Mon, 08 Dec 2025

Package qtl updated to version 1.74 with previous version 1.72 dated 2025-11-19

Title: Tools for Analyzing QTL Experiments
Description: Analysis of experimental crosses to identify genes (called quantitative trait loci, QTLs) contributing to variation in quantitative traits. Broman et al. (2003) <doi:10.1093/bioinformatics/btg112>.
Author: Karl W Broman [aut, cre] , Hao Wu [aut], Gary Churchill [ctb] , Saunak Sen [ctb] , Danny Arends [ctb] , Robert Corty [ctb], Timothee Flutre [ctb], Ritsert Jansen [ctb], Pjotr Prins [ctb] , Lars Ronnegard [ctb], Rohan Shah [ctb], Laura Shannon [ctb], [...truncated...]
Maintainer: Karl W Broman <broman@wisc.edu>

Diff between qtl versions 1.72 dated 2025-11-19 and 1.74 dated 2025-12-08

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Package DeCAFS updated to version 3.3.5 with previous version 3.3.3 dated 2023-01-06

Title: Detecting Changes in Autocorrelated and Fluctuating Signals
Description: Detect abrupt changes in time series with local fluctuations as a random walk process and autocorrelated noise as an AR(1) process. See Romano, G., Rigaill, G., Runge, V., Fearnhead, P. (2021) <doi:10.1080/01621459.2021.1909598>.
Author: Gaetano Romano [aut, cre], Guillem Rigaill [aut], Vincent Runge [aut], Paul Fearnhead [aut]
Maintainer: Gaetano Romano <g.romano@lancaster.ac.uk>

Diff between DeCAFS versions 3.3.3 dated 2023-01-06 and 3.3.5 dated 2025-12-08

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Package mlts updated to version 2.0.0 with previous version 1.0.0 dated 2024-06-27

Title: Multilevel Latent Time Series Models with 'R' and 'Stan'
Description: Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM). The models can be set up and modified with user-friendly functions and are fit to the data using 'Stan' for Bayesian inference. Path models and formulas for user-defined models can be easily created with functions using 'knitr'. Asparouhov, Hamaker, & Muthen (2018) <doi:10.1080/10705511.2017.1406803>.
Author: Kenneth Koslowski [aut, cre, cph] , Fabian Muench [aut] , Tobias Koch [aut] , Jana Holtmann [aut]
Maintainer: Kenneth Koslowski <kenneth.koslowski@uni-leipzig.de>

Diff between mlts versions 1.0.0 dated 2024-06-27 and 2.0.0 dated 2025-12-08

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Package modelbased updated to version 0.13.1 with previous version 0.13.0 dated 2025-08-30

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

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Package mlt.docreg updated to version 1.1-12 with previous version 1.1-11 dated 2025-10-08

Title: Most Likely Transformations: Documentation and Regression Tests
Description: Additional documentation, a package vignette and regression tests for package mlt.
Author: Torsten Hothorn [aut, cre]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

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Package ggdiceplot updated to version 1.0.1 with previous version 0.1.1 dated 2025-10-14

Title: Dice Plot Visualization for 'ggplot2'
Description: Provides 'ggplot2' extensions for creating dice-based visualizations where each dot position represents a specific categorical variable. The package includes geom_dice() for displaying presence/absence of categorical variables using traditional dice patterns. Each dice position (1-6) represents a different category, with dots shown only when that category is present. This allows intuitive visualization of up to 6 categorical variables simultaneously.
Author: Matthias Flotho [aut, cre]
Maintainer: Matthias Flotho <matthias.flotho@ccb.uni-saarland.de>

Diff between ggdiceplot versions 0.1.1 dated 2025-10-14 and 1.0.1 dated 2025-12-08

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Package alqrfe updated to version 1.3 with previous version 1.2 dated 2025-07-03

Title: Adaptive Lasso Quantile Regression with Fixed Effects
Description: Quantile regression with fixed effects solves longitudinal data, considering the individual intercepts as fixed effects. The parametric set of this type of problem used to be huge. Thus penalized methods such as Lasso are currently applied. Adaptive Lasso presents oracle proprieties, which include Gaussianity and correct model selection. Bayesian information criteria (BIC) estimates the optimal tuning parameter lambda. Plot tools are also available.
Author: Ian Meneghel Danilevicz [aut, cre] , Pascal Bondon [aut], Valderio A. Reisen [aut]
Maintainer: Ian Meneghel Danilevicz <iandanilevicz@gmail.com>

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Package rhosp (with last version 1.10) was removed from CRAN

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

2018-08-07 1.10
2015-07-20 1.07
2014-12-05 1.06
2013-12-11 1.05
2009-02-10 1.04
2008-02-16 1.03
2007-09-11 1.02
2006-08-03 1.01

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Package SimEUCartelLaw updated to version 1.0.4 with previous version 1.0.3 dated 2022-06-13

Title: Simulation of Legal Exemption System for European Cartel Law
Description: Monte Carlo simulations of a game-theoretic model for the legal exemption system of the European cartel law are implemented in order to estimate the (mean) deterrent effect of this system. The input and output parameters of the simulated cartel opportunities can be visualized by three-dimensional projections. A description of the model is given in Moritz et al. (2018) <doi:10.1515/bejeap-2017-0235>.
Author: Martin Becker [aut, cre]
Maintainer: Martin Becker <martin.becker@mx.uni-saarland.de>

Diff between SimEUCartelLaw versions 1.0.3 dated 2022-06-13 and 1.0.4 dated 2025-12-08

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Package MSCMT updated to version 1.4.1 with previous version 1.4.0 dated 2024-03-19

Title: Multivariate Synthetic Control Method Using Time Series
Description: Three generalizations of the synthetic control method (which has already an implementation in package 'Synth') are implemented: first, 'MSCMT' allows for using multiple outcome variables, second, time series can be supplied as economic predictors, and third, a well-defined cross-validation approach can be used. Much effort has been taken to make the implementation as stable as possible (including edge cases) without losing computational efficiency. A detailed description of the main algorithms is given in Becker and Klößner (2018) <doi:10.1016/j.ecosta.2017.08.002>.
Author: Martin Becker [aut, cre] , Stefan Kloessner [aut], Karline Soetaert [com], Jack Dongarra [cph], R.J. Hanson [cph], K.H. Haskell [cph], Cleve Moler [cph], LAPACK authors [cph]
Maintainer: Martin Becker <martin.becker@mx.uni-saarland.de>

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Package backbone updated to version 3.0.3 with previous version 3.0.2 dated 2025-10-06

Title: Extracts the Backbone from Networks
Description: An implementation of methods for extracting a sparse unweighted network (i.e. a backbone) from an unweighted network (e.g., Hamann et al., 2016 <doi:10.1007/s13278-016-0332-2>), a weighted network (e.g., Serrano et al., 2009 <doi:10.1073/pnas.0808904106>), or a weighted projection (e.g., Neal et al., 2021 <doi:10.1038/s41598-021-03238-3>).
Author: Zachary Neal [aut, cre] , Rachel Domagalski [ctb], Bruce Sagan [ctb], Karl Godard [ctb]
Maintainer: Zachary Neal <zpneal@msu.edu>

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Package pqrfe updated to version 1.3 with previous version 1.2 dated 2025-11-14

Title: Penalized Quantile Regression with Fixed Effects
Description: Quantile regression with fixed effects is a general model for longitudinal data. Here we proposed to solve it by several methods. The estimation methods include three loss functions as check, asymmetric least square and asymmetric Huber functions; and three structures as simple regression, fixed effects and fixed effects with penalized intercepts by LASSO.
Author: Ian Meneghel Danilevicz [aut, cre] , Valderio A Reisen [aut], Pascal Bondon [aut]
Maintainer: Ian Meneghel Danilevicz <iandanilevicz@gmail.com>

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Package mrap updated to version 1.0.1 with previous version 1.0.0 dated 2025-11-25

Title: Machine-Readable Data Analysis Results with Function Wrappers
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.
Author: Olga Lezhnina [aut] , Manuel Prinz [aut] , Markus Stocker [aut, cre] , Open Research Knowledge Graph Project and Contributors [cph]
Maintainer: Markus Stocker <markus.stocker@tib.eu>

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Package ipw updated to version 1.2.2 with previous version 1.2.1.1 dated 2025-07-28

Title: Estimate Inverse Probability Weights
Description: Functions to estimate the probability to receive the observed treatment, based on individual characteristics. The inverse of these probabilities can be used as weights when estimating causal effects from observational data via marginal structural models. Both point treatment situations and longitudinal studies can be analysed. The same functions can be used to correct for informative censoring.
Author: Hung Thai Tran [aut, cre], Willem M. van der Wal [aut], Ronald B. Geskus [aut]
Maintainer: Hung Thai Tran <hungtt@oucru.org>

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Package collinear updated to version 3.0.0 with previous version 2.0.0 dated 2024-11-08

Title: Automated Multicollinearity Management
Description: Provides a comprehensive and automated workflow for managing multicollinearity in data frames with numeric and/or categorical variables. The package integrates five robust methods into a single function: (1) target encoding of categorical variables based on response values (Micci-Barreca, 2001 (Micci-Barreca, D. 2001 <doi:10.1145/507533.507538>); (2) automated feature prioritization to preserve key predictors during filtering; (3 and 4) pairwise correlation and VIF filtering across all variable types (numeric–numeric, numeric–categorical, and categorical–categorical); (5) adaptive correlation and VIF thresholds. Together, these methods enable a reliable multicollinearity management in most use cases while maintaining model integrity. The package also supports parallel processing and progress tracking via the packages 'future' and 'progressr', and provides seamless integration with the 'tidymodels' ecosystem through a dedicated recipe step.
Author: Blas M. Benito [aut, cre, cph]
Maintainer: Blas M. Benito <blasbenito@gmail.com>

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Package broman updated to version 0.92 with previous version 0.90 dated 2025-10-10

Title: Karl Broman's R Code
Description: Miscellaneous R functions, including functions related to graphics (mostly for base graphics), permutation tests, running mean/median, and general utilities.
Author: Karl W Broman [aut, cre] , Aimee Teo Broman [ctb]
Maintainer: Karl W Broman <broman@wisc.edu>

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Package xoi updated to version 0.74 with previous version 0.72 dated 2023-03-22

Title: Tools for Analyzing Crossover Interference
Description: Analysis of crossover interference in experimental crosses, particularly regarding the gamma model. See, for example, Broman and Weber (2000) <doi:10.1086/302923>.
Author: Karl W Broman [aut, cre] , Il-Youp Kwak [ctb]
Maintainer: Karl W Broman <broman@wisc.edu>

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Package rlistings updated to version 0.2.13 with previous version 0.2.12 dated 2025-06-15

Title: Clinical Trial Style Data Readout Listings
Description: Listings are often part of the submission of clinical trial data in regulatory settings. We provide a framework for the specific formatting features often used when displaying large datasets in that context.
Author: Gabriel Becker [aut] , Adrian Waddell [aut], Joe Zhu [aut, cre] , Davide Garolini [aut] , Emily de la Rua [aut] , Abinaya Yogasekaram [ctb] , F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Joe Zhu <joe.zhu@roche.com>

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Package negenes updated to version 1.4 with previous version 1.2 dated 2025-05-11

Title: Estimating the Number of Essential Genes in a Genome
Description: Estimating the number of essential genes in a genome on the basis of data from a random transposon mutagenesis experiment, through the use of a Gibbs sampler. Lamichhane et al. (2003) <doi:10.1073/pnas.1231432100>.
Author: Karl W Broman [aut, cre]
Maintainer: Karl W Broman <broman@wisc.edu>

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Package moonboot updated to version 2.0.1 with previous version 1.0.1 dated 2025-02-21

Title: m-Out-of-n Bootstrap Functions
Description: Functions and examples based on the m-out-of-n bootstrap suggested by Politis, D.N. and Romano, J.P. (1994) <doi:10.1214/aos/1176325770>. Additionally there are functions to estimate the scaling factor tau and the subsampling size m. For a detailed description and a full list of references, see Dalitz, C. and Lögler, F. (2025) <doi:10.32614/RJ-2025-031>.
Author: Christoph Dalitz [aut, cre], Felix Loegler [aut]
Maintainer: Christoph Dalitz <christoph.dalitz@hs-niederrhein.de>

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Package knn.covertree updated to version 1.1 with previous version 1.0 dated 2019-10-28

Title: An Accurate kNN Implementation with Multiple Distance Measures
Description: Similarly to the 'FNN' package, this package allows calculation of the k nearest neighbors (kNN) of a data matrix. The implementation is based on cover trees introduced by Alina Beygelzimer, Sham Kakade, and John Langford (2006) <doi:10.1145/1143844.1143857>.
Author: Philipp Angerer [cre, aut] , David Crane [cph, aut]
Maintainer: Philipp Angerer <philipp.angerer@helmholtz-muenchen.de>

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Package fairGATE updated to version 0.1.1 with previous version 0.1.0 dated 2025-11-19

Title: Fair Gated Algorithm for Targeted Equity
Description: Tools for training and analysing fairness-aware gated neural networks for subgroup-aware prediction and interpretation in clinical datasets. Methods draw on prior work in mixture-of-experts neural networks by Jordan and Jacobs (1994) <doi:10.1007/978-1-4471-2097-1_113>, fairness-aware learning by Hardt, Price, and Srebro (2016) <doi:10.48550/arXiv.1610.02413>, and personalised treatment prediction for depression by Iniesta, Stahl, and McGuffin (2016) <doi:10.1016/j.jpsychires.2016.03.016>.
Author: Rhys Holland [aut, cre], Raquel Iniesta [aut]
Maintainer: Rhys Holland <rhys.holland@icloud.com>

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Package eco updated to version 4.0-6 with previous version 4.0-5 dated 2025-10-23

Title: Ecological Inference in 2x2 Tables
Description: Implements the Bayesian and likelihood methods proposed in Imai, Lu, and Strauss (2008 <doi:10.1093/pan/mpm017>) and (2011 <doi:10.18637/jss.v042.i05>) for ecological inference in 2 by 2 tables as well as the method of bounds introduced by Duncan and Davis (1953). The package fits both parametric and nonparametric models using either the Expectation-Maximization algorithms (for likelihood models) or the Markov chain Monte Carlo algorithms (for Bayesian models). For all models, the individual-level data can be directly incorporated into the estimation whenever such data are available. Along with in-sample and out-of-sample predictions, the package also provides a functionality which allows one to quantify the effect of data aggregation on parameter estimation and hypothesis testing under the parametric likelihood models.
Author: Kosuke Imai [aut, cre], Ying Lu [aut], Aaron Strauss [aut], Hubert Jin [ctb]
Maintainer: Kosuke Imai <imai@Harvard.Edu>

Diff between eco versions 4.0-5 dated 2025-10-23 and 4.0-6 dated 2025-12-08

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Package easybgm updated to version 0.3.1 with previous version 0.2.1 dated 2024-10-17

Title: Extracting and Visualizing Bayesian Graphical Models
Description: Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. The package supports fitting cross-sectional network models fitted using the packages 'BDgraph', 'bgms' and 'BGGM', as well as network comparison fitted using the 'bgms' and 'BBGM'. The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the posterior density of the parameters. In addition, for 'BDgraph' and 'bgms' it allows to assess the posterior structure space. Furthermore, the package comes with an extensive suite for visualizing results.
Author: Karoline Huth [aut, cre] , Sara Keetelaar [ctb], Nikola Sekulovski [ctb], Gali Geller [ctb]
Maintainer: Karoline Huth <k.huth@uva.nl>

Diff between easybgm versions 0.2.1 dated 2024-10-17 and 0.3.1 dated 2025-12-08

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Package Scalelink updated to version 1.0-2 with previous version 1.0 dated 2019-02-20

Title: Create Scale Linkage Scores
Description: Perform a 'probabilistic' linkage of two data files using a scaling procedure using the methods described in Goldstein, H., Harron, K. and Cortina-Borja, M. (2017) <doi:10.1002/sim.7287>.
Author: Chris Charlton [aut, cre], Harvey Goldstein [aut]
Maintainer: Chris Charlton <c.charlton@bristol.ac.uk>

Diff between Scalelink versions 1.0 dated 2019-02-20 and 1.0-2 dated 2025-12-08

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Package rncl updated to version 0.8.8 with previous version 0.8.7 dated 2023-01-08

Title: An Interface to the Nexus Class Library
Description: An interface to the Nexus Class Library which allows parsing of NEXUS, Newick and other phylogenetic tree file formats. It provides elements of the file that can be used to build phylogenetic objects such as ape's 'phylo' or phylobase's 'phylo4(d)'. This functionality is demonstrated with 'read_newick_phylo()' and 'read_nexus_phylo()'.
Author: Francois Michonneau [aut, cre] , Ben Bolker [aut] , Mark Holder [aut] , Paul Lewis [aut] , Brian O'Meara [aut]
Maintainer: Francois Michonneau <francois.michonneau@gmail.com>

Diff between rncl versions 0.8.7 dated 2023-01-08 and 0.8.8 dated 2025-12-08

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Package highfrequency updated to version 1.0.2 with previous version 1.0.1 dated 2023-10-04

Title: Tools for Highfrequency Data Analysis
Description: Provide functionality to manage, clean and match highfrequency trades and quotes data, calculate various liquidity measures, estimate and forecast volatility, detect price jumps and investigate microstructure noise and intraday periodicity. A detailed vignette can be found in the open-access paper "Analyzing Intraday Financial Data in R: The highfrequency Package" by Boudt, Kleen, and Sjoerup (2022, <doi:10.18637/jss.v104.i08>).
Author: Kris Boudt [aut, cre] , Jonathan Cornelissen [aut], Scott Payseur [aut], Giang Nguyen [ctb], Onno Kleen [aut] , Emil Sjoerup [aut]
Maintainer: Kris Boudt <kris.boudt@ugent.be>

Diff between highfrequency versions 1.0.1 dated 2023-10-04 and 1.0.2 dated 2025-12-08

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Package circlize updated to version 0.4.17 with previous version 0.4.16 dated 2024-02-20

Title: Circular Visualization
Description: Circular layout is an efficient way for the visualization of huge amounts of information. Here this package provides an implementation of circular layout generation in R as well as an enhancement of available software. The flexibility of the package is based on the usage of low-level graphics functions such that self-defined high-level graphics can be easily implemented by users for specific purposes. Together with the seamless connection between the powerful computational and visual environment in R, it gives users more convenience and freedom to design figures for better understanding complex patterns behind multiple dimensional data. The package is described in Gu et al. 2014 <doi:10.1093/bioinformatics/btu393>.
Author: Zuguang Gu [aut, cre]
Maintainer: Zuguang Gu <z.gu@dkfz.de>

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Package httr2 updated to version 1.2.2 with previous version 1.2.1 dated 2025-07-22

Title: Perform HTTP Requests and Process the Responses
Description: Tools for creating and modifying HTTP requests, then performing them and processing the results. 'httr2' is a modern re-imagining of 'httr' that uses a pipe-based interface and solves more of the problems that API wrapping packages face.
Author: Hadley Wickham [aut, cre], Posit Software, PBC [cph, fnd], Maximilian Girlich [ctb]
Maintainer: Hadley Wickham <hadley@posit.co>

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Package formatters updated to version 0.5.12 with previous version 0.5.11 dated 2025-04-09

Title: ASCII Formatting for Values and Tables
Description: We provide a framework for rendering complex tables to ASCII, and a set of formatters for transforming values or sets of values into ASCII-ready display strings.
Author: Gabriel Becker [aut] , Adrian Waddell [aut], Davide Garolini [aut] , Emily de la Rua [aut] , Abinaya Yogasekaram [ctb] , Joe Zhu [aut, cre] , F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Joe Zhu <joe.zhu@roche.com>

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Package eks updated to version 1.1.2 with previous version 1.1.1 dated 2025-07-08

Title: Tidy and Geospatial Kernel Smoothing
Description: Extensions of the kernel smoothing functions from the 'ks' package for compatibility with the tidyverse and geospatial ecosystems <doi:10.1007/s00180-024-01543-9>.
Author: Tarn Duong [aut, cre]
Maintainer: Tarn Duong <tarn.duong@gmail.com>

Diff between eks versions 1.1.1 dated 2025-07-08 and 1.1.2 dated 2025-12-08

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Package abess updated to version 0.4.11 with previous version 0.4.10 dated 2025-04-05

Title: Fast Best Subset Selection
Description: Extremely efficient toolkit for solving the best subset selection problem <https://www.jmlr.org/papers/v23/21-1060.html>. This package is its R interface. The package implements and generalizes algorithms designed in <doi:10.1073/pnas.2014241117> that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, Ising model reconstruction <doi:10.1080/01621459.2025.2571245>, (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection <doi:10.1287/ijoc.2022.1241> and sure independence screening <doi:10.1111/j.1467-9868.2008.00674.x> are also provided.
Author: Jin Zhu [aut, cre] , Zezhi Wang [aut], Liyuan Hu [aut], Junhao Huang [aut], Kangkang Jiang [aut], Yanhang Zhang [aut], Borui Tang [aut], Shiyun Lin [aut], Junxian Zhu [aut], Canhong Wen [aut], Heping Zhang [aut] , Xueqin Wang [aut] , spectra contribu [...truncated...]
Maintainer: Jin Zhu <zhuj1jqx@gmail.com>

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New package rdcor with initial version 1.0
Package: rdcor
Title: Rank Distance Correlation Coefficient
Version: 1.0
Date: 2025-12-01
Author: Michail Tsagris [aut, cre]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>
Depends: R (>= 4.0)
Imports: dcov, Rfast, Rfast2
Description: The rank distance correlation <doi:10.1080/01621459.2020.1782223> is computed. Included also is a function to perform permutation based testing.
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2025-12-01 19:11:54 UTC; mtsag
Repository: CRAN
Date/Publication: 2025-12-08 08:20:02 UTC

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Package plotor updated to version 0.8.0 with previous version 0.7.0 dated 2025-07-01

Title: Odds Ratio Tools for Logistic Regression
Description: Produces odds ratio analyses with comprehensive reporting tools. Generates plots, summary tables, and diagnostic checks for logistic regression models fitted with 'glm()' using binomial family. Provides visualisation methods, formatted reporting tables via 'gt', and tools to assess logistic regression model assumptions.
Author: Craig Parylo [aut, cre, cph]
Maintainer: Craig Parylo <craig.parylo2@nhs.net>

Diff between plotor versions 0.7.0 dated 2025-07-01 and 0.8.0 dated 2025-12-08

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Package pegas updated to version 1.4 with previous version 1.3 dated 2023-12-13

Title: Population and Evolutionary Genetics Analysis System
Description: Functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, including from VCF files, and for the analysis of population nucleotide sequences and micro-satellites including coalescent analyses, linkage disequilibrium, population structure (Fst, Amova) and equilibrium (HWE), haplotype networks, minimum spanning tree and network, and median-joining networks.
Author: Emmanuel Paradis [aut, cre, cph] , Thibaut Jombart [aut, cph] , Zhian N. Kamvar [aut, cph] , Brian Knaus [aut, cph] , Klaus Schliep [aut, cph] , Alastair Potts [aut, cph] , David Winter [aut, cph]
Maintainer: Emmanuel Paradis <Emmanuel.Paradis@ird.fr>

Diff between pegas versions 1.3 dated 2023-12-13 and 1.4 dated 2025-12-08

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New package micvar with initial version 0.1.0
Package: micvar
Title: Order Selection in Vector Autoregression by Mean Square Information Criteria
Version: 0.1.0
Description: Implements order selection for Vector Autoregressive (VAR) models using the Mean Square Information Criterion (MIC). Unlike standard methods such as AIC and BIC, MIC is likelihood-free. This method consistently estimates VAR order and has robust performance under model misspecification. For more details, see Hellstern and Shojaie (2025) <doi:10.48550/arXiv.2511.19761>.
License: GPL (>= 3)
Encoding: UTF-8
Imports: MASS, matrixcalc, Rdpack, stats
NeedsCompilation: no
Packaged: 2025-12-01 18:08:18 UTC; mike
Author: Michael Hellstern [aut, cre]
Maintainer: Michael Hellstern <mikeh1@uw.edu>
Repository: CRAN
Date/Publication: 2025-12-08 08:10:10 UTC

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New package MAIVE with initial version 0.1.10
Package: MAIVE
Title: Meta Analysis Instrumental Variable Estimator
Encoding: UTF-8
Version: 0.1.10
URL: https://meta-analysis.cz/maive/, https://github.com/meta-analysis-es/maive
BugReports: https://github.com/meta-analysis-es/maive/issues
Description: Meta-analysis traditionally assigns more weight to studies with lower standard errors, assuming higher precision. However, in observational research, precision must be estimated and is vulnerable to manipulation, such as p-hacking, to achieve statistical significance. This can lead to spurious precision, invalidating inverse-variance weighting and bias-correction methods like funnel plots. Common methods for addressing publication bias, including selection models, often fail or exacerbate the problem. This package introduces an instrumental variable approach to limit bias caused by spurious precision in meta-analysis. Methods are described in 'Irsova et al.' (2025) <doi:10.1038/s41467-025-63261-0>.
License: MIT + file LICENSE
Depends: R (>= 4.0.0)
Imports: stats, clubSandwich
Suggests: testthat, knitr, rmarkdown, varhandle
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-12-02 02:33:36 UTC; runner
Author: Zuzana Irsova [aut] , Pedro R. D. Bom [aut] , Tomas Havranek [aut] , Heiko Rachinger [aut] , Petr Cala [aut, cre]
Maintainer: Petr Cala <cala.p@seznam.cz>
Repository: CRAN
Date/Publication: 2025-12-08 08:40:02 UTC

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New package famish with initial version 0.2.0
Package: famish
Title: Flexibly Tune Families of Probability Distributions
Version: 0.2.0
Description: Fits probability distributions to data and plugs into the 'probaverse' suite of R packages so distribution objects are ready for further manipulation and evaluation. Supports methods such as maximum likelihood and L-moments, and provides diagnostics including empirical ranking and quantile score.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: checkmate, distionary, fitdistrplus, ismev, lmom, rlang, stats, vctrs
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
URL: https://famish.probaverse.com/, https://github.com/probaverse/famish/
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-12-01 18:44:33 UTC; vcoia
Author: Vincenzo Coia [aut, cre, cph]
Maintainer: Vincenzo Coia <vincenzo.coia@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-08 08:20:11 UTC

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New package distplyr with initial version 0.2.0
Package: distplyr
Title: Manipulate and Combine Probability Distributions
Version: 0.2.0
Description: Go beyond standard probability distributions such as the Normal or Exponential by combining, shifting, maximizing, and otherwise transforming distributions with simple, verb-based functions. Provides easy access to a broader space of distributions more representative of real-world systems such as river flows or insurance claims. Part of the probaverse framework of packages to support advanced statistical modeling and simulations with an intuitive workflow.
License: MIT + file LICENSE
Depends: R (>= 3.5.0)
Imports: ellipsis, rlang, vctrs, stats, checkmate, distionary
Suggests: covr, knitr, rmarkdown, testthat (>= 3.0.0), tibble
VignetteBuilder: knitr
Encoding: UTF-8
URL: https://distplyr.probaverse.com/, https://github.com/probaverse/distplyr
NeedsCompilation: no
Packaged: 2025-12-01 18:44:10 UTC; vcoia
Author: Vincenzo Coia [aut, cre, cph], Amogh Joshi [ctb], Shuyi Tan [ctb], Zhipeng Zhu [ctb]
Maintainer: Vincenzo Coia <vincenzo.coia@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-08 08:20:17 UTC

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New package charisma with initial version 1.0.0
Package: charisma
Title: Reproducible Color Characterization of Digital Images for Biological Studies
Version: 1.0.0
Description: Provides a standardized and reproducible framework for characterizing and classifying discrete color classes from digital images of biological organisms. The package automatically determines the presence or absence of 10 human-visible color categories (black, blue, brown, green, grey, orange, purple, red, white, yellow) using a biologically-inspired Color Look-Up Table (CLUT) that partitions HSV color space. Supports both fully automated and semi-automated (interactive) workflows with complete provenance tracking for reproducibility. Pre-processes images using the 'recolorize' package (Weller et al. 2024 <doi:10.1111/ele.14378>) for spatial-color binning, and integrates with 'pavo' (Maia et al. 2019 <doi:10.1111/2041-210X.13174>) for color pattern geometry statistics. Designed for high-throughput analysis and seamless integration with downstream evolutionary analyses.
License: MIT + file LICENSE
URL: https://github.com/shawntz/charisma, https://shawnschwartz.com/charisma/
BugReports: https://github.com/shawntz/charisma/issues
Encoding: UTF-8
LazyData: true
Depends: R (>= 4.0.0)
Imports: magrittr, plyr, dplyr, tibble, purrr, tidyr, parallel, recolorize, imager, abind, jpeg, png, grDevices, graphics, stats, tools, utils
Suggests: pavo, testthat (>= 3.0.0), knitr, rmarkdown, pkgdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-12-01 18:21:00 UTC; shawn.schwartz
Author: Shawn Schwartz [aut, cre, cph] , Whitney Tsai [aut] , Elizabeth Karan [aut] , Mark Juhn [aut], Allison Shultz [aut] , John McCormack [aut] , Thomas Smith [aut] , Michael Alfaro [aut]
Maintainer: Shawn Schwartz <shawn.t.schwartz@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-08 08:20:22 UTC

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Package BayesTools updated to version 0.2.23 with previous version 0.2.22 dated 2025-09-14

Title: Tools for Bayesian Analyses
Description: Provides tools for conducting Bayesian analyses and Bayesian model averaging (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, Hoeting et al., 1999, <doi:10.1214/ss/1009212519>). The package contains functions for creating a wide range of prior distribution objects, mixing posterior samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc... The tools for working with prior distribution span from visualization, generating 'JAGS' and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions.
Author: Frantisek Bartos [aut, cre]
Maintainer: Frantisek Bartos <f.bartos96@gmail.com>

Diff between BayesTools versions 0.2.22 dated 2025-09-14 and 0.2.23 dated 2025-12-08

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Package qtlpoly (with last version 0.2.4) was removed from CRAN

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

2024-03-25 0.2.4

Permanent link
Package mappoly (with last version 0.4.1) was removed from CRAN

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

2024-03-06 0.4.1

Permanent link
Package TriMatch updated to version 1.0.1 with previous version 1.0.0 dated 2025-04-03

Title: Propensity Score Matching of Non-Binary Treatments
Description: Propensity score matching for non-binary treatments.
Author: Jason Bryer [aut, cre]
Maintainer: Jason Bryer <jason@bryer.org>

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Package nmaplateplot updated to version 1.0.3 with previous version 1.0.2 dated 2024-04-07

Title: The Plate Plot for Network Meta-Analysis Results
Description: A graphical display of results from network meta-analysis (NMA). It is suitable for outcomes like odds ratio (OR), risk ratio (RR), risk difference (RD) and standardized mean difference (SMD). It also has an option to visually display and compare the surface under the cumulative ranking (SUCRA) of different treatments.
Author: Zhenxun Wang [aut, cre], Lifeng Lin [ctb], Shanshan Zhao [ctb], Haitao Chu [ctb]
Maintainer: Zhenxun Wang <wang6795@alumni.umn.edu>

Diff between nmaplateplot versions 1.0.2 dated 2024-04-07 and 1.0.3 dated 2025-12-08

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Package ggamma updated to version 1.0.2 with previous version 1.0.1 dated 2019-12-15

Title: Generalized Gamma Probability Distribution
Description: Density, distribution function, quantile function and random generation for the Generalized Gamma proposed in Stacy, E. W. (1962) <doi:10.1214/aoms/1177704481>.
Author: Matheus H. J. Saldanha [aut, cre], Adriano K. Suzuki [aut]
Maintainer: Matheus H. J. Saldanha <mhjsaldanha@gmail.com>

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Package DataPackageR updated to version 0.16.2 with previous version 0.16.1 dated 2024-09-17

Title: Construct Reproducible Analytic Data Sets as R Packages
Description: A framework to help construct R data packages in a reproducible manner. Potentially time consuming processing of raw data sets into analysis ready data sets is done in a reproducible manner and decoupled from the usual 'R CMD build' process so that data sets can be processed into R objects in the data package and the data package can then be shared, built, and installed by others without the need to repeat computationally costly data processing. The package maintains data provenance by turning the data processing scripts into package vignettes, as well as enforcing documentation and version checking of included data objects. Data packages can be version controlled on 'GitHub', and used to share data for manuscripts, collaboration and reproducible research.
Author: Greg Finak [aut, cph] , Paul Obrecht [ctb], Ellis Hughes [ctb] , Jimmy Fulp [ctb], Marie Vendettuoli [ctb] , Dave Slager [ctb, cre] , Jason Taylor [ctb], Kara Woo [rev] , William Landau [rev]
Maintainer: Dave Slager <dslager@fredhutch.org>

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Package common updated to version 1.1.4 with previous version 1.1.3 dated 2024-04-05

Title: Solutions for Common Problems in Base R
Description: Contains functions for solving commonly encountered problems while programming in R. This package is intended to provide a lightweight supplement to Base R, and will be useful for almost any R user.
Author: David Bosak [aut, cre], Duong Tran [ctb]
Maintainer: David Bosak <dbosak01@gmail.com>

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Package aster updated to version 1.3-7 with previous version 1.3-6 dated 2025-10-20

Title: Aster Models
Description: Aster models (Geyer, Wagenius, and Shaw, 2007, <doi:10.1093/biomet/asm030>; Shaw, Geyer, Wagenius, Hangelbroek, and Etterson, 2008, <doi:10.1086/588063>; Geyer, Ridley, Latta, Etterson, and Shaw, 2013, <doi:10.1214/13-AOAS653>) are exponential family regression models for life history analysis. They are like generalized linear models except that elements of the response vector can have different families (e. g., some Bernoulli, some Poisson, some zero-truncated Poisson, some normal) and can be dependent, the dependence indicated by a graphical structure. Discrete time survival analysis, life table analysis, zero-inflated Poisson regression, and generalized linear models that are exponential family (e. g., logistic regression and Poisson regression with log link) are special cases. Main use is for data in which there is survival over discrete time periods and there is additional data about what happens conditional on survival (e. g., number of offspring). Uses the ex [...truncated...]
Author: Charles J. Geyer [aut, cre]
Maintainer: Charles J. Geyer <geyer@umn.edu>

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Package IOHanalyzer (with last version 0.1.8.17) was removed from CRAN

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

2025-11-21 0.1.8.17
2024-03-01 0.1.8.10
2023-12-04 0.1.8.6

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Package d3po (with last version 1.0.2) was removed from CRAN

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

2025-11-25 1.0.2
2025-11-23 1.0.1
2023-06-19 0.5.5
2023-03-03 0.3.4
2021-09-23 0.3.2

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

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

2022-02-08 1.0.0

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

2022-09-30 0.3.0
2022-03-08 0.2.0

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

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

2025-05-02 1.1.1
2025-04-09 1.1.0
2025-01-24 1.0.0

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Package geosed (with last version 0.1.1) was removed from CRAN

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

2019-09-03 0.1.1

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Sun, 07 Dec 2025

Package RcmdrPlugin.EZR updated to version 1.70 with previous version 1.68 dated 2024-06-25

Title: R Commander Plug-in for the EZR (Easy R) Package
Description: EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10918-6, Nankodo, ISBN: 978-4-524-21861-5, Ohmsha, ISBN: 978-4-274-22632-8), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below. This report has been cited in more than 14,000 scientific articles.
Author: Yoshinobu Kanda [aut, cre]
Maintainer: Yoshinobu Kanda <ycanda-tky@umin.ac.jp>

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Package ctrdata updated to version 1.25.1 with previous version 1.25.0 dated 2025-08-26

Title: Retrieve and Analyze Clinical Trials Data from Public Registers
Description: A system for querying, retrieving and analyzing protocol- and results-related information on clinical trials from four public registers, the 'European Union Clinical Trials Register' ('EUCTR', <https://www.clinicaltrialsregister.eu/>), 'ClinicalTrials.gov' (<https://clinicaltrials.gov/> and also translating queries the retired classic interface), the 'ISRCTN' (<http://www.isrctn.com/>) and the 'European Union Clinical Trials Information System' ('CTIS', <https://euclinicaltrials.eu/>). Trial information is downloaded, converted and stored in a database ('PostgreSQL', 'SQLite', 'DuckDB' or 'MongoDB'; via package 'nodbi'). Protocols, statistical analysis plans, informed consent sheets and other documents in registers associated with trials can also be downloaded. Other functions implement trial concepts canonically across registers, identify deduplicated records, easily find and extract variables (fields) of interest even from complex nested data as used by the re [...truncated...]
Author: Ralf Herold [aut, cre] , Marek Kubica [cph] , Ivan Bozhanov [cph]
Maintainer: Ralf Herold <ralf.herold@mailbox.org>

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Package pqrBayes updated to version 1.2.0 with previous version 1.1.4 dated 2025-07-25

Title: Bayesian Penalized Quantile Regression
Description: Bayesian regularized quantile regression utilizing two major classes of shrinkage priors (the spike-and-slab priors and the horseshoe family of priors) leads to efficient Bayesian shrinkage estimation, variable selection and valid statistical inference. In this package, we have implemented robust Bayesian variable selection with spike-and-slab priors under high-dimensional linear regression models (Fan et al. (2024) <doi:10.3390/e26090794> and Ren et al. (2023) <doi:10.1111/biom.13670>), and regularized quantile varying coefficient models (Zhou et al.(2023) <doi:10.1016/j.csda.2023.107808>). In particular, valid robust Bayesian inferences under both models in the presence of heavy-tailed errors can be validated on finite samples. Additional models with spike-and-slab priors include robust Bayesian group LASSO and robust binary Bayesian LASSO (Fan and Wu (2025) <doi:10.1002/sta4.70078>). Besides, robust sparse Bayesian regression with the horseshoe family of (hor [...truncated...]
Author: Kun Fan [aut], Cen Wu [aut, cre], Jie Ren [aut], Xiaoxi Li [aut], Fei Zhou [aut]
Maintainer: Cen Wu <wucen@ksu.edu>

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Package ICRanks updated to version 3.2 with previous version 3.1 dated 2019-06-21

Title: Simultaneous Confidence Intervals for Ranks
Description: Algorithms to construct simultaneous confidence intervals for the ranks of means mu_1,...,mu_n based on an independent Gaussian sample using multiple testing techniques.
Author: Diaa Al Mohamad [aut, cre], Erik W. van Zwet [aut], Jelle J. Goeman [aut]
Maintainer: Diaa Al Mohamad <diaa.almohamad@gmail.com>

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Package elfDistr updated to version 1.0.1 with previous version 1.0.0 dated 2019-10-07

Title: Kumaraswamy Complementary Weibull Geometric (Kw-CWG) Probability Distribution
Description: Density, distribution function, quantile function and random generation for the Kumaraswamy Complementary Weibull Geometric (Kw-CWG) lifetime probability distribution proposed in Afify, A.Z. et al (2017) <doi:10.1214/16-BJPS322>.
Author: Matheus H. J. Saldanha [aut, cre], Adriano K. Suzuki [aut]
Maintainer: Matheus H. J. Saldanha <mhjsaldanha@gmail.com>

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Package DataGraph updated to version 1.2.15 with previous version 1.2.14 dated 2023-03-02

Title: Export Data from 'R' to 'DataGraph'
Description: Functions to pipe data from 'R' to 'DataGraph', a graphing and analysis application for mac OS. Create a live connection using either '.dtable' or '.dtbin' files that can be read by 'DataGraph'. Can save a data frame, collection of data frames and sequences of data frames and individual vectors. For more information see <https://community.visualdatatools.com/datagraph/knowledge-base/r-package/>.
Author: David Adalsteinsson [aut, cre]
Maintainer: David Adalsteinsson <david@visualdatatools.com>

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Package BEKKs updated to version 1.4.6 with previous version 1.4.5 dated 2024-11-25

Title: Multivariate Conditional Volatility Modelling and Forecasting
Description: Methods and tools for estimating, simulating and forecasting of so-called BEKK-models (named after Baba, Engle, Kraft and Kroner) based on the fast Berndt–Hall–Hall–Hausman (BHHH) algorithm described in Hafner and Herwartz (2008) <doi:10.1007/s00184-007-0130-y>. For an overview, we refer the reader to Fülle et al. (2024) <doi:10.18637/jss.v111.i04>.
Author: Markus J. Fuelle [aut, cre], Alexander Lange [aut], Christian M. Hafner [aut], Helmut Herwartz [aut]
Maintainer: Markus J. Fuelle <markus.fuelle@gmail.com>

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Package mixgb updated to version 2.0.3 with previous version 1.5.3 dated 2025-04-06

Title: Multiple Imputation Through 'XGBoost'
Description: Multiple imputation using 'XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2023) <doi:10.1080/10618600.2023.2252501>. The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process.
Author: Yongshi Deng [aut, cre] , Thomas Lumley [ths]
Maintainer: Yongshi Deng <agnes.yongshideng@gmail.com>

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Package Rcpp updated to version 1.1.0.8 with previous version 1.1.0 dated 2025-07-02

Title: Seamless R and C++ Integration
Description: The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at <https://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013, <doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2018, <doi:10.1080/00031305.2017.1375990>); see 'citation("Rcpp")' for details.
Author: Dirk Eddelbuettel [aut, cre] , Romain Francois [aut] , JJ Allaire [aut] , Kevin Ushey [aut] , Qiang Kou [aut] , Nathan Russell [aut], Inaki Ucar [aut] , Doug Bates [aut] , John Chambers [aut]
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package nodbi updated to version 0.14.0 with previous version 0.13.1 dated 2025-06-26

Title: 'NoSQL' Database Connector
Description: Simplified JSON document database access and manipulation, providing a common API across supported 'NoSQL' databases 'Elasticsearch', 'CouchDB', 'MongoDB' as well as 'SQLite/JSON1', 'PostgreSQL', and 'DuckDB'.
Author: Ralf Herold [aut, cre] , Scott Chamberlain [aut] , Rich FitzJohn [aut], Jeroen Ooms [aut], Ivan Tarbakou [cph]
Maintainer: Ralf Herold <ralf.herold@mailbox.org>

Diff between nodbi versions 0.13.1 dated 2025-06-26 and 0.14.0 dated 2025-12-07

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Package datetoiso updated to version 1.2.0 with previous version 1.1.1 dated 2025-11-18

Title: Convert and Impute Dates to ISO Standard ("International Organization for Standardization")
Description: Provides functions to convert and impute date values to the ISO 8601 standard format. The package automatically recognizes date patterns within a data frame and transforms them into consistent ISO-formatted dates. It also supports imputing missing month or day components in partial date strings using user-defined rules. Only one date format can be applied within a single data frame column.
Author: Lukasz Andrzejewski [aut, cre]
Maintainer: Lukasz Andrzejewski <lukasz.coding@gmail.com>

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Package CFM updated to version 0.8.0 with previous version 0.7.0 dated 2025-11-13

Title: Analyzing Censored Factor Models
Description: Provides generation and estimation of censored factor models for high-dimensional data with censored errors (normal, t, logistic). Includes Sparse Orthogonal Principal Components (SOPC), and evaluation metrics. Based on Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.
Author: Guangbao Guo [aut, cre], Tong Meng [aut]
Maintainer: Guangbao Guo <ggb11111111@163.com>

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Package Rwofost updated to version 0.8-7 with previous version 0.8-6 dated 2025-12-04

Title: WOFOST Crop Growth Simulation Model
Description: An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) <doi:10.1016/j.agsy.2018.06.018> for a recent review of the history and use of the model.
Author: Robert J. Hijmans [cre, aut], Huang Fang [ctb], C.A. van Diepen [ctb], Allard de Wit [ctb], Daniel van Kraalingen [ctb], Tamme van der Wal [ctb], C. Rappoldt [ctb], Hendrik Boogard [ctb], I.G.A.M. Noy [ctb], Alterra, Wageningen-UR [cph]
Maintainer: Robert J. Hijmans <r.hijmans@gmail.com>

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Package rSpectral readmission to version 1.0.0.14 with previous version 1.0.0.10 dated 2023-01-18

Title: Spectral Modularity Clustering
Description: Implements the network clustering algorithm described in Newman (2006) <doi:10.1103/PhysRevE.74.036104>. The complete iterative algorithm comprises of two steps. In the first step, the network is expressed in terms of its leading eigenvalue and eigenvector and recursively partition into two communities. Partitioning occurs if the maximum positive eigenvalue is greater than the tolerance (10e-5) for the current partition, and if it results in a positive contribution to the Modularity. Given an initial separation using the leading eigen step, 'rSpectral' then continues to maximise for the change in Modularity using a fine-tuning step - or variate thereof. The first stage here is to find the node which, when moved from one community to another, gives the maximum change in Modularity. This node’s community is then fixed and we repeat the process until all nodes have been moved. The whole process is repeated from this new state until the change in the Modularity, between the new and o [...truncated...]
Author: Colin Mclean [aut] , Anatoly Sorokin [aut, cre]
Maintainer: Anatoly Sorokin <lptolik@gmail.com>

This is a re-admission after prior archival of version 1.0.0.10 dated 2023-01-18

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Package rrpack updated to version 0.1-14 with previous version 0.1-13 dated 2022-06-16

Title: Reduced-Rank Regression
Description: Multivariate regression methodologies including classical reduced-rank regression (RRR) studied by Anderson (1951) <doi:10.1214/aoms/1177729580> and Reinsel and Velu (1998) <doi:10.1007/978-1-4757-2853-8>, reduced-rank regression via adaptive nuclear norm penalization proposed by Chen et al. (2013) <doi:10.1093/biomet/ast036> and Mukherjee et al. (2015) <doi:10.1093/biomet/asx080>, robust reduced-rank regression (R4) proposed by She and Chen (2017) <doi:10.1093/biomet/asx032>, generalized/mixed-response reduced-rank regression (mRRR) proposed by Luo et al. (2018) <doi:10.1016/j.jmva.2018.04.011>, row-sparse reduced-rank regression (SRRR) proposed by Chen and Huang (2012) <doi:10.1080/01621459.2012.734178>, reduced-rank regression with a sparse singular value decomposition (RSSVD) proposed by Chen et al. (2012) <doi:10.1111/j.1467-9868.2011.01002.x> and sparse and orthogonal factor regression (SOFAR) proposed by Uematsu et al. (2019) <d [...truncated...]
Author: Kun Chen [aut, cre] , Wenjie Wang [aut] , Jun Yan [ctb]
Maintainer: Kun Chen <kun.chen@uconn.edu>

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Package rintcal updated to version 1.3.1 with previous version 1.3.0 dated 2025-07-09

Title: Radiocarbon Calibration Curves
Description: The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided as a data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98), other curves (e.g., NOTCal04 [van der Plicht et al. 2004], Arnold & Libby 1951, Stuiver & Suess 1966, Pearson & Stuiver 1986) and postbomb curves. Also provided are functions to copy the curves into memory, and to read, query and plot the data underlying the IntCal20 curves.
Author: Maarten Blaauw [aut, cre] , Christopher Bronk Ramsey [ctb]
Maintainer: Maarten Blaauw <maarten.blaauw@qub.ac.uk>

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Package openxlsx2 updated to version 1.22 with previous version 1.21 dated 2025-10-31

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 nebula updated to version 1.5.6 with previous version 1.5.3 dated 2024-02-15

Title: Negative Binomial Mixed Models Using Large-Sample Approximation for Differential Expression Analysis of ScRNA-Seq Data
Description: A fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) <doi:10.1038/s42003-021-02146-6>.
Author: Liang He [aut, cre], Raghav Sharma [ctb]
Maintainer: Liang He <hyx520101@gmail.com>

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Package isoband updated to version 0.3.0 with previous version 0.2.7 dated 2022-12-20

Title: Generate Isolines and Isobands from Regularly Spaced Elevation Grids
Description: A fast C++ implementation to generate contour lines (isolines) and contour polygons (isobands) from regularly spaced grids containing elevation data.
Author: Hadley Wickham [aut] , Claus O. Wilke [aut] , Thomas Lin Pedersen [aut, cre] , Posit, PBC [cph, fnd]
Maintainer: Thomas Lin Pedersen <thomas.pedersen@posit.co>

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Package heplots updated to version 1.8.1 with previous version 1.7.8 dated 2025-08-18

Title: Visualizing Hypothesis Tests in Multivariate Linear Models
Description: Provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). It also provides other tools for analysis and graphical display of the models such as robust methods and homogeneity of variance covariance matrices. The related 'candisc' package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables.
Author: Michael Friendly [aut, cre] , John Fox [aut] , Georges Monette [aut] , Phil Chalmers [ctb] , Duncan Murdoch [ctb]
Maintainer: Michael Friendly <friendly@yorku.ca>

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Package fBasics updated to version 4052.98 with previous version 4041.97 dated 2024-08-19

Title: Rmetrics - Markets and Basic Statistics
Description: Provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. The covered fields include techniques of explorative data analysis and the investigation of distributional properties, including parameter estimation and hypothesis testing. Even more there are several utility functions for data handling and management.
Author: Diethelm Wuertz [aut] , Tobias Setz [aut], Yohan Chalabi [aut], Martin Maechler [ctb] , CRAN Team [ctb], Georgi N. Boshnakov [cre, aut]
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>

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Package Colossus updated to version 1.4.5 with previous version 1.4.3 dated 2025-10-30

Title: "Risk Model Regression and Analysis with Complex Non-Linear Models"
Description: Performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards <https://en.wikipedia.org/wiki/Proportional_hazards_model>, Poisson <https://en.wikipedia.org/wiki/Poisson_regression>, and Fine-Gray competing risks <https://www.publichealth.columbia.edu/research/population-health-methods/competing-risk-analysis> regression are supported. This work was sponsored by NASA Grants 80NSSC19M0161 and 80NSSC23M0129 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109.
Author: Eric Giunta [aut, cre] , Amir Bahadori [ctb] , Dan Andresen [ctb] , Linda Walsh [ctb] , Benjamin French [ctb] , Lawrence Dauer [ctb] , John Boice Jr [ctb] , Kansas State University [cph], NASA [fnd], NCRP [fnd], NRC [fnd]
Maintainer: Eric Giunta <egiunta@ksu.edu>

Diff between Colossus versions 1.4.3 dated 2025-10-30 and 1.4.5 dated 2025-12-07

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More information about Colossus at CRAN
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Sat, 06 Dec 2025

Package worldbank readmission to version 0.7.1 with previous version 0.7.0 dated 2025-11-10

Title: Client for World Banks's 'Indicators' and 'Poverty and Inequality Platform (PIP)' APIs
Description: Download and search data from the 'World Bank Indicators API', which provides access to nearly 16,000 time series indicators. See <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation> for further details about the API.
Author: Maximilian Muecke [aut, cre]
Maintainer: Maximilian Muecke <muecke.maximilian@gmail.com>

This is a re-admission after prior archival of version 0.7.0 dated 2025-11-10

Diff between worldbank versions 0.7.0 dated 2025-11-10 and 0.7.1 dated 2025-12-06

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Package traktok readmission to version 0.1.1 with previous version 0.1.0 dated 2025-11-24

Title: Collecting 'TikTok' Data
Description: Getting 'TikTok' data (<https://www.tiktok.com/>) through the official and unofficial APIs—in other words, you can track 'TikTok'.
Author: Johannes B. Gruber [aut, cre]
Maintainer: Johannes B. Gruber <JohannesB.Gruber@gmail.com>

This is a re-admission after prior archival of version 0.1.0 dated 2025-11-24

Diff between traktok versions 0.1.0 dated 2025-11-24 and 0.1.1 dated 2025-12-06

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More information about traktok at CRAN
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Package tmt updated to version 0.3.6-4 with previous version 0.3.6-0 dated 2025-06-20

Title: Estimation of the Rasch Model for Multistage Tests
Description: Provides conditional maximum likelihood (CML) item parameter estimation of both sequential and cumulative deterministic multistage designs (Zwitser & Maris, 2015, <doi:10.1007/s11336-013-9369-6>) and probabilistic sequential and cumulative multistage designs (Steinfeld & Robitzsch, 2024, <doi:10.1007/s41237-024-00228-3>). Supports CML item parameter estimation of conventional linear designs and additional functions for the likelihood ratio test (Andersen, 1973, <doi:10.1007/BF02291180>) as well as functions for simulating various types of multistage designs.
Author: Jan Steinfeld [cre, aut] , Alexander Robitzsch [aut]
Maintainer: Jan Steinfeld <jan.d.steinfeld@gmail.com>

Diff between tmt versions 0.3.6-0 dated 2025-06-20 and 0.3.6-4 dated 2025-12-06

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Package tidytreatment updated to version 0.3.2 with previous version 0.3.1 dated 2025-01-10

Title: Tidy Methods for Bayesian Treatment Effect Models
Description: Functions for extracting tidy data from Bayesian treatment effect models, in particular BART, but extensions are possible. Functionality includes extracting tidy posterior summaries as in 'tidybayes' <https://github.com/mjskay/tidybayes>, estimating (average) treatment effects, common support calculations, and plotting useful summaries of these.
Author: Joshua J Bon [aut, cre]
Maintainer: Joshua J Bon <joshuajbon@gmail.com>

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Package REFA updated to version 0.2.0 with previous version 0.1.0 dated 2023-11-19

Title: Robust Exponential Factor Analysis
Description: A robust alternative to the traditional principal component estimator is proposed within the framework of factor models, known as Robust Exponential Factor Analysis, specifically designed for the modeling of high-dimensional datasets with heavy-tailed distributions. The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance. For more detail of Robust Exponential Factor Analysis, please refer to Hu et al. (2026) <doi:10.1016/j.jmva.2025.105567>.
Author: Jiaqi Hu [cre, aut], Xueqin Wang [aut]
Maintainer: Jiaqi Hu <hujiaqi@mail.ustc.edu.cn>

Diff between REFA versions 0.1.0 dated 2023-11-19 and 0.2.0 dated 2025-12-06

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 15 files changed, 417 insertions(+), 176 deletions(-)

More information about REFA at CRAN
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Package osrm.backend updated to version 0.1.1 with previous version 0.1.0 dated 2025-12-03

Title: Bindings for 'Open Source Routing Machine'
Description: Install and control 'Open Source Routing Machine' ('OSRM') backend executables to prepare routing data and run/stop a local 'OSRM' server. For computations with the running server use the 'osrm' R package (<https://cran.r-project.org/package=osrm>).
Author: Egor Kotov [aut, cre, cph]
Maintainer: Egor Kotov <kotov.egor@gmail.com>

Diff between osrm.backend versions 0.1.0 dated 2025-12-03 and 0.1.1 dated 2025-12-06

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Package modeltuning updated to version 0.1.3 with previous version 0.1.2 dated 2025-10-28

Title: Model Selection and Tuning Utilities
Description: Provides a lightweight framework for model selection and hyperparameter tuning in R. The package offers intuitive tools for grid search, cross-validation, and combined grid search with cross-validation that work seamlessly with virtually any modeling package. Designed for flexibility and ease of use, it standardizes tuning workflows while remaining fully compatible with a wide range of model interfaces and estimation functions.
Author: Daniel Molitor [aut, cre]
Maintainer: Daniel Molitor <molitdj97@gmail.com>

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Package gumboot updated to version 1.0.2 with previous version 1.0.1 dated 2023-10-18

Title: Bootstrap Analyses of Sampling Uncertainty in Goodness-of-Fit Statistics
Description: Uses jackknife and bootstrap methods to quantify the sampling uncertainty in goodness-of-fit statistics. Full details are in Clark et al. (2021), "The abuse of popular performance metrics in hydrologic modeling", Water Resources Research, <doi:10.1029/2020WR029001>.
Author: Martyn Clark [aut], Kevin Shook [aut, trl, cre]
Maintainer: Kevin Shook <kshook@kshook.ca>

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Package gsDesign updated to version 3.8.0 with previous version 3.7.0 dated 2025-08-25

Title: Group Sequential Design
Description: Derives group sequential clinical trial designs and describes their properties. Particular focus on time-to-event, binary, and continuous outcomes. Largely based on methods described in Jennison, Christopher and Turnbull, Bruce W., 2000, "Group Sequential Methods with Applications to Clinical Trials" ISBN: 0-8493-0316-8.
Author: Keaven Anderson [aut, cre], Merck & Co., Inc., Rahway, NJ, USA and its affiliates [cph]
Maintainer: Keaven Anderson <keaven_anderson@merck.com>

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Package GrFA updated to version 0.2.2 with previous version 0.2.1 dated 2024-12-22

Title: Group Factor Analysis
Description: Implements statistical methods for group factor analysis, focusing on estimating the number of global and local factors and extracting them. Several algorithms are implemented, including Canonical Correlation-based Estimation by Choi et al. (2021) <doi:10.1016/j.jeconom.2021.09.008>, Generalised Canonical Correlation Estimation by Lin and Shin (2023) <doi:10.2139/ssrn.4295429>, Circularly Projected Estimation by Chen (2022) <doi:10.1080/07350015.2022.2051520>, and the Aggregated Projection Method by Hu et al. (2025) <doi:10.1080/01621459.2025.2491154>.
Author: Jiaqi Hu [cre, aut], Ting Li [aut], Xueqin Wang [aut]
Maintainer: Jiaqi Hu <hujiaqi@mail.ustc.edu.cn>

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Package FAfA updated to version 0.4 with previous version 0.3 dated 2025-05-23

Title: Factor Analysis for All
Description: Provides a comprehensive Shiny-based graphical user interface for conducting a wide range of factor analysis procedures. 'FAfA' (Factor Analysis for All) guides users through data uploading, assumption checking (descriptives, collinearity, multivariate normality, outliers), data wrangling (variable exclusion, data splitting), factor retention analysis (e.g., Parallel Analysis, Hull method, EGA), Exploratory Factor Analysis (EFA) with various rotation and extraction methods, Confirmatory Factor Analysis (CFA) for model testing, Reliability Analysis (e.g., Cronbach's Alpha, McDonald's Omega), Measurement Invariance testing across groups, and item weighting techniques. The application leverages established R packages such as 'lavaan' and 'psych' to perform these analyses, offering an accessible platform for researchers and students. Results are presented in user-friendly tables and plots, with options for downloading outputs.
Author: Abdullah Faruk KILIC [aut, cre]
Maintainer: Abdullah Faruk KILIC <afarukkilic@trakya.edu.tr>

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Package CimpleG updated to version 1.0.1 with previous version 1.0.0 dated 2025-12-03

Title: A Method to Identify Single CpG Sites for Classification and Deconvolution
Description: DNA methylation signatures are usually based on multivariate approaches that require hundreds of sites for predictions. 'CimpleG' is a method for the detection of small CpG methylation signatures used for cell-type classification and deconvolution. 'CimpleG' is time efficient and performs as well as top performing methods for cell-type classification of blood cells and other somatic cells, while basing its prediction on a single DNA methylation site per cell type (but users can also select more sites if they so wish). Users can train cell type classifiers ('CimpleG' based, and others) and directly apply these in a deconvolution of cell mixes context. Altogether, 'CimpleG' provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution. For more details see Maié et al. (2023) <doi:10.1186/s13059-023-03000-0>.
Author: Tiago F.V. Maie [aut, cre]
Maintainer: Tiago F.V. Maie <tiagomaie@hotmail.com>

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Package boutliers updated to version 2.1-3 with previous version 2.1-2 dated 2025-11-25

Title: Outlier Detection and Influence Diagnostics for Meta-Analysis
Description: Computational tools for outlier detection and influence diagnostics in meta-analysis (Noma et al. (2025) <doi:10.1101/2025.09.18.25336125>). Bootstrap distributions of influence statistics are computed, and explicit thresholds for identifying outliers are provided. These methods can also be applied to the analysis of influential centers or regions in multicenter or multiregional clinical trials (Aoki, Noma and Gosho (2021) <doi:10.1080/24709360.2021.1921944>, Nakamura and Noma (2021) <doi:10.5691/jjb.41.117>).
Author: Hisashi Noma [aut, cre], Kazushi Maruo [aut], Masahiko Gosho [aut]
Maintainer: Hisashi Noma <noma@ism.ac.jp>

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Package VWPre updated to version 1.2.5 with previous version 1.2.4 dated 2020-11-29

Title: Tools for Preprocessing Visual World Data
Description: Gaze data from the Visual World Paradigm requires significant preprocessing prior to plotting and analyzing the data. This package provides functions for preparing visual world eye-tracking data for statistical analysis and plotting. It can prepare data for linear analyses (e.g., ANOVA, Gaussian-family LMER, Gaussian-family GAMM) as well as logistic analyses (e.g., binomial-family LMER and binomial-family GAMM). Additionally, it contains various plotting functions for creating grand average and conditional average plots. See the vignette for samples of the functionality. Currently, the functions in this package are designed for handling data collected with SR Research Eyelink eye trackers using Sample Reports created in SR Research Data Viewer. While we would like to add functionality for data collected with other systems in the future, the current package is considered to be feature-complete; further updates will mainly entail maintenance and the addition of minor functionality.
Author: Vincent Porretta [aut, cre], Aki-Juhani Kyroelaeinen [aut], Jacolien van Rij [ctb], Juhani Jaervikivi [ctb]
Maintainer: Vincent Porretta <vincentporretta@gmail.com>

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Package statsExpressions updated to version 1.7.2 with previous version 1.7.1 dated 2025-07-27

Title: Tidy Dataframes and Expressions with Statistical Details
Description: Utilities for producing dataframes with rich details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for 'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.
Author: Indrajeet Patil [cre, aut, cph]
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

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Package tall updated to version 0.5.0 with previous version 0.4.0 dated 2025-10-23

Title: Text Analysis for All
Description: An R 'shiny' app designed for diverse text analysis tasks, offering a wide range of methodologies tailored to Natural Language Processing (NLP) needs. It is a versatile, general-purpose tool for analyzing textual data. 'tall' features a comprehensive workflow, including data cleaning, preprocessing, statistical analysis, and visualization, all integrated for effective text analysis.
Author: Massimo Aria [aut, cre, cph] , Maria Spano [aut] , Luca D'Aniello [aut] , Corrado Cuccurullo [ctb] , Michelangelo Misuraca [ctb]
Maintainer: Massimo Aria <aria@unina.it>

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Package spocc updated to version 1.2.4 with previous version 1.2.3 dated 2024-03-06

Title: Interface to Species Occurrence Data Sources
Description: A programmatic interface to many species occurrence data sources, including Global Biodiversity Information Facility ('GBIF'), 'iNaturalist', 'eBird', Integrated Digitized 'Biocollections' ('iDigBio'), 'VertNet', Ocean 'Biogeographic' Information System ('OBIS'), and Atlas of Living Australia ('ALA'). Includes functionality for retrieving species occurrence data, and combining those data.
Author: Hannah Owens [aut, cre] , Vijay Barve [aut] , Scott Chamberlain [aut] , Karthik Ram [ctb], Ted Hart [ctb], rOpenSci [fnd]
Maintainer: Hannah Owens <hannah.owens@gmail.com>

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Package testit updated to version 0.14 with previous version 0.13 dated 2021-04-14

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

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Package sugarbag updated to version 0.1.9 with previous version 0.1.6 dated 2022-11-09

Title: Create Tessellated Hexagon Maps
Description: Create a hexagon tile map display from spatial polygons. Each polygon is represented by a hexagon tile, placed as close to it's original centroid as possible, with a focus on maintaining spatial relationship to a focal point. Developed to aid visualisation and analysis of spatial distributions across Australia, which can be challenging due to the concentration of the population on the coast and wide open interior.
Author: Dianne Cook [aut, cre, ths] , Stephanie Kobakian [aut] , Matt Cowgill [ctb]
Maintainer: Dianne Cook <dicook@monash.edu>

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Package rqlm updated to version 4.1-1 with previous version 3.2-1 dated 2025-11-24

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

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Package ratematrix updated to version 1.2.5 with previous version 1.2.4 dated 2022-06-03

Title: Bayesian Estimation of the Evolutionary Rate Matrix
Description: The Evolutionary Rate Matrix is a variance-covariance matrix which describes both the rates of trait evolution and the evolutionary correlation among multiple traits. This package has functions to estimate these parameters using Bayesian MCMC. It is possible to test if the pattern of evolutionary correlations among traits has changed between predictive regimes painted along the branches of the phylogenetic tree. Regimes can be created a priori or estimated as part of the MCMC under a joint estimation approach. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions.
Author: Daniel Caetano [aut, cre], Luke Harmon [aut]
Maintainer: Daniel Caetano <caetanods1@gmail.com>

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Package MMDvariance updated to version 0.1.0 with previous version 0.0.9 dated 2018-07-27

Title: Detecting Differentially Variable Genes Using the Mixture of Marginal Distributions
Description: Gene selection based on variance using the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions. Please see the reference: Li X, Fu Y, Wang X, DeMeo DL, Tantisira K, Weiss ST, Qiu W. (2018) <doi:10.1155/2018/6591634>.
Author: Xuan Li [aut, ctb], Yuejiao Fu [aut, ctb], Xiaogang Wang [aut, ctb], Dawn L. DeMeo [aut, ctb], Kelan Tantisira [aut, ctb], Scott T. Weiss [aut, ctb], Weiliang Qiu [aut, cre]
Maintainer: Weiliang Qiu <weiliang.qiu@gmail.com>

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Package kerasnip updated to version 0.1.0 with previous version 0.0.3 dated 2025-09-18

Title: A Bridge Between 'keras' and 'tidymodels'
Description: Provides a seamless bridge between 'keras' and the 'tidymodels' frameworks. It allows for the dynamic creation of 'parsnip' model specifications for 'keras' models.
Author: David Diaz [aut, cre]
Maintainer: David Diaz <daviddrsch@gmail.com>

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Package EGAnet updated to version 2.4.0 with previous version 2.3.0 dated 2025-04-09

Title: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics
Description: Implements the Exploratory Graph Analysis (EGA) framework for dimensionality and psychometric assessment. EGA estimates the number of dimensions in psychological data using network estimation methods and community detection algorithms. A bootstrap method is provided to assess the stability of dimensions and items. Fit is evaluated using the Entropy Fit family of indices. Unique Variable Analysis evaluates the extent to which items are locally dependent (or redundant). Network loadings provide similar information to factor loadings and can be used to compute network scores. A bootstrap and permutation approach are available to assess configural and metric invariance. Hierarchical structures can be detected using Hierarchical EGA. Time series and intensive longitudinal data can be analyzed using Dynamic EGA, supporting individual, group, and population level assessments.
Author: Hudson Golino [aut, cre] , Alexander Christensen [aut] , Robert Moulder [ctb] , Luis E. Garrido [ctb] , Laura Jamison [ctb] , Dingjing Shi [ctb]
Maintainer: Hudson Golino <hfg9s@virginia.edu>

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Package CytoProfile updated to version 0.2.3 with previous version 0.2.2 dated 2025-09-04

Title: Cytokine Profiling Analysis Tool
Description: Provides comprehensive cytokine profiling analysis through quality control using biologically meaningful cutoffs on raw cytokine measurements and by testing for distributional symmetry to recommend appropriate transformations. Offers exploratory data analysis with summary statistics, enhanced boxplots, and barplots, along with univariate and multivariate analytical capabilities for in-depth cytokine profiling such as Principal Component Analysis based on Andrzej Maćkiewicz and Waldemar Ratajczak (1993) <doi:10.1016/0098-3004(93)90090-R>, Sparse Partial Least Squares Discriminant Analysis based on Lê Cao K-A, Boitard S, and Besse P (2011) <doi:10.1186/1471-2105-12-253>, Random Forest based on Breiman, L. (2001) <doi:10.1023/A:1010933404324>, and Extreme Gradient Boosting based on Tianqi Chen and Carlos Guestrin (2016) <doi:10.1145/2939672.2939785>.
Author: Shubh Saraswat [cre, aut, cph] , Xiaohua Douglas Zhang [aut]
Maintainer: Shubh Saraswat <shubh.saraswat00@gmail.com>

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Package bayesSSM updated to version 0.7.1 with previous version 0.7.0 dated 2025-08-26

Title: Bayesian Methods for State Space Models
Description: Implements methods for Bayesian analysis of State Space Models. Includes implementations of the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) <doi:10.1111/j.1467-9868.2009.00736.x> and automatic tuning inspired by Pitt et al. (2012) <doi:10.1016/j.jeconom.2012.06.004> and J. Dahlin and T. B. Schön (2019) <doi:10.18637/jss.v088.c02>.
Author: Bjarke Hautop [aut, cre, cph]
Maintainer: Bjarke Hautop <bjarke.hautop@gmail.com>

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Package odbc updated to version 1.6.4 with previous version 1.6.3 dated 2025-09-05

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

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Package neotoma2 updated to version 1.0.10 with previous version 1.0.9 dated 2025-11-25

Title: Working with the Neotoma Paleoecology Database
Description: Access and manipulation of data using the Neotoma Paleoecology Database. <https://api.neotomadb.org/api-docs/>.
Author: Dominguez Vidana Socorro [aut, cre] , Simon Goring [aut]
Maintainer: Dominguez Vidana Socorro <dominguezvid@wisc.edu>

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Package fastRG updated to version 0.4.0 with previous version 0.3.3 dated 2025-07-24

Title: Sample Generalized Random Dot Product Graphs in Linear Time
Description: Samples generalized random product graphs, a generalization of a broad class of network models. Given matrices X, S, and Y with with non-negative entries, samples a matrix with expectation X S Y^T and independent Poisson or Bernoulli entries using the fastRG algorithm of Rohe et al. (2017) <https://www.jmlr.org/papers/v19/17-128.html>. The algorithm first samples the number of edges and then puts them down one-by-one. As a result it is O(m) where m is the number of edges, a dramatic improvement over element-wise algorithms that which require O(n^2) operations to sample a random graph, where n is the number of nodes.
Author: Alex Hayes [aut, cre, cph] , Karl Rohe [aut, cph], Jun Tao [aut], Xintian Han [aut], Norbert Binkiewicz [aut]
Maintainer: Alex Hayes <alexpghayes@gmail.com>

Diff between fastRG versions 0.3.3 dated 2025-07-24 and 0.4.0 dated 2025-12-06

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More information about fastRG at CRAN
Permanent link

Package rswipl updated to version 10.0.0 with previous version 9.3.21 dated 2025-03-21

Title: Embed 'SWI'-'Prolog'
Description: Interface to 'SWI'-'Prolog', <https://www.swi-prolog.org/>. This package is normally not loaded directly, please refer to package 'rolog' instead. The purpose of this package is to provide the 'Prolog' runtime on systems that do not have a software installation of 'SWI'-'Prolog'.
Author: Matthias Gondan [aut, com, cre] , Jan Wielemaker [ctb, cph] , European Commission [fnd]
Maintainer: Matthias Gondan <Matthias.Gondan-Rochon@uibk.ac.at>

Diff between rswipl versions 9.3.21 dated 2025-03-21 and 10.0.0 dated 2025-12-06

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More information about rswipl at CRAN
Permanent link

Package GitStats updated to version 2.3.8 with previous version 2.3.7 dated 2025-10-16

Title: Standardized Git Repository Data
Description: Obtain standardized data from multiple 'Git' services, including 'GitHub' and 'GitLab'. Designed to be 'Git' service-agnostic, this package assists teams with activities spread across various 'Git' platforms by providing a unified way to access repository data.
Author: Maciej Banas [aut, cre], Kamil Koziej [aut], Karolina Marcinkowska [aut], Matt Secrest [aut]
Maintainer: Maciej Banas <banasmaciek@gmail.com>

Diff between GitStats versions 2.3.7 dated 2025-10-16 and 2.3.8 dated 2025-12-06

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More information about GitStats at CRAN
Permanent link

Package flevr updated to version 0.0.5 with previous version 0.0.4 dated 2023-11-30

Title: Flexible, Ensemble-Based Variable Selection with Potentially Missing Data
Description: Perform variable selection in settings with possibly missing data based on extrinsic (algorithm-specific) and intrinsic (population-level) variable importance. Uses a Super Learner ensemble to estimate the underlying prediction functions that give rise to estimates of variable importance. For more information about the methods, please see Williamson and Huang (2024) <doi:10.1515/ijb-2023-0059>.
Author: Brian D. Williamson [aut, cre]
Maintainer: Brian D. Williamson <brian.d.williamson@kp.org>

Diff between flevr versions 0.0.4 dated 2023-11-30 and 0.0.5 dated 2025-12-06

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More information about flevr at CRAN
Permanent link

Package feather updated to version 0.4.0 with previous version 0.3.5 dated 2019-09-14

Title: R Bindings to the Feather 'API'
Description: Read and write feather files, a lightweight binary columnar data store designed for maximum speed.
Author: Hadley Wickham [aut, cre], RStudio [cph], LevelDB Authors [ctb]
Maintainer: Hadley Wickham <hadley@rstudio.com>

Diff between feather versions 0.3.5 dated 2019-09-14 and 0.4.0 dated 2025-12-06

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More information about feather at CRAN
Permanent link

Package rsconnect updated to version 1.7.0 with previous version 1.6.2 dated 2025-11-18

Title: Deploy Docs, Apps, and APIs to 'Posit Connect', 'shinyapps.io', and 'RPubs'
Description: Programmatic deployment interface for 'RPubs', 'shinyapps.io', and 'Posit Connect'. Supported content types include R Markdown documents, Shiny applications, Plumber APIs, plots, and static web content.
Author: Aron Atkins [aut, cre], Toph Allen [aut], Hadley Wickham [aut], Jonathan McPherson [aut], JJ Allaire [aut], Posit Software, PBC [cph, fnd]
Maintainer: Aron Atkins <aron@posit.co>

Diff between rsconnect versions 1.6.2 dated 2025-11-18 and 1.7.0 dated 2025-12-06

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More information about rsconnect at CRAN
Permanent link

Package quickr updated to version 0.2.1 with previous version 0.2.0 dated 2025-08-25

Title: Compiler for R
Description: Compile R functions annotated with type and shape declarations for extremely fast performance and robust runtime type checking. Supports both just-in-time (JIT) and ahead-of-time (AOT) compilation. Compilation is performed by lowering R code to Fortran.
Author: Tomasz Kalinowski [aut, cre], Posit Software, PBC [cph, fnd]
Maintainer: Tomasz Kalinowski <tomasz@posit.co>

Diff between quickr versions 0.2.0 dated 2025-08-25 and 0.2.1 dated 2025-12-06

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More information about quickr at CRAN
Permanent link

Package projpred updated to version 2.10.0 with previous version 2.9.1 dated 2025-10-28

Title: Projection Predictive Feature Selection
Description: Performs projection predictive feature selection for generalized linear models (Piironen, Paasiniemi, and Vehtari, 2020, <doi:10.1214/20-EJS1711>) with or without multilevel or additive terms (Catalina, Bürkner, and Vehtari, 2022, <https://proceedings.mlr.press/v151/catalina22a.html>), for some ordinal and nominal regression models (Weber, Glass, and Vehtari, 2025, <doi:10.1007/s00180-024-01506-0>), and for many other regression models (using the latent projection by Catalina, Bürkner, and Vehtari, 2021, <doi:10.48550/arXiv.2109.04702>, which can also be applied to most of the former models). The package is compatible with the 'rstanarm' and 'brms' packages, but other reference models can also be used. See the vignettes and the documentation for more information and examples.
Author: Juho Piironen [aut], Markus Paasiniemi [aut], Alejandro Catalina [aut], Frank Weber [aut], Osvaldo Martin [cre, aut], Aki Vehtari [aut], Jonah Gabry [ctb], Marco Colombo [ctb], Paul-Christian Buerkner [ctb], Hamada S. Badr [ctb], Brian Sullivan [ctb], [...truncated...]
Maintainer: Osvaldo Martin <aloctavodia@gmail.com>

Diff between projpred versions 2.9.1 dated 2025-10-28 and 2.10.0 dated 2025-12-06

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More information about projpred at CRAN
Permanent link

Package MolgenisArmadillo updated to version 2.9.3 with previous version 2.9.1 dated 2025-06-13

Title: Armadillo Client for the Armadillo Service
Description: A set of functions to manage data shared on a 'MOLGENIS Armadillo' server.
Author: Mariska Slofstra [aut, cre] , Sido Haakma [aut] , Tommy de Boer [aut] , Fleur Kelpin [aut] , Morris Swertz [aut] , Tim Cadman [aut]
Maintainer: Mariska Slofstra <m.k.slofstra@umcg.nl>

Diff between MolgenisArmadillo versions 2.9.1 dated 2025-06-13 and 2.9.3 dated 2025-12-06

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Package LFM updated to version 0.3.3 with previous version 0.3.2 dated 2025-10-10

Title: Laplace Factor Model Analysis and Evaluation
Description: Enables the generation of Laplace factor models across diverse Laplace distributions and facilitates the application of Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Perturbation Principal Component (PPC), Stochastic Approximation Principal Component (SAPC), Sparse Principal Component (SPC) and other PC methods and Farm Test methods to these models. Evaluates the efficacy of these methods within the context of Laplace factor models by scrutinizing parameter estimation accuracy, mean square error, and the degree of sparsity.
Author: Guangbao Guo [aut, cre], Siqi Liu [aut]
Maintainer: Guangbao Guo <ggb11111111@163.com>

Diff between LFM versions 0.3.2 dated 2025-10-10 and 0.3.3 dated 2025-12-06

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More information about LFM at CRAN
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Package IPEDSuploadables updated to version 3.0.0 with previous version 2.11.0 dated 2025-09-09

Title: Transforms Institutional Data into Text Files for IPEDS Automated Import/Upload
Description: Starting from user-supplied institutional data, these scripts transform, aggregate, and reshape the information to produce key-value pair data files that are able to be uploaded to IPEDS (Integrated Postsecondary Education Data System) through their submission portal <https://surveys.nces.ed.gov/ipeds/>. Starting data specifications can be found in the vignettes. Final files are saved locally to a location of the user's choice. User-friendly readable files can also be produced for purposes of data review and validation.
Author: Alison Lanski [aut, cre], Shiloh Fling [aut], Edwin Welch [aut]
Maintainer: Alison Lanski <alanski@nd.edu>

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Package insight updated to version 1.4.4 with previous version 1.4.3 dated 2025-11-24

Title: Easy Access to Model Information for Various Model Objects
Description: A tool to provide an easy, intuitive and consistent access to information contained in various R models, like model formulas, model terms, information about random effects, data that was used to fit the model or data from response variables. 'insight' mainly revolves around two types of functions: Functions that find (the names of) information, starting with 'find_', and functions that get the underlying data, starting with 'get_'. The package has a consistent syntax and works with many different model objects, where otherwise functions to access these information are missing.
Author: Daniel Luedecke [aut, cre] , Dominique Makowski [aut, ctb] , Indrajeet Patil [aut, ctb] , Philip Waggoner [aut, ctb] , Mattan S. Ben-Shachar [aut, ctb] , Brenton M. Wiernik [aut, ctb] , Vincent Arel-Bundock [aut, ctb] , Etienne Bacher [aut, ctb] , Ale [...truncated...]
Maintainer: Daniel Luedecke <officialeasystats@gmail.com>

Diff between insight versions 1.4.3 dated 2025-11-24 and 1.4.4 dated 2025-12-06

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More information about insight at CRAN
Permanent link

Package crane updated to version 0.3.0 with previous version 0.2.0 dated 2025-08-29

Title: Supplements the 'gtsummary' Package for Pharmaceutical Reporting
Description: Tables summarizing clinical trial results are often complex and require detailed tailoring prior to submission to a health authority. The 'crane' package supplements the functionality of the 'gtsummary' package for creating these often highly bespoke tables in the pharmaceutical industry.
Author: Daniel D. Sjoberg [aut, cre] , Emily de la Rua [aut] , Davide Garolini [aut], Abinaya Yogasekaram [aut], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Daniel D. Sjoberg <danield.sjoberg@gmail.com>

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More information about crane at CRAN
Permanent link

Package blockr.core updated to version 0.1.1 with previous version 0.1.0 dated 2025-05-20

Title: Graphical Web-Framework for Data Manipulation and Visualization
Description: A framework for data manipulation and visualization using a web-based point and click user interface where analysis pipelines are decomposed into re-usable and parameterizable blocks.
Author: Nicolas Bennett [aut, cre], David Granjon [aut], Christoph Sax [aut], Karma Tarap [ctb], John Coene [ctb], Bristol Myers Squibb [fnd]
Maintainer: Nicolas Bennett <nicolas@cynkra.com>

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Package biostats updated to version 1.1.0 with previous version 1.0.0 dated 2025-11-13

Title: Biostatistics and Clinical Data Analysis
Description: Biostatistical and clinical data analysis, including descriptive statistics, exploratory data analysis, sample size and power calculations, statistical inference, and data visualization. Normality tests are implemented following Mishra et al. (2019) <doi:10.4103/aca.ACA_157_18>, omnibus test procedures are based on Blanca et al. (2017) <doi:10.3758/s13428-017-0918-2> and Field et al. (2012, ISBN:9781446200469), while sample size and power calculation methods follow Chow et al. (2017) <doi:10.1201/9781315183084>.
Author: Sebastian Quirarte-Justo [aut, cre] , Angela Carolina Montano-Ruiz [aut] , Jose Maria Torres-Arellano [aut] , Laboratorios Sophia S.A. de C.V. [cph, fnd]
Maintainer: Sebastian Quirarte-Justo <sebastian.quirarte@sophia.com.mx>

Diff between biostats versions 1.0.0 dated 2025-11-13 and 1.1.0 dated 2025-12-06

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Package ALFAM2 updated to version 4.2.14 with previous version 4.2 dated 2024-11-08

Title: Dynamic Model of Ammonia Emission from Field-Applied Manure
Description: An implementation of the ALFAM2 dynamic emission model for ammonia volatilization from field-applied animal slurry (manure with dry matter below about 15%). The model can be used to predict cumulative emission and emission rate of ammonia following field application of slurry. Predictions may be useful for emission inventory calculations, fertilizer management, assessment of mitigation strategies, or research aimed at understanding ammonia emission. Default parameter sets include effects of application method, slurry composition, and weather. The model structure is based on a simplified representation of the physical-chemical slurry-soil-atmosphere system. More information is available via citation("ALFAM2").
Author: Sasha D. Hafner [aut, cre] , Christoph Haeni [aut] , Roland Fuss [aut] , Frederik Dalby [aut] , Johanna Pedersen [ctb] , Valdemar Petersen [ctb]
Maintainer: Sasha D. Hafner <sasha.hafner@bce.au.dk>

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Package splineCox updated to version 0.0.7 with previous version 0.0.5 dated 2025-07-10

Title: A Two-Stage Estimation Approach to Cox Regression Using M-Spline Function
Description: Implements a two-stage estimation approach for Cox regression using five-parameter M-spline functions to model the baseline hazard. It allows for flexible hazard shapes and model selection based on log-likelihood criteria as described in Teranishi et al.(2025). In addition, the package provides functions for constructing and evaluating B-spline copulas based on five M-spline or I-spline basis functions, allowing users to flexibly model and compute bivariate dependence structures. Both the copula function and its density can be evaluated. Furthermore, the package supports computation of dependence measures such as Kendall's tau and Spearman's rho, derived analytically from the copula parameters.
Author: Ren Teranishi [aut, cre]
Maintainer: Ren Teranishi <ren.teranishi1227@gmail.com>

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Package ronfig updated to version 0.0.7 with previous version 0.0.6 dated 2025-11-21

Title: Load Configuration Values
Description: A simple approach to configuring R projects with different parameter values. Configurations are specified using a reduced subset of base R and parsed accordingly.
Author: Tim Taylor [aut, cre, cph]
Maintainer: Tim Taylor <tim.taylor@hiddenelephants.co.uk>

Diff between ronfig versions 0.0.6 dated 2025-11-21 and 0.0.7 dated 2025-12-06

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New package moodlequiz with initial version 0.2.0
Package: moodlequiz
Title: R Markdown format for 'Moodle' XML cloze quizzes
Version: 0.2.0
Description: Enables the creation of 'Moodle' quiz questions using literate programming with R Markdown. This makes it easy to quickly create a quiz that can be randomly replicated with new datasets, questions, and options for answers.
License: MIT + file LICENSE
Encoding: UTF-8
Language: es
Imports: rlang, rmarkdown, bookdown, xfun, yaml
URL: https://github.com/numbats/moodlequiz, https://numbats.github.io/moodlequiz/
BugReports: https://github.com/numbats/moodlequiz/issues
Suggests: knitr, testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2025-12-01 13:58:59 UTC; mitchell
Author: Mitchell O'Hara-Wild [aut, cre] , Emi Tanaka [aut]
Maintainer: Mitchell O'Hara-Wild <mail@mitchelloharawild.com>
Repository: CRAN
Date/Publication: 2025-12-06 12:10:07 UTC

More information about moodlequiz at CRAN
Permanent link

New package lvimp with initial version 1.0.0
Package: lvimp
Title: Perform Inference on Summaries of Longitudinal Algorithm-Agnostic Variable Importance
Version: 1.0.0
Description: Calculate point estimates of and valid confidence intervals for longitudinal summaries of nonparametric, algorithm-agnostic variable importance measures. For more details, see Williamson et al. (2024) <doi:10.48550/arXiv.2311.01638>.
License: MIT + file LICENSE
Imports: vimp
Suggests: knitr, rmarkdown, testthat, SuperLearner
URL: https://bdwilliamson.github.io/lvimp/
BugReports: https://github.com/bdwilliamson/lvimp/issues
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2025-12-01 17:05:16 UTC; L107067
Author: Brian D. Williamson [aut, cre]
Maintainer: Brian D. Williamson <brian.d.williamson@kp.org>
Repository: CRAN
Date/Publication: 2025-12-06 12:20:02 UTC

More information about lvimp at CRAN
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Package grec updated to version 1.6.3 with previous version 1.6.2 dated 2025-09-04

Title: Gradient-Based Recognition of Spatial Patterns in Environmental Data
Description: Provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.
Author: Wencheng Lau-Medrano [aut, cre]
Maintainer: Wencheng Lau-Medrano <luis.laum@gmail.com>

Diff between grec versions 1.6.2 dated 2025-09-04 and 1.6.3 dated 2025-12-06

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Package ggdibbler updated to version 0.6.1 with previous version 0.1.0 dated 2025-07-31

Title: Add Uncertainty to Data Visualisations
Description: A 'ggplot2' extension for visualising uncertainty with the goal of signal suppression. Usually, uncertainty visualisation focuses on expressing uncertainty as a distribution or probability, whereas 'ggdibbler' differentiates itself by viewing an uncertainty visualisation as an adjustment to an existing graphic that incorporates the inherent uncertainty in the estimates. You provide the code for an existing plot, but replace any of the variables with a vector of distributions, and it will convert the visualisation into it's signal suppression counterpart.
Author: Harriet Mason [aut, cre] , Dianne Cook [aut, ths] , Sarah Goodwin [aut, ths] , Susan VanderPlas [aut, ths]
Maintainer: Harriet Mason <harriet.m.mason@gmail.com>

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Package DLFM updated to version 0.1.4 with previous version 0.1.1 dated 2025-11-14

Title: Distributed Laplace Factor Model
Description: Distributed estimation method is based on a Laplace factor model to solve the estimates of load and specific variance. The philosophy of the package is described in Guangbao Guo. (2022). <doi:10.1007/s00180-022-01270-z>.
Author: Guangbao Guo [aut, cre], Siqi Liu [aut]
Maintainer: Guangbao Guo <ggb11111111@163.com>

Diff between DLFM versions 0.1.1 dated 2025-11-14 and 0.1.4 dated 2025-12-06

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New package zarr with initial version 0.1.1
Package: zarr
Title: Native and Extensible R Driver for 'Zarr'
Version: 0.1.1
Description: The 'Zarr' specification is widely used to build libraries for the storage and retrieval of n-dimensional array data from data stores ranging from local file systems to the cloud. This package is a native 'Zarr' implementation in R with support for all required features of 'Zarr' version 3. It is designed to be extensible such that new stores, codecs and extensions can be added easily.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: jsonlite, methods, R6
Suggests: bit64, blosc, digest, testthat (>= 3.0.0), zlib
URL: https://github.com/R-CF/zarr
BugReports: https://github.com/R-CF/zarr/issues
NeedsCompilation: no
Packaged: 2025-12-01 09:36:19 UTC; patrickvanlaake
Author: Patrick Van Laake [aut, cre, cph]
Maintainer: Patrick Van Laake <patrick@vanlaake.net>
Repository: CRAN
Date/Publication: 2025-12-06 11:50:02 UTC

More information about zarr at CRAN
Permanent link

Package StratPal updated to version 0.7.1 with previous version 0.7.0 dated 2025-11-24

Title: Stratigraphic Paleobiology Modeling Pipelines
Description: The fossil record is a joint expression of ecological, taphonomic, evolutionary, and stratigraphic processes (Holland and Patzkowsky, 2012, ISBN:978-0226649382). This package allowing to simulate biological processes in the time domain (e.g., trait evolution, fossil abundance, phylogenetic trees), and examine how their expression in the rock record (stratigraphic domain) is influenced based on age-depth models, ecological niche models, and taphonomic effects. Functions simulating common processes used in modeling trait evolution, biostratigraphy or event type data such as first/last occurrences are provided and can be used standalone or as part of a pipeline. The package comes with example data sets and tutorials in several vignettes, which can be used as a template to set up one's own simulation.
Author: Niklas Hohmann [aut, cre]
Maintainer: Niklas Hohmann <N.H.Hohmann@uu.nl>

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Package shinydrive updated to version 0.1.5 with previous version 0.1.3 dated 2022-09-29

Title: File Sharing Shiny Module
Description: Shiny module for easily sharing files between users. Admin can add, remove, edit and download file. User can only download file. It's also possible to manage files using R functions directly.
Author: Benoit Thieurmel [aut, cre], Thibaut Dubois [aut], Jeremy Boussaguet [ctb]
Maintainer: Benoit Thieurmel <bthieurmel@gmail.com>

Diff between shinydrive versions 0.1.3 dated 2022-09-29 and 0.1.5 dated 2025-12-06

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Package semlbci updated to version 0.11.5 with previous version 0.11.3 dated 2025-01-25

Title: Likelihood-Based Confidence Interval in Structural Equation Models
Description: Forms likelihood-based confidence intervals (LBCIs) for parameters in structural equation modeling, introduced in Cheung and Pesigan (2023) <doi:10.1080/10705511.2023.2183860>. Currently implements the algorithm illustrated by Pek and Wu (2018) <doi:10.1037/met0000163>, and supports the robust LBCI proposed by Falk (2018) <doi:10.1080/10705511.2017.1367254>.
Author: Shu Fai Cheung [aut, cre] , Ivan Jacob Agaloos Pesigan [ctb]
Maintainer: Shu Fai Cheung <shufai.cheung@gmail.com>

Diff between semlbci versions 0.11.3 dated 2025-01-25 and 0.11.5 dated 2025-12-06

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More information about semlbci at CRAN
Permanent link

Package DSMolgenisArmadillo updated to version 3.0.2 with previous version 3.0.1 dated 2025-09-16

Title: 'DataSHIELD' Client for 'MOLGENIS Armadillo'
Description: 'DataSHIELD' is an infrastructure and series of R packages that enables the remote and 'non-disclosive' analysis of sensitive research data. This package is the 'DataSHIELD' interface implementation to analyze data shared on a 'MOLGENIS Armadillo' server. 'MOLGENIS Armadillo' is a light-weight 'DataSHIELD' server using a file store and an 'RServe' server.
Author: Mariska Slofstra [aut, cre] , Sido Haakma [aut] , Tommy de Boer [aut] , Fleur Kelpin [aut] , MOLGENIS org [cph, fnd]
Maintainer: Mariska Slofstra <m.k.slofstra@umcg.nl>

Diff between DSMolgenisArmadillo versions 3.0.1 dated 2025-09-16 and 3.0.2 dated 2025-12-06

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More information about DSMolgenisArmadillo at CRAN
Permanent link

Package dockViewR updated to version 0.3.0 with previous version 0.2.0 dated 2025-07-10

Title: Layout Manager Widget for R and 'shiny' Apps
Description: Provides R bindings to the 'dockview' 'JavaScript' library <https://dockview.dev/>. Create fully customizable grid layouts (docks) in seconds to include in interactive R reports with R Markdown or 'Quarto' or in 'shiny' apps <https://shiny.posit.co/>. In 'shiny' mode, modify docks by dynamically adding, removing or moving panels or groups of panels from the server function. Choose among 8 stunning themes (dark and light), serialise the state of a dock to restore it later.
Author: David Granjon [aut, cre], Nelson Stevens [aut], Nicolas Bennett [aut], mathuo [cph], cynkra GmbH [fnd]
Maintainer: David Granjon <dgranjon@ymail.com>

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Package disclapmix updated to version 1.7.5 with previous version 1.7.4 dated 2022-06-29

Title: Discrete Laplace Mixture Inference using the EM Algorithm
Description: Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section).
Author: Mikkel Meyer Andersen [aut, cre], Poul Svante Eriksen [aut]
Maintainer: Mikkel Meyer Andersen <mikl@math.aau.dk>

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Package Countr updated to version 3.6 with previous version 3.5.8 dated 2024-01-14

Title: Flexible Univariate Count Models Based on Renewal Processes
Description: Flexible univariate count models based on renewal processes. The models may include covariates and can be specified with familiar formula syntax as in glm() and package 'flexsurv'. The methodology is described by Kharrat et all (2019) <doi:10.18637/jss.v090.i13> (included as vignette 'Countr_guide' in the package).
Author: Tarak Kharrat [aut] , Georgi N. Boshnakov [aut, cre]
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>

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Package clustcurv updated to version 3.0.1 with previous version 3.0.0 dated 2025-11-14

Title: Determining Groups in Multiples Curves
Description: A method for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods or other approaches with lower computational cost. The methodology can be applied both in regression and survival framework. Implemented methods are: Grouping multiple survival curves described by Villanueva et al. (2018) <doi:10.1002/sim.8016>.
Author: Nora M. Villanueva [aut, cre] , Marta Sestelo [aut]
Maintainer: Nora M. Villanueva <nmvillanueva@uvigo.es>

Diff between clustcurv versions 3.0.0 dated 2025-11-14 and 3.0.1 dated 2025-12-06

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Package CureAuxSP (with last version 0.0.1) was removed from CRAN

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

2024-02-28 0.0.1

Permanent link
Package worldbank (with last version 0.7.0) was removed from CRAN

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

2025-11-10 0.7.0
2025-08-29 0.6.1
2025-05-29 0.6.0
2025-01-27 0.5.0
2024-12-09 0.4.0
2024-08-26 0.3.0
2024-07-31 0.2.0
2024-07-02 0.1.0

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Fri, 05 Dec 2025

New package pater with initial version 1.0.0
Package: pater
Title: Turn a URL Pathname into a Regular Expression
Version: 1.0.0
Description: R's implementation of the JavaScript library 'path-to-regexp', it aims to provide R web frameworks features such as parameter handling among other URL path utilities.
License: MIT + file LICENSE
URL: https://github.com/JulioCollazos64/pater
BugReports: https://github.com/JulioCollazos64/pater/issues
Encoding: UTF-8
Suggests: testthat (>= 3.0.0), stringr
Imports: utils
NeedsCompilation: no
Packaged: 2025-12-01 06:47:45 UTC; julio
Author: Julio Collazos [aut, cre], path-to-regexp contributors [cph]
Maintainer: Julio Collazos <amarullazo626@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-05 20:20:02 UTC

More information about pater at CRAN
Permanent link

New package Ditwah with initial version 1.0.0
Package: Ditwah
Title: Ditwah Storm Data and Tools for Storm Monitoring and Early Warning November 2025, Sri Lanka
Version: 1.0.0
Description: The Ditwah storm began impacting Sri Lanka on 25 November 2025. Ditwah provides a collection of tidy, well-structured datasets to support storm data management, monitoring, and early warning applications in Sri Lanka. The publicly available data were converted to tidy data format for easy analysis. The package processes weather data, flood data and situation report data (families affected, etc.). The package also includes functions for analyzing river level progression and load dashboard visualizations to enhance situational awareness. This is also developed for educational purposes to support learning in data wrangling, visualization, and disaster analytics.
License: GPL (>= 3)
Encoding: UTF-8
Imports: dplyr, tibble
Depends: R (>= 4.1.0)
LazyData: true
NeedsCompilation: no
Packaged: 2025-12-01 02:37:05 UTC; DELL
Author: Thiyanga S. Talagala [aut, cre]
Maintainer: Thiyanga S. Talagala <ttalagala@sjp.ac.lk>
Repository: CRAN
Date/Publication: 2025-12-05 20:00:02 UTC

More information about Ditwah at CRAN
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Package vcr updated to version 2.1.0 with previous version 2.0.0 dated 2025-07-23

Title: Record 'HTTP' Calls to Disk
Description: Record test suite 'HTTP' requests and replays them during future runs. A port of the Ruby gem of the same name (<https://github.com/vcr/vcr/>). Works by recording real 'HTTP' requests/responses on disk in 'cassettes', and then replaying matching responses on subsequent requests.
Author: Scott Chamberlain [aut, cre] , Aaron Wolen [aut] , Maelle Salmon [aut] , Daniel Possenriede [aut] , Hadley Wickham [aut], rOpenSci [fnd]
Maintainer: Scott Chamberlain <myrmecocystus@gmail.com>

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Package TFORGE updated to version 0.1.15 with previous version 0.1.14 dated 2025-12-04

Title: Tests for Geophysical Eigenvalues
Description: The eigenvalues of observed symmetric matrices are often of intense scientific interest. This package offers single sample tests for the eigenvalues of the population mean or the eigenvalue-multiplicity of the population mean. For k-samples, this package offers tests for equal eigenvalues between samples. Included is support for matrices with constraints common to geophysical tensors (constant trace, sum of squared eigenvalues, or both) and eigenvectors are usually considered nuisance parameters. Pivotal bootstrap methods enable these tests to have good performance for small samples (n=15 for 3x3 matrices). These methods were developed and studied by Hingee, Scealy and Wood (2026, "Nonparametric bootstrap inference for the eigenvalues of geophysical tensors", accepted by the Journal of American Statistical Association). Also available is a 2-sample test using a Gaussian orthogonal ensemble approximation and an eigenvalue-multiplicity test that assumes orthogonally-invariant covariance.
Author: Kassel Liam Hingee [aut, cre] , Art B. Owen [cph] , Board of Trustees Leland Stanford Junior University [cph]
Maintainer: Kassel Liam Hingee <kassel.hingee@anu.edu.au>

Diff between TFORGE versions 0.1.14 dated 2025-12-04 and 0.1.15 dated 2025-12-05

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Package PublicationBiasBenchmark updated to version 0.1.3 with previous version 0.1.2 dated 2025-11-26

Title: Benchmark for Publication Bias Correction Methods
Description: Implements a unified interface for benchmarking meta-analytic publication bias correction methods through simulation studies (see Bartoš et al., 2025, <doi:10.48550/arXiv.2510.19489>). It provides 1) predefined data-generating mechanisms from the literature, 2) functions for running meta-analytic methods on simulated data, 3) pre-simulated datasets and pre-computed results for reproducible benchmarks, 4) tools for visualizing and comparing method performance.
Author: Frantisek Bartos [aut, cre] , Samuel Pawel [aut] , Bjoern S. Siepe [aut]
Maintainer: Frantisek Bartos <f.bartos96@gmail.com>

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Package gtsummary updated to version 2.5.0 with previous version 2.4.0 dated 2025-08-28

Title: Presentation-Ready Data Summary and Analytic Result Tables
Description: Creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.
Author: Daniel D. Sjoberg [aut, cre] , Joseph Larmarange [aut] , Michael Curry [aut] , Emily de la Rua [aut] , Jessica Lavery [aut] , Karissa Whiting [aut] , Emily C. Zabor [aut] , Xing Bai [ctb], Malcolm Barrett [ctb] , Esther Drill [ctb] , Jessica Flynn [c [...truncated...]
Maintainer: Daniel D. Sjoberg <danield.sjoberg@gmail.com>

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Package GRAB updated to version 0.2.4 with previous version 0.2.3 dated 2025-08-19

Title: Genome-Wide Robust Analysis for Biobank Data (GRAB)
Description: Provides a comprehensive suite of genome-wide association study (GWAS) methods specifically designed for biobank-scale data, including but not limited to, robust approaches for time-to-event traits (Li et al., 2025 <doi:10.1038/s43588-025-00864-z>) and ordinal categorical traits (Bi et al., 2021 <doi:10.1016/j.ajhg.2021.03.019>). The package also offers general frameworks for GWAS of any trait type (Bi et al., 2020 <doi:10.1016/j.ajhg.2020.06.003>), while accounting for sample relatedness (Xu et al., 2025 <doi:10.1038/s41467-025-56669-1>) or population structure (Ma et al., 2025 <doi:10.1186/s13059-025-03827-9>). By accurately approximating score statistic distributions using saddlepoint approximation (SPA), these methods can effectively control type I error rates for rare variants and in the presence of unbalanced phenotype distributions. Additionally, the package includes functions for simulating genotype and phenotype data to support research and method [...truncated...]
Author: Wenjian Bi [aut], Wei Zhou [aut], Rounak Dey [aut], Zhangchen Zhao [aut], Seunggeun Lee [aut], Woody Miao [cre]
Maintainer: Woody Miao <miaolin@pku.edu.cn>

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Package filibustr readmission to version 0.5.1 with previous version 0.5.0 dated 2025-10-26

Title: Data Utilities for Congressional Research
Description: Provides easy-to-understand and consistent interfaces for accessing data on the U.S. Congress. The functions in 'filibustr' streamline the process for importing data on Congress into R, removing the need to download and work from CSV files and the like. Data sources include 'Voteview' (<https://voteview.com/>), the U.S. Senate website (<https://www.senate.gov/>), and more.
Author: Max Feinleib [aut, cre, cph]
Maintainer: Max Feinleib <mhfeinleib@gmail.com>

This is a re-admission after prior archival of version 0.5.0 dated 2025-10-26

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Package excessmort updated to version 0.8.2 with previous version 0.8.1 dated 2025-04-07

Title: Excess Mortality
Description: Implementation of method for estimating excess mortality and other health related outcomes from weekly or daily count data described in Acosta and Irizarry (2021) "A Flexible Statistical Framework for Estimating Excess Mortality".
Author: Rafael A. Irizarry [aut, cre], Rolando Acosta [aut], Monica Robles-Fontan [aut], Ferenci Tamas [ctb]
Maintainer: Rafael A. Irizarry <rafael_irizarry@dfci.harvard.edu>

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Package CopernicusClimate updated to version 0.0.4 with previous version 0.0.3 dated 2025-10-23

Title: Search Download and Handle Data from Copernicus Climate Data Service
Description: Subset and download data from EU Copernicus Climate Data Service: <https://cds.climate.copernicus.eu/>. Import information about the Earth's past, present and future climate from Copernicus into R without the need of external software.
Author: Pepijn de Vries [aut, cre]
Maintainer: Pepijn de Vries <pepijn.devries@outlook.com>

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Package nhdplusTools updated to version 1.4.1 with previous version 1.4.0 dated 2025-11-19

Title: NHDPlus Tools
Description: Tools for traversing and working with National Hydrography Dataset Plus (NHDPlus) data. All methods implemented in 'nhdplusTools' are available in the NHDPlus documentation available from the US Environmental Protection Agency <https://www.epa.gov/waterdata/basic-information>.
Author: David Blodgett [aut, cre] , Mike Johnson [aut] , Marc Weber [ctb] , Josh Erickson [ctb], Lauren Koenig [ctb]
Maintainer: David Blodgett <dblodgett@usgs.gov>

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Package LMMsolver updated to version 1.0.12 with previous version 1.0.11 dated 2025-08-20

Title: Linear Mixed Models with Sparse Matrix Methods and Smoothing
Description: Provides tools for fitting linear mixed models using sparse matrix methods and variance component estimation. Applications include spline-based modeling of spatial and temporal trends using penalized splines (Boer, 2023) <doi:10.1177/1471082X231178591>.
Author: Martin Boer [aut] , Bart-Jan van Rossum [aut, cre]
Maintainer: Bart-Jan van Rossum <bart-jan.vanrossum@wur.nl>

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Package DNAtools updated to version 0.2-5 with previous version 0.2-4 dated 2022-03-17

Title: Tools for Analysing Forensic Genetic DNA Data
Description: Computationally efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also called F- designation) is implemented. Dedicated functions ease plotting the results. See Tvedebrink et al. (2012) <doi:10.1016/j.fsigen.2011.08.001>. Compute the distribution of the numbers of alleles in DNA mixtures. See Tvedebrink (2013) <doi:10.1016/j.fsigss.2013.10.142>.
Author: Torben Tvedebrink [aut], James Curran [aut], Mikkel Meyer Andersen [aut, cre]
Maintainer: Mikkel Meyer Andersen <mikl@math.aau.dk>

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

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

2024-04-19 0.1.0

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Package memshare updated to version 1.1.0 with previous version 1.0.3 dated 2025-11-08

Title: Shared Memory Multithreading
Description: This project extends 'R' with a mechanism for efficient parallel data access by utilizing 'C++' shared memory. Large data objects can be accessed and manipulated directly from 'R' without redundant copying, providing both speed and memory efficiency.
Author: Julian Maerte [aut, ctr] , Romain Francois [ctb], Michael Thrun [aut, ths, rev, cph, cre]
Maintainer: Michael Thrun <m.thrun@gmx.net>

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Package visor updated to version 0.1.1 with previous version 0.1.0 dated 2025-04-07

Title: Geospatial Tools for Visibility Analysis
Description: Provides tools for visibility analysis in geospatial data. It offers functionality to perform isovist calculations, using arbitrary geometries as both viewpoints and occluders.
Author: Claudiu Forgaci [aut, cre, cph] , Francesco Nattino [aut] , Netherlands eScience Center [fnd]
Maintainer: Claudiu Forgaci <c.forgaci@tudelft.nl>

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Package terrainmeshr updated to version 1.0.1 with previous version 0.1.0 dated 2020-04-29

Title: Triangulate and Simplify 3D Terrain Meshes
Description: Provides triangulations of regular height fields, based on the methods described in "Fast Polygonal Approximation of Terrains and Height Fields" Michael Garland and Paul S. Heckbert (1995) <https://www.mgarland.org/files/papers/scape.pdf> using code from the 'hmm' library written by Michael Fogleman <https://github.com/fogleman/hmm>.
Author: Tyler Morgan-Wall [aut, cph, cre] , Michael Fogleman [ctb, cph]
Maintainer: Tyler Morgan-Wall <tylermw@gmail.com>

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Package supernova updated to version 3.0.1 with previous version 3.0.0 dated 2024-02-07

Title: Judd, McClelland, & Ryan Formatting for ANOVA Output
Description: Produces ANOVA tables in the format used by Judd, McClelland, and Ryan (2017, ISBN: 978-1138819832) in their introductory textbook, Data Analysis. This includes proportional reduction in error and formatting to improve ease the transition between the book and R.
Author: Adam Blake [cre, aut] , Jeff Chrabaszcz [aut], Ji Son [aut] , Jim Stigler [aut]
Maintainer: Adam Blake <adam@coursekata.org>

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Package highlight updated to version 0.5.2 with previous version 0.5.1 dated 2023-01-18

Title: Syntax Highlighter
Description: Syntax highlighter for R code based on the results of the R parser. Rendering in HTML and latex markup. Custom Sweave driver performing syntax highlighting of R code chunks.
Author: Hadley Wickham [cre], Romain Francois [aut], Andre Simon [ctb]
Maintainer: Hadley Wickham <hadley@rstudio.com>

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Package hce updated to version 0.8.8 with previous version 0.8.5 dated 2025-08-22

Title: Design and Analysis of Hierarchical Composite Endpoints
Description: Simulate and analyze hierarchical composite endpoints. Includes implementation for the kidney hierarchical composite endpoint as defined in Heerspink HL et al (2023) “Development and validation of a new hierarchical composite end point for clinical trials of kidney disease progression” (Journal of the American Society of Nephrology 34 (2): 2025–2038, <doi:10.1681/ASN.0000000000000243>). Win odds, also called Wilcoxon-Mann-Whitney or success odds, is the main analysis method. Other win statistics (win probability, win ratio, net benefit) are also implemented in the univariate case, provided there is no censoring. The win probability analysis is based on the Brunner-Munzel test and uses the DeLong-DeLong-Clarke-Pearson variance estimator, as described by Brunner and Konietschke (2025) in “An unbiased rank-based estimator of the Mann–Whitney variance including the case of ties” (Statistical Papers 66 (1): 20, <doi:10.1007/s00362-024-01635-0>). Includes implementation of a new [...truncated...]
Author: Samvel B. Gasparyan [aut, cre]
Maintainer: Samvel B. Gasparyan <gasparyan.co@gmail.com>

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Package bookdown updated to version 0.46 with previous version 0.45 dated 2025-10-03

Title: Authoring Books and Technical Documents with R Markdown
Description: Output formats and utilities for authoring books and technical documents with R Markdown.
Author: Yihui Xie [aut, cre] , Christophe Dervieux [ctb] , JJ Allaire [ctb], Albert Kim [ctb], Alessandro Samuel-Rosa [ctb], Andrzej Oles [ctb], Atsushi Yasumoto [ctb] , Aust Frederik [ctb] , Bastiaan Quast [ctb], Ben Marwick [ctb], Chester Ismay [ctb], Clif [...truncated...]
Maintainer: Yihui Xie <xie@yihui.name>

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Package funtimes updated to version 10.0 with previous version 9.1 dated 2023-03-21

Title: Functions for Time Series Analysis
Description: Nonparametric estimators and tests for time series analysis. The functions use bootstrap techniques and robust nonparametric difference-based estimators to test for the presence of possibly non-monotonic trends and for synchronicity of trends in multiple time series.
Author: Vyacheslav Lyubchich [aut, cre] , Yulia R. Gel [aut], Alexander Brenning [ctb], Calvin Chu [ctb], Xin Huang [ctb], Umar Islambekov [ctb], Palina Niamkova [ctb], Dorcas Ofori-Boateng [ctb], Ethan D. Schaeffer [ctb], Srishti Vishwakarma [aut], Xingyu W [...truncated...]
Maintainer: Vyacheslav Lyubchich <lyubchich@umces.edu>

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Thu, 04 Dec 2025

Package rankdifferencetest updated to version 2025.12.4 with previous version 2025.4.21 dated 2025-04-22

Title: Kornbrot's Rank Difference Test
Description: Implements Kornbrot's rank difference test as described in <doi:10.1111/j.2044-8317.1990.tb00939.x>. This method is a modified Wilcoxon signed-rank test which produces consistent and meaningful results for ordinal or monotonically-transformed data.
Author: Brett Klamer [aut, cre]
Maintainer: Brett Klamer <code@brettklamer.com>

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Package GCCfactor updated to version 1.1.4 with previous version 1.1.2 dated 2025-11-28

Title: GCC Estimation of the Multilevel Factor Model
Description: Provides methods for model selection, estimation, inference, and simulation for the multilevel factor model, based on the principal component estimation and generalised canonical correlation approach. Details can be found in "Generalised Canonical Correlation Estimation of the Multilevel Factor Model." Lin and Shin (2025) <doi:10.2139/ssrn.4295429>.
Author: Rui Lin [aut, cre], Yongcheol Shin [aut]
Maintainer: Rui Lin <ruilin1081@gmail.com>

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More information about GCCfactor at CRAN
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Package Rwofost updated to version 0.8-6 with previous version 0.8-3 dated 2021-10-01

Title: WOFOST Crop Growth Simulation Model
Description: An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) <doi:10.1016/j.agsy.2018.06.018> for a recent review of the history and use of the model.
Author: Robert J. Hijmans [cre, aut], Huang Fang [ctb], C.A. van Diepen [ctb], Allard de Wit [ctb], Daniel van Kraalingen [ctb], Tamme van der Wal [ctb], C. Rappoldt [ctb], Hendrik Boogard [ctb], I.G.A.M. Noy [ctb], Alterra, Wageningen-UR [cph]
Maintainer: Robert J. Hijmans <r.hijmans@gmail.com>

Diff between Rwofost versions 0.8-3 dated 2021-10-01 and 0.8-6 dated 2025-12-04

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More information about Rwofost at CRAN
Permanent link

Package armspp updated to version 0.0.3 with previous version 0.0.2 dated 2019-05-24

Title: Adaptive Rejection Metropolis Sampling (ARMS) via 'Rcpp'
Description: An efficient 'Rcpp' implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) <doi:10.2307/2986138>. This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density.
Author: Michael Bertolacci [aut, cre]
Maintainer: Michael Bertolacci <m.bertolacci@gmail.com>

Diff between armspp versions 0.0.2 dated 2019-05-24 and 0.0.3 dated 2025-12-04

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More information about armspp at CRAN
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Package xrf updated to version 0.3.0 with previous version 0.2.2 dated 2022-10-04

Title: eXtreme RuleFit
Description: An implementation of the RuleFit algorithm as described in Friedman & Popescu (2008) <doi:10.1214/07-AOAS148>. eXtreme Gradient Boosting ('XGBoost') is used to build rules, and 'glmnet' is used to fit a sparse linear model on the raw and rule features. The result is a model that learns similarly to a tree ensemble, while often offering improved interpretability and achieving improved scoring runtime in live applications. Several algorithms for reducing rule complexity are provided, most notably hyperrectangle de-overlapping. All algorithms scale to several million rows and support sparse representations to handle tens of thousands of dimensions.
Author: Karl Holub [aut, cre]
Maintainer: Karl Holub <karljholub@gmail.com>

Diff between xrf versions 0.2.2 dated 2022-10-04 and 0.3.0 dated 2025-12-04

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More information about xrf at CRAN
Permanent link

Package mlsurvlrnrs updated to version 0.0.7 with previous version 0.0.6 dated 2025-09-09

Title: R6-Based ML Survival Learners for 'mlexperiments'
Description: Enhances 'mlexperiments' <https://CRAN.R-project.org/package=mlexperiments> with additional machine learning ('ML') learners for survival analysis. The package provides R6-based survival learners for the following algorithms: 'glmnet' <https://CRAN.R-project.org/package=glmnet>, 'ranger' <https://CRAN.R-project.org/package=ranger>, 'xgboost' <https://CRAN.R-project.org/package=xgboost>, and 'rpart' <https://CRAN.R-project.org/package=rpart>. These can be used directly with the 'mlexperiments' R package.
Author: Lorenz A. Kapsner [cre, aut, cph]
Maintainer: Lorenz A. Kapsner <lorenz.kapsner@gmail.com>

Diff between mlsurvlrnrs versions 0.0.6 dated 2025-09-09 and 0.0.7 dated 2025-12-04

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More information about mlsurvlrnrs at CRAN
Permanent link

Package ambiorix updated to version 2.2.2 with previous version 2.2.1 dated 2025-07-29

Title: Web Framework Inspired by 'Express.js'
Description: A web framework inspired by 'express.js' to build any web service from multi-page websites to 'RESTful' application programming interfaces.
Author: John Coene [aut] , Opifex [fnd], Kennedy Mwavu [cre] , Julio Collazos [ctb]
Maintainer: Kennedy Mwavu <mwavukennedy@gmail.com>

Diff between ambiorix versions 2.2.1 dated 2025-07-29 and 2.2.2 dated 2025-12-04

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More information about ambiorix at CRAN
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Package armadillo4r updated to version 0.7.0 with previous version 0.6.0 dated 2025-11-03

Title: An 'Armadillo' Interface
Description: Provides function declarations and inline function definitions that facilitate communication between R and the 'Armadillo' 'C++' library for linear algebra and scientific computing. This implementation is derived from Vargas Sepulveda and Schneider Malamud (2024) <doi:10.1016/j.softx.2025.102087>.
Author: Mauricio Vargas Sepulveda [aut, cre] , Jonathan Schneider Malamud [ctb], Conrad Sanderson [aut] )
Maintainer: Mauricio Vargas Sepulveda <m.vargas.sepulveda@gmail.com>

Diff between armadillo4r versions 0.6.0 dated 2025-11-03 and 0.7.0 dated 2025-12-04

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 armadillo4r-0.7.0/armadillo4r/DESCRIPTION                                                                 |    6 
 armadillo4r-0.7.0/armadillo4r/MD5                                                                         | 1318 -
 armadillo4r-0.7.0/armadillo4r/NEWS.md                                                                     |    7 
 armadillo4r-0.7.0/armadillo4r/R/unvendor.R                                                                |   29 
 armadillo4r-0.7.0/armadillo4r/R/vendor.R                                                                  |   12 
 armadillo4r-0.7.0/armadillo4r/R/version.R                                                                 |    4 
 armadillo4r-0.7.0/armadillo4r/README.md                                                                   |    2 
 armadillo4r-0.7.0/armadillo4r/configure                                                                   |   30 
 armadillo4r-0.7.0/armadillo4r/inst/extdata/configure                                                      |   25 
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 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/CubeToMatOp_bones.hpp                    |   49 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/CubeToMatOp_meat.hpp                     |   47 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Cube_bones.hpp                           | 1031 
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 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Op_bones.hpp                             |   77 
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 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/compiler_check.hpp                       |   83 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/compiler_setup.hpp                       |  636 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/compiler_setup_post.hpp                  |   16 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/cond_rel.hpp                             |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/config.hpp                               |  326 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/config.hpp.cmake                         |   83 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/constants.hpp                            |  459 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/constants_old.hpp                        |  130 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/csv_name.hpp                             |  216 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/debug.hpp                                | 1852 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/def_arpack.hpp                           |  290 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/def_atlas.hpp                            |  154 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/def_blas.hpp                             |  354 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/def_fftw3.hpp                            |   57 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/def_lapack.hpp                           | 3918 ---
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/def_superlu.hpp                          |  180 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/diagmat_proxy.hpp                        |  509 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/diagview_bones.hpp                       |  140 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/diagview_meat.hpp                        | 1307 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/diskio_bones.hpp                         |  494 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/diskio_meat.hpp                          | 6863 ++---
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/distr_param.hpp                          |  111 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eGlueCube_bones.hpp                      |   62 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eGlueCube_meat.hpp                       |  189 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eGlue_bones.hpp                          |   62 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eGlue_meat.hpp                           |  185 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eOpCube_bones.hpp                        |   78 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eOpCube_meat.hpp                         |  219 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eOp_bones.hpp                            |   73 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eOp_meat.hpp                             |  172 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eglue_core_bones.hpp                     |  100 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eglue_core_meat.hpp                      | 1938 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eop_aux.hpp                              |  552 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eop_core_bones.hpp                       |  200 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eop_core_meat.hpp                        | 1690 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fft_engine_fftw3.hpp                     |  202 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fft_engine_kissfft.hpp                   |  497 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/field_bones.hpp                          |  627 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/field_meat.hpp                           | 3552 +-
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fill.hpp                                 |  284 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_accu.hpp                              | 1143 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_all.hpp                               |  100 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_any.hpp                               |  100 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_approx_equal.hpp                      |  702 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_as_scalar.hpp                         |  544 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_balance.hpp                           |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_chi2rnd.hpp                           |  207 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_chol.hpp                              |  174 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_circshift.hpp                         |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_clamp.hpp                             |  147 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_cond_rcond.hpp                        |   81 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_conv.hpp                              |   80 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_conv_to.hpp                           | 1258 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_cor.hpp                               |   48 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_cov.hpp                               |   48 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_cross.hpp                             |   36 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_cumprod.hpp                           |   90 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_cumsum.hpp                            |   90 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_det.hpp                               |   77 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_diagmat.hpp                           |   95 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_diags_spdiags.hpp                     |  163 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_diagvec.hpp                           |   61 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_diff.hpp                              |   91 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_dot.hpp                               |  439 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_eig_gen.hpp                           |  222 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_eig_pair.hpp                          |  179 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_eig_sym.hpp                           |  190 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_eigs_gen.hpp                          |  509 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_eigs_sym.hpp                          |  309 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_elem.hpp                              | 1428 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_expmat.hpp                            |  100 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_eye.hpp                               |  124 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_fft.hpp                               |  158 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_fft2.hpp                              |  147 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_find.hpp                              |  568 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_find_unique.hpp                       |   67 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_flip.hpp                              |   75 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_hess.hpp                              |  194 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_hist.hpp                              |   81 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_histc.hpp                             |   59 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_index_max.hpp                         |  179 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_index_min.hpp                         |  179 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_inplace_strans.hpp                    |  106 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_inplace_trans.hpp                     |  131 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_interp1.hpp                           |  431 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_interp2.hpp                           |  350 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_intersect.hpp                         |   62 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_inv.hpp                               |  143 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_inv_sympd.hpp                         |  142 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_join.hpp                              |  574 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_kmeans.hpp                            |   55 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_kron.hpp                              |   95 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_log_det.hpp                           |  163 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_log_normpdf.hpp                       |  230 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_logmat.hpp                            |  143 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_lu.hpp                                |   84 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_max.hpp                               |  312 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_mean.hpp                              |  160 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_median.hpp                            |   74 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_min.hpp                               |  312 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_misc.hpp                              |  703 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_mvnrnd.hpp                            |  117 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_n_unique.hpp                          |  141 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_nonzeros.hpp                          |   42 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_norm.hpp                              |  442 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_normalise.hpp                         |  123 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_normcdf.hpp                           |  220 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_normpdf.hpp                           |  228 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_numel.hpp                             |   92 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_omit.hpp                              |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_ones.hpp                              |  167 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_orth_null.hpp                         |   95 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_pinv.hpp                              |  126 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_polyfit.hpp                           |   64 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_polyval.hpp                           |   36 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_powext.hpp                            |  199 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_powmat.hpp                            |  113 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_princomp.hpp                          |  166 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_prod.hpp                              |   82 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_qr.hpp                                |  149 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_quantile.hpp                          |   57 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_qz.hpp                                |   60 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_rande.hpp                             |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_randg.hpp                             |  276 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_randi.hpp                             |  301 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_randn.hpp                             |  410 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_randperm.hpp                          |  182 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_randu.hpp                             |  402 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_range.hpp                             |   62 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_rank.hpp                              |   58 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_regspace.hpp                          |  319 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_repelem.hpp                           |   45 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_repmat.hpp                            |   88 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_reshape.hpp                           |  197 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_resize.hpp                            |  158 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_reverse.hpp                           |  102 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_roots.hpp                             |   63 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_schur.hpp                             |  107 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_shuffle.hpp                           |   88 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_size.hpp                              |  347 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_solve.hpp                             |  263 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_sort.hpp                              |  166 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_sort_index.hpp                        |  117 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_speye.hpp                             |   91 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_spones.hpp                            |   40 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_sprandn.hpp                           |  132 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_sprandu.hpp                           |  132 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_spsolve.hpp                           |  227 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_sqrtmat.hpp                           |  137 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_stddev.hpp                            |  155 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_strans.hpp                            |  105 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_sum.hpp                               |  159 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_svd.hpp                               |  266 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_svds.hpp                              |  435 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_sylvester.hpp                         |  154 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_symmat.hpp                            |  142 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_toeplitz.hpp                          |   59 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trace.hpp                             |  751 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trans.hpp                             |   93 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trapz.hpp                             |   53 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trig.hpp                              |  517 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trimat.hpp                            |  150 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trimat_ind.hpp                        |  143 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trunc_exp.hpp                         |   99 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trunc_log.hpp                         |  106 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_unique.hpp                            |   58 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_var.hpp                               |  158 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_vecnorm.hpp                           |  456 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_vectorise.hpp                         |  111 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_wishrnd.hpp                           |  213 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_zeros.hpp                             |  209 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_affmul_bones.hpp                    |   64 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_affmul_meat.hpp                     |  685 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_atan2_bones.hpp                     |   46 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_atan2_meat.hpp                      |  250 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_conv_bones.hpp                      |   62 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_conv_meat.hpp                       |  462 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_cor_bones.hpp                       |   36 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_cor_meat.hpp                        |   76 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_cov_bones.hpp                       |   36 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_cov_meat.hpp                        |   72 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_cross_bones.hpp                     |   35 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_cross_meat.hpp                      |   84 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_hist_bones.hpp                      |   55 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_hist_meat.hpp                       |  308 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_histc_bones.hpp                     |   55 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_histc_meat.hpp                      |  212 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_hypot_bones.hpp                     |   46 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_hypot_meat.hpp                      |  187 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_intersect_bones.hpp                 |   45 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_intersect_meat.hpp                  |  156 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_join_bones.hpp                      |  124 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_join_meat.hpp                       |  553 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_kron_bones.hpp                      |   50 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_kron_meat.hpp                       |  175 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_max_bones.hpp                       |   46 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_max_meat.hpp                        |  201 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_min_bones.hpp                       |   46 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_min_meat.hpp                        |  201 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_mixed_bones.hpp                     |  136 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_mixed_meat.hpp                      |  716 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_mvnrnd_bones.hpp                    |   66 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_mvnrnd_meat.hpp                     |  212 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_polyfit_bones.hpp                   |   54 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_polyfit_meat.hpp                    |  204 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_polyval_bones.hpp                   |   40 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_polyval_meat.hpp                    |   97 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_powext_bones.hpp                    |   98 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_powext_meat.hpp                     |  775 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_quantile_bones.hpp                  |   67 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_quantile_meat.hpp                   |  303 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_relational_bones.hpp                |  190 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_relational_meat.hpp                 |  527 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_solve_bones.hpp                     |  299 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_solve_meat.hpp                      |  964 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_times_bones.hpp                     |  254 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_times_meat.hpp                      | 1583 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_times_misc_bones.hpp                |  122 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_times_misc_meat.hpp                 |  782 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_toeplitz_bones.hpp                  |   27 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_toeplitz_meat.hpp                   |   72 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_trapz_bones.hpp                     |   64 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_trapz_meat.hpp                      |  231 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/gmm_diag_bones.hpp                       |  305 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/gmm_diag_meat.hpp                        | 3560 +-
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/gmm_full_bones.hpp                       |  289 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/gmm_full_meat.hpp                        | 3683 +-
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/gmm_misc_bones.hpp                       |  142 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/gmm_misc_meat.hpp                        |  203 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/hdf5_misc.hpp                            |  846 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/hdf5_name.hpp                            |  133 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/include_hdf5.hpp                         |   63 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/include_superlu.hpp                      |  633 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/injector_bones.hpp                       |   79 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/injector_meat.hpp                        |  424 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/memory.hpp                               |  301 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mp_misc.hpp                              |  101 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtGlueCube_bones.hpp                     |   37 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtGlueCube_meat.hpp                      |   51 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtGlue_bones.hpp                         |   47 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtGlue_meat.hpp                          |   66 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtOpCube_bones.hpp                       |   66 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtOpCube_meat.hpp                        |  135 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtOp_bones.hpp                           |   72 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtOp_meat.hpp                            |  138 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtSpGlue_bones.hpp                       |   43 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtSpGlue_meat.hpp                        |   50 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtSpOp_bones.hpp                         |   62 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtSpOp_meat.hpp                          |  102 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtSpReduceOp_bones.hpp                   |   67 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtSpReduceOp_meat.hpp                    |   50 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mul_gemm.hpp                             |  732 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mul_gemm_mixed.hpp                       |  378 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mul_gemv.hpp                             |  731 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mul_herk.hpp                             |  667 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mul_syrk.hpp                             |  647 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_DenseGenMatProd_bones.hpp         |   34 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_DenseGenMatProd_meat.hpp          |   47 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_DoubleShiftQR_bones.hpp           |   64 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_DoubleShiftQR_meat.hpp            |  322 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_EigsSelect.hpp                    |   57 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_GenEigsSolver_bones.hpp           |  118 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_GenEigsSolver_meat.hpp            |  418 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SortEigenvalue.hpp                |  235 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SparseGenMatProd_bones.hpp        |   38 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SparseGenMatProd_meat.hpp         |   58 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SparseGenRealShiftSolve_bones.hpp |   52 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SparseGenRealShiftSolve_meat.hpp  |  180 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SymEigsShiftSolver_bones.hpp      |   33 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SymEigsShiftSolver_meat.hpp       |   45 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SymEigsSolver_bones.hpp           |  108 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SymEigsSolver_meat.hpp            |  404 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_TridiagEigen_bones.hpp            |   38 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_TridiagEigen_meat.hpp             |  165 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_UpperHessenbergEigen_bones.hpp    |   56 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_UpperHessenbergEigen_meat.hpp     |  201 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_UpperHessenbergQR_bones.hpp       |   46 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_UpperHessenbergQR_meat.hpp        |  234 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_cx_attrib.hpp                     |   44 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_accu_bones.hpp                        |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_accu_meat.hpp                         |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_all_bones.hpp                         |   93 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_all_meat.hpp                          |  605 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_any_bones.hpp                         |   93 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_any_meat.hpp                          |  599 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_chi2rnd_bones.hpp                     |   47 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_chi2rnd_meat.hpp                      |  189 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_chol_bones.hpp                        |   34 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_chol_meat.hpp                         |   89 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_circshift_bones.hpp                   |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_circshift_meat.hpp                    |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_clamp_bones.hpp                       |  111 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_clamp_meat.hpp                        |  744 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_col_as_mat_bones.hpp                  |   25 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_col_as_mat_meat.hpp                   |   49 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cond_bones.hpp                        |   34 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cond_meat.hpp                         |  217 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cor_bones.hpp                         |   30 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cor_meat.hpp                          |  144 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cov_bones.hpp                         |   30 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cov_meat.hpp                          |  116 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cumprod_bones.hpp                     |   51 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cumprod_meat.hpp                      |  226 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cumsum_bones.hpp                      |   50 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cumsum_meat.hpp                       |  226 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cx_scalar_bones.hpp                   |  221 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cx_scalar_meat.hpp                    |  739 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_det_bones.hpp                         |   58 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_det_meat.hpp                          |  223 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_diagmat_bones.hpp                     |   72 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_diagmat_meat.hpp                      |  944 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_diagvec_bones.hpp                     |   65 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_diagvec_meat.hpp                      |  609 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_diff_bones.hpp                        |   55 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_diff_meat.hpp                         |  311 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_dot_bones.hpp                         |  167 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_dot_meat.hpp                          |  806 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_dotext_bones.hpp                      |   57 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_dotext_meat.hpp                       |  227 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_elem_bones.hpp                        |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_elem_meat.hpp                         |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_expmat_bones.hpp                      |   58 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_expmat_meat.hpp                       |  337 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_fft_bones.hpp                         |   67 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_fft_meat.hpp                          |  437 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_find_bones.hpp                        |  156 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_find_meat.hpp                         | 1025 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_find_unique_bones.hpp                 |   84 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_find_unique_meat.hpp                  |  162 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_flip_bones.hpp                        |   73 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_flip_meat.hpp                         |  395 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_hist_bones.hpp                        |   30 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_hist_meat.hpp                         |  167 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_htrans_bones.hpp                      |  161 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_htrans_meat.hpp                       |  499 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_index_max_bones.hpp                   |   65 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_index_max_meat.hpp                    |  509 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_index_min_bones.hpp                   |   65 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_index_min_meat.hpp                    |  509 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_inv_gen_bones.hpp                     |  223 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_inv_gen_meat.hpp                      |  574 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_inv_spd_bones.hpp                     |   92 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_inv_spd_meat.hpp                      |  472 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_log_det_bones.hpp                     |   60 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_log_det_meat.hpp                      |  303 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_logmat_bones.hpp                      |  112 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_logmat_meat.hpp                       |  763 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_max_bones.hpp                         |  177 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_max_meat.hpp                          | 1725 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_mean_bones.hpp                        |  148 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_mean_meat.hpp                         |  837 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_median_bones.hpp                      |   95 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_median_meat.hpp                       |  399 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_min_bones.hpp                         |  179 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_min_meat.hpp                          | 1725 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_nonzeros_bones.hpp                    |   35 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_nonzeros_meat.hpp                     |  127 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_norm2est_bones.hpp                    |   65 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_norm2est_meat.hpp                     |  317 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_norm_bones.hpp                        |   92 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_norm_meat.hpp                         | 1366 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_normalise_bones.hpp                   |   48 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_normalise_meat.hpp                    |  166 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_omit_bones.hpp                        |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_omit_meat.hpp                         |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_orth_null_bones.hpp                   |   50 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_orth_null_meat.hpp                    |  245 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_pinv_bones.hpp                        |   74 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_pinv_meat.hpp                         |  461 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_powmat_bones.hpp                      |   61 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_powmat_meat.hpp                       |  386 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_princomp_bones.hpp                    |   89 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_princomp_meat.hpp                     |  366 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_prod_bones.hpp                        |   34 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_prod_meat.hpp                         |  232 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_range_bones.hpp                       |   31 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_range_meat.hpp                        |   93 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_rank_bones.hpp                        |   45 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_rank_meat.hpp                         |  273 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_rcond_bones.hpp                       |   23 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_rcond_meat.hpp                        |  119 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_relational_bones.hpp                  |  181 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_relational_meat.hpp                   |  746 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_repelem_bones.hpp                     |   34 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_repelem_meat.hpp                      |  104 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_repmat_bones.hpp                      |   44 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_repmat_meat.hpp                       |  193 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_reshape_bones.hpp                     |   65 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_reshape_meat.hpp                      |  360 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_resize_bones.hpp                      |   55 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_resize_meat.hpp                       |  233 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_reverse_bones.hpp                     |   49 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_reverse_meat.hpp                      |  210 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_roots_bones.hpp                       |   42 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_roots_meat.hpp                        |  170 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_row_as_mat_bones.hpp                  |   25 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_row_as_mat_meat.hpp                   |   64 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_shuffle_bones.hpp                     |   47 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_shuffle_meat.hpp                      |  254 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sort_bones.hpp                        |   62 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sort_index_bones.hpp                  |  169 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sort_index_meat.hpp                   |  229 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sort_meat.hpp                         |  415 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_as_dense_bones.hpp                 |   25 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_as_dense_meat.hpp                  |   27 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_diagvec_bones.hpp                  |   28 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_diagvec_meat.hpp                   |   59 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_max_bones.hpp                      |   86 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_max_meat.hpp                       |  739 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_mean_bones.hpp                     |   80 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_mean_meat.hpp                      |  428 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_min_bones.hpp                      |   86 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_min_meat.hpp                       |  787 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_minus_bones.hpp                    |   69 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_minus_meat.hpp                     |  180 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_nonzeros_bones.hpp                 |   28 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_nonzeros_meat.hpp                  |  101 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_plus_bones.hpp                     |   41 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_plus_meat.hpp                      |   94 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_stddev_bones.hpp                   |   37 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_stddev_meat.hpp                    |  181 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_sum_bones.hpp                      |   28 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_sum_meat.hpp                       |  156 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_var_bones.hpp                      |   70 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_var_meat.hpp                       |  348 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_vecnorm_bones.hpp                  |   55 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_vecnorm_meat.hpp                   |  233 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sqrtmat_bones.hpp                     |  108 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sqrtmat_meat.hpp                      |  673 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_stddev_bones.hpp                      |   55 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_stddev_meat.hpp                       |  211 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_strans_bones.hpp                      |  107 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_strans_meat.hpp                       |  636 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sum_bones.hpp                         |   97 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sum_meat.hpp                          |  747 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_symmat_bones.hpp                      |   75 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_symmat_meat.hpp                       |  325 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_toeplitz_bones.hpp                    |   43 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_toeplitz_meat.hpp                     |  137 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_trimat_bones.hpp                      |   93 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_trimat_meat.hpp                       |  484 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_unique_bones.hpp                      |   91 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_unique_meat.hpp                       |  220 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_var_bones.hpp                         |   85 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_var_meat.hpp                          |  466 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_vecnorm_bones.hpp                     |   69 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_vecnorm_meat.hpp                      |  303 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_vectorise_bones.hpp                   |  116 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_vectorise_meat.hpp                    |  580 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_wishrnd_bones.hpp                     |   64 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_wishrnd_meat.hpp                      |  368 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_cube_div.hpp                    |  217 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_cube_minus.hpp                  |  234 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_cube_plus.hpp                   |  234 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_cube_relational.hpp             |  333 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_cube_schur.hpp                  |  135 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_cube_times.hpp                  |  129 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_div.hpp                         |  440 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_minus.hpp                       |  570 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_ostream.hpp                     |  181 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_plus.hpp                        |  499 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_relational.hpp                  |  819 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_schur.hpp                       |  446 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_times.hpp                       |  556 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/podarray_bones.hpp                       |   94 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/podarray_meat.hpp                        |  340 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/promote_type.hpp                         |  855 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/restrictors.hpp                          |  738 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/running_stat_bones.hpp                   |  146 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/running_stat_meat.hpp                    |  522 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/running_stat_vec_bones.hpp               |  242 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/running_stat_vec_meat.hpp                |  771 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/sp_auxlib_bones.hpp                      |  413 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/sp_auxlib_meat.hpp                       | 3902 +--
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/span.hpp                                 |   81 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spdiagview_bones.hpp                     |  135 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spdiagview_meat.hpp                      | 1297 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_join_bones.hpp                    |  109 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_join_meat.hpp                     |  438 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_kron_bones.hpp                    |   45 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_kron_meat.hpp                     |  157 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_max_bones.hpp                     |   68 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_max_meat.hpp                      |  231 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_merge_bones.hpp                   |   36 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_merge_meat.hpp                    |  625 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_min_bones.hpp                     |   68 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_min_meat.hpp                      |  229 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_minus_bones.hpp                   |   80 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_minus_meat.hpp                    |  408 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_plus_bones.hpp                    |   70 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_plus_meat.hpp                     |  348 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_relational_bones.hpp              |  103 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_relational_meat.hpp               |  564 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_schur_bones.hpp                   |   86 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_schur_meat.hpp                    |  470 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_times_bones.hpp                   |   76 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_times_meat.hpp                    |  386 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_accu_bones.hpp                      |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_accu_meat.hpp                       |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_circshift_bones.hpp                 |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_circshift_meat.hpp                  |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_diagmat_bones.hpp                   |   85 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_diagmat_meat.hpp                    |  566 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_htrans_bones.hpp                    |   49 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_htrans_meat.hpp                     |   87 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_misc_bones.hpp                      |  372 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_misc_meat.hpp                       |  766 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_norm_bones.hpp                      |   45 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_norm_meat.hpp                       |  170 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_normalise_bones.hpp                 |   28 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_normalise_meat.hpp                  |  147 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_omit_bones.hpp                      |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_omit_meat.hpp                       |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_relational_bones.hpp                |  163 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_relational_meat.hpp                 |  671 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_repmat_bones.hpp                    |   40 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_repmat_meat.hpp                     |  143 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_reverse_bones.hpp                   |   35 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_reverse_meat.hpp                    |  208 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_strans_bones.hpp                    |   53 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_strans_meat.hpp                     |  183 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_symmat_bones.hpp                    |   43 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_symmat_meat.hpp                     |  106 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_trimat_bones.hpp                    |   83 
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Package rspacer updated to version 0.3.1 with previous version 0.3.0 dated 2025-10-17

Title: 'RSpace' API Wrapper
Description: Wrapper for the 'RSpace' Electronic Lab Notebook (<https://www.researchspace.com/>) API. This packages provides convenience functions to browse, search, create, and edit your 'RSpace' documents. In addition, it enables filling 'RSpace' templates from R Markdown/Quarto templates or tabular data (e.g., 'Excel' files). This R package is not developed or endorsed by 'Research Space'.
Author: Gerhard Burger [aut, cre] , Hanneke Leegwater [aut] , Leiden University [cph, fnd]
Maintainer: Gerhard Burger <burger.ga@gmail.com>

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Package MIC updated to version 2.0.0 with previous version 1.2.0 dated 2025-10-12

Title: Analysis of Antimicrobial Minimum Inhibitory Concentration Data
Description: Analyse, plot, and tabulate antimicrobial minimum inhibitory concentration (MIC) data. Validate the results of an MIC experiment by comparing observed MIC values to a gold standard assay, in line with standards from the International Organization for Standardization (2021) <https://www.iso.org/standard/79377.html>.
Author: Alessandro Gerada [aut, cre, cph]
Maintainer: Alessandro Gerada <alessandro.gerada@liverpool.ac.uk>

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Package ganttify updated to version 0.1.7 with previous version 0.1.1 dated 2025-10-28

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

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Package superb updated to version 1.0.1 with previous version 1.0.0 dated 2025-08-18

Title: Summary Plots with Adjusted Error Bars
Description: Computes standard error and confidence interval of various descriptive statistics under various designs and sampling schemes. The main function, superb(), return a plot. It can also be used to obtain a dataframe with the statistics and their precision intervals so that other plotting environments (e.g., Excel) can be used. See Cousineau and colleagues (2021) <doi:10.1177/25152459211035109> or Cousineau (2017) <doi:10.5709/acp-0214-z> for a review as well as Cousineau (2005) <doi:10.20982/tqmp.01.1.p042>, Morey (2008) <doi:10.20982/tqmp.04.2.p061>, Baguley (2012) <doi:10.3758/s13428-011-0123-7>, Cousineau & Laurencelle (2016) <doi:10.1037/met0000055>, Cousineau & O'Brien (2014) <doi:10.3758/s13428-013-0441-z>, Calderini & Harding <doi:10.20982/tqmp.15.1.p001> for specific references. The documentation is available at <https://dcousin3.github.io/superb/> .
Author: Denis Cousineau [aut, cre], Bradley Harding [ctb], Marc-Andre Goulet [ctb], Jesika Walker [art, pre]
Maintainer: Denis Cousineau <denis.cousineau@uottawa.ca>

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Package stan4bart updated to version 0.0-11 with previous version 0.0-10 dated 2024-09-04

Title: Bayesian Additive Regression Trees with Stan-Sampled Parametric Extensions
Description: Fits semiparametric linear and multilevel models with non-parametric additive Bayesian additive regression tree (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) components and Stan (Stan Development Team (2021) <https://mc-stan.org/>) sampled parametric ones. Multilevel models can be expressed using 'lme4' syntax (Bates, Maechler, Bolker, and Walker (2015) <doi:10.18637/jss.v067.i01>).
Author: Vincent Dorie [aut, cre] , Ben Goodrich [ctb] , Jonah Gabry [ctb] , Imad Ali [ctb] , Sam Brilleman [ctb] , Paul-Christian Burkner [ctb] , Joshua Pritikin [ctb] , Andrew Gelman [ctb] , Bob Carpenter [ctb] , Matt Hoffman [ctb] , Daniel Lee [ctb] , Mich [...truncated...]
Maintainer: Vincent Dorie <vdorie@gmail.com>

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Package Anthropometry updated to version 1.21 with previous version 1.20 dated 2025-07-23

Title: Statistical Methods for Anthropometric Data
Description: Statistical methodologies especially developed to analyze anthropometric data. These methods are aimed at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the statistical concept of data depth, statistical shape analysis and archetypal analysis. Please see Vinue (2017) <doi:10.18637/jss.v077.i06>.
Author: Guillermo Vinue [aut, cre], Irene Epifanio [aut], Amelia Simo [aut], M. Victoria Ibanez [aut], Juan Domingo [aut], Guillermo Ayala [aut]
Maintainer: Guillermo Vinue <guillermo.vinue@uv.es>

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Package shinymrp updated to version 0.10.0 with previous version 0.9.1 dated 2025-11-11

Title: Interface for Multilevel Regression and Poststratification
Description: Dual interfaces, graphical and programmatic, designed for intuitive applications of Multilevel Regression and Poststratification (MRP). Users can apply the method to a variety of datasets, from electronic health records to sample survey data, through an end-to-end Bayesian data analysis workflow. The package provides robust tools for data cleaning, exploratory analysis, flexible model building, and insightful result visualization. For more details, see Si et al. (2020) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2020002/article/00003-eng.pdf?st=iF1_Fbrh> and Si (2025) <doi:10.1214/24-STS932>.
Author: Toan Tran [cre, aut, cph], Jonah Gabry [aut, cph], Yajuan Si [aut, cph]
Maintainer: Toan Tran <trannttoan97@gmail.com>

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Package rtpcr updated to version 2.0.4 with previous version 2.0.3 dated 2025-11-08

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

Diff between rtpcr versions 2.0.3 dated 2025-11-08 and 2.0.4 dated 2025-12-04

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Package nanoarrow updated to version 0.7.0-2 with previous version 0.7.0-1 dated 2025-09-24

Title: Interface to the 'nanoarrow' 'C' Library
Description: Provides an 'R' interface to the 'nanoarrow' 'C' library and the 'Apache Arrow' application binary interface. Functions to import and export 'ArrowArray', 'ArrowSchema', and 'ArrowArrayStream' 'C' structures to and from 'R' objects are provided alongside helpers to facilitate zero-copy data transfer among 'R' bindings to libraries implementing the 'Arrow' 'C' data interface.
Author: Dewey Dunnington [aut, cre] , Apache Arrow [aut, cph], Apache Software Foundation [cph]
Maintainer: Dewey Dunnington <dewey@dunnington.ca>

Diff between nanoarrow versions 0.7.0-1 dated 2025-09-24 and 0.7.0-2 dated 2025-12-04

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Package MixedPsy updated to version 1.3.0 with previous version 1.2.0 dated 2025-02-18

Title: Statistical Tools for the Analysis of Psychophysical Data
Description: Tools for the analysis of psychophysical data in R. This package allows to estimate the Point of Subjective Equivalence (PSE) and the Just Noticeable Difference (JND), either from a psychometric function or from a Generalized Linear Mixed Model (GLMM). Additionally, the package allows plotting the fitted models and the response data, simulating psychometric functions of different shapes, and simulating data sets. For a description of the use of GLMMs applied to psychophysical data, refer to Moscatelli et al. (2012).
Author: Alessandro Moscatelli [aut, cre] , Priscilla Balestrucci [aut]
Maintainer: Alessandro Moscatelli <moskante@gmail.com>

Diff between MixedPsy versions 1.2.0 dated 2025-02-18 and 1.3.0 dated 2025-12-04

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Package E2E updated to version 0.1.2 with previous version 0.1.1 dated 2025-11-28

Title: Ensemble Learning Framework for Diagnostic and Prognostic Modeling
Description: Provides a framework to build and evaluate diagnosis or prognosis models using stacking, voting, and bagging ensemble techniques with various base learners. The package also includes tools for visualization and interpretation of models. The development version of the package is available on 'GitHub' at <https://github.com/xiaojie0519/E2E>. The methods are based on the foundational work of Breiman (1996) <doi:10.1007/BF00058655> on bagging and Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> on stacking.
Author: Shanjie Luan [aut, cre]
Maintainer: Shanjie Luan <Luan20050519@163.com>

Diff between E2E versions 0.1.1 dated 2025-11-28 and 0.1.2 dated 2025-12-04

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New package dauR with initial version 1.0
Package: dauR
Version: 1.0
Title: Datasets for "Sampling and Data Analysis Using R: Theory and Practice"
Maintainer: Professor Dr. Mohammad Shahidul Islam <shahed.stat@gmail.com>
Description: Provides several datasets used throughout the book "Sampling and Data Analysis Using R: Theory and Practice" by Islam (2025, ISBN:978-984-35-8644-5). The datasets support teaching and learning of statistical concepts such as sampling methods, descriptive analysis, estimation and basic data handling. These curated data objects allow instructors, students and researchers to reproduce examples, practice data manipulation and perform hands-on analysis using R.
License: CC BY 4.0
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5)
NeedsCompilation: no
Packaged: 2025-11-30 14:47:35 UTC; shahe
Author: Professor Dr. Mohammad Shahidul Islam [aut, cre]
Repository: CRAN
Date/Publication: 2025-12-04 15:10:08 UTC

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New package cifmodeling with initial version 0.9.4
Package: cifmodeling
Title: Visualization and Polytomous Modeling of Survival and Competing Risks
Version: 0.9.4
Description: A publication-ready toolkit for modern survival and competing risks analysis with a minimal, formula-based interface. Both nonparametric estimation and direct polytomous regression of cumulative incidence functions (CIFs) are supported. The main functions 'cifcurve()', 'cifplot()', and 'cifpanel()' estimate survival and CIF curves and produce high-quality graphics with risk tables, censoring and competing-event marks, and multi-panel or inset layouts built on 'ggplot2' and 'ggsurvfit'. The modeling function 'polyreg()' performs direct polytomous regression for coherent joint modeling of all cause-specific CIFs to estimate risk ratios, odds ratios, or subdistribution hazard ratios at user-specified time points. All core functions adopt a formula-and-data syntax and return tidy and extensible outputs that integrate smoothly with 'modelsummary', 'broom', and the broader 'tidyverse' ecosystem. Key numerical routines are implemented in C++ via 'Rcpp'.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 4.1.0)
Suggests: survival, mets, modelsummary, gtsummary, knitr, rmarkdown, testthat (>= 3.0.0), pkgdown
LinkingTo: Rcpp
Imports: Rcpp, nleqslv, boot, ggsurvfit, ggplot2, patchwork, scales, generics, lifecycle
VignetteBuilder: knitr
URL: https://gestimation.github.io/cifmodeling/, https://github.com/gestimation/cifmodeling
BugReports: https://github.com/gestimation/cifmodeling/issues
NeedsCompilation: yes
Packaged: 2025-11-30 12:04:32 UTC; Shiro
Author: Shiro Tanaka [aut, cre, cph] , Shigetaka Kobari [ctb], Chisato Honda [ctb]
Maintainer: Shiro Tanaka <gestimation@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-04 15:10:13 UTC

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Package airGRdatasets updated to version 0.2.3 with previous version 0.2.2 dated 2025-11-18

Title: Hydro-Meteorological Catchments Datasets for the 'airGR' Packages
Description: Sample of hydro-meteorological datasets extracted from the 'CAMELS-FR' French database <doi:10.57745/WH7FJR>. It provides metadata and catchment-scale aggregated hydro-meteorological time series on a pool of French catchments for use by the 'airGR' packages.
Author: Olivier Delaigue [aut, cre] , Pierre Brigode [aut] , Guillaume Thirel [aut] , Benoît Genot [ctb], Guilherme Mendoza Guimaraes [ctb]
Maintainer: Olivier Delaigue <airGR@inrae.fr>

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Package tram updated to version 1.3-1 with previous version 1.3-0 dated 2025-11-18

Title: Transformation Models
Description: Formula-based user-interfaces to specific transformation models implemented in package 'mlt' (<DOI:10.32614/CRAN.package.mlt>, <DOI:10.32614/CRAN.package.mlt.docreg>). Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to transformation models for clustered data is provided (Barbanti and Hothorn, 2022, <DOI:10.1093/biostatistics/kxac048>) and a tutorial explains applications in survival analysis (Siegfried et al., 2025, <DOI:10.48550/arXiv.2402.06428>). Multivariate conditional transformation models (Klein et al, 2022, <DOI:10.1111/sjos.12501>) and shift-scale transformation models (Siegfried et al, 2023, <DOI:10.108 [...truncated...]
Author: Torsten Hothorn [aut, cre] , Luisa Barbanti [ctb] , Sandra Siegfried [aut] , Lucas Kook [aut] , Susanne Dandl [ctb] , Brian Ripley [ctb], Bill Venables [ctb], Douglas M. Bates [ctb], Nadja Klein [ctb]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

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Package singleCellHaystack updated to version 1.0.3 with previous version 1.0.2 dated 2024-01-11

Title: A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data
Description: One key exploratory analysis step in single-cell genomics data analysis is the prediction of features with different activity levels. For example, we want to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, spatial DEGs in spatial transcriptomics data, or differentially accessible regions (DARs) in single-cell ATAC-seq data. 'singleCellHaystack' predicts differentially active features in single cell omics datasets without relying on the clustering of cells into arbitrary clusters. 'singleCellHaystack' uses Kullback-Leibler divergence to find features (e.g., genes, genomic regions, etc) that are active in subsets of cells that are non-randomly positioned inside an input space (such as 1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For the theoretical background of 'singleCellHaystack' we refer to our original paper Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3> and our update Vandenbon and Diez [...truncated...]
Author: Alexis Vandenbon [aut, cre] , Diego Diez [aut]
Maintainer: Alexis Vandenbon <alexis.vandenbon@gmail.com>

Diff between singleCellHaystack versions 1.0.2 dated 2024-01-11 and 1.0.3 dated 2025-12-04

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New package TFORGE with initial version 0.1.14
Package: TFORGE
Title: Tests for Geophysical Eigenvalues
Version: 0.1.14
Description: The eigenvalues of observed symmetric matrices are often of intense scientific interest. This package offers single sample tests for the eigenvalues of the population mean or the eigenvalue-multiplicity of the population mean. For k-samples, this package offers tests for equal eigenvalues between samples. Included is support for matrices with constraints common to geophysical tensors (constant trace, sum of squared eigenvalues, or both) and eigenvectors are usually considered nuisance parameters. Pivotal bootstrap methods enable these tests to have good performance for small samples (n=15 for 3x3 matrices). These methods were developed and studied by Hingee, Scealy and Wood (2026, "Nonparametric bootstrap inference for the eigenvalues of geophysical tensors", accepted by the Journal of American Statistical Association). Also available is a 2-sample test using a Gaussian orthogonal ensemble approximation and an eigenvalue-multiplicity test that assumes orthogonally-invariant covariance.
URL: https://github.com/kasselhingee/TFORGE
Imports: mvtnorm, purrr, Rdpack
Suggests: testthat
License: GPL (>= 3)
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2025-11-29 00:34:24 UTC; kassel
Author: Kassel Liam Hingee [aut, cre] , Art B. Owen [cph] , Board of Trustees Leland Stanford Junior University [cph]
Maintainer: Kassel Liam Hingee <kassel.hingee@anu.edu.au>
Repository: CRAN
Date/Publication: 2025-12-04 14:40:40 UTC

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New package SFOCDs with initial version 1.0.0
Package: SFOCDs
Title: Space Filling Optimal Covariate Designs
Version: 1.0.0
Description: We have designed this package to address experimental scenarios involving multiple covariates. It focuses on construction of Optimal Covariate Designs (OCDs), checking space filling property of the developed design. The primary objective of the package is to generate OCDs using four methods viz., M array method, Juxtapose method, Orthogonal Integer Array and Hadamard method. The package also evaluates space filling properties of both the base design and OCDs using the MaxPro criterion, providing a meaningful basis for comparison. In addition, it includes tool to visualize the spread offered by the design points in the form of scatterplot, which help users to assess distribution and coverage of design points.
Maintainer: Ashutosh Dalal <ashutosh.dalal97@gmail.com>
License: GPL (>= 2)
Encoding: UTF-8
Repository: CRAN
NeedsCompilation: no
Packaged: 2025-11-29 12:13:50 UTC; Ashutosh
Author: Neethu RS [aut, ctb], Cini Varghese [aut, ctb], Mohd Harun [aut, ctb], Anindita Datta [aut, ctb], Ashutosh Dalal [aut, ctb, cre]
Date/Publication: 2025-12-04 14:50:02 UTC

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Package SDModels updated to version 2.0.0 with previous version 1.0.13 dated 2025-06-05

Title: Spectrally Deconfounded Models
Description: Screen for and analyze non-linear sparse direct effects in the presence of unobserved confounding using the spectral deconfounding techniques (Ćevid, Bühlmann, and Meinshausen (2020)<jmlr.org/papers/v21/19-545.html>, Guo, Ćevid, and Bühlmann (2022) <doi:10.1214/21-AOS2152>). These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. 'SDModels' provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) <doi:10.1145/3711116>) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) <doi:10.48550/arXiv.2502.03969>).
Author: Markus Ulmer [aut, cre, cph] , Cyrill Scheidegger [aut]
Maintainer: Markus Ulmer <markus.ulmer@stat.math.ethz.ch>

Diff between SDModels versions 1.0.13 dated 2025-06-05 and 2.0.0 dated 2025-12-04

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Package qdap updated to version 2.4.6.1 with previous version 2.4.6 dated 2023-05-11

Title: Bridging the Gap Between Qualitative Data and Quantitative Analysis
Description: Automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse including frequency counts of sentence types, words, sentences, turns of talk, syllables and other assorted analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R packages that undertake higher level analysis and visualization of text. This affords the user a more efficient and targeted analysis. 'qdap' is designed for transcript analysis, however, many functions are applicable to other areas of Text Mining/ Natural Language Processing.
Author: Tyler Rinker [aut, cre], Bryan Goodrich [ctb], Dason Kurkiewicz [ctb]
Maintainer: Tyler Rinker <tyler.rinker@gmail.com>

Diff between qdap versions 2.4.6 dated 2023-05-11 and 2.4.6.1 dated 2025-12-04

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Package polle updated to version 1.6.2 with previous version 1.6.1 dated 2025-12-01

Title: Policy Learning
Description: Package for learning and evaluating (subgroup) policies via doubly robust loss functions. Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.
Author: Andreas Nordland [aut, cre], Klaus Holst [aut]
Maintainer: Andreas Nordland <andreasnordland@gmail.com>

Diff between polle versions 1.6.1 dated 2025-12-01 and 1.6.2 dated 2025-12-04

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Package istat readmission to version 1.1.1 with previous version 1.0 dated 2024-09-10

Title: Download and Manipulate Data from Istat
Description: Download data from ISTAT (Italian Institute of Statistics) database, both old and new provider (respectively, <http://dati.istat.it/> and <https://esploradati.istat.it/databrowser/>). Additional functions for manipulating data are provided. Moreover, a 'shiny' application called 'shinyIstat' can be used to search, download and filter datasets in an easier way.
Author: Elena Gradi [aut, cre], Alissa Lelli [aut], Daniela Ichim [aut]
Maintainer: Elena Gradi <elenaagradi@gmail.com>

This is a re-admission after prior archival of version 1.0 dated 2024-09-10

Diff between istat versions 1.0 dated 2024-09-10 and 1.1.1 dated 2025-12-04

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New package dentomedical with initial version 0.1.3
Package: dentomedical
Title: Publication-Ready Descriptive, Bivariate, Regression, and Diagnostic Accuracy Tools for Medical and Dental Data
Version: 0.1.3
Description: The 'dentomedical' package provides a comprehensive suite of tools for medical and dental research. It includes automated descriptive statistics, bivariate analysis with intelligent test selection, logistic regression, and diagnostic accuracy assessment. All functions generate structured, publication-ready tables using 'flextable', ensuring reproducibility and clarity suitable for manuscripts, reports, and clinical research workflows.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: dplyr, stats, flextable, tibble, MASS, broom, tidyr
Depends: R (>= 4.0.0)
URL: https://github.com/umarhussain-git/dentomedical
BugReports: https://github.com/umarhussain-git/dentomedical/issues
NeedsCompilation: no
Suggests: testthat, knitr, rmarkdown
Packaged: 2025-11-30 04:03:28 UTC; DR. Umar Hussain
Author: Umar Hussain [aut, cre]
Maintainer: Umar Hussain <drumarhussain@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-04 15:00:09 UTC

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Package crmPack updated to version 2.0.1 with previous version 2.0.0 dated 2025-11-29

Title: Object-Oriented Implementation of Dose Escalation Designs
Description: Implements a wide range of dose escalation designs. The focus is on model-based designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. Bayesian inference is performed via MCMC sampling in JAGS, and it is easy to setup a new design with custom JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanes Bove et al. (2019) <doi:10.18637/jss.v089.i10>.
Author: Daniel Sabanes Bove [aut, cre], Wai Yin Yeung [aut], Burak Kuersad Guenhan [aut], Giuseppe Palermo [aut], Thomas Jaki [aut], Jiawen Zhu [aut], Ziwei Liao [aut], Dimitris Kontos [aut], Marlene Schulte-Goebel [aut], Doug Kelkhoff [aut] , Oliver Boix [a [...truncated...]
Maintainer: Daniel Sabanes Bove <daniel.sabanes_bove@rconis.com>

Diff between crmPack versions 2.0.0 dated 2025-11-29 and 2.0.1 dated 2025-12-04

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Package clinUtils updated to version 0.2.2 with previous version 0.2.1 dated 2025-09-03

Title: General Utility Functions for Analysis of Clinical Data
Description: Utility functions to facilitate the import, the reporting and analysis of clinical data. Example datasets in 'SDTM' and 'ADaM' format, containing a subset of patients/domains from the 'CDISC Pilot 01 study' are also available as R datasets to demonstrate the package functionalities.
Author: Laure Cougnaud [aut, cre], Michela Pasetto [aut], Arne De Roeck [rev] , Open Analytics [cph]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>

Diff between clinUtils versions 0.2.1 dated 2025-09-03 and 0.2.2 dated 2025-12-04

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New package blockedFF with initial version 0.1.0
Package: blockedFF
Title: Generation of Blocked Fractional Factorial Designs (Two-Level and Three-Level)
Version: 0.1.0
Description: Provides computational tools to generate efficient blocked and unblocked fractional factorial designs for two-level and three-level factors using the generalized Minimum Aberration (MA) criterion and related optimization algorithms. Methodological foundations include the general theory of minimum aberration as described by Cheng and Tang (2005) <doi:10.1214/009053604000001228>, and the catalogue of three-level regular fractional factorial designs developed by Xu (2005) <doi:10.1007/s00184-005-0408-x>. The main functions dol2() and dol3() generate blocked two-level and three-level fractional factorial designs, respectively, using beam search, optimization-based ranking, confounding assessment, and structured output suitable for complete factorial situations.
License: GPL-3
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2025-11-30 03:54:24 UTC; B.N Mandal
Author: Sunil Kumar Yadav [aut], Sukanta Dash [aut, cre], Anil Kumar [aut]
Maintainer: Sukanta Dash <sukanta.iasri@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-04 15:00:14 UTC

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Package wiqid (with last version 0.3.3) was removed from CRAN

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

2022-11-17 0.3.3
2022-06-20 0.3.1
2020-06-10 0.3.0
2020-01-08 0.2.3
2019-06-05 0.2.2
2019-01-02 0.2.1
2018-04-20 0.1.4
2018-04-20 0.1.5
2017-11-18 0.1.3
2017-09-10 0.1.2
2017-06-09 0.1.1
2016-10-05 0.1.0

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

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2020-06-25 0.1.0

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Package nse (with last version 1.21) was removed from CRAN

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2022-11-10 1.21

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Package vDiveR (with last version 2.1.0) was removed from CRAN

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

2025-09-19 2.1.0
2024-11-22 2.0.1
2024-09-12 2.0.0
2024-01-09 1.2.1
2023-09-12 1.2.0
2023-04-15 1.1.0
2023-01-23 1.0.0

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Package scipub (with last version 1.2.3) was removed from CRAN

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2024-03-08 1.2.3
2021-03-18 1.2.2
2020-10-07 1.2.1
2020-09-22 1.2.0
2020-06-04 1.1.0
2020-05-18 1.0.0

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