dated 2026-08-20

 DESCRIPTION         |    6 +++---
 MD5                 |    6 +++---
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 4 files changed, 11 insertions(+), 7 deletions(-)

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Package vecvec updated to version 1.3.0 with previous version 1.2.0 dated 2026-06-23

Title: Construct Mixed Type Data Structures with Vectors of Vectors
Description: Mixed type vectors are useful for combining semantically similar classes. Some examples of semantically related classes include time across different granularities (e.g. daily, monthly, annual) and probability distributions (e.g. Normal, Uniform, Poisson). These groups of vector types typically share common statistical operations which vary in results with the attributes of each vector. The 'vecvec' data structure facilitates efficient storage and computation across multiple vectors within the same object.
Author: Mitchell O'Hara-Wild [aut, cre]
Maintainer: Mitchell O'Hara-Wild <mail@mitchelloharawild.com>

Diff between vecvec versions 1.2.0 dated 2026-06-23 and 1.3.0 dated 2026-08-20

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 tests/testthat/test-replacement.R |   44 ++++++++++++
 tests/testthat/test-utils.R       |only
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 30 files changed, 1095 insertions(+), 154 deletions(-)

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New package themescopeR with initial version 0.1.1
Package: themescopeR
Title: Social Representation Analysis via Semantic Network Mapping
Version: 0.1.1
Description: Implements the ThemeScope framework for detecting and visualising social representations in large-scale digital text corpora. From raw documents it builds, via 'udpipe' annotation, sentence-level word co-occurrence networks and derives two community-level indicators grounded in Social Representation Theory: the Prototypical Salience Index (PSI) for anchoring and the Concreteness Score (CS) for objectification. Communities are located in a two-dimensional, theoretically grounded representational map. The whole pipeline is usable from the R console; an optional 'shiny' graphical interface calls the same exported functions. The method is described in Misuraca, Spano and D'Aniello (2026) <doi:10.1177/01655515261454276>.
License: MIT + file LICENSE
URL: https://github.com/lucadaniello/themescopeR
BugReports: https://github.com/lucadaniello/themescopeR/issues
Encoding: UTF-8
Language: en-GB
LazyData: true
Depends: R (>= 4.1.0)
Imports: cli (>= 3.6.0), dplyr (>= 1.1.0), ggplot2 (>= 3.4.0), ggrepel (>= 0.9.0), igraph (>= 2.1.0), Matrix (>= 1.6.0), methods, readr, readxl, rlang, stats, tools, udpipe (>= 0.8.11), utils
Suggests: bslib, DT, ggraph (>= 2.1.0), htmltools, knitr, plotly, rmarkdown, shiny, shinycssloaders, testthat (>= 3.0.0), visNetwork, writexl
NeedsCompilation: no
Packaged: 2026-08-19 10:10:22 UTC; lucadaniello
Author: Luca D'Aniello [aut, cre] , Michelangelo Misuraca [aut] , Maria Spano [aut]
Maintainer: Luca D'Aniello <luca.daniello@unina.it>
Repository: CRAN
Date/Publication: 2026-08-20 14:10:02 UTC

More information about themescopeR at CRAN
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New package scmix with initial version 0.1.1
Package: scmix
Title: Bayesian Model-Based Clustering with Sparse Conditional Mixture Models
Version: 0.1.1
Description: Fits Bayesian sparse conditional (Gaussian) mixture models for model-based clustering. Each mixture component factorizes into a chain of univariate polynomial regressions with per-component, per-equation Bayesian variable selection under a centered Zellner g-prior; the number of clusters is selected within a single run via an overfitted sparse mixture (Dirichlet concentration 1/K). The blocked Gibbs sampler draws the selection sets exactly by enumeration (or by validated single-flip Metropolis-Hastings in higher dimension), is provably well-posed under a documented proper fallback prior, and reports a label-invariant consensus partition (Dahl's least-squares criterion). Companion package to Dong, Liao, and Lee (2026), "Replacing three nested searches with one sweep: a Bayesian treatment of sparse conditional mixture clustering". Multiple-imputation functionality for the same engine is also exposed.
License: GPL (>= 3)
Encoding: UTF-8
Depends: R (>= 4.1)
Imports: graphics, stats
Suggests: knitr, mclust, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-08-15 17:13:34 UTC; july
Author: Aqi Dong [aut, cre], Yang-Li Liao [aut], Danhyang Lee [aut]
Maintainer: Aqi Dong <donga2@erau.edu>
Repository: CRAN
Date/Publication: 2026-08-20 14:30:02 UTC

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New package RtForecastR with initial version 0.1.0
Package: RtForecastR
Title: Real-Time Effective Reproduction Number Estimation and Forecasting
Version: 0.1.0
Description: Filtered (real-time/causal) and smoothed (retrospective) estimation of the time-varying effective reproduction number (Rt) from case-count time series, using the EpiFilter algorithm of Parag (2021) <doi:10.1371/journal.pcbi.1009347>, together with a one-step-ahead in-sample prediction check, a genuine out-of-sample one-step forecast with predictive intervals, elimination probability P(Rt < 1), and forecast calibration metrics (mean absolute error, mean squared error, root mean squared error, empirical coverage, and the weighted interval score of Bracher et al. (2021) <doi:10.1371/journal.pcbi.1008618>). Disease-agnostic: works for any pathogen given a known generation interval.
Language: en-US
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5)
Imports: graphics, grDevices, stats, utils
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
URL: https://github.com/rajsubediresearch/RtForecastR
BugReports: https://github.com/rajsubediresearch/RtForecastR/issues
NeedsCompilation: no
Packaged: 2026-08-19 12:33:48 UTC; razsu
Author: Raj Subedi [aut, cre, cph] ; author of R/recursPredict.R's configurable-grid maxI extension and R/recursPredictQuantiles.R), Kris V. Parag [ctb, cph] ; files R/epiFilter.R, R/epiSmoother.R and R/recursPredict.R are unmodified or lightly modified port [...truncated...]
Maintainer: Raj Subedi <rajsubediresearch@gmail.com>
Repository: CRAN
Date/Publication: 2026-08-20 14:12:38 UTC

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New package ofemeantest with initial version 1.0.0
Package: ofemeantest
Title: On Farm Experimentation Mean Test
Version: 1.0.0
Description: Provides tools for comparing treatments in unreplicated on-farm experiments (OFE) using georeferenced data. The package includes functions for constructing spatial grids, estimating the effective sample size from spatial autocorrelation, performing repeated permutation tests, adjusting pairwise comparisons, and visualizing the results. It implements the OFE-mean test described in Córdoba, Paccioretti and Balzarini (2025) <doi:10.1007/s11119-024-10206-0>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: multcompView, permuco, sf, spatialreg, spdep, withr
Suggests: ggplot2, knitr, rmarkdown, testthat (>= 3.0.0)
Depends: R (>= 4.1.0)
VignetteBuilder: knitr
URL: https://ppaccioretti.github.io/ofemeantest/, https://github.com/PPaccioretti/ofemeantest
BugReports: https://github.com/PPaccioretti/ofemeantest/issues
NeedsCompilation: no
Packaged: 2026-08-19 12:43:09 UTC; ariel
Author: Mariano Cordoba [aut, cph], Pablo Paccioretti [aut, cre, cph], Monica Balzarini [aut]
Maintainer: Pablo Paccioretti <pablopaccioretti@agro.unc.edu.ar>
Repository: CRAN
Date/Publication: 2026-08-20 14:20:14 UTC

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New package nwaa with initial version 0.1.4
Package: nwaa
Title: USGS NWAA Data Download
Version: 0.1.4
Description: Download and parse model outputs from the U.S. Geological Survey (USGS) National Water Availability Assessment (NWAA) Data Companion web services. Provides typed, validated wrappers for the published NWAA models across the Water Use, Water Quantity, and Integrated Water Availability families, returning results at HUC12 spatial resolution.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-US
Depends: R (>= 4.1.0)
Imports: httr2, jsonlite, readr, rlang, tibble
Suggests: httptest2, knitr, pkgdown, rmarkdown, sf, testthat (>= 3.0.0)
VignetteBuilder: knitr
URL: https://laljeet.github.io/nwaa/, https://github.com/laljeet/nwaa
BugReports: https://github.com/laljeet/nwaa/issues
NeedsCompilation: no
Packaged: 2026-08-19 16:10:13 UTC; laljeetsangha
Author: Laljeet Sangha [aut, cre]
Maintainer: Laljeet Sangha <lsanghah2o@gmail.com>
Repository: CRAN
Date/Publication: 2026-08-20 14:40:02 UTC

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New package mditools with initial version 0.1.0
Package: mditools
Title: Microdata Infrastructure Tools for Firm-Level Microdata Research
Version: 0.1.0
License: GPL-3
URL: https://github.com/Secretariat-CompNet/mditools
BugReports: https://github.com/Secretariat-CompNet/mditools/issues
Description: Supports the full analysis pipeline for researchers working with firm-level microdata. Provides data tools for panel preparation (import, outlier detection, classification harmonization), analytical methods (production function estimation, capital stock measurement, markups, intensity measures, distributions, regression, clustering), and disclosure tools for tagging outputs with dominance and observation counts before aggregation and publication. Production function estimation implements methods by Ackerberg, Caves and Frazer (2015) <doi:10.3982/ECTA13408>, Levinsohn and Petrin (2003) <doi:10.1111/1467-937X.00246>, Wooldridge (2009) <doi:10.1016/j.econlet.2009.04.026>, Petrin, Poi and Levinsohn (2004) <doi:10.1177/1536867X0400400202>, and Arellano and Bond (1991) <doi:10.2307/2297968> with the "too many instruments" correction by Roodman (2009) <doi:10.1111/j.1468-0084.2008.00542.x>. Markup estimation follows De Loecker and Warzynski (2012) <doi:1 [...truncated...]
Encoding: UTF-8
Imports: data.table, fixest, haven, readxl, Matrix, cluster, dbscan, mclust, stats, utils, graphics, grDevices
Suggests: testthat (>= 3.0.0), arrow
NeedsCompilation: no
Packaged: 2026-08-19 16:23:58 UTC; johanna
Author: Daniele Aglio [aut], Eric Bartelsman [aut], Mirja Haelbig [aut], Marco Miorandi [aut], Johanna Weiss [aut, cre], Alessandro Zona Mattioli [aut], Julian Diaz-Acosta [ctb], Alberto Ferreira [ctb], Javier Miranda [ctb], Marcelo Piemonte Ribeiro [ctb], Re [...truncated...]
Maintainer: Johanna Weiss <johanna.weiss@iwhesl.onmicrosoft.com>
Repository: CRAN
Date/Publication: 2026-08-20 14:50:02 UTC

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New package lineager with initial version 0.1.1
Package: lineager
Title: Row-Level Data Provenance and Exclusion Tracking
Version: 0.1.1
Description: Provides row-level data provenance tracking for analytical pipelines. Tags datasets with unique lineage identifiers that persist through filter, join, and derive operations. Requires documented reasons for every row exclusion, capturing who was removed, why, and at which pipeline stage. Variable derivations are registered as structured specifications linking output variables back to their source. Any row in any downstream dataset can be traced back to its origin via lg_trace(). Generates structured HTML provenance reports suitable for regulatory submissions, internal audit, or analytical documentation. General-purpose: works for clinical data, machine learning pipelines, financial modelling, epidemiology, or any workflow where row-level accountability matters. Optional features support pharmaceutical users including population flag definitions, source-to-analysis variable mapping, and Reviewer's Guide-aligned report output. Complements the 'regulog' package for tamper-evident session-l [...truncated...]
License: MIT + file LICENSE
URL: https://reprostats.org, https://github.com/repro-stats/lineager
BugReports: https://github.com/repro-stats/lineager/issues
Imports: dplyr (>= 1.1.0), magrittr (>= 2.0.3)
Suggests: admiral, DiagrammeR, ggplot2, haven, knitr, mockery, rmarkdown, testthat (>= 3.0.0), tibble
Encoding: UTF-8
Language: en-GB
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-08-19 12:35:38 UTC; ndohpenn
Author: Ndoh Penn [aut, cre]
Maintainer: Ndoh Penn <ndohpenn9@gmail.com>
Repository: CRAN
Date/Publication: 2026-08-20 14:20:20 UTC

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Package labelled updated to version 2.16.1 with previous version 2.16.0 dated 2025-10-22

Title: Manipulating Labelled Data
Description: Work with labelled data imported from 'SPSS' or 'Stata' with 'haven' or 'foreign'. This package provides useful functions to deal with "haven_labelled" and "haven_labelled_spss" classes introduced by 'haven' package.
Author: Joseph Larmarange [aut, cre] , Daniel Ludecke [ctb], Hadley Wickham [ctb], Michal Bojanowski [ctb], Francois Briatte [ctb]
Maintainer: Joseph Larmarange <joseph@larmarange.net>

Diff between labelled versions 2.16.0 dated 2025-10-22 and 2.16.1 dated 2026-08-20

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 24 files changed, 497 insertions(+), 342 deletions(-)

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New package kpp2019 with initial version 0.0.1
Package: kpp2019
Title: Kenya Population Projections 2019
Version: 0.0.1
Description: Provides population projection data for Kenya and its 47 counties from 2020 to 2045, derived from the 2019 Kenya Population and Housing Census and subsequent projections published by the Kenya National Bureau of Statistics (KNBS). Includes annual and five-year age and sex distributions, along with components of population change such as births, deaths, and migration.
License: CC BY 4.0
URL: https://kpp2019.damurka.com, https://github.com/damurka/kpp2019
BugReports: https://github.com/damurka/kpp2019/issues
Suggests: dplyr, ggplot2, knitr, purrr, readxl, rmarkdown, spelling, stringr, tidyr, testthat (>= 3.0.0)
Depends: R (>= 2.10)
Encoding: UTF-8
Language: en-GB
LazyData: true
NeedsCompilation: no
Packaged: 2026-08-19 12:07:26 UTC; Murage
Author: David Kariuki [aut, cre, cph]
Maintainer: David Kariuki <hello@damurka.com>
Repository: CRAN
Date/Publication: 2026-08-20 14:10:09 UTC

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Package guideR updated to version 0.12.0 with previous version 0.11.0 dated 2026-08-03

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

Diff between guideR versions 0.11.0 dated 2026-08-03 and 0.12.0 dated 2026-08-20

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New package hydrogeofetch with initial version 2.0.0
Package: hydrogeofetch
Title: Hydrologic Geospatial Fabric Extraction Tool Chain
Version: 2.0.0
Description: Traverses and works with National Hydrography Dataset Plus (NHDPlus) data. All methods implemented in 'hydrogeofetch' are available in the NHDPlus documentation available from the US Environmental Protection Agency <https://www.epa.gov/waterdata/basic-information>. Previously published as 'nhdplusTools'.
URL: https://doi-usgs.github.io/nhdplusTools/ https://github.com/doi-usgs/nhdplusTools/
BugReports: https://github.com/doi-usgs/nhdplusTools/issues/
Depends: R (>= 4.1)
Imports: hydroloom, dataRetrieval, dplyr, sf, units, jsonlite, httr2, xml2, utils, data.table, methods, arrow, tools, zip, memoise, digest
Suggests: testthat, knitr, rmarkdown, ggmap, ggplot2, lwgeom, gifski, leaflet, httptest2, StreamCatTools, terra, maptiles, mapsf
License: CC0
Encoding: UTF-8
VignetteBuilder: knitr
LazyData: true
LazyDataCompression: xz
Language: en-US
NeedsCompilation: no
Packaged: 2026-08-17 13:07:00 UTC; root
Author: David Blodgett [aut, cre] , Mike Johnson [ctb] , Marc Weber [ctb] , Josh Erickson [ctb], Lauren Koenig [ctb]
Maintainer: David Blodgett <dblodgett@usgs.gov>
Repository: CRAN
Date/Publication: 2026-08-20 14:40:08 UTC

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New package grout with initial version 0.1.0
Package: grout
Title: Abstract Raster Tiling Schemes
Version: 0.1.0
Description: Impose a tiling scheme on a raster grid defined by its dimension and extent. Computes tile counts, pixel offsets (suitable for spatial library windowed reads), and geographic extents for each tile. Also supports standard Web Mercator and geodetic tiling profiles with zoom levels, and provides a spatial map of what each chunk covers for interrogating virtual raster stores. Based on the analysis of Lamb (1994, ISBN-13: 978-0748403158) "Tiling very large rasters" in 'Advances in GIS Research: Proceedings of the Sixth International Symposium on Spatial Data Handling', volume 1, pages 449-461.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: graphics, tibble, utils, vaster (>= 0.6.0)
Suggests: testthat (>= 3.0.0), wk
URL: https://github.com/hypertidy/grout
BugReports: https://github.com/hypertidy/grout/issues
NeedsCompilation: no
Packaged: 2026-08-19 12:57:18 UTC; mdsumner
Author: Michael D. Sumner [aut, cre, cph]
Maintainer: Michael D. Sumner <mdsumner@gmail.com>
Repository: CRAN
Date/Publication: 2026-08-20 14:30:13 UTC

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Package ggquiver updated to version 0.5.0 with previous version 0.4.0 dated 2025-12-18

Title: Quiver Plots for 'ggplot2'
Description: An extension of 'ggplot2' to provide quiver plots to visualise vector fields. This functionality is implemented using a geom to produce a new graphical layer, which allows aesthetic options. This layer can be overlaid on a map to improve visualisation of mapped data.
Author: Mitchell O'Hara-Wild [aut, cre]
Maintainer: Mitchell O'Hara-Wild <mail@mitchelloharawild.com>

Diff between ggquiver versions 0.4.0 dated 2025-12-18 and 0.5.0 dated 2026-08-20

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Package fluffy updated to version 1.0.1 with previous version 1.0.0 dated 2026-06-10

Title: Schema-Based Validation of 'R' Objects with User-Defined Rules
Description: A schema-based validation framework for 'R' objects using user-defined rules. Provides three 'S7' classes 'Registry', 'Schema', and 'Validator' to manage rules, define list-based schemas, and validate data in a flexible and extensible manner.
Author: Luke Jenkins [aut, cre, cph]
Maintainer: Luke Jenkins <luke-jenkins-dev@outlook.com>

Diff between fluffy versions 1.0.0 dated 2026-06-10 and 1.0.1 dated 2026-08-20

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Package favr updated to version 2.0.0 with previous version 1.0.0 dated 2025-12-15

Title: Function Argument Validation
Description: Validate function arguments succinctly with informative error messages.
Author: Luke Jenkins [aut, cre, cph]
Maintainer: Luke Jenkins <luke-jenkins-dev@outlook.com>

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Package cheddar updated to version 0.1-640 with previous version 0.1-639 dated 2024-07-24

Title: Analysis and Visualisation of Ecological Communities
Description: Provides a flexible, extendable representation of an ecological community and a range of functions for analysis and visualisation, focusing on food web, body mass and numerical abundance data. Allows inter-web comparisons such as examining changes in community structure over environmental, temporal or spatial gradients.
Author: Lawrence Hudson [aut, cre], Dan Reuman [aut], Rob Emerson [ctb]
Maintainer: Lawrence Hudson <quicklizard@googlemail.com>

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Package broom.helpers updated to version 1.23.0 with previous version 1.22.0 dated 2025-09-17

Title: Helpers for Model Coefficients Tibbles
Description: Provides suite of functions to work with regression model 'broom::tidy()' tibbles. The suite includes functions to group regression model terms by variable, insert reference and header rows for categorical variables, add variable labels, and more.
Author: Joseph Larmarange [aut, cre] , Daniel D. Sjoberg [aut]
Maintainer: Joseph Larmarange <joseph@larmarange.net>

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Package BayesBinMix updated to version 1.4.2 with previous version 1.4.1 dated 2017-07-04

Title: Bayesian Estimation of Mixtures of Multivariate Bernoulli Distributions
Description: Fully Bayesian inference for estimating the number of clusters and related parameters to heterogeneous binary data.
Author: Panagiotis Papastamoulis [aut, cre]
Maintainer: Panagiotis Papastamoulis <papapast@yahoo.gr>

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New package alepe with initial version 0.1.0
Package: alepe
Title: Access the Open Data API of the Legislative Assembly of Pernambuco
Version: 0.1.0
Description: A tidy interface to the open data API of the Legislative Assembly of the State of Pernambuco, Brazil ('ALEPE', <https://dadosabertos.alepe.pe.gov.br>). Retrieve data on representatives, staff, positions, departments, remuneration, contracts, procurement processes, and legislative propositions as tibbles with clean names and parsed column types. Requests are cached locally and retried with exponential backoff; network failures are handled gracefully.
License: MIT + file LICENSE
URL: https://github.com/StrategicProjects/alepe, https://strategicprojects.github.io/alepe/
BugReports: https://github.com/StrategicProjects/alepe/issues
Depends: R (>= 4.1)
Imports: cli, httr2 (>= 1.0.0), readr, rlang (>= 1.1.0), tibble
Suggests: dplyr, ggplot2, httptest2, jsonlite, knitr, rmarkdown, testthat (>= 3.0.0), tidyr, withr
VignetteBuilder: knitr
Encoding: UTF-8
Language: en-US
NeedsCompilation: no
Packaged: 2026-08-19 12:53:57 UTC; leite
Author: Andre Leite [aut, cre] , Marcos Wasiliew [aut], Hugo Vasconcelos [aut] , Carlos Amorim [aut] , Diogo Bezerra [aut] , Julia Nascimento Barreto [aut]
Maintainer: Andre Leite <leite@castlab.org>
Repository: CRAN
Date/Publication: 2026-08-20 14:22:29 UTC

More information about alepe at CRAN
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Package xegaPermGene updated to version 1.0.0.2 with previous version 1.0.0.1 dated 2025-04-16

Title: Operations on Permutation Genes
Description: An implementation of representation-dependent gene level operations for genetic algorithms with genes representing permutations: Initialization of genes, mutation, and crossover. The crossover operation provided is position-based crossover (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8). For mutation, several variants are included: Order-based mutation (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8), randomized Lin-Kernighan heuristics (Croes, G. A. (1958) <doi:10.1287/opre.6.6.791> and Lin, S. and Kernighan. B. W. (1973) <doi:10.1287/opre.21.2.498>), and randomized greedy operators. A random mix operator for mutation selects a mutation variant randomly.
Author: Andreas Geyer-Schulz [aut, cre]
Maintainer: Andreas Geyer-Schulz <Andreas.Geyer-Schulz@kit.edu>

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Package xegaGaGene updated to version 1.0.0.7 with previous version 1.0.0.6 dated 2026-02-16

Title: Binary Gene Operations for Genetic Algorithms
Description: Representation-dependent gene level operations of a genetic algorithm with binary coded genes for the R-package 'xega' <https://CRAN.R-project.org/package=xega>: Initialization of random binary genes, several gene maps for binary genes, several mutation operators, several crossover operators with 1 and 2 kids, replication pipelines for 1 and 2 kids, and, last but not least, function factories for configuration. See Goldberg, D. E. (1989, ISBN:0-201-15767-5). For crossover operators, see Syswerda, G. (1989, ISBN:1-55860-066-3), Spears, W. and De Jong, K. (1991, ISBN:1-55860-208-9). For mutation operators, see Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7). For 'xega''s architecture, see Geyer-Schulz, A. (2025) <doi:10.5445/IR/1000187255>.
Author: Andreas Geyer-Schulz [aut, cre]
Maintainer: Andreas Geyer-Schulz <Andreas.Geyer-Schulz@kit.edu>

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Package xega updated to version 0.9.1.0 with previous version 0.9.0.23 dated 2026-02-17

Title: Extended Evolutionary and Genetic Algorithms
Description: Implementation of a scalable, highly configurable, and e(x)tended architecture for (e)volutionary and (g)enetic (a)lgorithms. Multiple representations (binary, real-coded, permutation, and derivation-tree), a rich collection of genetic operators, as well as an extended processing pipeline are provided for genetic algorithms (Goldberg, D. E. (1989, ISBN:0-201-15767-5)), differential evolution (Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) <doi:10.1007/3-540-31306-0>), simulated annealing (Aarts, E., and Korst, J. (1989, ISBN:0-471-92146-7)), grammar-based genetic programming (Geyer-Schulz (1997, ISBN:978-3-7908-0830-X)), grammatical evolution (Ryan, C., O'Neill, M., and Collins, J. J. (2018) <doi:10.1007/978-3-319-78717-6>), and grammatical differential evolution (O'Neill, M. and Brabazon, A. (2006) in Arabinia, H. (2006, ISBN:978-193-241596-3). All algorithms reuse basic adaptive mechanisms for performance optimization. For the architecture, see Geyer-Sc [...truncated...]
Author: Andreas Geyer-Schulz [aut, cre]
Maintainer: Andreas Geyer-Schulz <Andreas.Geyer-Schulz@kit.edu>

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Package TKCat updated to version 1.2.3 with previous version 1.2.2 dated 2026-06-29

Title: Tailored Knowledge Catalog
Description: Facilitate the management of data from knowledge resources that are frequently used alone or together in research environments. In 'TKCat', knowledge resources are manipulated as modeled database (MDB) objects. These objects provide access to the data tables along with a general description of the resource and a detailed data model documenting the tables, their fields and their relationships. These MDBs are then gathered in catalogs that can be easily explored and shared. Finally, 'TKCat' provides tools to easily subset, filter and combine MDBs and create new catalogs suited for specific needs.
Author: Patrice Godard [aut, cre, cph]
Maintainer: Patrice Godard <patrice.godard@gmail.com>

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Package tidySEM updated to version 0.2.11 with previous version 0.2.10 dated 2026-01-11

Title: Tidy Structural Equation Modeling
Description: A tidy workflow for generating, estimating, reporting, and plotting structural equation models using 'lavaan', 'OpenMx', or 'Mplus'. Throughout this workflow, elements of syntax, results, and graphs are represented as 'tidy' data, making them easy to customize. Includes functionality to estimate latent class analyses, and to plot 'dagitty' and 'igraph' objects.
Author: Caspar J. van Lissa [aut, cre] , Mauricio Garnier-Villarreal [ctb] , Frank C Gootjes [ctb]
Maintainer: Caspar J. van Lissa <c.j.vanlissa@tilburguniversity.edu>

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Package robust2sls updated to version 0.2.4 with previous version 0.2.3 dated 2025-05-20

Title: Outlier Robust Two-Stage Least Squares Inference and Testing
Description: An implementation of easy tools for outlier robust inference in two-stage least squares (2SLS) models. The user specifies a reference distribution against which observations are classified as outliers or not. After removing the outliers, adjusted standard errors are automatically provided. Furthermore, several statistical tests for the false outlier detection rate can be calculated. The outlier removing algorithm can be iterated a fixed number of times or until the procedure converges. The algorithms and robust inference are described in more detail in Jiao (2019) <https://drive.google.com/file/d/1qPxDJnLlzLqdk94X9wwVASptf1MPpI2w/view>.
Author: Jonas Kurle [aut, cre]
Maintainer: Jonas Kurle <mail@jonaskurle.com>

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Package rebmix updated to version 2.17.2 with previous version 2.17.1 dated 2025-11-10

Title: Finite Mixture Modeling, Clustering & Classification
Description: Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or circular von Mises parametric families.
Author: Marko Nagode [aut, cre] , Branislav Panic [ctb] , Jernej Klemenc [ctb] , Simon Oman [ctb]
Maintainer: Marko Nagode <marko.nagode@fs.uni-lj.si>

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Package openxlsx2 updated to version 1.29 with previous version 1.28 dated 2026-07-02

Title: Read, Write and Edit 'xlsx' Files
Description: Simplifies the creation of 'xlsx' files by providing a high level interface to writing, styling and editing worksheets.
Author: Jordan Mark Barbone [aut] , Jan Marvin Garbuszus [aut, cre], Olivier Roy [ctb], openxlsx authors [cph] , Arseny Kapoulkine [ctb, cph]
Maintainer: Jan Marvin Garbuszus <jan.garbuszus@ruhr-uni-bochum.de>

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Package nuggets updated to version 2.2.3 with previous version 2.2.2 dated 2026-07-22

Title: Fast and Extensible Pattern Discovery in Tabular Data
Description: Fast and extensible framework for discovering interesting patterns in tabular data. The package searches combinations of fuzzy or Boolean predicates and evaluates the resulting subgroups using statistical, logical, or structural measures. It supports a broad range of pattern-discovery tasks, including association rules (Agrawal et al., 1994, <https://www.vldb.org/conf/1994/P487.PDF>), contrast patterns (Chen, 2022, <doi:10.48550/arXiv.2209.13556>), emerging patterns (Dong et al., 1999, <doi:10.1145/312129.312191>), subgroup discovery (Atzmueller, 2015, <doi:10.1002/widm.1144>), and conditional correlations (Hájek, 1978, <doi:10.1007/978-3-642-66943-9>). User-defined functions may be supplied to guide custom pattern searches, making the framework applicable beyond traditional association-rule mining. Efficient implementation enables pattern discovery on large and dense data sets. Package includes methods for visualization and supports interactive explo [...truncated...]
Author: Michal Burda [aut, cre]
Maintainer: Michal Burda <michal.burda@osu.cz>

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Package fracreg updated to version 1.1.0 with previous version 1.0.1 dated 2026-08-05

Title: Fractional Response Regressions
Description: Provides routines for the estimation and specification analysis of fractional response models. Includes univariate one-part, two-part, and double-inflated three-part fractional models. Further incorporates estimators for panel data settings and addresses unobserved heterogeneity and endogeneity via correlated random effects and control function approaches. Extends fractional methodology to multivariate data via fractional multinomial logit models and handles high-dimensional multicollinear data via fractional ridge regression. Calculates analytical partial effects across all model types and includes generalised goodness-of-functional-form (GGOFF) and Regression Equation Specification Error Test (RESET) hypothesis tests. Methods are described in Papke and Wooldridge (1996) <doi:10.1002/(SICI)1099-1255(199611)11:6%3C619::AID-JAE418%3E3.0.CO;2-1>, Papke and Wooldridge (2008) <doi:10.1016/j.jeconom.2008.05.009>, Buis (2008) <http://maartenbuis.nl/software/likelihoodFmlogit.p [...truncated...]
Author: Sulman Olieko Owili [aut, cre]
Maintainer: Sulman Olieko Owili <oliekosulman@gmail.com>

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Package ambiR updated to version 0.2.0 with previous version 0.1.1 dated 2025-12-19

Title: Calculate AZTI’s Marine Biotic Index
Description: Calculate AZTI’s Marine Biotic Index - AMBI. The included list of benthic fauna species according to their sensitivity to pollution. Matching species in sample data to the list allows the calculation of fractions of individuals in the different sensitivity categories and thereafter the AMBI index. The Shannon Diversity Index H' and the Danish benthic fauna quality index DKI (Dansk Kvalitetsindeks) can also be calculated, as well as the multivariate M-AMBI index. Borja, A., Franco, J. ,Pérez, V. (2000) "A marine biotic index to establish the ecological quality of soft bottom benthos within European estuarine and coastal environments" <doi:10.1016/S0025-326X(00)00061-8>.
Author: Ciaran J. Murray [aut, cre, cph] , Angel Borja [aut] , Sarai Pouso [aut] , Inigo Muxika [aut] , Joxe Mikel Garmendia [aut] , Steen Knudsen [ctb] , GES4SEAS [fnd]
Maintainer: Ciaran J. Murray <cjm@niva-dk.dk>

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Package SSLfmm updated to version 0.2.0 with previous version 0.1.0 dated 2025-12-09

Title: Semi-Supervised Learning with Mixed Missingness in Finite Mixture Models
Description: Semi-supervised Gaussian finite mixture models for partially labelled data under complete-case, missing completely at random (MCAR), entropy-dependent missing at random (MAR), and mixed MCAR/MAR label-missingness formulations. For the mixed formulation, the source of a missing label may be observed or latent. The package supports equal and component-specific covariance matrices, model fitting, simulation, initialization, prediction, classification performance assessment, and entropy-based diagnostics. A semi-synthetic Blood Transfusion data set is included to illustrate the applied workflow.
Author: Geoffrey J. McLachlan [aut] , Jinran Wu [aut, cre]
Maintainer: Jinran Wu <jinran.wu@uq.edu.au>

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Package FESta updated to version 1.0.2 with previous version 1.0.1 dated 2026-08-03

Title: Fishing Effort Standardization
Description: Marine fisheries governance and management rely heavily on reliable indicators of stock abundance and fishing pressure to ensure the sustainable utilization of marine resources. Catch Per Unit Effort (CPUE) is widely used as an index of relative abundance, but direct comparison of catch rates is often affected by differences in fishing effort, vessel characteristics, gear efficiency, and operational practices. The FESta package provides methods for fishing effort and CPUE standardization, including vessel-based, gear-based, relative effort, derived effort, generalized linear models, generalized additive models, generalized linear mixed models, ordered quantile transformation models, and multi-gear standardization techniques for fisheries stock assessment and monitoring. To cite our package run this command, citation("FESta").
Author: Eldho Varghese [aut, cre], Jayasankar J [aut], Ashutosh Dalal [aut, ctb], Sathianandan T V [aut], Sreepriya V [aut, ctb], Reshma Gills [ctb], Grinson George [ctb]
Maintainer: Eldho Varghese <eldhoiasri@gmail.com>

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Package BayesRTMB updated to version 0.3.0 with previous version 0.2.4 dated 2026-07-24

Title: Bayesian Inference Using 'RTMB'
Description: Provides tools for Markov chain Monte Carlo (MCMC) and Maximum A Posteriori (MAP) estimation utilizing the 'RTMB' package. It supports various statistical models including generalized linear mixed models, factor analysis, item response theory, and multidimensional unfolding. The package allows users to easily transition between frequentist and Bayesian paradigms using a unified interface. Automatic differentiation and Laplace approximation follow Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>, and MCMC sampling uses the No-U-Turn Sampler described by Hoffman and Gelman (2014) <https://jmlr.org/papers/v15/hoffman14a.html>.
Author: Hiroshi Shimizu [aut, cre]
Maintainer: Hiroshi Shimizu <simizu706@gmail.com>

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Package virustotal updated to version 0.7.0 with previous version 0.6.0 dated 2026-04-13

Title: R Client for the 'VirusTotal' API
Description: Provides a comprehensive R interface to the 'VirusTotal' API v3.0 <https://docs.virustotal.com/>, a Google service that analyzes files and URLs for viruses, worms, trojans and other malware. Features include file/URL scanning, domain categorization, passive DNS information, IP reputation analysis, IoC relationships, sandbox analysis, and comment/voting systems. Implements rate limiting, error handling, and response validation for robust security analysis workflows.
Author: Gaurav Sood [aut, cre]
Maintainer: Gaurav Sood <gsood07@gmail.com>

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 164 files changed, 2164 insertions(+), 1431 deletions(-)

More information about virustotal at CRAN
Permanent link

Package tuber updated to version 2.0.0 with previous version 1.4.1 dated 2026-08-02

Title: Client for the YouTube Data API
Description: Search public YouTube data and retrieve channels, videos, playlists, comments, captions, live broadcasts, and reference data. Authenticated methods support common uploads, playlist changes, comment moderation, and media updates. See the YouTube Data API documentation at <https://developers.google.com/youtube/v3/>.
Author: Gaurav Sood [aut, cre], Kate Lyons [ctb], John Muschelli [ctb]
Maintainer: Gaurav Sood <gsood07@gmail.com>

Diff between tuber versions 1.4.1 dated 2026-08-02 and 2.0.0 dated 2026-08-20

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 tuber-2.0.0/tuber/R/read_sbv.R                                  |    4 
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 tuber-2.0.0/tuber/R/tuber.R                                     |  232 ++--
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Package TCpRepDesigns updated to version 0.0.2 with previous version 0.0.1 dated 2026-06-25

Title: Partially Replicated Test-Control Designs for Early Generation Varietal Trials
Description: Provides functions for generating partially replicated (p-rep) test-control designs for early generation varietal trials conducted across multiple environments. The package implements three construction methods for obtaining p-rep test-control designs with one or more control treatments. The package extends the partially replicated design framework of Vinaykumar et al. (2026) <doi:10.1007/s12355-025-01684-1> to accommodate test-control comparisons in breeding trials. Functions are provided for generating randomized and non-randomized layouts and for displaying the design parameters and treatment allocations for each environment. The proposed designs are useful for large-scale varietal evaluation trials where a large number of test lines are assessed under limited experimental resources.
Author: Vinaykumar L.N. [aut, cre], Cini Varghese [aut, ctb], Mohd Harun [aut, ctb], Sayantani Karmakar [aut, ctb], Vinayaka [aut, ctb]
Maintainer: Vinaykumar L.N. <vinaymandya123@gmail.com>

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Package statease updated to version 1.4.0 with previous version 1.3.0 dated 2026-07-02

Title: Simplified Statistical Analysis with Plain-English Interpretation
Description: A toolkit for common statistical analyses including descriptive statistics, Student's t-tests (one-sample, independent, and paired), one-way and two-way Analysis of Variance (ANOVA), Multivariate Analysis of Variance (MANOVA), chi-square tests, Fisher's Exact Test, McNemar's Test, correlation analysis, simple and multiple linear regression, logistic regression, Friedman Test, and non-parametric tests (Mann-Whitney U, Wilcoxon Signed Rank, and Kruskal-Wallis). Each function automatically interprets results in plain English, reporting effect sizes, confidence intervals, and p-value interpretations, and prints relevant assumption checks by default. A context argument allows users to describe their study design, echoed back alongside the interpretation as a reminder to read results in that context. Post-hoc tests are automatically applied following significant results. A master function automatically detects the appropriate test based on the structure of the input data. Methods are based o [...truncated...]
Author: Uwakmfon Paul [aut, cre, cph]
Maintainer: Uwakmfon Paul <uwakmfon31@gmail.com>

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Package hashtable updated to version 1.0.1 with previous version 1.0.0 dated 2026-07-27

Title: Hash Table and Hash Set
Description: It provides three implementations of hash tables and hash maps: 1. using 'std::unordered_map' and 'std::unordered_set' C++ libraries, 2. wrapping around the 'fastmatch' package, 3. using R environment.
Author: Zuguang Gu [aut, cre]
Maintainer: Zuguang Gu <guzuguang@suat-sz.edu.cn>

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Package dowser updated to version 2.5.1 with previous version 2.4.1 dated 2026-03-26

Title: B Cell Receptor Phylogenetics Toolkit
Description: Provides a set of functions for inferring, visualizing, and analyzing B cell phylogenetic trees. Provides methods to 1) reconstruct unmutated ancestral sequences, 2) build B cell phylogenetic trees using multiple methods, 3) visualize trees with metadata at the tips, 4) reconstruct intermediate sequences, 5) detect biased ancestor-descendant relationships among metadata types Workflow examples available at documentation site (see URL). Citations: Hoehn et al (2022) <doi:10.1371/journal.pcbi.1009885>, Hoehn et al (2021) <doi:10.1101/2021.01.06.425648>.
Author: Kenneth Hoehn [aut, cre], Cole Jensen [aut], Jessie Fielding [aut], Hunter Melton [aut], Susanna Marquez [ctb], Jason Vander Heiden [ctb], Erick Matsen [ctb], Steven Kleinstein [aut, cph]
Maintainer: Kenneth Hoehn <kenneth.b.hoehn@dartmouth.edu>

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Package dataSDA updated to version 0.2.7 with previous version 0.2.6 dated 2026-06-12

Title: Datasets and Basic Statistics for Symbolic Data Analysis
Description: Provides benchmark datasets and foundational tools for Symbolic Data Analysis (SDA). The package includes functions for constructing symbolic data objects from classical data, converting among different interval-valued data formats, managing interval-valued, histogram-valued, modal-valued, and multi-valued data, and performing basic descriptive statistics. It is designed to support teaching, methodological research, and the development of SDA techniques.
Author: Po-Wei Chen [aut], Chun-houh Chen [aut], Han-Ming Wu [cre]
Maintainer: Han-Ming Wu <wuhm@g.nccu.edu.tw>

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Package blockCV updated to version 4.0-0 with previous version 3.2-0 dated 2025-08-21

Title: Spatial and Environmental Blocking for Cross-Validation
Description: Creates spatially or environmentally separated, or group-preserving, training and testing folds for k-fold, leave-group-out, and leave-one-out cross-validation. Provides spatial blocking, clustering, buffering, and nearest-neighbour distance-matching methods, together with tools to visualise folds, summarise fold sizes and class balance, and assess train–test separation and environmental novelty. Also estimates spatial autocorrelation ranges in point samples and continuous raster covariates to provide an initial distance scale for designing spatial folds. Methods are described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.
Author: Roozbeh Valavi [aut, cre] , Jane Elith [aut], Jose Lahoz-Monfort [aut], Ian Flint [aut], Gurutzeta Guillera-Arroita [aut]
Maintainer: Roozbeh Valavi <valavi.r@gmail.com>

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Package bbssr updated to version 2.0.0 with previous version 1.0.2 dated 2025-06-18

Title: Blinded Sample Size Re-Estimation for Binary Endpoints
Description: Tools for blinded sample size re-estimation (BSSR) in two-arm clinical trials with binary endpoints, together with the exact power and sample size calculations that the re-estimation relies on. Five exact statistical tests are implemented: Pearson chi-squared, Fisher exact, Fisher mid-p, Z-pooled exact unconditional, and Boschloo exact unconditional. Each test is available with a one-sided or a two-sided alternative, and the exact unconditional tests can be combined with the Berger-Boos procedure. Sample sizes can be re-estimated either at the planning stage, to study the operating characteristics of a design, or from the blinded data of a trial that is under way. Statistical methods based on Mehrotra et al. (2003) <doi:10.1111/1541-0420.00051>, Berger and Boos (1994) <doi:10.1080/01621459.1994.10476836> and Kieser (2020) <doi:10.1007/978-3-030-49528-2_21>.
Author: Gosuke Homma [aut, cre]
Maintainer: Gosuke Homma <my.name.is.gosuke@gmail.com>

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Package tidyweather updated to version 0.3.0 with previous version 0.2.0 dated 2026-02-19

Title: Analysis the Weather Data for Agriculture
Description: Functions are collected to analyse weather data for agriculture purposes including to read weather records in multiple formats, calculate extreme climate index. Demonstration data are included the SILO daily climate data (licensed under CC BY 4.0, <https://www.longpaddock.qld.gov.au/silo/>).
Author: Bangou Zheng [aut, cre]
Maintainer: Bangou Zheng <zheng.bangyou@gmail.com>

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Package salmonMSE updated to version 3.0.0 with previous version 2.1.0 dated 2026-04-25

Title: Management Strategy Evaluation for Salmon Species
Description: Simulation tools to evaluate the long-term effects of salmon management strategies, including a combination of habitat, harvest, and habitat actions. The stochastic age-structured operating model accommodates complex life histories, including freshwater survival across early life stages, juvenile survival and fishery exploitation in the marine life stage, partial maturity by age class, and fitness impacts of hatchery programs on natural spawning populations. 'salmonMSE' also provides an age-structured conditioning model to develop operating models fitted to data.
Author: Quang Huynh [aut, cre]
Maintainer: Quang Huynh <quang@bluematterscience.com>

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Package Rdrw updated to version 1.0.4 with previous version 1.0.3 dated 2026-07-02

Title: Univariate and Multivariate Damped Random Walk Processes
Description: Provides tools for fitting and simulating univariate and multivariate damped random walk processes, also known as Ornstein-Uhlenbeck processes or first-order continuous-time autoregressive models, CAR(1) or CARMA(1, 0). The package supports irregularly spaced observation times, heteroscedastic measurement errors, missing measurements across multivariate time series, and polynomial mean trends in normalized time. The current implementation models up to ten time series jointly. Kalman filtering is used to evaluate the likelihood efficiently. Polynomial mean coefficients are handled conditionally within the Kalman recursion, reducing the dimension of numerical maximum likelihood optimization and permitting exact Gaussian conditional updates during Bayesian posterior sampling. Maximum likelihood estimation is computationally efficient and suitable for large-scale data analysis, while Bayesian posterior sampling is better suited to small-scale analyses requiring more careful uncertainty qua [...truncated...]
Author: Zhirui Hu [aut], Hyungsuk Tak [aut, cre]
Maintainer: Hyungsuk Tak <hyungsuk.tak@gmail.com>

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Package rcatfish updated to version 1.0.5 with previous version 1.0.4 dated 2026-07-07

Title: An R Interface to the California Academy of Sciences Eschmeyer's Catalog of Fishes
Description: Accesses the California Academy of Sciences Eschmeyer's Catalog of Fishes in R using web requests. The Catalog of fishes is the leading authority in fish taxonomy. Functions in the package allow users to search for fish taxa and valid names, retrieve taxonomic references, retrieve monthly taxonomic changes, obtain natural history collection information, and see the number of species by taxonomic group. For more information on the Catalog: Fricke, R., Eschmeyer, W. N. & R. van der Laan (eds) 2025. ESCHMEYER'S CATALOG OF FISHES <https://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp>.
Author: Samuel R. Borstein [aut, cre], Brandon Dominy [aut], Brian O'Meara [aut]
Maintainer: Samuel R. Borstein <sam@borstein.com>

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Package nimbleMacros updated to version 0.1.3 with previous version 0.1.1 dated 2025-03-11

Title: Macros Generating 'nimble' Code
Description: Macros to generate 'nimble' code from a concise syntax. Included are macros for generating linear modeling code using a formula-based syntax and for building for() loops. For more details review the 'nimble' manual: <https://r-nimble.org/manual/cha-user-defined.html#sec:user-macros>.
Author: Ken Kellner [cre, aut], Perry de Valpine [aut], Christopher Paciorek [aut], Daniel Turek [aut]
Maintainer: Ken Kellner <contact@kenkellner.com>

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Package inti updated to version 0.7.3 with previous version 0.7.2 dated 2026-07-28

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

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Package exametrika updated to version 2.0.0 with previous version 1.15.0 dated 2026-07-15

Title: Test Data Engineering
Description: Implements comprehensive test data engineering methods as described in Shojima (2022, ISBN:978-9811699856). Provides statistical techniques for engineering and processing test data: Classical Test Theory (CTT) with reliability coefficients for continuous ability assessment; Item Response Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank Analysis (LRA) for ordinal clustering with automatic determination of cluster numbers; Biclustering methods including infinite relational models for simultaneous clustering of examinees and items without predefined cluster numbers; and Bayesian Network Models (BNM) for visualizing inter-item dependencies. Features local dependence analysis through LRA and biclustering, parameter estimation, dimensionality assessment, and network structure visualization for educational, psychological, and social science research.
Author: Koji Kosugi [aut, cre]
Maintainer: Koji Kosugi <kosugitti@gmail.com>

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Package DEoptimR updated to version 1.2-1 with previous version 1.2-0 dated 2026-06-07

Title: Differential Evolution Optimization in Pure R
Description: Differential Evolution (DE) stochastic heuristic algorithms for global optimization of problems with and without general constraints. The aim is to curate a collection of its variants that (1) do not sacrifice simplicity of design, (2) are essentially tuning-free, and (3) can be efficiently implemented directly in the R language. Currently, it provides implementations of the algorithms 'jDE' by Brest et al. (2006) <doi:10.1109/TEVC.2006.872133> for single-objective optimization and 'NCDE' by Qu et al. (2012) <doi:10.1109/TEVC.2011.2161873> for multimodal optimization (single-objective problems with multiple solutions).
Author: Eduardo L. T. Conceicao [aut, cre], Martin Maechler [ctb]
Maintainer: Eduardo L. T. Conceicao <mail@eduardoconceicao.org>

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Package BsplineQuantReg updated to version 0.2.5 with previous version 0.2.2 dated 2026-07-28

Title: 'Constrained Quantile Regression with B-Splines'
Description: Quantile regression with B-splines under shape constraints. The initial version with cubic splines is now augmented with splines of degree 1 to 4. Constraints for degrees 3 (monotone) and 4 (monotone and convex) use the Karlin-Studden SOCP characterization for the sign of the polynomial, while other constraints applied at the knots are added as linear problems. The method for cubic splines is described in 'Abbes (2026)' <doi:10.5281/zenodo.17427913>. Other formulations are simple consequences of the other given references. All B-spline and polynomial functions have been rewritten for consistency. This package provides an original B-spline library for conversion between PP-form and B-spline representation, evaluation, differentiation, callable and non-callable objects, print human readable pp forms, view basis, all based on "De Boor\'s" theory. It also extends to multiple knots to catch up singularities. This feature is robust in the package including for constrained regression. T [...truncated...]
Author: Alexandre Abbes [aut, cre]
Maintainer: Alexandre Abbes <alexandre.abbes@proton.me>

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Package BioTooltipR updated to version 0.1.1 with previous version 0.1.0 dated 2026-06-30

Title: Add Bio Tooltips to HTML Reports
Description: Provides lightweight helpers for adding gene and chemical tooltips to 'R Markdown', 'Quarto', 'shiny', 'pkgdown', and other HTML outputs. The package emits small HTML spans with module-specific data attributes and attaches the browser-side 'bio-tooltips' JavaScript and CSS assets through 'htmltools'. Entity lookup and rendering are handled in the browser by 'bio-tooltips'; this package does not query biological databases from R during package checks.
Author: Matthew J. Meier [aut, cre]
Maintainer: Matthew J. Meier <matthew.meier@hc-sc.gc.ca>

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Package bayesqm updated to version 0.2.0 with previous version 0.1.0 dated 2026-06-17

Title: Bayesian Q Methodology: Exact Rank-Order Likelihood for Forced Q Sorts
Description: A Bayesian analysis for Q methodology, alongside the classical one. Models the forced Q sort as an ordered partition of the statements through an exact rank-order likelihood (the design quotas fix the partition margins, so the likelihood of the observed sorting event is exact), fits it by a parameter-expanded Gibbs sampler in R with no compiled code and a convergence gate on rotation-invariant functionals, resolves rotational ambiguity via the MatchAlign post-processing of Poworoznek et al. (2025) <doi:10.1214/25-BA1544>, and returns the familiar Q tables as posterior summaries: credible intervals for bounded participant loadings, flag probabilities with an explicit unclassified state, quota-respecting factor arrays, distinguishing and consensus statements judged against a posterior critical difference and a grid-width equivalence region, one posterior false-discovery rule for all published claims, and a two-signal posterior-predictive workflow for the number of factors.
Author: Raymond Dacosta Azadda [aut, cre] , Henry Ofoe Agbi-Kaiser [aut] , Hannah D. Robinson [aut] , AK-ACE Team [aut], Karsten Hueffer [aut], Taa'aii Peter [aut], Stacy Rasmus [aut]
Maintainer: Raymond Dacosta Azadda <rdazadda@alaska.edu>

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Package tteICE updated to version 1.1.5 with previous version 1.1.4 dated 2026-03-16

Title: Treatment Effect Estimation for Time-to-Event Data with Intercurrent Events
Description: Analysis of treatment effects in clinical trials with time-to-event outcomes is complicated by intercurrent events. This package implements methods for estimating and inferring the cumulative incidence functions for time-to-event (TTE) outcomes with intercurrent events (ICE) under the five strategies outlined in the ICH E9 (R1) addendum, see Deng (2025) <doi:10.1002/sim.70091>. This package can be used for analyzing data from both randomized controlled trials and observational studies. In general, the data involve a primary outcome event and, potentially, an intercurrent event. Two data structures are allowed: competing risks, where only the time to the first event is recorded, and semicompeting risks, where the times to both the primary outcome event and intercurrent event (or censoring) are recorded. For estimation methods, users can choose nonparametric estimation (which does not use covariates) and semiparametrically efficient estimation.
Author: Yuhao Deng [aut], Yi Zhou [cre]
Maintainer: Yi Zhou <yzhou@pku.edu.cn>

Diff between tteICE versions 1.1.4 dated 2026-03-16 and 1.1.5 dated 2026-08-20

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Package REDCapR updated to version 1.7.0 with previous version 1.6.0 dated 2025-10-08

Title: Interaction Between R and REDCap
Description: Encapsulates functions to streamline calls from R to the REDCap API. REDCap (Research Electronic Data CAPture) is a web application for building and managing online surveys and databases developed at Vanderbilt University. The Application Programming Interface (API) offers an avenue to access and modify data programmatically, improving the capacity for literate and reproducible programming.
Author: Will Beasley [aut, cre] , David Bard [ctb] , Thomas Wilson [ctb], John J Aponte [ctb], Rollie Parrish [ctb] , Benjamin Nutter [ctb], Andrew Peters [ctb] , Hao Zhu [ctb] , Janosch Linkersdoerfer [ctb] , Jonathan Mang [ctb] , Felix Torres [ctb], Philip [...truncated...]
Maintainer: Will Beasley <wibeasley@hotmail.com>

Diff between REDCapR versions 1.6.0 dated 2025-10-08 and 1.7.0 dated 2026-08-20

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 REDCapR-1.7.0/REDCapR/DESCRIPTION                                                                           |    8 
 REDCapR-1.7.0/REDCapR/LICENSE                                                                               |    4 
 REDCapR-1.7.0/REDCapR/MD5                                                                                   |  775 +-
 REDCapR-1.7.0/REDCapR/NAMESPACE                                                                             |  113 
 REDCapR-1.7.0/REDCapR/NEWS.md                                                                               |   28 
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 REDCapR-1.7.0/REDCapR/R/skippers.R                                                                          |   38 
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 REDCapR-1.7.0/REDCapR/README.md                                                                             |  190 
 REDCapR-1.7.0/REDCapR/build/vignette.rds                                                                    |binary
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Package phylopath updated to version 1.4.0 with previous version 1.3.1 dated 2025-09-06

Title: Perform Phylogenetic Path Analysis
Description: A comprehensive and easy to use R implementation of confirmatory phylogenetic path analysis as described by Von Hardenberg and Gonzalez-Voyer (2012) <doi:10.1111/j.1558-5646.2012.01790.x>. Note that the required package 'ggm' depends on 'graph' from Bioconductor, which is not installed automatically; the simplest route is install.packages("BiocManager"); BiocManager::install("phylopath").
Author: Wouter van der Bijl [aut, cre]
Maintainer: Wouter van der Bijl <wouter@zoology.ubc.ca>

Diff between phylopath versions 1.3.1 dated 2025-09-06 and 1.4.0 dated 2026-08-20

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Permanent link

Package outliertree updated to version 1.10.0-2 with previous version 1.10.0-1 dated 2026-03-05

Title: Explainable Outlier Detection Through Decision Tree Conditioning
Description: Outlier detection method that flags suspicious values within observations, constrasting them against the normal values in a user-readable format, potentially describing conditions within the data that make a given outlier more rare. Full procedure is described in Cortes (2020) <doi:10.48550/arXiv.2001.00636>. Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.
Author: David Cortes [aut, cre]
Maintainer: David Cortes <david.cortes.rivera@gmail.com>

Diff between outliertree versions 1.10.0-1 dated 2026-03-05 and 1.10.0-2 dated 2026-08-20

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Package ncpen readmission to version 1.0.1 with previous version 1.0.0 dated 2018-11-17

Title: Unified Algorithm for Non-Convex Penalized Estimation for Generalized Linear Models
Description: An efficient unified nonconvex penalized estimation algorithm for Gaussian (linear), binomial Logit (logistic), Poisson, multinomial Logit, and Cox proportional hazard regression models. The unified algorithm is implemented based on the convex concave procedure and the algorithm can be applied to most of the existing nonconvex penalties. The algorithm also supports convex penalty: least absolute shrinkage and selection operator (LASSO). Supported nonconvex penalties include smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), truncated LASSO penalty (TLP), clipped LASSO (CLASSO), sparse ridge (SRIDGE), modified bridge (MBRIDGE) and modified log (MLOG). For high-dimensional data (data set with many variables), the algorithm selects relevant variables producing a parsimonious regression model. Kim, D., Lee, S. and Kwon, S. (2021) <doi:10.32614/RJ-2021-003>, Lee, S., Kwon, S. and Kim, Y. (2016) <doi:10.1016/j.csda.2015.08.019>, Kwon, S., Lee, S. and Kim, [...truncated...]
Author: Dongshin Kim [aut, cre, cph], Sunghoon Kwon [aut, cph], Sangin Lee [aut, cph]
Maintainer: Dongshin Kim <dongshin.kim@outlook.com>

This is a re-admission after prior archival of version 1.0.0 dated 2018-11-17

Diff between ncpen versions 1.0.0 dated 2018-11-17 and 1.0.1 dated 2026-08-20

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Package mirt updated to version 1.47 with previous version 1.46.1 dated 2026-03-07

Title: Multidimensional Item Response Theory
Description: Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported, as well as a wide family of probabilistic unfolding models.
Author: Phil Chalmers [aut, cre] , Joshua Pritikin [ctb], Alexander Robitzsch [ctb], Mateusz Zoltak [ctb], KwonHyun Kim [ctb], Carl F. Falk [ctb], Adam Meade [ctb], Lennart Schneider [ctb], David King [ctb], Chen-Wei Liu [ctb], Ogreden Oguzhan [ctb], Samuel [...truncated...]
Maintainer: Phil Chalmers <rphilip.chalmers@gmail.com>

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Permanent link

Package lava updated to version 1.9.3 with previous version 1.9.2 dated 2026-06-30

Title: Latent Variable Models
Description: A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.
Author: Klaus K. Holst [aut, cre], Benedikt Sommer [ctb], Brice Ozenne [ctb], Thomas Gerds [ctb]
Maintainer: Klaus K. Holst <klaus@holst.it>

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More information about lava at CRAN
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Package tirt updated to version 0.4.0 with previous version 0.3.1 dated 2026-03-24

Title: Testlet Item Response Theory
Description: Implementation of Testlet and Item Response Theory. A light-version yet comprehensive and streamlined framework for psychometric analysis using unidimensional and multidimensional Item Response Theory (IRT; Baker & Kim (2004) <doi:10.1201/9781482276725>) and Testlet Response Theory (TRT; Wainer et al., (2007) <doi:10.1017/CBO9780511618765>). Designed for researchers, this package supports the estimation of item and person parameters for a wide variety of models, including binary (i.e., Rasch, 2-Parameter Logistic, 3-Parameter Logistic) and polytomous (Partial Credit Model, Generalized Partial Credit Model, Graded Response Model) formats. It also supports the estimation of Testlet models (Rasch Testlet, 2-Parameter Logistic Testlet, 3-Parameter Logistic Testlet, Bifactor, Partial Credit Model Testlet, Graded Response), allowing users to account for local item dependence in bundled items. A key feature is the specialized support for combination use and joint estimation of [...truncated...]
Author: Jiawei Xiong [aut, cre], Cheng Tang [ctb], Qidi Liu [ctb]
Maintainer: Jiawei Xiong <jiawei.xiong@uga.edu>

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Package SVEMnet updated to version 3.5.0 with previous version 3.2.3 dated 2026-07-02

Title: Self-Validated Ensemble Models with Lasso and Relaxed Elastic Net Regression
Description: Tools for fitting self-validated ensemble models (SVEM; Lemkus et al. (2021) <doi:10.1016/j.chemolab.2021.104439>) in small-sample design-of-experiments and related workflows, using elastic net and relaxed elastic net regression via 'glmnet' (Friedman et al. (2010) <doi:10.18637/jss.v033.i01>). Fractional random-weight bootstraps with anti-correlated validation copies are used to tune penalty paths by validation-weighted AIC/BIC. Supports Gaussian and binomial responses, deterministic expansion helpers for shared factor spaces, prediction with bootstrap uncertainty, a random-search optimizer that respects mixture constraints and combines multiple responses via desirability functions, and a batch sequential-design proposer based on Thompson sampling from the bootstrap ensemble (Thompson (1933) <doi:10.1093/biomet/25.3-4.285>; Kandasamy et al. (2018) <doi:10.48550/arXiv.1705.09236>). Also includes a permutation-based whole-model test for Gaussian SVEM fits (Karl ( [...truncated...]
Author: Andrew T. Karl [cre, aut]
Maintainer: Andrew T. Karl <akarl@asu.edu>

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Package RIFanalysis updated to version 0.9.2 with previous version 0.9.1 dated 2026-08-08

Title: Relative Importance Factor Analysis
Description: Tools for estimating, comparing, and visualizing Relative Importance Factor (RIF) indices based on rank-frequency distributions and discrete power-law models. The package provides reproducible workflows for data preparation, model fitting, goodness-of-fit assessment, bootstrap inference, and publication-ready outputs. The implemented methodology is described in Llinas et al. (2026) <doi:10.3390/math14060966>.
Author: Humberto J. Llinas M. [aut, cre], Humberto J. Llinas S. [aut], Javier A. De la Hoz M. [aut], Brian J. Llinas M. [aut], Jose J. Padilla [aut]
Maintainer: Humberto J. Llinas M. <lhumberto@uninorte.edu.co>

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Package praznik updated to version 13.0.0 with previous version 12.0.0 dated 2025-11-11

Title: Tools for Information-Based Feature Selection and Scoring
Description: A toolbox of fast, native and parallel implementations of various information-based importance criteria estimators and feature selection filters based on them, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) <https://www.jmlr.org/papers/v13/brown12a.html>. Contains, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information ('JMI') method by Yang and Moody (1999) <https://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data>; double input symmetrical relevance ('DISR') method by Meyer and Bontempi (2006) <doi:10.1007/11732242_9> as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) <doi:10.1016/j.eswa.2015.07.007>.
Author: Miron B. Kursa [aut, cre]
Maintainer: Miron B. Kursa <m@mbq.me>

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Package isodistrreg updated to version 0.5.2 with previous version 0.1.0 dated 2021-03-22

Title: Isotonic Distributional Regression (IDR)
Description: Distributional regression under stochastic order restrictions for numeric and binary response variables and partially ordered covariates, including right-censored responses via Survival-IDR. See Henzi, Ziegel, Gneiting (2021) <doi:10.1111/rssb.12450> and Bladt, Henzi, van den Heuvel, Ziegel (2026) <doi:10.48550/arXiv.2608.02914>.
Author: Bram van den Heuvel [aut, cre], Alexander Henzi [aut], Martin Bladt [aut], Johanna Ziegel [ths]
Maintainer: Bram van den Heuvel <bram.vandenheuvel@stat.math.ethz.ch>

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Package fru updated to version 1.0.0 with previous version 0.0.7 dated 2026-05-07

Title: A Blazing Fast Implementation of Random Forest
Description: Yet another implementation of the Random Forest method by Breiman (2001) <doi:10.1023/A:1010933404324>, written in Rust and tailored towards stability, correctness, efficiency and scalability on modern multi-core machines. Handles both classification and regression, as well as provides permutation feature importance via a novel, highly optimised algorithm.
Author: Miron Bartosz Kursa [aut, cre] , Krzysztof Piotr Piwonski [aut]
Maintainer: Miron Bartosz Kursa <m@mbq.me>

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Package FCPS updated to version 1.4.1 with previous version 1.4.0 dated 2026-07-15

Title: Fundamental Clustering Problems Suite
Description: Over sixty clustering algorithms are provided in this package with consistent input and output, which enables the user to try out algorithms swiftly. Additionally, 26 statistical approaches for the estimation of the number of clusters as well as the mirrored density plot (MD-plot) of clusterability are implemented. The packages is published in Thrun, M.C., Stier Q.: "Fundamental Clustering Algorithms Suite" (2021), SoftwareX, <DOI:10.1016/j.softx.2020.100642>. Moreover, the fundamental clustering problems suite (FCPS) offers a variety of clustering challenges any algorithm should handle when facing real world data, see Thrun, M.C., Ultsch A.: "Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems" (2020), Data in Brief, <DOI:10.1016/j.dib.2020.105501>.
Author: Michael Thrun [aut, cre, cph] , Peter Nahrgang [ctr, ctb], Felix Pape [ctr, ctb], Vasyl Pihur [ctb], Guy Brock [ctb], Susmita Datta [ctb], Somnath Datta [ctb], Luis Winckelmann [com], Alfred Ultsch [dtc, ctb], Quirin Stier [ctb, rev]
Maintainer: Michael Thrun <m.thrun@gmx.net>

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Package broadcast updated to version 0.1.9.5 with previous version 0.1.9 dated 2026-05-29

Title: Broadcasted Array Operations Like 'NumPy'
Description: Implements efficient 'NumPy'-like broadcasted operations for atomic and recursive arrays. In the context of operations involving 2 (or more) arrays, “broadcasting” (AKA singleton expansion) refers to efficiently recycling array dimensions, without making copies. Besides linking to 'Rcpp', 'broadcast' does not use any external libraries in any way; 'broadcast' was essentially made from scratch and can be installed out-of-the-box. The implementations available in 'broadcast' include, but are not limited to, the following. 1) Broadcasted element-wise operations on any 2 arrays; they support a large set of relational, arithmetic, Boolean, string, and bit-wise operations. 2) A faster, more memory efficient, and broadcasted abind-like function, for binding arrays along an arbitrary dimension. 3) Broadcasted ifelse-like and apply-like functions. 4) Casting functions, that cast subset-groups of an array to a new dimension, cast nested lists to dimensional lists, and vice-versa. 5) A few linear [...truncated...]
Author: Tony Wilkes [aut, cre, cph]
Maintainer: Tony Wilkes <tonywilkes.nl@gmail.com>

Diff between broadcast versions 0.1.9 dated 2026-05-29 and 0.1.9.5 dated 2026-08-20

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Package acR updated to version 0.3.3 with previous version 0.3.2 dated 2026-08-07

Title: Content Analysis in R: Integrated Qualitative (LLMs) and Quantitative Pipeline
Description: Provides an integrated pipeline for content analysis combining qualitative coding assisted by large language models (LLMs) with classical quantitative text analysis. Includes modules for pre-processing (tokenization, stopwords for Brazilian Portuguese), descriptive statistics, keyness, co-occurrence networks, word clouds (including comparative and X-ray variants), sentiment analysis via OpLexicon, Latent Dirichlet Allocation (LDA), inter-coder reliability metrics (Krippendorff, Gwet), and modern visualizations based on ggplot2. Special focus on Brazilian corpora and political-institutional codebooks. Inspired by Maerz and Benoit (2025) <https://quallmer.github.io/quallmer/>.
Author: Anderson Henrique [aut, cre]
Maintainer: Anderson Henrique <anderson.henrique@usp.br>

Diff between acR versions 0.3.2 dated 2026-08-07 and 0.3.3 dated 2026-08-20

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Package vasicekreg updated to version 1.1.0 with previous version 1.0.2 dated 2026-01-12

Title: Regression Modeling Using Vasicek Distribution
Description: Provides density, cumulative distribution, quantile, and random generation functions for Vasicek distributions with standard normal and standard logistic kernels. The normal-kernel distribution is parameterized by either its mean or a fixed quantile, whereas the logistic-kernel distribution uses a fixed-quantile parameterization. Zero-adjusted, one-adjusted, and zero-and-one-adjusted extensions of the normal-kernel mean parameterization are also provided for responses that include boundary values. The corresponding 'NVASIM', 'NVASIQ', 'LVASIQ', 'ZANVASIM', 'OANVASIM', and 'ZOANVASIM' families are available for fitting Generalized Additive Models for Location, Scale and Shape, as introduced by Rigby and Stasinopoulos (2005, <doi:10.1111/j.1467-9876.2005.00510.x>). Some functions are written in 'C++' using 'Rcpp', developed by Eddelbuettel and Francois (2011, <doi:10.18637/jss.v040.i08>).
Author: Josmar Mazucheli [aut, cre], Bruna Alves [ctb]
Maintainer: Josmar Mazucheli <jmazucheli@gmail.com>

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Package TrackTrap updated to version 1.0.1 with previous version 1.0.0 dated 2026-06-13

Title: Model Cumulative Growing Degree-Days for Pest Monitoring
Description: Pest monitoring is crucial, especially during the early season, to understand the distribution and the proliferation of the target pest. Raw count data from pest monitoring/traps can be coupled with derived environmental variables such as growing degree-day ('GDD') to get useful insights about the pest phenology. This package pulls temperature data from the 'Daymet' application programming interface ('API', <https://daymet.ornl.gov>), or 'Open-Meteo' ('API', <https://open-meteo.com/>) or manual user-supplied CSV file from the California Irrigation Management Information System ('CIMIS', <https://cimis.water.ca.gov>), for a user-specified time period and calculates cumulative growing degree-days. Users provide intended date range, pest of concern, and the geographic coordinates of the trap location to track pest emergence and phenology throughout the growing season.
Author: Santosh Bhandari [aut, cre], Lalit Kharel [aut], Mahesh Ghimire [aut]
Maintainer: Santosh Bhandari <santoshbhandari4556@gmail.com>

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Package WDI updated to version 2.8.0 with previous version 2.7.10 dated 2026-04-05

Title: World Development Indicators and Other World Bank Data
Description: Search and download data from over 40 databases hosted by the World Bank, including the World Development Indicators ('WDI'), International Debt Statistics, Doing Business, Human Capital Index, and Sub-national Poverty indicators.
Author: Vincent Arel-Bundock [aut, cre] , Etienne Bacher [ctb]
Maintainer: Vincent Arel-Bundock <vincent.arel-bundock@umontreal.ca>

Diff between WDI versions 2.7.10 dated 2026-04-05 and 2.8.0 dated 2026-08-20

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Package unmarked updated to version 1.5.2 with previous version 1.5.1 dated 2025-09-26

Title: Models for Data from Unmarked Animals
Description: Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Author: Richard Chandler [aut], Ken Kellner [cre, aut], Ian Fiske [aut], David Miller [aut], Andy Royle [aut], Jeff Hostetler [aut], Rebecca Hutchinson [aut], Adam Smith [aut], Lea Pautrel [aut], Marc Kery [ctb], Mike Meredith [ctb], Auriel Fournier [ctb], A [...truncated...]
Maintainer: Ken Kellner <contact@kenkellner.com>

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 man/pcountOpen.Rd                   |  139 ++++++++++++++++++------------------
 man/predict.Rd                      |    5 -
 man/unmarkedFitList-class.rd        |    1 
 man/unmarkedMultFrame.Rd            |    1 
 src/Makevars                        |    2 
 src/Makevars.win                    |    2 
 tests/testthat/test_distsamp.R      |    4 -
 tests/testthat/test_formatInputs.R  |   22 ++---
 tests/testthat/test_makePiFun.R     |    6 -
 tests/testthat/test_occu.R          |    6 -
 tests/testthat/test_occuFP.R        |   14 +++
 tests/testthat/test_occuPEN.R       |   27 ++++++
 tests/testthat/test_pcount.R        |    2 
 tests/testthat/test_pcount.spHDS.R  |    2 
 tests/testthat/test_pcountOpen.R    |    2 
 tests/testthat/test_plotEffects.R   |   32 ++++++++
 tests/testthat/test_unmarkedFrame.R |   28 +++++++
 tests/testthat/test_utils.R         |   27 ++++++
 42 files changed, 401 insertions(+), 207 deletions(-)

More information about unmarked at CRAN
Permanent link

Package tweedieDistr updated to version 0.2.0 with previous version 0.1.1 dated 2026-07-23

Title: Tweedie Distribution
Description: Provides density, distribution function, quantile function, and random generation for the Tweedie distribution under the compound Poisson-Gamma parameterisation with power parameter in (1, 2). The density is evaluated using the series expansion of Dunn and Smyth (2005) <doi:10.1007/s11222-005-4070-y>, implemented in C++ via 'Rcpp' and 'RcppArmadillo' for performance. A constructor compatible with the 'distributional' package is also provided for use in tidy modelling workflows.
Author: Stefano Damato [aut, cre]
Maintainer: Stefano Damato <stefanodamato128@gmail.com>

Diff between tweedieDistr versions 0.1.1 dated 2026-07-23 and 0.2.0 dated 2026-08-20

 DESCRIPTION      |    6 +-
 MD5              |   14 ++---
 NEWS.md          |    4 +
 src/dtweedie.cpp |  142 +++++++++++++++++++++++++++++++++++--------------------
 src/ptweedie.cpp |   10 ++-
 src/qtweedie.cpp |   42 ++++++++++------
 src/utils.cpp    |   14 ++++-
 src/utils.h      |    2 
 8 files changed, 150 insertions(+), 84 deletions(-)

More information about tweedieDistr at CRAN
Permanent link

Package tinytable updated to version 0.18.0 with previous version 0.17.0 dated 2026-06-26

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

Diff between tinytable versions 0.17.0 dated 2026-06-26 and 0.18.0 dated 2026-08-20

 tinytable-0.17.0/tinytable/R/last_style.R                                                                 |only
 tinytable-0.18.0/tinytable/DESCRIPTION                                                                    |    7 
 tinytable-0.18.0/tinytable/MD5                                                                            |  385 +++---
 tinytable-0.18.0/tinytable/NEWS.md                                                                        |   26 
 tinytable-0.18.0/tinytable/R/aaa_class.R                                                                  |   73 -
 tinytable-0.18.0/tinytable/R/ansi.R                                                                       |   16 
 tinytable-0.18.0/tinytable/R/build_tt.R                                                                   |   82 -
 tinytable-0.18.0/tinytable/R/colors.R                                                                     |   71 +
 tinytable-0.18.0/tinytable/R/expand_style.R                                                               |  169 ++
 tinytable-0.18.0/tinytable/R/format_apply.R                                                               |  118 --
 tinytable-0.18.0/tinytable/R/format_tt.R                                                                  |  193 +--
 tinytable-0.18.0/tinytable/R/format_vector_escape.R                                                       |    7 
 tinytable-0.18.0/tinytable/R/format_vector_numeric.R                                                      |  176 +--
 tinytable-0.18.0/tinytable/R/format_vector_replace.R                                                      |   47 
 tinytable-0.18.0/tinytable/R/grid_build.R                                                                 |  234 +---
 tinytable-0.18.0/tinytable/R/grid_finalize.R                                                              |   77 -
 tinytable-0.18.0/tinytable/R/grid_group.R                                                                 |   56 
 tinytable-0.18.0/tinytable/R/grid_style.R                                                                 |  172 --
 tinytable-0.18.0/tinytable/R/group_tt.R                                                                   |  261 +---
 tinytable-0.18.0/tinytable/R/group_tt_i.R                                                                 |   28 
 tinytable-0.18.0/tinytable/R/group_tt_j.R                                                                 |   24 
 tinytable-0.18.0/tinytable/R/html_build.R                                                                 |   29 
 tinytable-0.18.0/tinytable/R/html_finalize.R                                                              |   67 -
 tinytable-0.18.0/tinytable/R/html_group.R                                                                 |   97 -
 tinytable-0.18.0/tinytable/R/html_style.R                                                                 |  354 +++---
 tinytable-0.18.0/tinytable/R/json.R                                                                       |  215 ++-
 tinytable-0.18.0/tinytable/R/nse.R                                                                        |   40 
 tinytable-0.18.0/tinytable/R/plot_tt.R                                                                    |  230 ++-
 tinytable-0.18.0/tinytable/R/print.R                                                                      |   98 -
 tinytable-0.18.0/tinytable/R/rbind2.R                                                                     |    6 
 tinytable-0.18.0/tinytable/R/sanity.R                                                                     |  444 +++----
 tinytable-0.18.0/tinytable/R/save_tt.R                                                                    |   17 
 tinytable-0.18.0/tinytable/R/style_maps.R                                                                 |only
 tinytable-0.18.0/tinytable/R/style_tt.R                                                                   |  290 ++--
 tinytable-0.18.0/tinytable/R/subset.R                                                                     |  156 ++
 tinytable-0.18.0/tinytable/R/tabularray_build.R                                                           |    1 
 tinytable-0.18.0/tinytable/R/tabularray_group.R                                                           |   13 
 tinytable-0.18.0/tinytable/R/tabularray_helpers.R                                                         |  101 -
 tinytable-0.18.0/tinytable/R/tabularray_style.R                                                           |  582 +++-------
 tinytable-0.18.0/tinytable/R/tabulator_build.R                                                            |   19 
 tinytable-0.18.0/tinytable/R/tabulator_columns.R                                                          |  111 +
 tinytable-0.18.0/tinytable/R/tabulator_data.R                                                             |   43 
 tinytable-0.18.0/tinytable/R/tabulator_finalize.R                                                         |   42 
 tinytable-0.18.0/tinytable/R/tabulator_group.R                                                            |    6 
 tinytable-0.18.0/tinytable/R/tabulator_options.R                                                          |   34 
 tinytable-0.18.0/tinytable/R/tabulator_plot.R                                                             |    5 
 tinytable-0.18.0/tinytable/R/tabulator_search.R                                                           |   25 
 tinytable-0.18.0/tinytable/R/tabulator_style.R                                                            |   40 
 tinytable-0.18.0/tinytable/R/tabulator_stylesheet.R                                                       |   13 
 tinytable-0.18.0/tinytable/R/theme_empty.R                                                                |    7 
 tinytable-0.18.0/tinytable/R/theme_html_tabulator.R                                                       |   33 
 tinytable-0.18.0/tinytable/R/theme_latex.R                                                                |   40 
 tinytable-0.18.0/tinytable/R/theme_markdown.R                                                             |   10 
 tinytable-0.18.0/tinytable/R/theme_revealjs.R                                                             |   68 -
 tinytable-0.18.0/tinytable/R/theme_rotate.R                                                               |   24 
 tinytable-0.18.0/tinytable/R/theme_striped.R                                                              |   27 
 tinytable-0.18.0/tinytable/R/theme_tt.R                                                                   |   47 
 tinytable-0.18.0/tinytable/R/theme_typst.R                                                                |   20 
 tinytable-0.18.0/tinytable/R/tt.R                                                                         |   10 
 tinytable-0.18.0/tinytable/R/typst_style.R                                                                |  513 ++++----
 tinytable-0.18.0/tinytable/R/typst_tt.R                                                                   |  298 +++--
 tinytable-0.18.0/tinytable/R/utils.R                                                                      |  160 ++
 tinytable-0.18.0/tinytable/README.md                                                                      |    2 
 tinytable-0.18.0/tinytable/build/partial.rdb                                                              |binary
 tinytable-0.18.0/tinytable/inst/templates/typst.typ                                                       |    4 
 tinytable-0.18.0/tinytable/inst/tinytable.css                                                             |   16 
 tinytable-0.18.0/tinytable/inst/tinytable_dark.css                                                        |   16 
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 tinytable-0.18.0/tinytable/inst/tinytest/_tinysnapshot/html-footnote.html                                 |   16 
 tinytable-0.18.0/tinytable/inst/tinytest/_tinysnapshot/html-formatting.html                               |   16 
 tinytable-0.18.0/tinytable/inst/tinytest/_tinysnapshot/html-images.html                                   |    4 
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 tinytable-0.18.0/tinytable/inst/tinytest/_tinysnapshot/typst-tblr.typ                                     |   11 
 tinytable-0.18.0/tinytable/inst/tinytest/helpers.R                                                        |   21 
 tinytable-0.18.0/tinytable/inst/tinytest/test-bugfix.R                                                    |   33 
 tinytable-0.18.0/tinytable/inst/tinytest/test-format.R                                                    |   47 
 tinytable-0.18.0/tinytable/inst/tinytest/test-group.R                                                     |   88 +
 tinytable-0.18.0/tinytable/inst/tinytest/test-group_matrix.R                                              |   31 
 tinytable-0.18.0/tinytable/inst/tinytest/test-html.R                                                      |   46 
 tinytable-0.18.0/tinytable/inst/tinytest/test-json.R                                                      |only
 tinytable-0.18.0/tinytable/inst/tinytest/test-latex.R                                                     |   31 
 tinytable-0.18.0/tinytable/inst/tinytest/test-line_type.R                                                 |only
 tinytable-0.18.0/tinytable/inst/tinytest/test-markdown.R                                                  |    9 
 tinytable-0.18.0/tinytable/inst/tinytest/test-nse.R                                                       |only
 tinytable-0.18.0/tinytable/inst/tinytest/test-pdf.R                                                       |   16 
 tinytable-0.18.0/tinytable/inst/tinytest/test-plot_tt.R                                                   |   27 
 tinytable-0.18.0/tinytable/inst/tinytest/test-rbind2.R                                                    |   12 
 tinytable-0.18.0/tinytable/inst/tinytest/test-style-resolution.R                                          |only
 tinytable-0.18.0/tinytable/inst/tinytest/test-style.R                                                     |  116 +
 tinytable-0.18.0/tinytable/inst/tinytest/test-subset.R                                                    |only
 tinytable-0.18.0/tinytable/inst/tinytest/test-tabulator.R                                                 |   14 
 tinytable-0.18.0/tinytable/inst/tinytest/test-tt.R                                                        |   35 
 tinytable-0.18.0/tinytable/inst/tinytest/test-typst.R                                                     |   92 +
 tinytable-0.18.0/tinytable/inst/tinytest/test-utils.R                                                     |only
 tinytable-0.18.0/tinytable/man/print.tinytable.Rd                                                         |    1 
 tinytable-0.18.0/tinytable/man/rbind2-tinytable-tinytable-method.Rd                                       |    2 
 tinytable-0.18.0/tinytable/man/save_tt.Rd                                                                 |    3 
 tinytable-0.18.0/tinytable/man/style_tt.Rd                                                                |   11 
 tinytable-0.18.0/tinytable/man/theme_empty.Rd                                                             |    5 
 tinytable-0.18.0/tinytable/man/theme_markdown.Rd                                                          |    4 
 tinytable-0.18.0/tinytable/man/tt.Rd                                                                      |    3 
 200 files changed, 4888 insertions(+), 4285 deletions(-)

More information about tinytable at CRAN
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