Tue, 25 Nov 2025

Package testthat updated to version 3.3.1 with previous version 3.3.0 dated 2025-11-13

Title: Unit Testing for R
Description: Software testing is important, but, in part because it is frustrating and boring, many of us avoid it. 'testthat' is a testing framework for R that is easy to learn and use, and integrates with your existing 'workflow'.
Author: Hadley Wickham [aut, cre], Posit Software, PBC [cph, fnd], R Core team [ctb] )
Maintainer: Hadley Wickham <hadley@posit.co>

Diff between testthat versions 3.3.0 dated 2025-11-13 and 3.3.1 dated 2025-11-25

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Package nlrx updated to version 0.4.6 with previous version 0.4.5 dated 2024-01-29

Title: Setup, Run and Analyze 'NetLogo' Model Simulations from 'R' via 'XML'
Description: Setup, run and analyze 'NetLogo' (<https://www.netlogo.org>) model simulations in 'R'. 'nlrx' experiments use a similar structure as 'NetLogos' Behavior Space experiments. However, 'nlrx' offers more flexibility and additional tools for running and analyzing complex simulation designs and sensitivity analyses. The user defines all information that is needed in an intuitive framework, using class objects. Experiments are submitted from 'R' to 'NetLogo' via 'XML' files that are dynamically written, based on specifications defined by the user. By nesting model calls in future environments, large simulation design with many runs can be executed in parallel. This also enables simulating 'NetLogo' experiments on remote high performance computing machines. In order to use this package, 'Java' and 'NetLogo' (>= 5.3.1) need to be available on the executing system.
Author: Jan Salecker [aut] , Marco Sciaini [aut] , Marina Papadopoulou [rev] , Sebastian Hanss [cre]
Maintainer: Sebastian Hanss <nlrx@mailbox.org>

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Package mcmodule updated to version 1.1.1 with previous version 1.1.0 dated 2025-10-22

Title: Modular Monte Carlo Risk Analysis
Description: Framework for building modular Monte Carlo risk analysis models. It extends the capabilities of 'mc2d' to facilitate working with multiple risk pathways, variates and scenarios. It provides tools to organize risk analysis in independent flexible modules, perform multivariate Monte Carlo node operations, automate the creation of Monte Carlo nodes and visualize risk analysis models. For more details see Ciria (2025) <https://nataliaciria.github.io/mcmodule/articles/mcmodule>.
Author: Natalia Ciria [aut, cre, cph] , Alberto Allepuz [ths] , Giovanna Ciaravino [ths]
Maintainer: Natalia Ciria <nataliaciria@hotmail.com>

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Package lavaangui updated to version 0.3.1 with previous version 0.3.0 dated 2025-10-16

Title: Graphical User Interface with Integrated 'Diagrammer' for 'Lavaan'
Description: Provides a graphical user interface with an integrated diagrammer for latent variable models from the 'lavaan' package. It offers two core functions: first, lavaangui() launches a web application that allows users to specify models by drawing path diagrams, fitting them, assessing model fit, and more; second, plot_lavaan() creates interactive path diagrams from models specified in 'lavaan'. Karch (2024) <doi: 10.1080/10705511.2024.2420678> contains a tutorial.
Author: Julian D. Karch [aut, cre, cph]
Maintainer: Julian D. Karch <j.d.karch@fsw.leidenuniv.nl>

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Package gtreg updated to version 0.4.2 with previous version 0.4.1 dated 2025-02-27

Title: Regulatory Tables for Clinical Research
Description: Creates tables suitable for regulatory agency submission by leveraging the 'gtsummary' package as the back end. Tables can be exported to HTML, Word, PDF and more. Highly customized outputs are available by utilizing existing styling functions from 'gtsummary' as well as custom options designed for regulatory tables.
Author: Shannon Pileggi [aut, cre, cph] , Daniel D. Sjoberg [aut]
Maintainer: Shannon Pileggi <shannon.pileggi@gmail.com>

Diff between gtreg versions 0.4.1 dated 2025-02-27 and 0.4.2 dated 2025-11-25

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Package ivo.table updated to version 0.7.1 with previous version 0.7.0 dated 2025-09-30

Title: Nicely Formatted Contingency Tables and Frequency Tables
Description: Nicely formatted frequency tables and contingency tables (1-way, 2-way, 3-way and 4-way tables), that can easily be exported to HTML or 'Office' documents. Designed to work with pipes.
Author: Mans Thulin [aut, cre], Kajsa Grind [aut], Stefan Furne [aut]
Maintainer: Mans Thulin <mans@statistikkonsult.com>

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Package Qval updated to version 1.2.4 with previous version 1.2.3 dated 2025-06-02

Title: The Q-Matrix Validation Methods Framework
Description: Provide a variety of Q-matrix validation methods for the generalized cognitive diagnosis models, including the method based on the generalized deterministic input, noisy, and gate model (G-DINA) by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7> discrimination index (the GDI method) by de la Torre and Chiu (2016) <DOI:10.1007/s11336-015-9467-8>, the Hull method by Najera et al. (2021) <DOI:10.1111/bmsp.12228>, the stepwise Wald test method (the Wald method) by Ma and de la Torre (2020) <DOI:10.1111/bmsp.12156>, the multiple logistic regression‑based Q‑matrix validation method (the MLR-B method) by Tu et al. (2022) <DOI:10.3758/s13428-022-01880-x>, the beta method based on signal detection theory by Li and Chen (2024) <DOI:10.1111/bmsp.12371> and Q-matrix validation based on relative fit index by Chen et al. (2013) <DOI:10.1111/j.1745-3984.2012.00185.x>. Different research methods and iterative procedures during Q-matrix validating are availa [...truncated...]
Author: Haijiang Qin [aut, cre, cph] , Lei Guo [aut, cph]
Maintainer: Haijiang Qin <haijiang133@outlook.com>

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Package psyntur updated to version 0.1.1 with previous version 0.1.0 dated 2021-09-15

Title: Helper Tools for Teaching Statistical Data Analysis
Description: Provides functions and data-sets that are helpful for teaching statistics and data analysis. It was originally designed for use when teaching students in the Psychology Department at Nottingham Trent University.
Author: Mark Andrews [aut, cre], Jens Roeser [aut]
Maintainer: Mark Andrews <mark.andrews@ntu.ac.uk>

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Package paws.common updated to version 0.8.7 with previous version 0.8.6 dated 2025-11-19

Title: Paws Low-Level Amazon Web Services API
Description: Functions for making low-level API requests to Amazon Web Services <https://aws.amazon.com>. The functions handle building, signing, and sending requests, and receiving responses. They are designed to help build higher-level interfaces to individual services, such as Simple Storage Service (S3).
Author: David Kretch [aut], Adam Banker [aut], Dyfan Jones [cre], Amazon.com, Inc. [cph]
Maintainer: Dyfan Jones <dyfan.r.jones@gmail.com>

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Package MultBiplotR updated to version 25.11.15 with previous version 23.11.0 dated 2023-11-21

Title: Multivariate Analysis Using Biplots in R
Description: Several multivariate techniques from a biplot perspective. It is the translation (with many improvements) into R of the previous package developed in 'Matlab'. The package contains some of the main developments of my team during the last 30 years together with some more standard techniques. Package includes: Classical Biplots, HJ-Biplot, Canonical Biplots, MANOVA Biplots, Correspondence Analysis, Canonical Correspondence Analysis, Canonical STATIS-ACT, Logistic Biplots for binary and ordinal data, Multidimensional Unfolding, External Biplots for Principal Coordinates Analysis or Multidimensional Scaling, among many others. References can be found in the help of each procedure.
Author: Jose Luis Vicente Villardon [aut], Laura Vicente-Gonzalez [aut], Elisa Frutos-Bernal [aut], Jose Luis Vicente Villardon [cre]
Maintainer: Jose Luis Vicente Villardon <villardon@usal.es>

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Package moocore updated to version 0.1.10 with previous version 0.1.9 dated 2025-11-01

Title: Core Mathematical Functions for Multi-Objective Optimization
Description: Fast implementations of mathematical operations and performance metrics for multi-objective optimization, including filtering and ranking of dominated vectors according to Pareto optimality, hypervolume metric, C.M. Fonseca, L. Paquete, M. López-Ibáñez (2006) <doi:10.1109/CEC.2006.1688440>, epsilon indicator, inverted generational distance, computation of the empirical attainment function, V.G. da Fonseca, C.M. Fonseca, A.O. Hall (2001) <doi:10.1007/3-540-44719-9_15>, and Vorob'ev threshold, expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) <doi:10.1016/j.ejor.2014.07.032>, among others.
Author: Manuel Lopez-Ibanez [aut, cre] , Carlos Fonseca [ctb], Luis Paquete [ctb], Andreia P. Guerreiro [ctb], Mickael Binois [ctb], Michael H. Buselli [cph] , Wessel Dankers [cph] , NumPy Developers [cph] , Jean-Sebastien Roy [cph] , Makoto Matsumoto [cph] [...truncated...]
Maintainer: Manuel Lopez-Ibanez <manuel.lopez-ibanez@manchester.ac.uk>

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Package CptNonPar updated to version 0.3.1 with previous version 0.3.0 dated 2025-04-16

Title: Nonparametric Change Point Detection for Multivariate Time Series
Description: Implements the nonparametric moving sum procedure for detecting changes in the joint characteristic function (NP-MOJO) for multiple change point detection in multivariate time series. See McGonigle, E. T., Cho, H. (2025) <doi:10.1093/biomet/asaf024> for description of the NP-MOJO methodology.
Author: Euan T. McGonigle [aut, cre], Haeran Cho [aut]
Maintainer: Euan T. McGonigle <e.t.mcgonigle@soton.ac.uk>

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Package rrapply updated to version 1.2.8 with previous version 1.2.7 dated 2024-06-26

Title: Revisiting Base Rapply
Description: The minimal 'rrapply'-package contains a single function rrapply(), providing an extended implementation of 'R'-base rapply() by allowing to recursively apply a function to elements of a nested list based on a general condition function and including the possibility to prune or aggregate nested list elements from the result. In addition, special arguments can be supplied to access the name, location, parents and siblings in the nested list of the element under evaluation. The rrapply() function builds upon rapply()'s native 'C' implementation and requires no other package dependencies.
Author: Joris Chau [aut, cre]
Maintainer: Joris Chau <joris.chau@openanalytics.eu>

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Package parameters updated to version 0.28.3 with previous version 0.28.2 dated 2025-09-10

Title: Processing of Model Parameters
Description: Utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), this package implements features like bootstrapping or simulating of parameters and models, feature reduction (feature extraction and variable selection) as well as functions to describe data and variable characteristics (e.g. skewness, kurtosis, smoothness or distribution).
Author: Daniel Luedecke [aut, cre] , Dominique Makowski [aut] , Mattan S. Ben-Shachar [aut] , Indrajeet Patil [aut] , Soeren Hoejsgaard [aut], Brenton M. Wiernik [aut] , Zen J. Lau [ctb], Vincent Arel-Bundock [ctb] , Jeffrey Girard [ctb] , Christina Maimone [re [...truncated...]
Maintainer: Daniel Luedecke <officialeasystats@gmail.com>

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Package HRTnomaly updated to version 25.11.22 with previous version 25.2.25 dated 2025-02-26

Title: Historical, Relational, and Tail Anomaly-Detection Algorithms
Description: The presence of outliers in a dataset can substantially bias the results of statistical analyses. To correct for outliers, micro edits are manually performed on all records. A set of constraints and decision rules is typically used to aid the editing process. However, straightforward decision rules might overlook anomalies arising from disruption of linear relationships. Computationally efficient methods are provided to identify historical, tail, and relational anomalies at the data-entry level (Sartore et al., 2024; <doi:10.6339/24-JDS1136>). A score statistic is developed for each anomaly type, using a distribution-free approach motivated by the Bienaymé-Chebyshev's inequality, and fuzzy logic is used to detect cellwise outliers resulting from different types of anomalies. Each data entry is individually scored and individual scores are combined into a final score to determine anomalous entries. In contrast to fuzzy logic, Bayesian bootstrap and a Bayesian test based on empiric [...truncated...]
Author: Luca Sartore [aut] , Luca Sartore [cre] , Lu Chen [aut] , Justin van Wart [aut], Andrew Dau [aut] , Valbona Bejleri [aut]
Maintainer: Luca Sartore <drwolf85@gmail.com>

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Package gdi updated to version 1.10.0 with previous version 1.6.0 dated 2024-04-21

Title: Volumetric Analysis using Graphic Double Integration
Description: Tools implementing an automated version of the graphic double integration technique (GDI) for volume implementation, and some other related utilities for paleontological image-analysis. GDI was first employed by Jerison (1973) <ISBN:9780323141086> and Hurlburt (1999) <doi:10.1080/02724634.1999.10011145> and is primarily used for volume or mass estimation of (extinct) animals. The package 'gdi' aims to make this technique as convenient and versatile as possible. The core functions of 'gdi' provide utilities for automatically measuring diameters from digital silhouettes provided as image files and calculating volume via graphic double integration with simple elliptical, superelliptical (following Motani 2001 <doi:10.1666/0094-8373(2001)027%3C0735:EBMFST%3E2.0.CO;2>) or complex cross-sectional geometries (see also Zhao 2024 <doi:10.7717/peerj.17479>). Additionally, the package provides functions for estimating the center of mass position (COM), the moment of inerti [...truncated...]
Author: Darius Nau [aut, cre]
Maintainer: Darius Nau <dariusnau@gmx.at>

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Package deepspat updated to version 0.3.1 with previous version 0.3.0 dated 2025-11-12

Title: Deep Compositional Spatial Models
Description: Deep compositional spatial models are standard spatial covariance models coupled with an injective warping function of the spatial domain. The warping function is constructed through a composition of multiple elemental injective functions in a deep-learning framework. The package implements two cases for the univariate setting; first, when these warping functions are known up to some weights that need to be estimated, and, second, when the weights in each layer are random. In the multivariate setting only the former case is available. Estimation and inference is done using `tensorflow`, which makes use of graphics processing units. For more details see Zammit-Mangion et al. (2022) <doi:10.1080/01621459.2021.1887741>, Vu et al. (2022) <doi:10.5705/ss.202020.0156>, Vu et al. (2023) <doi:10.1016/j.spasta.2023.100742>, and Shao et al. (2025) <doi:10.48550/arXiv.2505.12548>.
Author: Andrew Zammit-Mangion [aut], Quan Vu [aut, cre], Xuanjie Shao [aut]
Maintainer: Quan Vu <quanvustats@gmail.com>

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Package d3po updated to version 1.0.2 with previous version 1.0.1 dated 2025-11-23

Title: Fast and Beautiful Interactive Visualization for 'Markdown' and 'Shiny'
Description: Apache licensed alternative to 'Highcharter' which provides functions for both fast and beautiful interactive visualization for 'Markdown' and 'Shiny'.
Author: Mauricio Vargas Sepulveda [aut, cre, cph] , John Coene [aut], Ariel Alvarado [ctb], Sylvain Lesage [ctb], Curran Kelleher [ctb], Fernando Becerra [ctb], Natural Earth [dtc], R Consortium [fnd]
Maintainer: Mauricio Vargas Sepulveda <m.vargas.sepulveda@gmail.com>

Diff between d3po versions 1.0.1 dated 2025-11-23 and 1.0.2 dated 2025-11-25

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Package AssumpSure updated to version 1.1.4 with previous version 1.1.3 dated 2025-11-12

Title: 'shiny' Application for Statistical Test Assumption Checking and Guidance
Description: A 'shiny' application to assess statistical assumptions and guide users toward appropriate tests. The app is designed for researchers with minimal statistical training and provides diagnostics, plots, and test recommendations for a wide range of analyses. Many statistical assumptions are implemented using the package 'rstatix' (Kassambara, 2019) <doi:10.32614/CRAN.package.rstatix> and 'performance' (Lüdecke et al., 2021) <doi:10.21105/joss.03139>.
Author: Ahmed Bargheet [aut, cre]
Maintainer: Ahmed Bargheet <ahmed.bargheet@yahoo.com>

Diff between AssumpSure versions 1.1.3 dated 2025-11-12 and 1.1.4 dated 2025-11-25

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Package abn updated to version 3.1.12 with previous version 3.1.9 dated 2025-06-26

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

Diff between abn versions 3.1.9 dated 2025-06-26 and 3.1.12 dated 2025-11-25

 abn-3.1.12/abn/DESCRIPTION                                                                             |   14 
 abn-3.1.12/abn/MD5                                                                                     |   99 -
 abn-3.1.12/abn/NAMESPACE                                                                               |    8 
 abn-3.1.12/abn/R/RcppExports.R                                                                         |   54 
 abn-3.1.12/abn/R/abn-methods.R                                                                         |   46 
 abn-3.1.12/abn/R/build_score_cache.R                                                                   |    4 
 abn-3.1.12/abn/R/build_score_cache_mle.R                                                               |   62 
 abn-3.1.12/abn/R/export.R                                                                              |only
 abn-3.1.12/abn/R/fitabn.R                                                                              |    3 
 abn-3.1.12/abn/R/fitabn_mle.R                                                                          |  312 ++-
 abn-3.1.12/abn/README.md                                                                               |    2 
 abn-3.1.12/abn/build/vignette.rds                                                                      |binary
 abn-3.1.12/abn/configure                                                                               |   18 
 abn-3.1.12/abn/configure.ac                                                                            |    2 
 abn-3.1.12/abn/inst/CITATION                                                                           |    7 
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 abn-3.1.12/abn/man/fitAbn.Rd                                                                           |    5 
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 abn-3.1.12/abn/src/RcppExports.cpp                                                                     |   84 
 abn-3.1.12/abn/src/irls_binomial_fast.cpp                                                              |   12 
 abn-3.1.12/abn/src/irls_binomial_fast_br.cpp                                                           |   15 
 abn-3.1.12/abn/src/irls_gaussian_fast.cpp                                                              |    9 
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 abn-3.1.12/abn/tests/testthat/test-abn-methods.R                                                       |   23 
 abn-3.1.12/abn/tests/testthat/test-abn-toolbox.R                                                       |   44 
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 abn-3.1.9/abn/man/factorial_fast.Rd                                                                    |only
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New package r5rgui with initial version 0.1.0
Package: r5rgui
Title: Graphical User Interface for 'r5r' Router
Version: 0.1.0
Description: Interactively build and explore public transit routes with 'r5r' package via a graphical user interface in a 'shiny' app. The underlying routing methods are described in Pereira et al. (2021) <doi:10.32866/001c.21262>.
License: MIT + file LICENSE
URL: https://github.com/e-kotov/r5rgui, https://www.ekotov.pro/r5rgui/
BugReports: https://github.com/e-kotov/r5rgui/issues
Imports: DT, mapgl, r5r, sf, shiny
Suggests: mockery, quarto, rlang, shinytest2, testthat (>= 3.0.0)
Encoding: UTF-8
Language: en
NeedsCompilation: no
Packaged: 2025-11-20 23:13:18 UTC; ek
Author: Egor Kotov [aut, cre, cph]
Maintainer: Egor Kotov <kotov.egor@gmail.com>
Repository: CRAN
Date/Publication: 2025-11-25 07:50:02 UTC

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New package BLSloadR with initial version 0.2
Package: BLSloadR
Title: Download Time Series Data from the U.S. Bureau of Labor Statistics
Version: 0.2
Description: These functions provide a convenient interface for downloading data from the U.S. Bureau of Labor Statistics <https://www.bls.gov>. The functions in this package utilize flat files produced by the Bureau of Labor Statistics, which contain full series history. These files include employment, unemployment, wages, prices, industry and occupational data at a national, state, and sub-state level, depending on the series. Individual functions are included for those programs which have data available at the state level. The core functions provide direct access to the Current Employment Statistics (CES) <https://www.bls.gov/ces/>, Local Area Unemployment Statistics (LAUS) <https://www.bls.gov/lau/>, Occupational Employment and Wage Statistics (OEWS) <https://www.bls.gov/oes/> and Alternative Measures of Labor Underutilization (SALT) <https://www.bls.gov/lau/stalt.htm> data produced by the Bureau of Labor Statistics.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: data.table (>= 1.16), httr (>= 1.4.7), dplyr (>= 1.1), stringr (>= 1.5), lubridate (>= 1.9), rvest (>= 1.0.4), readxl (>= 1.4.5), sf (>= 1.0), tidyselect (>= 1.2), tigris (>= 2.0), zoo (>= 1.8), htmltools, rstudioapi
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
URL: https://schmidtdetr.github.io/BLSloadR/
NeedsCompilation: no
Packaged: 2025-11-20 22:02:19 UTC; deschmidt
Author: Nevada Department of Employment, Training, and Rehabilitation [cph], David Schmidt [aut, cre]
Maintainer: David Schmidt <deschmidt@detr.nv.gov>
Repository: CRAN
Date/Publication: 2025-11-25 07:20:02 UTC

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Package filibustr (with last version 0.5.0) was removed from CRAN

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

2025-10-26 0.5.0
2025-08-19 0.4.1
2025-05-11 0.4.0
2025-03-31 0.3.0

Permanent link
Package terminalgraphics updated to version 0.2.0 with previous version 0.1.1 dated 2025-08-28

Title: Graphical Output in Terminals
Description: Defines a graphics device and functions for graphical output in terminal emulators that support graphical output. Currently terminals that support the Terminal Graphics Protocol (<https://sw.kovidgoyal.net/kitty/graphics-protocol/>) and terminal supporting Sixel (<https://en.wikipedia.org/wiki/Sixel>) are supported.
Author: Jan van der Laan [aut, cre] , Doug Kelkhoff [ctb]
Maintainer: Jan van der Laan <r@eoos.dds.nl>

Diff between terminalgraphics versions 0.1.1 dated 2025-08-28 and 0.2.0 dated 2025-11-25

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Package splusTimeDate updated to version 2.5.9 with previous version 2.5.8 dated 2024-09-12

Title: Times and Dates from 'S-PLUS'
Description: A collection of classes and methods for working with times and dates. The code was originally available in 'S-PLUS'.
Author: Stephen Kaluzny [aut, cre], TIBCO Software Inc. [aut, cph]
Maintainer: Stephen Kaluzny <spkaluzny@gmail.com>

Diff between splusTimeDate versions 2.5.8 dated 2024-09-12 and 2.5.9 dated 2025-11-25

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Package pomp updated to version 6.4 with previous version 6.3 dated 2025-05-09

Title: Statistical Inference for Partially Observed Markov Processes
Description: Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.
Author: Aaron A. King [aut, cre] , Edward L. Ionides [aut] , Carles Breto [aut] , Stephen P. Ellner [ctb] , Matthew J. Ferrari [ctb] , Sebastian Funk [ctb] , Steven G. Johnson [ctb], Bruce E. Kendall [ctb] , Michael Lavine [ctb], Dao Nguyen [ctb] , Eamon B. [...truncated...]
Maintainer: Aaron A. King <kingaa@umich.edu>

Diff between pomp versions 6.3 dated 2025-05-09 and 6.4 dated 2025-11-25

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Package multimark updated to version 2.1.7 with previous version 2.1.6 dated 2023-03-10

Title: Capture-Mark-Recapture Analysis using Multiple Non-Invasive Marks
Description: Traditional and spatial capture-mark-recapture analysis with multiple non-invasive marks. The models implemented in 'multimark' combine encounter history data arising from two different non-invasive "marks", such as images of left-sided and right-sided pelage patterns of bilaterally asymmetrical species, to estimate abundance and related demographic parameters while accounting for imperfect detection. Bayesian models are specified using simple formulae and fitted using Markov chain Monte Carlo. Addressing deficiencies in currently available software, 'multimark' also provides a user-friendly interface for performing Bayesian multimodel inference using non-spatial or spatial capture-recapture data consisting of a single conventional mark or multiple non-invasive marks. See McClintock (2015) <doi:10.1002/ece3.1676> and Maronde et al. (2020) <doi:10.1002/ece3.6990>.
Author: Brett T. McClintock [aut, cre], Acho Arnold [ctb, cph] , Barry Brown [ctb] , James Lovato [ctb] , John Burkardt [ctb] , Cleve Moler [ctb] , Arjun Gopalaswamy [ctb]
Maintainer: Brett T. McClintock <brett.mcclintock@noaa.gov>

Diff between multimark versions 2.1.6 dated 2023-03-10 and 2.1.7 dated 2025-11-25

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 8 files changed, 23 insertions(+), 17 deletions(-)

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New package mrap with initial version 1.0.0
Package: mrap
Title: Machine-Readable Data Analysis Results with Function Wrappers
Version: 1.0.0
Description: You can use the set of wrappers for analytical schemata to reduce the effort in writing machine-readable data. The set of all-in-one wrappers will cover widely used functions from data analysis packages.
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://gitlab.com/TIBHannover/lki/knowledge-loom/mrap-r
BugReports: https://gitlab.com/TIBHannover/lki/knowledge-loom/mrap-r/-/issues
Imports: dtreg, jsonlite, stringr
Suggests: knitr, lme4, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-11-20 17:43:48 UTC; LezhninaO
Author: Olga Lezhnina [aut, cre] , Manuel Prinz [aut] , Markus Stocker [aut] , Open Research Knowledge Graph Project and Contributors [cph]
Maintainer: Olga Lezhnina <olga.lezhnina@tib.eu>
Repository: CRAN
Date/Publication: 2025-11-25 07:00:08 UTC

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Package mlr3measures updated to version 1.2.0 with previous version 1.1.0 dated 2025-09-04

Title: Performance Measures for 'mlr3'
Description: Implements multiple performance measures for supervised learning. Includes over 40 measures for regression and classification. Additionally, meta information about the performance measures can be queried, e.g. what the best and worst possible performances scores are.
Author: Michel Lang [aut] , Martin Binder [ctb], Marc Becker [cre, aut] , Lona Koers [aut]
Maintainer: Marc Becker <marcbecker@posteo.de>

Diff between mlr3measures versions 1.1.0 dated 2025-09-04 and 1.2.0 dated 2025-11-25

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Package groupedHyperframe updated to version 0.3.2 with previous version 0.3.1 dated 2025-10-30

Title: Grouped Hyper Data Frame: An Extension of Hyper Data Frame
Description: An S3 class 'groupedHyperframe' that inherits from hyper data frame. Batch processes on point-pattern hyper column. Aggregation of function-value-table hyper column(s) and numeric hyper column(s) over a nested grouping structure.
Author: Tingting Zhan [aut, cre] , Inna Chervoneva [ctb]
Maintainer: Tingting Zhan <tingtingzhan@gmail.com>

Diff between groupedHyperframe versions 0.3.1 dated 2025-10-30 and 0.3.2 dated 2025-11-25

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Package ggside updated to version 0.4.1 with previous version 0.4.0 dated 2025-09-13

Title: Side Grammar Graphics
Description: The grammar of graphics as shown in 'ggplot2' has provided an expressive API for users to build plots. 'ggside' extends 'ggplot2' by allowing users to add graphical information about one of the main panel's axis using a familiar 'ggplot2' style API with tidy data. This package is particularly useful for visualizing metadata on a discrete axis, or summary graphics on a continuous axis such as a boxplot or a density distribution.
Author: Justin Landis [aut, cre]
Maintainer: Justin Landis <jtlandis314@gmail.com>

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Package GeoThinneR updated to version 2.1.0 with previous version 2.0.0 dated 2025-04-24

Title: Efficient Spatial Thinning of Species Occurrences
Description: Provides efficient geospatial thinning algorithms to reduce the density of coordinate data while maintaining spatial relationships. Implements K-D Tree and brute-force distance-based thinning, as well as grid-based and precision-based thinning methods. For more information on the methods, see Elseberg et al. (2012) <https://hdl.handle.net/10446/86202>.
Author: Jorge Mestre-Tomas [aut, cre]
Maintainer: Jorge Mestre-Tomas <jorge.mestre.tomas@csic.es>

Diff between GeoThinneR versions 2.0.0 dated 2025-04-24 and 2.1.0 dated 2025-11-25

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Package candisc updated to version 1.1.0 with previous version 1.0.0 dated 2025-11-05

Title: Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis
Description: Functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA' design and is equivalent to canonical correlation analysis between a set of quantitative response variables and a set of dummy variables coded from the factor variable. The 'candisc' package generalizes this to higher-way 'MANOVA' designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are now provided for canonical correlation analysis when all predictors are quantitative. Methods for linear discriminant analysis are now included.
Author: Michael Friendly [aut, cre] , John Fox [aut]
Maintainer: Michael Friendly <friendly@yorku.ca>

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Package BayesMallows updated to version 2.2.6 with previous version 2.2.5 dated 2025-06-27

Title: Bayesian Preference Learning with the Mallows Rank Model
Description: An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>; Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>; Sorensen et al., R Journal, 2020 <doi:10.32614/RJ-2020-026>; Stein, PhD Thesis, 2023 <https://eprints.lancs.ac.uk/id/eprint/195759>). Both Metropolis-Hastings and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank mo [...truncated...]
Author: Oystein Sorensen [aut, cre] , Waldir Leoncio [aut], Valeria Vitelli [aut] , Marta Crispino [aut], Qinghua Liu [aut], Cristina Mollica [aut], Luca Tardella [aut], Anja Stein [aut]
Maintainer: Oystein Sorensen <oystein.sorensen.1985@gmail.com>

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