Mon, 09 Feb 2026

New package hemat with initial version 1.1.2
Package: hemat
Title: Hematimetric Indices Calculator
Version: 1.1.2
Maintainer: Andre Luis Fernandes dos Santos <andrvet@alumni.usp.br>
Description: Tools to calculate Mean Corpuscular Volume, Mean Corpuscular Hemoglobin, and Mean Corpuscular Hemoglobin Concentration, which are essential for assessing red blood cell health and diagnosing blood disorders.
License: GPL (>= 2)
Suggests: testthat (>= 3.3.1)
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-02-06 10:53:33 UTC; andre
Author: Andre Luis Fernandes dos Santos [aut, cre]
Repository: CRAN
Date/Publication: 2026-02-09 19:20:02 UTC

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New package heaping with initial version 0.1.0
Package: heaping
Title: Correction of Heaping on Individual Level
Version: 0.1.0
Description: Provides methods for correcting heaping (digit preference) in survey data at the individual record level. Age heaping, where respondents disproportionately report ages ending in 0 or 5, is a common phenomenon that can distort demographic analyses. Unlike traditional smoothing methods that only correct aggregated statistics, this package corrects individual values by replacing a calculated proportion of heaped observations with draws from fitted truncated distributions (log-normal, normal, or uniform). Supports 5-year and 10-year heaping patterns, single heap correction, and optional model-based adjustment to preserve covariate relationships.
License: GPL (>= 2)
URL: https://github.com/matthias-da/heaping
BugReports: https://github.com/matthias-da/heaping/issues
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
Depends: R (>= 3.5.0)
Imports: fitdistrplus, EnvStats, stats
Suggests: VIM, ranger, data.table, ggplot2, simPop, testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-06 13:38:50 UTC; matthias
Author: Matthias Templ [aut, cre] , Bernhard Meindl [ctb]
Maintainer: Matthias Templ <matthias.templ@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-09 19:20:07 UTC

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New package GAReg with initial version 0.1.0
Package: GAReg
Title: Genetic Algorithms in Regression
Version: 0.1.0
Description: Provides a genetic algorithm framework for regression problems requiring discrete optimization over model spaces with unknown or varying dimension, where gradient-based methods and exhaustive enumeration are impractical. Uses a compact chromosome representation for tasks including spline knot placement and best-subset variable selection, with constraint-preserving crossover and mutation, exact uniform initialization under spacing constraints, steady-state replacement, and optional island-model parallelization from Lu, Lund, and Lee (2010, <doi:10.1214/09-AOAS289>). The computation is built on the 'GA' engine of Scrucca (2017, <doi:10.32614/RJ-2017-008>) and 'changepointGA' engine from Li and Lu (2024, <doi:10.48550/arXiv.2410.15571>). In challenging high-dimensional settings, 'GAReg' enables efficient search and delivers near-optimal solutions when alternative algorithms are not well-justified.
License: Apache License (== 2.0)
Depends: R (>= 4.3.0)
Imports: stats, splines, utils, methods, changepointGA, GA
URL: https://github.com/mli171/GAReg
BugReports: https://github.com/mli171/GAReg/issues
Suggests: MASS, knitr, rmarkdown
Encoding: UTF-8
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-06 14:33:17 UTC; mli171
Author: Mo Li [aut, cre], QiQi Lu [aut], Robert Lund [aut], Xueheng Shi [aut]
Maintainer: Mo Li <mo.li@louisiana.edu>
Repository: CRAN
Date/Publication: 2026-02-09 19:20:18 UTC

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Package frontmatter updated to version 0.2.0 with previous version 0.1.0 dated 2026-01-14

Title: Parse Front Matter from Documents
Description: Extracts and parses structured metadata ('YAML' or 'TOML') from the beginning of text documents. Front matter is a common pattern in 'Quarto' documents, 'R Markdown' documents, static site generators, documentation systems, content management tools and even 'Python' and 'R' scripts where metadata is placed at the top of a document, separated from the main content by delimiter fences.
Author: Garrick Aden-Buie [aut, cre] , Posit Software, PBC [cph, fnd]
Maintainer: Garrick Aden-Buie <garrick@posit.co>

Diff between frontmatter versions 0.1.0 dated 2026-01-14 and 0.2.0 dated 2026-02-09

 frontmatter-0.1.0/frontmatter/R/default_parsers.R                      |only
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 frontmatter-0.2.0/frontmatter/NAMESPACE                                |    3 
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 frontmatter-0.2.0/frontmatter/R/formats.R                              |only
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 18 files changed, 430 insertions(+), 57 deletions(-)

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New package egnyte with initial version 0.1.2
Package: egnyte
Title: Read and Write Files from 'Egnyte'
Version: 0.1.2
Description: Provides functions to read and write files from 'Egnyte' cloud storage using the 'Egnyte' API <https://developers.egnyte.com/docs>. Supports both API key and 'OAuth' 2.0 authentication for file transfer operations.
Depends: R (>= 4.1.0)
License: Apache License 2.0 | file LICENSE
URL: https://atorus-research.github.io/egnyte/
BugReports: https://github.com/atorus-research/egnyte/issues
Encoding: UTF-8
Imports: cli, httr2
Suggests: haven, jsonlite, knitr, readr, readxl, rmarkdown, testthat (>= 3.0.0), withr, writexl
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-06 14:00:51 UTC; mstackhouse
Author: Mike Stackhouse [aut, cre]
Maintainer: Mike Stackhouse <mike.stackhouse@atorusresearch.com>
Repository: CRAN
Date/Publication: 2026-02-09 19:20:13 UTC

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New package appraise with initial version 0.1.1
Package: appraise
Title: Bias-Aware Evidence Synthesis in Systematic Reviews
Version: 0.1.1
Description: Implements a bias-aware framework for evidence synthesis in systematic reviews and health technology assessments, as described in Kabali (2025) <doi:10.1111/jep.70272>. The package models study-level effect estimates by explicitly accounting for multiple sources of bias through prior distributions and propagates uncertainty using posterior simulation. Evidence across studies is combined using posterior mixture distributions rather than a single pooled likelihood, enabling probabilistic inference on clinically or policy-relevant thresholds. The methods are designed to support transparent decision-making when study relevance and bias vary across the evidence base.
License: GPL-3
Encoding: UTF-8
Imports: stats
Suggests: sn, VGAM, cmdstanr, rmarkdown, knitr
Additional_repositories: https://stan-dev.r-universe.dev/
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-06 19:07:35 UTC; 12896
Author: Conrad Kabali [aut, cre]
Maintainer: Conrad Kabali <conrad.kabali@utoronto.ca>
Repository: CRAN
Date/Publication: 2026-02-09 20:00:02 UTC

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Package tidycensus updated to version 1.7.5 with previous version 1.7.3 dated 2025-07-24

Title: Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames
Description: An integrated R interface to several United States Census Bureau APIs (<https://www.census.gov/data/developers/data-sets.html>) and the US Census Bureau's geographic boundary files. Allows R users to return Census and ACS data as tidyverse-ready data frames, and optionally returns a list-column with feature geometry for mapping and spatial analysis.
Author: Kyle Walker [aut, cre], Matt Herman [aut], Kris Eberwein [ctb]
Maintainer: Kyle Walker <kyle@walker-data.com>

Diff between tidycensus versions 1.7.3 dated 2025-07-24 and 1.7.5 dated 2026-02-09

 DESCRIPTION          |   20 +++++++++---------
 MD5                  |   14 ++++++-------
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 8 files changed, 51 insertions(+), 51 deletions(-)

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Package OmopViewer updated to version 0.7.0 with previous version 0.6.0 dated 2025-12-10

Title: Visualise OMOP Results using 'shiny' Applications
Description: Visualise results obtained from analysing data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model using 'shiny' applications.
Author: Marti Catala [aut, cre] , Yuchen Guo [aut] , Edward Burn [ctb] , Nuria Mercade-Besora [ctb] , Elin Rowlands [ctb] , Cecilia Campanile [ctb]
Maintainer: Marti Catala <marti.catalasabate@ndorms.ox.ac.uk>

Diff between OmopViewer versions 0.6.0 dated 2025-12-10 and 0.7.0 dated 2026-02-09

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 OmopViewer-0.7.0/OmopViewer/R/about.R                               |   50 
 OmopViewer-0.7.0/OmopViewer/R/appDynamic.R                          |  594 +--
 OmopViewer-0.7.0/OmopViewer/R/appStatic.R                           |  737 ++--
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 OmopViewer-0.7.0/OmopViewer/inst/brand/sad_robot.yml                |   25 
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 OmopViewer-0.7.0/OmopViewer/inst/doc/dynamic_app.Rmd                |   66 
 OmopViewer-0.7.0/OmopViewer/inst/doc/dynamic_app.html               |  759 ++--
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 OmopViewer-0.7.0/OmopViewer/inst/doc/edit_static_content.html       |  906 ++---
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 OmopViewer-0.7.0/OmopViewer/inst/doc/export_static_app.html         | 1637 ++++-----
 OmopViewer-0.7.0/OmopViewer/inst/functions.R                        |  857 ++--
 OmopViewer-0.7.0/OmopViewer/inst/logos/ohdsi_logo.svg               | 1784 +++++-----
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 OmopViewer-0.7.0/OmopViewer/man/getPanel.Rd                         |   46 
 OmopViewer-0.7.0/OmopViewer/man/launchDynamicApp.Rd                 |   28 
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 OmopViewer-0.7.0/OmopViewer/man/panelDetailsFromResult.Rd           |   51 
 OmopViewer-0.7.0/OmopViewer/tests/testthat.R                        |   24 
 OmopViewer-0.7.0/OmopViewer/tests/testthat/_snaps/appStatic.md      |   14 
 OmopViewer-0.7.0/OmopViewer/tests/testthat/test-appStatic.R         |  453 +-
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 OmopViewer-0.7.0/OmopViewer/tests/testthat/test-choices.R           |  336 -
 OmopViewer-0.7.0/OmopViewer/tests/testthat/test-functions.R         |   30 
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 68 files changed, 7575 insertions(+), 7412 deletions(-)

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New package ggmlR with initial version 0.5.1
Package: ggmlR
Title: 'GGML' Tensor Operations for Machine Learning
Version: 0.5.1
Description: Provides 'R' bindings to the 'GGML' tensor library for efficient machine learning computation. Implements core tensor operations including element-wise arithmetic, reshaping, and matrix multiplication. Supports neural network layers (attention, convolutions, normalization), activation functions, and quantization. Features optimization/training API with 'AdamW' (Adam with Weight decay) and 'SGD' (Stochastic Gradient Descent) optimizers, 'MSE' (Mean Squared Error) and cross-entropy losses. Multi-backend support with CPU and optional 'Vulkan' GPU (Graphics Processing Unit) acceleration. See <https://github.com/ggml-org/ggml> for more information about the underlying library.
License: MIT + file LICENSE
URL: https://github.com/Zabis13/ggmlR
BugReports: https://github.com/Zabis13/ggmlR/issues
Encoding: UTF-8
SystemRequirements: C++17, GNU make
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-02-05 18:46:14 UTC; yuri
Author: Yuri Baramykov [aut, cre], Georgi Gerganov [ctb, cph] , Jeffrey Quesnelle [ctb, cph] , Bowen Peng [ctb, cph] , Mozilla Foundation [ctb, cph]
Maintainer: Yuri Baramykov <lbsbmsu@mail.ru>
Repository: CRAN
Date/Publication: 2026-02-09 19:00:02 UTC

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Package targets updated to version 1.12.0 with previous version 1.11.4 dated 2025-09-13

Title: Dynamic Function-Oriented 'Make'-Like Declarative Pipelines
Description: Pipeline tools coordinate the pieces of computationally demanding analysis projects. The 'targets' package is a 'Make'-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, <doi:10.21105/joss.00550>).
Author: William Michael Landau [aut, cre] , Matthew T. Warkentin [ctb], Mark Edmondson [ctb] , Samantha Oliver [rev] , Tristan Mahr [rev] , Eli Lilly and Company [cph, fnd]
Maintainer: William Michael Landau <will.landau.oss@gmail.com>

Diff between targets versions 1.11.4 dated 2025-09-13 and 1.12.0 dated 2026-02-09

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More information about targets at CRAN
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Package tarchetypes updated to version 0.14.0 with previous version 0.13.2 dated 2025-09-13

Title: Archetypes for Targets
Description: Function-oriented Make-like declarative pipelines for Statistics and data science are supported in the 'targets' R package. As an extension to 'targets', the 'tarchetypes' package provides convenient user-side functions to make 'targets' easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the 'targets' R package. by Will Landau (2018) <doi:10.21105/joss.00550>.
Author: William Michael Landau [aut, cre] , Rudolf Siegel [ctb] , Samantha Oliver [rev] , Tristan Mahr [rev] , Eli Lilly and Company [cph, fnd]
Maintainer: William Michael Landau <will.landau.oss@gmail.com>

Diff between tarchetypes versions 0.13.2 dated 2025-09-13 and 0.14.0 dated 2026-02-09

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 94 files changed, 927 insertions(+), 736 deletions(-)

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Package historicalborrow updated to version 1.1.1 with previous version 1.1.0 dated 2024-09-10

Title: Non-Longitudinal Bayesian Historical Borrowing Models
Description: Historical borrowing in clinical trials can improve precision and operating characteristics. This package supports a hierarchical model and a mixture model to borrow historical control data from other studies to better characterize the control response of the current study. It also quantifies the amount of borrowing through benchmark models (independent and pooled). Some of the methods are discussed by Viele et al. (2013) <doi:10.1002/pst.1589>.
Author: William Michael Landau [aut, cre] , Albert Man [rev], Eli Lilly and Company [cph]
Maintainer: William Michael Landau <will.landau.oss@gmail.com>

Diff between historicalborrow versions 1.1.0 dated 2024-09-10 and 1.1.1 dated 2026-02-09

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 9 files changed, 48 insertions(+), 46 deletions(-)

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Package BCSreg updated to version 1.1.1 with previous version 1.1.0 dated 2026-01-12

Title: Box-Cox Symmetric Regression for Non-Negative Data
Description: A collection of tools for regression analysis of non-negative data, including strictly positive and zero-inflated observations, based on the class of the Box-Cox symmetric (BCS) distributions and its zero-adjusted extension. The BCS distributions are a class of flexible probability models capable of describing different levels of skewness and tail-heaviness. The package offers a comprehensive regression modeling framework, including estimation and tools for evaluating goodness-of-fit.
Author: Felipe Queiroz [aut] , Rodrigo Medeiros [aut, cre, cph]
Maintainer: Rodrigo Medeiros <rodrigo.matheus@ufrn.br>

Diff between BCSreg versions 1.1.0 dated 2026-01-12 and 1.1.1 dated 2026-02-09

 DESCRIPTION                |    6 +++---
 MD5                        |    6 +++---
 NEWS.md                    |    3 +++
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 4 files changed, 22 insertions(+), 19 deletions(-)

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Package ThreeWay updated to version 1.1.4 with previous version 1.1.3 dated 2015-09-07

Title: Three-Way Component Analysis
Description: Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models.
Author: Maria Antonietta Del Ferraro [aut], Paolo Giordani [aut, cre], Henk A.L. Kiers [aut]
Maintainer: Paolo Giordani <paolo.giordani@uniroma1.it>

Diff between ThreeWay versions 1.1.3 dated 2015-09-07 and 1.1.4 dated 2026-02-09

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Package SIBER updated to version 2.1.10 with previous version 2.1.9 dated 2023-10-19

Title: Stable Isotope Bayesian Ellipses in R
Description: Fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche.
Author: Andrew Jackson [aut, cre] , Andrew Parnell [aut]
Maintainer: Andrew Jackson <jacksoan@tcd.ie>

Diff between SIBER versions 2.1.9 dated 2023-10-19 and 2.1.10 dated 2026-02-09

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 SIBER-2.1.10/SIBER/NEWS.md                                     |    3 
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Package sentencepiece updated to version 0.2.5 with previous version 0.2.4 dated 2025-11-27

Title: Text Tokenization using Byte Pair Encoding and Unigram Modelling
Description: Unsupervised text tokenizer allowing to perform byte pair encoding and unigram modelling. Wraps the 'sentencepiece' library <https://github.com/google/sentencepiece> which provides a language independent tokenizer to split text in words and smaller subword units. The techniques are explained in the paper "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing" by Taku Kudo and John Richardson (2018) <doi:10.18653/v1/D18-2012>. Provides as well straightforward access to pretrained byte pair encoding models and subword embeddings trained on Wikipedia using 'word2vec', as described in "BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages" by Benjamin Heinzerling and Michael Strube (2018) <http://www.lrec-conf.org/proceedings/lrec2018/pdf/1049.pdf>.
Author: Jan Wijffels [aut, cre, cph] , BNOSAC [cph] , Google Inc. [ctb, cph] , The Abseil Authors [ctb, cph] , Google Inc. [ctb, cph] ), Kenton Varda [ctb, cph] ), Sanjay Ghemawat [ctb, cph] ), Jeff Dean [ctb, cph] ), Laszlo Csomor [ctb, cph] ), Wink Sav [...truncated...]
Maintainer: Jan Wijffels <jwijffels@bnosac.be>

Diff between sentencepiece versions 0.2.4 dated 2025-11-27 and 0.2.5 dated 2026-02-09

 DESCRIPTION  |    9 +++++----
 MD5          |    6 +++---
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 4 files changed, 13 insertions(+), 7 deletions(-)

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Package deepgp updated to version 1.2.1 with previous version 1.2.0 dated 2026-01-08

Title: Bayesian Deep Gaussian Processes using MCMC
Description: Performs Bayesian posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2023, <doi:10.48550/arXiv.2012.08015>). See Sauer (2023, <http://hdl.handle.net/10919/114845>) for comprehensive methodological details and <https://bitbucket.org/gramacylab/deepgp-ex/> for a variety of coding examples. Models are trained through MCMC including elliptical slice sampling of latent Gaussian layers and Metropolis-Hastings sampling of kernel hyperparameters. Gradient-enhancement and gradient predictions are offered following Booth (2025, <doi:10.48550/arXiv.2512.18066>). Vecchia approximation for faster computation is implemented following Sauer, Cooper, and Gramacy (2023, <doi:10.48550/arXiv.2204.02904>). Optional monotonic warpings are implemented following Barnett et al. (2025, <doi:10.48550/arXiv.2408.01540>). Downstream tasks include sequential design through active learning Cohn/integrated mean squared error (ALC/IMSE; Sauer, G [...truncated...]
Author: Annie S. Booth [aut, cre]
Maintainer: Annie S. Booth <annie_booth@vt.edu>

Diff between deepgp versions 1.2.0 dated 2026-01-08 and 1.2.1 dated 2026-02-09

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 8 files changed, 154 insertions(+), 125 deletions(-)

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New package toonlite with initial version 0.1.0
Package: toonlite
Title: Read, Write, Validate, Stream, and Convert TOON Data
Version: 0.1.0
Description: A minimal-dependency, performance-first R package for reading, writing, validating, streaming, and converting TOON (Token-Oriented Object Notation) data. Optimized for very large tabular files with robust diagnostics. Supports lossless JSON conversion and tabular CSV/Parquet/Feather conversion.
License: MIT + file LICENSE
URL: https://github.com/aljrico/toonlite
BugReports: https://github.com/aljrico/toonlite/issues
Encoding: UTF-8
SystemRequirements: C++17
Suggests: testthat (>= 3.0.0), jsonlite, arrow
NeedsCompilation: yes
Packaged: 2026-02-05 13:47:19 UTC; alejandrojimenezrico
Author: Alejandro Jimenez Rico [aut, cre]
Maintainer: Alejandro Jimenez Rico <aljrico@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-09 13:10:02 UTC

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New package tabbitR with initial version 0.1.3
Package: tabbitR
Title: Weighted Cross-Tabulations Exported to 'Excel'
Version: 0.1.3
Description: Produces weighted cross-tabulation tables for one or more outcome variables across one or more breakdown variables, and exports them directly to 'Excel'. For each outcome-by-breakdown combination, the package creates a weighted percentage table and a corresponding unweighted count table, with transparent handling of missing values and light, readable formatting. Designed to support social survey analysis workflows that require large sets of consistent, publication-ready tables.
Language: en-GB
License: MIT + file LICENSE
Encoding: UTF-8
Imports: haven, openxlsx, stats
URL: https://github.com/smmcandrew/tabbitR
BugReports: https://github.com/smmcandrew/tabbitR/issues
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-05 21:46:01 UTC; siobh
Author: Siobhan McAndrew [aut, cre]
Maintainer: Siobhan McAndrew <siobhan.mcandrew@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-09 13:50:02 UTC

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New package statuser with initial version 0.1.8
Package: statuser
Title: Statistical Tools Designed for End Users
Version: 0.1.8
Description: The statistical tools in this package do one of four things: 1) Enhance basic statistical functions with more flexible inputs, smarter defaults, and richer, clearer, and ready-to-use output (e.g., t.test2()) 2) Produce publication-ready commonly needed figures with one line of code (e.g., plot_cdf()) 3) Implement novel analytical tools developed by the authors (e.g., twolines()) 4) Deliver niche functions of high value to the authors that are not easily available elsewhere (e.g., clear(), convert_to_sql(), resize_images()).
License: GPL-3
Encoding: UTF-8
Imports: mgcv, rsvg, magick, labelled, sandwich, lmtest, utils
Suggests: testthat (>= 3.0.0), crayon, quantreg, estimatr, marginaleffects, broom, modelsummary
NeedsCompilation: no
Packaged: 2026-02-05 12:14:59 UTC; uri_s
Author: Uri Simonsohn [aut, cre]
Maintainer: Uri Simonsohn <urisohn@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-09 13:10:08 UTC

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New package ohvbd with initial version 1.0.0
Package: ohvbd
Title: One Health VBD Hub
Version: 1.0.0
Description: Interface with the One Health VBD (vector-borne disease) Hub <https://vbdhub.org/> and related repositories (VectorByte <https://www.vectorbyte.org>, GBIF <https://www.gbif.org> and AREAdata <https://pearselab.github.io/areadata/>) directly to find, download, and subset vector-borne disease data.
License: GPL (>= 3)
Encoding: UTF-8
URL: https://github.com/fwimp/ohvbd, https://ohvbd.vbdhub.org
BugReports: https://github.com/fwimp/ohvbd/issues
Imports: cli (>= 3.0.0), curl (>= 6.4.0), data.table, dplyr, httr2 (>= 1.1.1), lubridate, rlang (>= 1.1.0), stats, stringr, terra, tibble, tidyr, utils
Suggests: knitr, parsermd, rgbif, rmarkdown, rnaturalearth, testthat (>= 3.0.0), vcr, withr
VignetteBuilder: knitr
Depends: R (>= 4.1.0)
NeedsCompilation: no
Packaged: 2026-02-05 16:10:17 UTC; Francis
Author: Francis Windram [aut, cre, cph]
Maintainer: Francis Windram <francis.windram17@imperial.ac.uk>
Repository: CRAN
Date/Publication: 2026-02-09 13:30:02 UTC

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New package ocrRBBR with initial version 0.1.0
Package: ocrRBBR
Title: Explain Gene Expression with Boolean Rules of Chromatin States
Version: 0.1.0
Description: Infers Boolean rules among cis-regulatory regions using paired chromatin accessibility and gene expression data at bulk and single-cell levels. Links regulatory regions to target genes, providing insights into gene regulation mechanisms.
License: GPL-3
URL: https://github.com/CompBioIPM/ocrRBBR
Depends: R (>= 4.4)
Imports: doParallel, dplyr, foreach, GenomeInfoDb, GenomicRanges, glmnet, Matrix, parallel, rtracklayer, S4Vectors, stats, utils
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
NeedsCompilation: no
Packaged: 2026-02-05 18:55:34 UTC; seyed_000
Author: Seyed Amir Malekpour [aut, cre]
Maintainer: Seyed Amir Malekpour <a.malekpour@ipm.ir>
Repository: CRAN
Date/Publication: 2026-02-09 13:50:07 UTC

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New package mvfmr with initial version 0.1.0
Package: mvfmr
Title: Functional Multivariable Mendelian Randomization
Version: 0.1.0
Description: Implements Multivariable Functional Mendelian Randomization (MV-FMR) to estimate time-varying causal effects of multiple longitudinal exposures on health outcomes. Extends univariable functional Mendelian Randomisation (MR) (Tian et al., 2024 <doi:10.1002/sim.10222>) to the multivariable setting, enabling joint estimation of multiple time-varying exposures with pleiotropy and mediation scenarios. Key features include: (1) data-driven cross-validation for basis component selection, (2) handling of mediation pathways between exposures, (3) support for both continuous and binary outcomes using Generalized Method of Moments (GMM) and control function approaches, (4) one-sample and two-sample MR designs, (5) bootstrap inference and instrument diagnostics including Q-statistics for overidentification testing. Methods are described in Fontana et al. (2025) <doi:10.48550/arXiv.2512.19064>.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: fdapace, ggplot2 (>= 3.0.0), parallel, doParallel, foreach, pROC, progress, glmnet, gridExtra, stats
Suggests: dplyr, tidyr, testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-05 16:47:13 UTC; nicole.fontana
Author: Nicole Fontana [aut, cre], Francesca Ieva [aut, ths], Piercesare Secchi [aut, ths]
Maintainer: Nicole Fontana <nicole.fontana@polimi.it>
Repository: CRAN
Date/Publication: 2026-02-09 13:30:09 UTC

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New package MaxIntTools with initial version 0.1.0
Package: MaxIntTools
Title: Testing Maximal Interaction in Two-Mode Clustering via a Permutation Based Procedure
Version: 0.1.0
Description: Performs maximal interaction two-mode clustering, permutation tests, scree plots, and interaction visualizations for bicluster analysis. See Ahmed et al. (2025) <doi:10.17605/OSF.IO/AWGXB>, Ahmed et al. (2023) <doi:10.1007/s00357-023-09434-2>, Ahmed et al. (2021) <doi:10.1007/s11634-021-00441-y>.
License: GPL-3
Encoding: UTF-8
Imports: ggplot2, MASS, reshape, pracma
NeedsCompilation: no
Packaged: 2026-02-05 13:49:03 UTC; ahmed
Author: Zaheer Ahmed [aut, cre, cph], Jan Schepers [ctb], Uwe Ligges [ctb], Alberto Cassese [ctb], Gerard van Breukelen [ctb], Katja Ickstadt [ctb], Edmund Wascher [ctb]
Maintainer: Zaheer Ahmed <ahmed@ifado.de>
Repository: CRAN
Date/Publication: 2026-02-09 13:10:21 UTC

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New package lpmec with initial version 1.1.4
Package: lpmec
Title: Measurement Error Analysis and Correction Under Identification Restrictions
Version: 1.1.4
Author: Connor Jerzak [aut, cre], Stephen Jessee [aut]
Description: Implements methods for analyzing latent variable models with measurement error correction, including Item Response Theory (IRT) models. Provides tools for various correction methods such as Bayesian Markov Chain Monte Carlo (MCMC), over-imputation, bootstrapping for robust standard errors, Ordinary Least Squares (OLS), and Instrumental Variables (IV) based approaches. Supports flexible specification of observable indicators and groupings for latent variable analyses in social sciences and other fields. Methods are described in a working paper (2025) <doi:10.48550/arXiv.2507.22218>.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
LazyData: false
Maintainer: Connor Jerzak <connor.jerzak@gmail.com>
Imports: reticulate, stats, sensemakr, pscl, AER, sandwich, mvtnorm, Amelia, emIRT, gtools
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
SystemRequirements: Python (>= 3.10) with jax, numpy, numpyro (optional; for NumPyro backend via reticulate)
VignetteBuilder: knitr
URL: https://github.com/cjerzak/lpmec-software
BugReports: https://github.com/cjerzak/lpmec-software/issues
NeedsCompilation: no
Packaged: 2026-02-05 17:34:00 UTC; cjerzak
Repository: CRAN
Date/Publication: 2026-02-09 13:30:14 UTC

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New package HaDeX2 with initial version 1.0.0
Package: HaDeX2
Title: Analysis and Visualisation of Hydrogen/Deuterium Exchange Mass Spectrometry Data
Version: 1.0.0
Description: Processing, analysis and visualization of Hydrogen Deuterium eXchange monitored by Mass Spectrometry experiments (HDX-MS). 'HaDeX2' introduces a new standardized and reproducible workflow for the analysis of the HDX-MS data, including uncertainty propagation, data aggregation and visualization on 3D structure. Additionally, it covers data exploration, quality control and generation of publication-quality figures. All functionalities are also available in the accompanying 'shiny' app.
Depends: R (>= 3.5)
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: checkmate, data.table, dplyr, ggplot2, glue, gridExtra, magick, purrr, readr, readxl, r3dmol (>= 0.1.2), remotes, stringi, tidyr, ggiraph
Suggests: bookdown, digest, knitr, magrittr, pander, renv, rmarkdown, microbenchmark, testthat, vdiffr, scales, shiny, spelling
VignetteBuilder: knitr
Language: en-US
URL: https://hadexversum.github.io/HaDeX2/
NeedsCompilation: no
Packaged: 2026-02-05 15:05:03 UTC; User
Author: Weronika Puchala [cre, aut] , Michal Burdukiewicz [aut] , Dominik Rafacz [ctb], Krystyna Grzesiak [ctb]
Maintainer: Weronika Puchala <puchala.weronika@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-09 13:30:26 UTC

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Package fmrihrf updated to version 0.2.0 with previous version 0.1.0 dated 2025-09-16

Title: Hemodynamic Response Functions for fMRI Data Analysis
Description: Creates, manipulates, and evaluates hemodynamic response functions and event-related regressors for functional magnetic resonance imaging data analysis. Supports multiple basis sets including Canonical, Gamma, Gaussian, B-spline, and Fourier bases. Features decorators for time-shifting and blocking, and efficient convolution algorithms for regressor construction. Methods are based on standard fMRI analysis techniques as described in Jezzard et al. (2001, ISBN:9780192630711).
Author: Bradley Buchsbaum [aut, cre]
Maintainer: Bradley Buchsbaum <brad.buchsbaum@gmail.com>

Diff between fmrihrf versions 0.1.0 dated 2025-09-16 and 0.2.0 dated 2026-02-09

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Package fabletools updated to version 0.6.0 with previous version 0.5.1 dated 2025-09-01

Title: Core Tools for Packages in the 'fable' Framework
Description: Provides tools, helpers and data structures for developing models and time series functions for 'fable' and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis.
Author: Mitchell O'Hara-Wild [aut, cre] , Rob Hyndman [aut], Earo Wang [aut] , Di Cook [ctb], George Athanasopoulos [ctb], David Holt [ctb]
Maintainer: Mitchell O'Hara-Wild <mail@mitchelloharawild.com>

Diff between fabletools versions 0.5.1 dated 2025-09-01 and 0.6.0 dated 2026-02-09

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Package coreheat readmission to version 0.3.2 with previous version 0.2.2 dated 2021-09-19

Title: Correlation Heatmaps
Description: Create correlation heatmaps from a numeric matrix. Ensembl Gene ID row names can be converted to Gene Symbols using, e.g., BioMart. Optionally, data can be clustered and filtered by correlation, tree cutting and/or number of missing values. Genes of interest can be highlighted in the plot and correlation significance be indicated by asterisks encoding corresponding P-Values. Plot dimensions and label measures are adjusted automatically by default. The plot features rely on the heatmap.n2() function in the 'heatmapFlex' package.
Author: Vidal Fey [aut, cre], Henri Sara [aut]
Maintainer: Vidal Fey <vidal.fey@gmail.com>

This is a re-admission after prior archival of version 0.2.2 dated 2021-09-19

Diff between coreheat versions 0.2.2 dated 2021-09-19 and 0.3.2 dated 2026-02-09

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New package compositional.mle with initial version 1.0.2
Package: compositional.mle
Title: Compositional Maximum Likelihood Estimation
Version: 1.0.2
Description: Provides composable optimization strategies for maximum likelihood estimation (MLE). Solvers are first-class functions that combine via sequential chaining, parallel racing, and random restarts. Implements gradient ascent, Newton-Raphson, quasi-Newton (BFGS), and derivative-free methods with support for constrained optimization and tracing. Returns 'mle' objects compatible with 'algebraic.mle' for downstream analysis. Methods based on Nocedal J, Wright SJ (2006) "Numerical Optimization" <doi:10.1007/978-0-387-40065-5>.
License: MIT + file LICENSE
Encoding: UTF-8
ByteCompile: true
Depends: R (>= 3.5.0), algebraic.mle
Imports: MASS, numDeriv
Suggests: rmarkdown, dplyr, knitr, ggplot2, tibble, testthat (>= 3.0.0), cli, future, hypothesize
URL: https://github.com/queelius/compositional.mle, https://queelius.github.io/compositional.mle/
BugReports: https://github.com/queelius/compositional.mle/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-05 09:31:43 UTC; spinoza
Author: Alexander Towell [aut, cre]
Maintainer: Alexander Towell <queelius@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-09 12:20:02 UTC

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Package nametagger updated to version 0.1.7 with previous version 0.1.6 dated 2026-01-30

Title: Named Entity Recognition in Texts using 'NameTag'
Description: Wraps the 'nametag' library <https://github.com/ufal/nametag>, allowing users to find and extract entities (names, persons, locations, addresses, ...) in raw text and build your own entity recognition models. Based on a maximum entropy Markov model which is described in Strakova J., Straka M. and Hajic J. (2013) <https://ufal.mff.cuni.cz/~straka/papers/2013-tsd_ner.pdf>.
Author: Jan Wijffels [aut, cre, cph] , BNOSAC [cph] , Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic [cph], Milan Straka [ctb, cph] , Jana Strakova [ctb, cph]
Maintainer: Jan Wijffels <jwijffels@bnosac.be>

Diff between nametagger versions 0.1.6 dated 2026-01-30 and 0.1.7 dated 2026-02-09

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Package fpeek updated to version 0.2.0 with previous version 0.1.2 dated 2021-03-31

Title: Check Text Files Content at a Glance
Description: Tools to help text files importation. It can return the number of lines; print the first and last lines; convert encoding; guess delimiters and file encoding. Operations are made without reading the entire file before starting, resulting in good performances with large files. This package provides an alternative to a simple use of the 'head', 'tail', 'wc' and 'iconv' programs that are not always available on machine where R is installed.
Author: David Gohel [aut, cre]
Maintainer: David Gohel <david.gohel@ardata.fr>

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New package AUKtest with initial version 0.1.0
Package: AUKtest
Title: Calculate the AUK Estimator
Version: 0.1.0
Description: Computes the Area Under the Kendall (AUK) estimator for multivariate independence. The AUK estimator is based on the survival copula and quantifies the deviation from the null hypothesis of independence. The methodology implemented in this package is based on the work of 'Afendras', 'Markatou', and 'Papantonis' (2025) <doi:10.1016/j.jmva.2025.105589>.
Depends: R (>= 4.0.0)
License: GPL-3
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-02-05 07:16:49 UTC; nikospapantonis
Author: Nikos Papantonis [aut, cre], Georgios Afendras [aut], Marianthi Markatou [aut]
Maintainer: Nikos Papantonis <feipapantonis@yahoo.gr>
Repository: CRAN
Date/Publication: 2026-02-09 12:00:02 UTC

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Package TUvalues updated to version 1.1.1 with previous version 1.1.0 dated 2025-12-16

Title: Tools for Calculating Allocations in Game Theory using Exact and Approximated Methods
Description: The main objective of cooperative Transferable-Utility games (TU-games) is to allocate a good among the agents involved. The package implements major solution concepts including the Shapley value, Banzhaf value, and egalitarian rules, alongside their extensions for structured games: the Owen value and Banzhaf-Owen value for games with a priori unions, and the Myerson value for communication games on networks. To address the inherent exponential computational complexity of exact evaluation, the package offers both exact algorithms and linear approximation methods based on sampling, enabling the analysis of large-scale games. Additionally, it supports core set-based solutions, allowing computation of the vertices and the centroid of the core.
Author: Maria D. Guillen [cre, aut] , Juan Carlos Goncalves [aut]
Maintainer: Maria D. Guillen <maria.guilleng@umh.es>

Diff between TUvalues versions 1.1.0 dated 2025-12-16 and 1.1.1 dated 2026-02-09

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Package tsibble updated to version 1.2.0 with previous version 1.1.6 dated 2025-01-30

Title: Tidy Temporal Data Frames and Tools
Description: Provides a 'tbl_ts' class (the 'tsibble') for temporal data in an data- and model-oriented format. The 'tsibble' provides tools to easily manipulate and analyse temporal data, such as filling in time gaps and aggregating over calendar periods.
Author: Earo Wang [aut] , Di Cook [aut, ths] , Rob Hyndman [aut, ths] , Mitchell O'Hara-Wild [aut, cre] , Tyler Smith [ctb], Wil Davis [ctb]
Maintainer: Mitchell O'Hara-Wild <mail@mitchelloharawild.com>

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Package gmp updated to version 0.7-5.1 with previous version 0.7-5 dated 2024-08-23

Title: Multiple Precision Arithmetic
Description: Multiple Precision Arithmetic (big integers and rationals, prime number tests, matrix computation), "arithmetic without limitations" using the C library GMP (GNU Multiple Precision Arithmetic).
Author: Antoine Lucas [aut, cre] , Immanuel Scholz [aut], Rainer Boehme [ctb], Sylvain Jasson [ctb], Martin Maechler [ctb]
Maintainer: Antoine Lucas <antoinelucas@gmail.com>

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Package feasts updated to version 0.5.0 with previous version 0.4.2 dated 2025-08-27

Title: Feature Extraction and Statistics for Time Series
Description: Provides a collection of features, decomposition methods, statistical summaries and graphics functions for the analysing tidy time series data. The package name 'feasts' is an acronym comprising of its key features: Feature Extraction And Statistics for Time Series.
Author: Mitchell O'Hara-Wild [aut, cre], Rob Hyndman [aut], Earo Wang [aut], Di Cook [ctb], Thiyanga Talagala [ctb] , Leanne Chhay [ctb]
Maintainer: Mitchell O'Hara-Wild <mail@mitchelloharawild.com>

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Package AER updated to version 1.2-16 with previous version 1.2-15 dated 2025-06-18

Title: Applied Econometrics with R
Description: Functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. ISBN 978-0-387-77316-2. <doi:10.1007/978-0-387-77318-6> (See the vignette "AER" for a package overview.)
Author: Christian Kleiber [aut] , Achim Zeileis [aut, cre]
Maintainer: Achim Zeileis <Achim.Zeileis@R-project.org>

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Package vcdExtra updated to version 0.9.1 with previous version 0.9.0 dated 2026-01-21

Title: 'vcd' Extensions and Additions
Description: Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data and the 'gnm' package for Generalized Nonlinear Models. In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and adds a 3D version in 'mosaic3d'. Additionally, methods are provided for comparing and visualizing lists of 'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer.
Author: Michael Friendly [aut, cre] , David Meyer [ctb], Achim Zeileis [ctb] , Duncan Murdoch [ctb], Heather Turner [ctb] , David Firth [ctb], Daniel Sabanes Bove [ctb], Matt Kumar [ctb], Shuguang Sun [ctb]
Maintainer: Michael Friendly <friendly@yorku.ca>

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Package simstudy updated to version 0.9.2 with previous version 0.9.1 dated 2025-12-16

Title: Simulation of Study Data
Description: Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR).
Author: Keith Goldfeld [aut, cre] , Jacob Wujciak-Jens [aut]
Maintainer: Keith Goldfeld <keith.goldfeld@nyulangone.org>

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Package pumBayes readmission to version 1.0.2 with previous version 1.0.1 dated 2025-10-22

Title: Bayesian Estimation of Probit Unfolding Models for Binary Preference Data
Description: Bayesian estimation and analysis methods for Probit Unfolding Models (PUMs), a novel class of scaling models designed for binary preference data. These models allow for both monotonic and non-monotonic response functions. The package supports Bayesian inference for both static and dynamic PUMs using Markov chain Monte Carlo (MCMC) algorithms with minimal or no tuning. Key functionalities include posterior sampling, hyperparameter selection, data preprocessing, model fit evaluation, and visualization. The methods are particularly suited to analyzing voting data, such as from the U.S. Congress or Supreme Court, but can also be applied in other contexts where non-monotonic responses are expected. For methodological details, see Shi et al. (2025) <doi:10.48550/arXiv.2504.00423>.
Author: Skylar Shi [aut, cre] , Abel Rodriguez [aut] , Rayleigh Lei [aut] , Jonathan Olmsted [cph]
Maintainer: Skylar Shi <dshi98@uw.edu>

This is a re-admission after prior archival of version 1.0.1 dated 2025-10-22

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Package pctax updated to version 0.1.7 with previous version 0.1.3 dated 2024-12-02

Title: Professional Comprehensive Omics Data Analysis
Description: Provides a comprehensive suite of tools for analyzing omics data. It includes functionalities for alpha diversity analysis, beta diversity analysis, differential abundance analysis, community assembly analysis, visualization of phylogenetic tree, and functional enrichment analysis. With a progressive approach, the package offers a range of analysis methods to explore and understand the complex communities. It is designed to support researchers and practitioners in conducting in-depth and professional omics data analysis.
Author: Chen Peng [aut, cre]
Maintainer: Chen Peng <bfzede@gmail.com>

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Package MetaNet updated to version 0.2.8 with previous version 0.2.7 dated 2025-06-26

Title: Network Analysis for Omics Data
Description: Comprehensive network analysis package. Calculate correlation network fastly, accelerate lots of analysis by parallel computing. Support for multi-omics data, search sub-nets fluently. Handle bigger data, more than 10,000 nodes in each omics. Offer various layout method for multi-omics network and some interfaces to other software ('Gephi', 'Cytoscape', 'ggplot2'), easy to visualize. Provide comprehensive topology indexes calculation, including ecological network stability.
Author: Chen Peng [aut, cre]
Maintainer: Chen Peng <bfzede@gmail.com>

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Package ggtime updated to version 0.2.0 with previous version 0.1.0 dated 2025-06-10

Title: Grammar of Graphics and Plot Helpers for Time Series Visualization
Description: Extends the capabilities of 'ggplot2' by providing grammatical elements and plot helpers designed for visualizing temporal patterns. The package implements a grammar of temporal graphics, which leverages calendar structures to highlight changes over time. The package also provides plot helper functions to quickly produce commonly used time series graphics, including time plots, season plots, and seasonal sub-series plots.
Author: Mitchell O'Hara-Wild [aut, cre] , Cynthia A. Huang [aut] , Matthew Kay [aut] , Rob Hyndman [aut] , Earo Wang [ctb]
Maintainer: Mitchell O'Hara-Wild <mail@mitchelloharawild.com>

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Package Familias updated to version 2.6.4 with previous version 2.6.3 dated 2025-05-12

Title: Probabilities for Pedigrees Given DNA Data
Description: An interface to the core 'Familias' functions which are programmed in C++. The implementation is described in Egeland, Mostad and Olaisen (1997) <doi:10.1016/S1355-0306(97)72202-0> and Simonsson and Mostad (2016) <doi:10.1016/j.fsigen.2016.04.005>.
Author: Petter Mostad [aut], Thore Egeland [aut], Franco Marsico [aut, cre], Ivar Simonsson [aut]
Maintainer: Franco Marsico <franco.lmarsico@gmail.com>

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Package pglm updated to version 0.2-4 with previous version 0.2-3 dated 2021-07-19

Title: Panel Generalized Linear Models
Description: Estimation of panel models for glm-like models: this includes binomial models (logit and probit), count models (poisson and negbin) and ordered models (logit and probit), as described in: Baltagi (2013) Econometric Analysis of Panel Data, ISBN-13:978-1-118-67232-7, Hsiao (2014) Analysis of Panel Data <doi:10.1017/CBO9781139839327> and Croissant and Millo (2018), Panel Data Econometrics with R, ISBN:978-1-118-94918-4.
Author: Yves Croissant [aut, cre]
Maintainer: Yves Croissant <yves.croissant@univ-reunion.fr>

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Package mize updated to version 0.2.5 with previous version 0.2.4 dated 2020-08-30

Title: Unconstrained Numerical Optimization Algorithms
Description: Optimization algorithms implemented in R, including conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited memory BFGS (L-BFGS) methods. Most internal parameters can be set through the call interface. The solvers hold up quite well for higher-dimensional problems.
Author: James Melville [aut, cre]
Maintainer: James Melville <jlmelville@gmail.com>

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Package nanonext updated to version 1.8.0 with previous version 1.7.2 dated 2025-11-03

Title: Lightweight Toolkit for Messaging, Concurrency and the Web
Description: R binding for NNG (Nanomsg Next Gen), a successor to ZeroMQ. A toolkit for messaging, concurrency and the web. High-performance socket messaging over in-process, IPC, TCP, WebSocket and secure TLS transports implements 'Scalability Protocols', a standard for common communications patterns including publish/subscribe, request/reply and survey. A threaded concurrency framework with intuitive 'aio' objects that resolve automatically upon completion of asynchronous operations, and synchronisation primitives that allow R to wait on events signalled by concurrent threads. A unified HTTP server hosting REST endpoints, WebSocket connections and streaming on a single port, with a built-in HTTP client.
Author: Charlie Gao [aut, cre] , Posit Software, PBC [cph, fnd] , Hibiki AI Limited [cph], R Consortium [fnd]
Maintainer: Charlie Gao <charlie.gao@posit.co>

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Package lattice updated to version 0.22-9 with previous version 0.22-7 dated 2025-04-02

Title: Trellis Graphics for R
Description: A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements. See ?Lattice for an introduction.
Author: Deepayan Sarkar [aut, cre] , Felix Andrews [ctb], Kevin Wright [ctb] , Neil Klepeis [ctb], Johan Larsson [ctb] , Zhijian Wen [cph] , Paul Murrell [ctb], Stefan Eng [ctb] , Achim Zeileis [ctb] , Alexandre Courtiol [ctb]
Maintainer: Deepayan Sarkar <deepayan.sarkar@r-project.org>

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Package ivreg updated to version 0.6-6 with previous version 0.6-5 dated 2025-01-19

Title: Instrumental-Variables Regression by '2SLS', '2SM', or '2SMM', with Diagnostics
Description: Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). The main ivreg() model-fitting function is designed to provide a workflow as similar as possible to standard lm() regression. A wide range of methods is provided for fitted ivreg model objects, including extensive functionality for computing and graphing regression diagnostics in addition to other standard model tools.
Author: John Fox [aut] , Christian Kleiber [aut] , Achim Zeileis [aut, cre] , Nikolas Kuschnig [ctb] , R Core Team [ctb]
Maintainer: Achim Zeileis <Achim.Zeileis@R-project.org>

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Package gdverse updated to version 1.6 with previous version 1.5.1 dated 2025-10-15

Title: Analysis of Spatial Stratified Heterogeneity
Description: Detecting spatial associations via spatial stratified heterogeneity, accounting for spatial dependencies, interpretability, complex interactions, and robust stratification. In addition, it supports the spatial stratified heterogeneity family described in Lv et al. (2025)<doi:10.1111/tgis.70032>.
Author: Wenbo Lv [aut, cre, cph] , Yangyang Lei [aut] , Fangmei Liu [aut] , Jianwu Yan [aut] , Yongze Song [aut] , Wufan Zhao [aut]
Maintainer: Wenbo Lv <lyu.geosocial@gmail.com>

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Package gdtools updated to version 0.5.0 with previous version 0.4.4 dated 2025-10-06

Title: Font Metrics and Font Management Utilities for R Graphics
Description: Compute text metrics (width, ascent, descent) using 'Cairo' and 'FreeType', independently of the active graphic device. Font lookup is delegated to 'systemfonts'. Additional utilities let users register 'Google Fonts' or bundled 'Liberation' fonts, check font availability, and assemble 'htmlDependency' objects so that fonts are correctly embedded in 'Shiny' applications, 'R Markdown' documents, and 'htmlwidgets' outputs such as 'ggiraph'.
Author: David Gohel [aut, cre], Hadley Wickham [aut], Lionel Henry [aut], Jeroen Ooms [aut] , Yixuan Qiu [ctb], R Core Team [cph] , ArData [cph], RStudio [cph]
Maintainer: David Gohel <david.gohel@ardata.fr>

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Package eam updated to version 1.1.0 with previous version 1.0.1 dated 2026-01-17

Title: Evidence Accumulation Models
Description: Simulation-based evidence accumulation models for analyzing responses and reaction times in single- and multi-response tasks. The package includes simulation engines for five representative models: the Diffusion Decision Model (DDM), Leaky Competing Accumulator (LCA), Linear Ballistic Accumulator (LBA), Racing Diffusion Model (RDM), and Levy Flight Model (LFM), and extends these frameworks to multi-response settings. The package supports user-defined functions for item-level parameterization and the incorporation of covariates, enabling flexible customization and the development of new model variants based on existing architectures. Inference is performed using simulation-based methods, including Approximate Bayesian Computation (ABC) and Amortized Bayesian Inference (ABI), which allow parameter estimation without requiring tractable likelihood functions. In addition to core inference tools, the package provides modules for parameter recovery, posterior predictive checks, and model com [...truncated...]
Author: Guangyu Zhu [aut] , Guang Yang [aut, cre]
Maintainer: Guang Yang <guang.spike.yang@gmail.com>

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Sun, 08 Feb 2026

Package spatialAtomizeR updated to version 0.2.5 with previous version 0.2.4 dated 2025-12-17

Title: Spatial Analysis with Misaligned Data Using Atom-Based Regression Models
Description: Implements atom-based regression models (ABRM) for analyzing spatially misaligned data. Provides functions for simulating misaligned spatial data, preparing NIMBLE model inputs, running MCMC diagnostics, and comparing different spatial analysis methods including dasymetric mapping. All main functions return S3 objects with print(), summary(), and plot() methods for intuitive result exploration. Methods are described in Nethery et al. (2023) <doi:10.1101/2023.01.10.23284410>. Further methodological details and software implementation are described in Qian et al. (in review).
Author: Yunzhe Qian [aut, cre], Rachel Nethery [aut], Nancy Krieger [ctb] , Nykesha Johnson [ctb]
Maintainer: Yunzhe Qian <qyzanemos@gmail.com>

Diff between spatialAtomizeR versions 0.2.4 dated 2025-12-17 and 0.2.5 dated 2026-02-08

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Package RCMinification updated to version 1.4 with previous version 1.2 dated 2023-01-17

Title: Random Coefficient Minification Time Series Models
Description: Data sets, and functions for simulating and fitting nonlinear time series with minification and nonparametric models.
Author: L. Han [aut, cre]
Maintainer: L. Han <lengyi.han@ubc.ca>

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Package MassWateR updated to version 2.2.1 with previous version 2.2.0 dated 2025-05-29

Title: Quality Control and Analysis of Massachusetts Water Quality Data
Description: Methods for quality control and exploratory analysis of surface water quality data collected in Massachusetts, USA. Functions are developed to facilitate data formatting for the Water Quality Exchange Network <https://www.epa.gov/waterdata/water-quality-data-upload-wqx> and reporting of data quality objectives to state agencies. Quality control methods are from Massachusetts Department of Environmental Protection (2020) <https://www.mass.gov/orgs/massachusetts-department-of-environmental-protection>.
Author: Marcus Beck [aut, cre] , Jill Carr [aut], Ben Wetherill [aut]
Maintainer: Marcus Beck <mbeck@tbep.org>

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Package bhetGP updated to version 1.0.2 with previous version 1.0.1 dated 2025-07-18

Title: Bayesian Heteroskedastic Gaussian Processes
Description: Performs Bayesian posterior inference for heteroskedastic Gaussian processes. Models are trained through MCMC including elliptical slice sampling (ESS) of latent noise processes and Metropolis-Hastings sampling of kernel hyperparameters. Replicates are handled efficientyly through a Woodbury formulation of the joint likelihood for the mean and noise process (Binois, M., Gramacy, R., Ludkovski, M. (2018) <doi:10.1080/10618600.2018.1458625>) For large data, Vecchia-approximation for faster computation is leveraged (Sauer, A., Cooper, A., and Gramacy, R., (2023), <doi:10.1080/10618600.2022.2129662>). Incorporates 'OpenMP' and SNOW parallelization and utilizes 'C'/'C++' under the hood.
Author: Parul V. Patil [aut, cre]
Maintainer: Parul V. Patil <parulvijay@vt.edu>

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Package nse readmission to version 1.22 with previous version 1.21 dated 2022-11-10

Title: Numerical Standard Errors Computation in R
Description: Collection of functions designed to calculate numerical standard error (NSE) of univariate time series as described in Ardia et al. (2018) <doi:10.1515/jtse-2017-0011> and Ardia and Bluteau (2017) <doi:10.21105/joss.00172>.
Author: David Ardia [aut] , Keven Bluteau [aut, cre]
Maintainer: Keven Bluteau <keven.bluteau@usherbrooke.ca>

This is a re-admission after prior archival of version 1.21 dated 2022-11-10

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Package camtrapR updated to version 3.0.3 with previous version 3.0.2 dated 2026-01-25

Title: Camera Trap Data Management and Analysis Framework
Description: Management and analysis of camera trap wildlife data through an integrated workflow. Provides functions for image/video organization and metadata extraction, species/individual identification. Creates detection histories for occupancy and spatial capture-recapture analyses, with support for multi-season studies. Includes tools for fitting community occupancy models in JAGS and NIMBLE, and an interactive dashboard for survey data visualization and analysis. Features visualization of species distributions and activity patterns, plus export capabilities for GIS and reports. Emphasizes automation and reproducibility while maintaining flexibility for different study designs.
Author: Juergen Niedballa [aut, cre] , Alexandre Courtiol [aut] , Rahel Sollmann [aut] , John Mathai [ctb], Seth Timothy Wong [ctb] , An The Truong Nguyen [ctb] , Azlan bin Mohamed [ctb] , Andrew Tilker [ctb] , Roshan Guharajan [ctb] , Ioannis Alexiou [ctb] [...truncated...]
Maintainer: Juergen Niedballa <camtrapr@gmail.com>

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Package autotab updated to version 0.1.3 with previous version 0.1.2 dated 2026-02-06

Title: Variational Autoencoders for Heterogeneous Tabular Data
Description: Build and train a variational autoencoder (VAE) for mixed-type tabular data (continuous, binary, categorical). Models are implemented using 'TensorFlow' and 'Keras' via the 'reticulate' interface, enabling reproducible VAE training for heterogeneous tabular datasets.
Author: Sarah Milligan [aut, cre]
Maintainer: Sarah Milligan <slm1999@bu.edu>

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Package forestmangr updated to version 0.9.9 with previous version 0.9.8 dated 2024-12-01

Title: Forest Mensuration and Management
Description: Processing forest inventory data with methods such as simple random sampling, stratified random sampling and systematic sampling. There are also functions for yield and growth predictions and model fitting, linear and nonlinear grouped data fitting, and statistical tests. References: Kershaw Jr., Ducey, Beers and Husch (2016). <doi:10.1002/9781118902028>.
Author: Sollano Rabelo Braga [aut, cre, cph], Marcio Leles Romarco de Oliveira [aut], Eric Bastos Gorgens [aut]
Maintainer: Sollano Rabelo Braga <sollanorb@gmail.com>

Diff between forestmangr versions 0.9.8 dated 2024-12-01 and 0.9.9 dated 2026-02-08

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Package rattle updated to version 5.6.2 with previous version 5.5.1 dated 2022-03-21

Title: R Data Science Supporting Rattle
Description: The R Analytic Tool To Learn Easily (Rattle) provides a collection of utilities functions for the data scientist. This package (v5.6.0) supports the companion graphical interface with the aim to provide a simple and intuitive introduction to R for data science, allowing a user to quickly load data from a CSV file transform and explore the data, and to build and evaluate models. A key aspect of the GUI is that all R commands are logged and commented through the log tab. This can be saved as a standalone R script file and as an aid for the user to learn R or to copy-and-paste directly into R itself. If you want to use the older Rattle implementing the GUI in RGtk2 (which is no longer available from CRAN) then please install the Rattle package v5.5.1. See rattle.togaware.com for instructions on installing the modern Rattle graphical user interface.
Author: Graham Williams [aut, cph, cre], Mark Vere Culp [cph], Ed Cox [ctb], Anthony Nolan [ctb], Denis White [cph], Daniele Medri [ctb], Akbar Waljee [ctb] , Brian Ripley [cph] , Jose Magana [ctb] , Surendra Tipparaju [ctb] , Durga Prasad Chappidi [ctb] , D [...truncated...]
Maintainer: Graham Williams <Graham.Williams@togaware.com>

Diff between rattle versions 5.5.1 dated 2022-03-21 and 5.6.2 dated 2026-02-08

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Package ggbrick updated to version 0.3.2 with previous version 0.3.1 dated 2025-09-14

Title: Waffle Style Chart with a Brick Layout in 'ggplot2'
Description: A new take on the bar chart. Similar to a waffle style chart but instead of squares the layout resembles a brick wall.
Author: Daniel Oehm [aut, cre]
Maintainer: Daniel Oehm <danieloehm@gmail.com>

Diff between ggbrick versions 0.3.1 dated 2025-09-14 and 0.3.2 dated 2026-02-08

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 4 files changed, 9 insertions(+), 11 deletions(-)

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Package PhenotypeR updated to version 0.3.2 with previous version 0.3.1 dated 2026-01-28

Title: Assess Study Cohorts Using a Common Data Model
Description: Phenotype study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model. Diagnostics are run at the database, code list, cohort, and population level to assess whether study cohorts are ready for research.
Author: Edward Burn [aut, cre] , Marti Catala [aut] , Xihang Chen [aut] , Marta Alcalde-Herraiz [aut] , Nuria Mercade-Besora [aut] , Albert Prats-Uribe [aut]
Maintainer: Edward Burn <edward.burn@ndorms.ox.ac.uk>

Diff between PhenotypeR versions 0.3.1 dated 2026-01-28 and 0.3.2 dated 2026-02-08

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New package shinyEditor with initial version 0.1.0
Package: shinyEditor
Title: 'Ace' and 'Monaco' Editors Bindings for 'shiny' Application
Version: 0.1.0
Date: 2026-02-05
Maintainer: zearoby <949386232@qq.com>
Description: 'Ace' and 'Monaco' editor bindings to enable a rich text widget within 'shiny' application and provide more features, e.g. text comparison, spell checking and an extra 'SAS' code highlight mode.
License: MIT + file LICENSE
URL: https://github.com/zearoby/shinyEditor
BugReports: https://github.com/zearoby/shinyEditor/issues
Encoding: UTF-8
Depends: R (>= 4.0.0)
Imports: htmlwidgets, jsonlite, memoise, shinyjs, shiny, systemfonts, methods, yaml
Suggests: devtools, testthat
NeedsCompilation: no
Packaged: 2026-02-05 01:51:51 UTC; admin
Author: zearoby [aut, cre]
Repository: CRAN
Date/Publication: 2026-02-08 17:00:03 UTC

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New package scaledescr with initial version 0.1.3
Package: scaledescr
Title: Descriptive, Reliability, and Inferential Tables for Psychometric Scales and Demographic Data
Version: 0.1.3
Description: Provides functions to format and summarise already computed outputs from commonly used statistical and psychometric functions into compact, single-row tables and simple graphs, with utilities to export results to CSV, Word, and Excel formats. The package does not implement new statistical methods or estimation procedures; instead, it organises and presents results obtained from existing functions such as psych::describe(), psych::alpha(), stats::t.test(), and gtsummary::tbl_summary() to streamline reporting workflows in clinical and psychological research.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: dplyr, gtsummary, purrr, rlang, stats, officer, openxlsx, utils
Suggests: psych, testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2026-02-05 03:12:52 UTC; HP
Author: Darshan Dharaiya [aut, cre]
Maintainer: Darshan Dharaiya <dharaiya.darshan@gmail.com>
Depends: R (>= 4.1.0)
Repository: CRAN
Date/Publication: 2026-02-08 17:00:10 UTC

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New package roadDB with initial version 0.1.0
Package: roadDB
Title: Access Data from the ROCEEH Out of Africa Database (ROAD)
Version: 0.1.0
Maintainer: Christian Sommer <christian.sommer@uni-tuebingen.de>
Description: Provides an R interface to the ROCEEH Out of Africa Database (ROAD) (<https://www.roceeh.uni-tuebingen.de/roadweb/smarty_road_simple_search.php>), a comprehensive resource for archaeological, anthropological, paleoenvironmental and geographic data from Africa and Eurasia dating from 3,000,000 to 20,000 years BP. The package allows users to retrieve data from the online database at different levels of detail and customize search requests. Functions return `data frame` objects compatible with other R packages used in prehistoric and paleoenvironmental science, supporting reproducible workflows as an input provider.
License: CC BY-SA 4.0
Encoding: UTF-8
Suggests: ggplot2, knitr, rmarkdown, testthat (>= 3.0.0)
Imports: RPostgres, assertthat, utils, dplyr, stringr, glue
NeedsCompilation: no
Packaged: 2026-02-04 13:29:53 UTC; somme
Author: Zara Kanaeva [aut] , Andrew Kandel [ctb] , Jesper Borre Pedersen [aut] , Christian Sommer [aut, cre] , Timo Patrick Streicher [aut] , Research Center: The Role of Culture in Early Expansions of Humans [cph], Heidelberg Academy of Sciences and Humani [...truncated...]
Repository: CRAN
Date/Publication: 2026-02-08 16:10:02 UTC

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New package QuantileOnQuantile with initial version 1.0.3
Package: QuantileOnQuantile
Title: Quantile-on-Quantile Regression Analysis
Version: 1.0.3
Description: Implements the Quantile-on-Quantile (QQ) regression methodology developed by Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013>. QQ regression estimates the effect that quantiles of one variable have on quantiles of another, capturing the dependence between distributions. The package provides functions for QQ regression estimation, 3D surface visualization with 'MATLAB'-style color schemes ('Jet', 'Viridis', 'Plasma'), heatmaps, contour plots, and quantile correlation analysis. Uses 'quantreg' for quantile regression and 'plotly' for interactive visualizations. Particularly useful for examining relationships between financial variables, oil prices, and stock returns under different market conditions.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 3.5.0)
Author: Merwan Roudane [aut, cre, cph], Nicholas Sim [ctb] , Hongtao Zhou [ctb]
Maintainer: Merwan Roudane <merwanroudane920@gmail.com>
Imports: quantreg (>= 5.0), plotly (>= 4.0.0), stats, utils
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), htmlwidgets
VignetteBuilder: knitr
URL: https://github.com/merwanroudane/qq
BugReports: https://github.com/merwanroudane/qq/issues
NeedsCompilation: no
Packaged: 2026-02-04 18:40:54 UTC; merwan
Repository: CRAN
Date/Publication: 2026-02-08 16:40:02 UTC

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New package HNPclassifier with initial version 0.1.0
Package: HNPclassifier
Title: Hierarchical Neyman-Pearson Classification for Ordered Classes
Version: 0.1.0
Description: The Hierarchical Neyman-Pearson (H-NP) classification framework extends the Neyman-Pearson classification paradigm to multi-class settings where classes have a natural priority ordering. This is particularly useful for classification in unbalanced dataset, for example, disease severity classification, where under-classification errors (misclassifying patients into less severe categories) are more consequential than other misclassifications. The package implements H-NP umbrella algorithms that controls under-classification errors under user specified control levels with high probability. It supports the creation of H-NP classifiers using scoring functions based on built-in classification methods (including logistic regression, support vector machines, and random forests), as well as user-trained scoring functions. For theoretical details, please refer to Lijia Wang, Y. X. Rachel Wang, Jingyi Jessica Li & Xin Tong (2024) <doi:10.1080/01621459.2023.2270657>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: dplyr, e1071, nnet, randomForest
NeedsCompilation: no
Packaged: 2026-02-04 18:16:28 UTC; scsma
Author: Che Shen [aut, cre] , Lujia Yang [aut] , Lijia Wang [aut] , Shunan Yao [aut]
Maintainer: Che Shen <chshen3-c@my.cityu.edu.hk>
Repository: CRAN
Date/Publication: 2026-02-08 16:40:07 UTC

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Package highs updated to version 1.12.0-3 with previous version 1.12.0-2 dated 2026-02-03

Title: 'HiGHS' Optimization Solver
Description: R interface to 'HiGHS', an optimization solver for solving mixed integer optimization problems with quadratic or linear objective and linear constraints.
Author: Florian Schwendinger [aut, cre], Dirk Schumacher [aut], Julian Hall [cph], Ivet Galabova [cph], Leona Gottwald [cph], Michael Feldmeier [cph]
Maintainer: Florian Schwendinger <FlorianSchwendinger@gmx.at>

Diff between highs versions 1.12.0-2 dated 2026-02-03 and 1.12.0-3 dated 2026-02-08

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New package fuzzystring with initial version 0.0.1
Package: fuzzystring
Title: Fast Fuzzy String Joins for Data Frames
Version: 0.0.1
Description: Perform fuzzy joins on data frames using approximate string matching. Implements all standard join types (inner, left, right, full, semi, anti) with support for multiple string distance metrics from the 'stringdist' package including Levenshtein, Damerau-Levenshtein, Jaro-Winkler, and Soundex. Features a high-performance 'data.table' backend with 'C++' row binding for efficient processing of large datasets. Ideal for matching misspellings, inconsistent labels, messy user input, or reconciling datasets with slight variations in identifiers. Optionally returns distance metrics alongside matched records.
License: MIT + file LICENSE
Depends: R (>= 4.1)
Imports: data.table, Rcpp, stringdist
LinkingTo: Rcpp
Suggests: dplyr, ggplot2, knitr, qdapDictionaries, readr, rmarkdown, rvest, stringr, testthat (>= 3.0.0), tidyr
Encoding: UTF-8
LazyData: true
URL: https://github.com/PaulESantos/fuzzystring, https://paulesantos.github.io/fuzzystring/
BugReports: https://github.com/PaulESantos/fuzzystring/issues
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-02-05 01:33:47 UTC; PC
Author: Paul E. Santos Andrade [aut, cre] , David Robinson [ctb]
Maintainer: Paul E. Santos Andrade <paulefrens@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-08 17:00:15 UTC

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New package bunddev with initial version 0.1.0
Package: bunddev
Title: Discover and Call 'Bund.dev' APIs
Version: 0.1.0
Author: Michael Buecker [aut, cre]
Maintainer: Michael Buecker <michael.buecker@fh-muenster.de>
Depends: R (>= 4.1.0)
Description: Provides a registry of APIs listed on <https://bund.dev> and a core 'OpenAPI' client layer to explore specs and perform requests. Adapter helpers return tidy tibbles for supported APIs, with optional response caching and rate limiting guidance.
License: MIT + file LICENSE
Encoding: UTF-8
Suggests: ggplot2, knitr, rmarkdown, testthat (>= 3.0.0), withr
Imports: cli, dplyr, httr2, jsonlite, purrr, rlang, stringr, tibble, tidyr, tools, xml2, yaml
VignetteBuilder: knitr, rmarkdown
NeedsCompilation: no
Packaged: 2026-02-04 19:46:14 UTC; michael
Repository: CRAN
Date/Publication: 2026-02-08 16:50:02 UTC

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Package easy.glmnet readmission to version 1.1 with previous version 1.0 dated 2024-09-11

Title: Functions to Simplify the Use of 'glmnet' for Machine Learning
Description: Provides several functions to simplify using the 'glmnet' package: converting data frames into matrices ready for 'glmnet'; b) imputing missing variables multiple times; c) fitting and applying prediction models straightforwardly; d) assigning observations to folds in a balanced way; e) cross-validate the models; f) selecting the most representative model across imputations and folds; and g) getting the relevance of the model regressors; as described in several publications: Solanes et al. (2022) <doi:10.1038/s41537-022-00309-w>, Palau et al. (2023) <doi:10.1016/j.rpsm.2023.01.001>, Salazar de Pablo et al. (2025) <doi:10.1038/s41380-025-03244-1>.
Author: Joaquim Radua [aut, cre]
Maintainer: Joaquim Radua <quimradua@gmail.com>

This is a re-admission after prior archival of version 1.0 dated 2024-09-11

Diff between easy.glmnet versions 1.0 dated 2024-09-11 and 1.1 dated 2026-02-08

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Package treesitter.c updated to version 0.0.4.2 with previous version 0.0.1 dated 2025-12-10

Title: 'R' Bindings to the 'C' Grammar for Tree-Sitter
Description: Provides bindings to a 'C' grammar for Tree-sitter, to be used alongside the 'treesitter' package. Tree-sitter builds concrete syntax trees for source files and can efficiently update them or generate code like producing R C API wrappers from C functions, structs and global definitions from header files.
Author: Sounkou Mahamane Toure [aut, cre], Tree-sitter authors [cph] , Eli Bendersky and Co-authors [cph]
Maintainer: Sounkou Mahamane Toure <sounkoutoure@gmail.com>

Diff between treesitter.c versions 0.0.1 dated 2025-12-10 and 0.0.4.2 dated 2026-02-08

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Package mLLMCelltype updated to version 2.0.0 with previous version 1.3.2 dated 2025-09-02

Title: Cell Type Annotation Using Large Language Models
Description: Automated cell type annotation for single-cell RNA sequencing data using consensus predictions from multiple large language models. Integrates with Seurat objects and provides uncertainty quantification for annotations. Supports various LLM providers including OpenAI, Anthropic, and Google. For details see Yang et al. (2025) <doi:10.1101/2025.04.10.647852>.
Author: Chen Yang [aut, cre, cph]
Maintainer: Chen Yang <cafferychen777@tamu.edu>

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Package HTGM3D readmission to version 1.0.1 with previous version 1.0 dated 2025-05-30

Title: Three Dimensional High Throughput 'GoMiner'
Description: The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. To capture the benefit of all three ontologies, I developed 'HTGM3D', a three-dimensional version of 'GoMiner'.
Author: Barry Zeeberg [aut, cre]
Maintainer: Barry Zeeberg <barryz2013@gmail.com>

This is a re-admission after prior archival of version 1.0 dated 2025-05-30

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Package SKNN updated to version 4.1.3 with previous version 4.1.2 dated 2025-09-18

Title: A Super K-Nearest Neighbor (SKNN) Classification Algorithm
Description: It's a Super K-Nearest Neighbor(SKNN) classification method with using kernel density to describe weight of the distance between a training observation and the testing sample. Comparison of performance between SKNN and KNN shows that SKNN is significantly superior to KNN.
Author: Yarong Yang [aut, cre], Nader Ebrahimi [ctb], Yoram Rubin [ctb], Jacob Zhang [ctb]
Maintainer: Yarong Yang <Yi.YA_yaya@hotmail.com>

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Permanent link

Package seekr updated to version 0.1.4 with previous version 0.1.3 dated 2025-05-10

Title: Extract Matching Lines from Matching Files
Description: Provides a simple interface to recursively list files from a directory, filter them using a regular expression, read their contents, and extract lines that match a user-defined pattern. The package returns a dataframe containing the matched lines, their line numbers, file paths, and the corresponding matched substrings. Designed for quick code base exploration, log inspection, or any use case involving pattern-based file and line filtering.
Author: Sacha Martingay [aut, cre, cph]
Maintainer: Sacha Martingay <martingay.sacha@hotmail.com>

Diff between seekr versions 0.1.3 dated 2025-05-10 and 0.1.4 dated 2026-02-08

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More information about seekr at CRAN
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2025-02-07 0.10.0

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2026-02-04 0.6.0
2024-07-29 0.5.9
2024-03-28 0.5.8
2023-03-10 0.5.7
2023-01-31 0.5.6
2022-10-31 0.5.5
2022-08-06 0.5.4
2022-06-07 0.5.3
2022-05-18 0.5.2
2022-03-11 0.5.1
2022-01-26 0.5.0
2021-09-14 0.4.2
2021-08-04 0.4.1
2021-07-31 0.4.0
2021-06-07 0.3.0
2021-04-19 0.2.0

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2025-07-14 2.1.1

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2021-09-13 0.1.3
2021-07-26 0.1.2

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

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2020-06-29 0.4.0
2018-10-19 0.3.0
2017-12-21 0.2.0
2017-05-05 0.1.4
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Package refdb (with last version 0.1.2) was removed from CRAN

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2025-12-19 0.1.2
2022-09-22 0.1.1
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Package ShinyLink (with last version 0.5.5) was removed from CRAN

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2025-12-15 0.5.5
2023-01-18 0.2.2

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Package wklsr (with last version 0.2.6) was removed from CRAN

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2026-01-27 0.2.6
2026-01-19 0.2.5

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2020-06-16 1.0.0
2017-04-28 0.1.1
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Package ooplah (with last version 0.2.0) was removed from CRAN

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Package brfinance (with last version 0.6.0) was removed from CRAN

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2025-12-21 0.6.0
2025-10-19 0.2.2
2025-10-17 0.2.1
2025-10-16 0.2.0

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Package WikidataR (with last version 2.3.3) was removed from CRAN

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2021-11-16 2.3.3
2021-07-12 2.3.1
2021-07-05 2.2.0
2021-05-25 2.1.5
2017-09-22 1.4.0
2017-05-17 1.3.0
2017-02-05 1.2.0
2016-06-13 1.1.0
2016-01-14 1.0.1
2015-06-01 1.0.0

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Package taxotools (with last version 0.0.148) was removed from CRAN

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2025-05-27 0.0.148

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Package Rdpack updated to version 2.6.6 with previous version 2.6.5 dated 2026-01-23

Title: Update and Manipulate Rd Documentation Objects
Description: Functions for manipulation of R documentation objects, including functions reprompt() and ereprompt() for updating 'Rd' documentation for functions, methods and classes; 'Rd' macros for citations and import of references from 'bibtex' files for use in 'Rd' files and 'roxygen2' comments; 'Rd' macros for evaluating and inserting snippets of 'R' code and the results of its evaluation or creating graphics on the fly; and many functions for manipulation of references and Rd files.
Author: Georgi N. Boshnakov [aut, cre] , Duncan Murdoch [ctb]
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>

Diff between Rdpack versions 2.6.5 dated 2026-01-23 and 2.6.6 dated 2026-02-08

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Package geoarrow updated to version 0.4.2 with previous version 0.4.1 dated 2025-11-19

Title: Extension Types for Spatial Data for Use with 'Arrow'
Description: Provides extension types and conversions to between R-native object types and 'Arrow' columnar types. This includes integration among the 'arrow', 'nanoarrow', 'sf', and 'wk' packages such that spatial metadata is preserved wherever possible. Extension type implementations ensure first-class geometry data type support in the 'arrow' and 'nanoarrow' packages.
Author: Dewey Dunnington [aut, cre] , Anthony North [ctb], Apache Software Foundation [cph], Ulf Adams [cph], Daniel Lemire [cph], Joao Paulo Magalhaes [cph]
Maintainer: Dewey Dunnington <dewey@dunnington.ca>

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Package disagmethod updated to version 0.1.1 with previous version 0.1.0 dated 2026-01-30

Title: Autoregressive Integrated Moving Average (ARIMA) Based Disaggregation Methods
Description: We have the code for disaggregation as found in Wei and Stram (1990, <doi:10.1111/j.2517-6161.1990.tb01799.x>), and Hodgess and Wei (1996, "Temporal Disaggregation of Time Series" in Statistical Science I, Nova Publishing). The disaggregation models have different orders of the moving average component. These are based on ARIMA models rather than differencing or using similar time series.
Author: Erin Hodgess [aut, cre]
Maintainer: Erin Hodgess <erinm.hodgess@gmail.com>

Diff between disagmethod versions 0.1.0 dated 2026-01-30 and 0.1.1 dated 2026-02-08

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Package cgam updated to version 1.31 with previous version 1.30 dated 2026-01-16

Title: Constrained Generalized Additive Model
Description: A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with [...truncated...]
Author: Mary Meyer [aut], Xiyue Liao [aut, cre]
Maintainer: Xiyue Liao <xliao@sdsu.edu>

Diff between cgam versions 1.30 dated 2026-01-16 and 1.31 dated 2026-02-08

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Sat, 07 Feb 2026

Package LRTesteR updated to version 1.3.1 with previous version 1.3.0 dated 2025-05-24

Title: Likelihood Ratio Tests and Confidence Intervals
Description: A collection of hypothesis tests and confidence intervals based on the likelihood ratio <https://en.wikipedia.org/wiki/Likelihood-ratio_test>.
Author: Greg McMahan [aut, cre]
Maintainer: Greg McMahan <gmcmacran@gmail.com>

Diff between LRTesteR versions 1.3.0 dated 2025-05-24 and 1.3.1 dated 2026-02-07

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Package FLORAL updated to version 0.7.0 with previous version 0.6.0 dated 2025-11-21

Title: Fit Log-Ratio Lasso Regression for Compositional Data
Description: Log-ratio Lasso regression for continuous, binary, and survival outcomes with (longitudinal) compositional features. See Fei and others (2024) <doi:10.1016/j.crmeth.2024.100899>.
Author: Teng Fei [aut, cre, cph] , Tyler Funnell [aut] , Nicholas Waters [aut] , Sandeep Raj [aut]
Maintainer: Teng Fei <feit1@mskcc.org>

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Package projectLSA updated to version 0.0.8 with previous version 0.0.7 dated 2026-01-30

Title: Shiny Application for Latent Structure Analysis with a Graphical User Interface
Description: Provides an interactive Shiny-based toolkit for conducting latent structure analyses, including Latent Profile Analysis (LPA), Latent Class Analysis (LCA), Latent Trait Analysis (LTA/IRT), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). The implementation is grounded in established methodological frameworks: LPA is supported through 'tidyLPA' (Rosenberg et al., 2018) <doi:10.21105/joss.00978>, LCA through 'poLCA' (Linzer & Lewis, 2011) <doi:10.32614/CRAN.package.poLCA> & 'glca' (Kim & Kim, 2024) <doi:10.32614/CRAN.package.glca>, LTA/IRT via 'mirt' (Chalmers, 2012) <doi:10.18637/jss.v048.i06>, and EFA via 'psych' (Revelle, 2025). SEM and CFA functionalities build upon the 'lavaan' framework (Rosseel, 2012) <doi:10.18637/jss.v048.i02>. Users can upload datasets or use built-in examples, fit models, compare fit indices, visualize results, and export outputs without programming.
Author: Hasan Djidu [aut, cre] , Heri Retnawati [ctb] , Samsul Hadi [ctb] , Haryanto [ctb]
Maintainer: Hasan Djidu <hasandjidu@gmail.com>

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Package rib updated to version 0.30.4 with previous version 0.25.3 dated 2025-02-10

Title: An Implementation of 'Interactive Brokers' API
Description: Allows interaction with 'Interactive Brokers' 'Trader Workstation' <https://interactivebrokers.github.io/tws-api/>. Handles the connection over the network and the exchange of messages. Data is encoded and decoded between user and wire formats. Data structures and functionality closely mirror the official implementations.
Author: Luca Billi [aut, cre]
Maintainer: Luca Billi <noreply.section+dev@gmail.com>

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Package tdsa updated to version 1.1-2 with previous version 1.1-1 dated 2025-05-15

Title: Time-Dependent Sensitivity Analysis
Description: Functions that can be used to calculate time-dependent state and parameter sensitivities for both continuous- and discrete-time deterministic models. See Ng et al. (2023) <doi:10.1086/726143> for more information about time-dependent sensitivity analysis.
Author: Wee Hao Ng [aut, cre] , Christopher R. Myers [ctb] , Scott H. McArt [ctb] , Stephen P. Ellner [ctb]
Maintainer: Wee Hao Ng <wn68@cornell.edu>

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Package ambit updated to version 0.2.3 with previous version 0.1.2 dated 2022-08-19

Title: Simulation and Estimation of Ambit Processes
Description: Simulation and estimation tools for various types of ambit processes, including trawl processes and weighted trawl processes.
Author: Almut E. D. Veraart [aut, cre, cph]
Maintainer: Almut E. D. Veraart <a.veraart@imperial.ac.uk>

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Package cocoon updated to version 0.3.0 with previous version 0.2.1 dated 2025-09-07

Title: Extract, Format, and Print Statistical Output
Description: Provides functions that format statistical output in a way that can be inserted into R Markdown documents. This is analogous to the apa_print() functions in the 'papaja' package but prints Markdown or LaTeX syntax.
Author: Jeffrey R. Stevens [aut, cre, cph]
Maintainer: Jeffrey R. Stevens <jeffrey.r.stevens@protonmail.com>

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Package nflfastR updated to version 5.2.0 with previous version 5.1.0 dated 2025-05-14

Title: Functions to Efficiently Access NFL Play by Play Data
Description: A set of functions to access National Football League play-by-play data from <https://www.nfl.com/>.
Author: Sebastian Carl [aut], Ben Baldwin [cre, aut], Lee Sharpe [ctb], Maksim Horowitz [ctb], Ron Yurko [ctb], Samuel Ventura [ctb], Tan Ho [ctb], John Edwards [ctb]
Maintainer: Ben Baldwin <bbaldwin206@gmail.com>

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Package healthcare.antitrust updated to version 0.1.5 with previous version 0.1.4 dated 2024-01-08

Title: Healthcare Antitrust Analysis
Description: Antitrust analysis of healthcare markets. Contains functions to implement the semiparametric estimation technique described in Raval, Rosenbaum, and Tenn (2017) "A Semiparametric Discrete Choice Model: An Application to Hospital Mergers" <doi:10.1111/ecin.12454>.
Author: Matthew T Panhans [aut, cre]
Maintainer: Matthew T Panhans <mpanhans@gmail.com>

Diff between healthcare.antitrust versions 0.1.4 dated 2024-01-08 and 0.1.5 dated 2026-02-07

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Package exdqlm updated to version 0.3.0 with previous version 0.2.0 dated 2026-01-12

Title: Extended Dynamic Quantile Linear Models
Description: Routines for Bayesian estimation and analysis of dynamic quantile linear models utilizing the extended asymmetric Laplace error distribution, also known as extended dynamic quantile linear models (exDQLM) described in Barata et al (2020) <doi:10.1214/21-AOAS1497>.
Author: Raquel Barata [aut, cre], Raquel Prado [ths], Bruno Sanso [ths], Antonio Aguirre [aut]
Maintainer: Raquel Barata <raquel.a.barata@gmail.com>

Diff between exdqlm versions 0.2.0 dated 2026-01-12 and 0.3.0 dated 2026-02-07

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Package RFOC updated to version 3.4-14 with previous version 3.4-10 dated 2023-09-06

Title: Graphics for Spherical Distributions and Earthquake Focal Mechanisms
Description: Graphics for statistics on a sphere, as applied to geological fault data, crystallography, earthquake focal mechanisms, radiation patterns, ternary plots and geographical/geological maps. Non-double couple plotting of focal spheres and source type maps are included for statistical analysis of moment tensors.
Author: Jonathan M. Lees [aut, cre], Keehoon Kim [ctb]
Maintainer: Jonathan M. Lees <jonathan.lees@unc.edu>

Diff between RFOC versions 3.4-10 dated 2023-09-06 and 3.4-14 dated 2026-02-07

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Package volker updated to version 3.3.1 with previous version 3.3.0 dated 2026-01-10

Title: High-Level Functions for Tabulating, Charting and Reporting Survey Data
Description: Craft polished tables and plots in Markdown reports. Simply choose whether to treat your data as counts or metrics, and the package will automatically generate well-designed default tables and plots for you. Boiled down to the basics, with labeling features and simple interactive reports. All functions are 'tidyverse' compatible.
Author: Jakob Juenger [aut, cre, cph] , Henrieke Kotthoff [aut, ctb] , Chantal Gaertner [ctb] , Sophia Rinne [ctb]
Maintainer: Jakob Juenger <jakob.juenger@uni-muenster.de>

Diff between volker versions 3.3.0 dated 2026-01-10 and 3.3.1 dated 2026-02-07

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Package somhca updated to version 0.3.0 with previous version 0.2.0 dated 2025-01-27

Title: Self-Organising Maps Coupled with Hierarchical Cluster Analysis
Description: Implements self-organising maps combined with hierarchical cluster analysis (SOM-HCA) for clustering and visualization of high-dimensional data. The package includes functions to estimate the optimal map size based on various quality measures and to generate a model using the selected dimensions. It also performs hierarchical clustering on the map nodes to group similar units. Documentation about the SOM-HCA method is provided in Pastorelli et al. (2024) <doi:10.1002/xrs.3388>.
Author: Gianluca Pastorelli [aut, cre]
Maintainer: Gianluca Pastorelli <gianluca.pastorelli@gmail.com>

Diff between somhca versions 0.2.0 dated 2025-01-27 and 0.3.0 dated 2026-02-07

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Package Westerlund updated to version 0.1.2 with previous version 0.1.1 dated 2026-01-17

Title: Panel Cointegration Tests Based on Westerlund (2007)
Description: Implements a functional approximation of the four panel cointegration tests developed by Westerlund (2007) <doi:10.1111/j.1468-0084.2007.00477.x>. The tests are based on structural rather than residual dynamics and allow for heterogeneity in both the long-run cointegrating relationship and the short-run dynamics. The package includes logic for automated lag and lead selection via AIC/BIC, Bartlett kernel long-run variance estimation, and a bootstrap procedure to handle cross-sectional dependence. It also includes a bootstrapping distribution visualization function for diagnostic purposes.
Author: Bosco Hung [aut, cre]
Maintainer: Bosco Hung <bosco.hung@st-annes.ox.ac.uk>

Diff between Westerlund versions 0.1.1 dated 2026-01-17 and 0.1.2 dated 2026-02-07

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Package tweedie updated to version 3.0.12 with previous version 3.0.10 dated 2026-02-06

Title: Evaluation of Tweedie Exponential Family Models
Description: Maximum likelihood computations for Tweedie families, including the series expansion (Dunn and Smyth, 2005; <doi:10.1007/s11222-005-4070-y>) and the Fourier inversion (Dunn and Smyth, 2008; <doi:10.1007/s11222-007-9039-6>), and related methods.
Author: Peter K. Dunn [cre, aut]
Maintainer: Peter K. Dunn <pdunn2@usc.edu.au>

Diff between tweedie versions 3.0.10 dated 2026-02-06 and 3.0.12 dated 2026-02-07

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New package SppTrend with initial version 0.4
Package: SppTrend
Title: Analyzing Linear Trends in Species Occurrence Data
Version: 0.4
Description: Provides a methodology to analyze how species occurrences change over time, particularly in relation to spatial and thermal factors. It facilitates the development of explanatory hypotheses about the impact of environmental shifts on species by analyzing historical presence data that includes temporal and geographic information. Approach described in Lobo et al., 2023 <doi:10.1002/ece3.10674>.
License: MIT + file LICENSE
Encoding: UTF-8
Suggests: testthat (>= 3.0.0), devtools
Imports: dplyr, tidyr, terra, stats, stringr, tidyselect, ggplot2, patchwork, sf
NeedsCompilation: no
Packaged: 2026-02-04 09:15:16 UTC; mario
Author: Mario Mingarro [aut, cre] , Emilio Garcia-Rosello [aut] , Jorge M. Lobo [aut]
Maintainer: Mario Mingarro <mario_mingarro@mncn.csic.es>
Repository: CRAN
Date/Publication: 2026-02-07 12:50:02 UTC

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New package readosense with initial version 0.0.1
Package: readosense
Title: Imports Log and Data Files from Eosense Flux Chambers
Version: 0.0.1
Date: 2026-01-30
Description: Imports log and data files from "Eosense" ecosystem gas flux chambers into dataframes that can directly be used with "fluxible" by Gaudard et al (2025) <doi:10.1111/2041-210X.70161>.
License: GPL (>= 3)
Encoding: UTF-8
Imports: dplyr, lubridate, purrr, readr, rlang, tidyr
Suggests: testthat (>= 3.0.0)
Depends: R (>= 4.1)
URL: https://jogaudard.github.io/readosense/
BugReports: https://github.com/jogaudard/readosense/issues
NeedsCompilation: no
Packaged: 2026-02-04 09:45:54 UTC; jga051
Author: Joseph Gaudard [aut, cre]
Maintainer: Joseph Gaudard <joseph.gaudard@pm.me>
Repository: CRAN
Date/Publication: 2026-02-07 12:40:02 UTC

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New package optimflex with initial version 0.1.0
Package: optimflex
Title: Derivative-Based Optimization with User-Defined Convergence Criteria
Version: 0.1.0
Description: Provides a derivative-based optimization framework that allows users to combine eight convergence criteria. Unlike standard optimization functions, this package includes a built-in mechanism to verify the positive definiteness of the Hessian matrix at the point of convergence. This additional check helps prevent the solver from falsely identifying non-optimal solutions, such as saddle points, as valid minima.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: numDeriv, utils
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-04 07:35:53 UTC; bene
Author: Eunseong Cho [aut, cre]
Maintainer: Eunseong Cho <bene@kw.ac.kr>
Repository: CRAN
Date/Publication: 2026-02-07 12:40:07 UTC

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Package LabRS updated to version 0.2.0 with previous version 0.1.0 dated 2018-12-18

Title: Laboratorio di Ricerca Sociale con R
Description: Libreria di dati, scripts e funzioni che accompagna il libro "Ricerca sociale con R. Concetti e funzioni base per la ricerca sociale".
Author: Agnese Vardanega [aut, cre]
Maintainer: Agnese Vardanega <avardanega@unite.it>

Diff between LabRS versions 0.1.0 dated 2018-12-18 and 0.2.0 dated 2026-02-07

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Package fastrmodels updated to version 2.1.0 with previous version 2.0.0 dated 2025-05-15

Title: Models for the 'nflfastR' Package
Description: A data package that hosts all models for the 'nflfastR' package.
Author: Sebastian Carl [cre, aut], Ben Baldwin [aut]
Maintainer: Sebastian Carl <mrcaseb@gmail.com>

Diff between fastrmodels versions 2.0.0 dated 2025-05-15 and 2.1.0 dated 2026-02-07

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Package dream readmission to version 1.0.1 with previous version 1.0.0 dated 2026-01-20

Title: Dynamic Relational Event Analysis and Modeling
Description: A set of tools for relational and event analysis, including two- and one-mode network brokerage and structural measures, and helper functions optimized for relational event analysis with large datasets, including creating relational risk sets, computing network statistics, estimating relational event models, and simulating relational event sequences. For more information on relational event models, see Butts (2008) <doi:10.1111/j.1467-9531.2008.00203.x>, Lerner and Lomi (2020) <doi:10.1017/nws.2019.57>, Bianchi et al. (2024) <doi:10.1146/annurev-statistics-040722-060248>, and Butts et al. (2023) <doi:10.1017/nws.2023.9>. In terms of the structural measures in this package, see Leal (2025) <doi:10.1177/00491241251322517>, Burchard and Cornwell (2018) <doi:10.1016/j.socnet.2018.04.001>, and Fujimoto et al. (2018) <doi:10.1017/nws.2018.11>. This package was developed with support from the National Science Foundation’s (NSF) Human Networks and Data [...truncated...]
Author: Kevin A. Carson [aut, cre] , Diego F. Leal [aut]
Maintainer: Kevin A. Carson <kacarson@arizona.edu>

This is a re-admission after prior archival of version 1.0.0 dated 2026-01-20

Diff between dream versions 1.0.0 dated 2026-01-20 and 1.0.1 dated 2026-02-07

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Package theorytools updated to version 0.1.3 with previous version 0.1.2 dated 2025-07-13

Title: FAIR Theory Construction
Description: An integrated suite of tools for creating, maintaining, and reusing FAIR (Findable, Accessible, Interoperable, Reusable) theories. Designed to support transparent and collaborative theory development, the package enables users to formalize theories, track changes with version control, assess pre-empirical coherence, and derive testable hypotheses. Aligning with open science principles and workflows, 'theorytools' facilitates the systematic improvement of theoretical frameworks and enhances their discoverability and usability.
Author: Caspar J. Van Lissa [aut, cre]
Maintainer: Caspar J. Van Lissa <c.j.vanlissa@tilburguniversity.edu>

Diff between theorytools versions 0.1.2 dated 2025-07-13 and 0.1.3 dated 2026-02-07

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Package network updated to version 1.20.0 with previous version 1.19.0 dated 2024-12-09

Title: Classes for Relational Data
Description: Tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.
Author: Carter T. Butts [aut, cre], David Hunter [ctb], Mark Handcock [ctb], Skye Bender-deMoll [ctb], Jeffrey Horner [ctb], Li Wang [ctb], Pavel N. Krivitsky [ctb] , Brendan Knapp [ctb] , Michal Bojanowski [ctb] , Chad Klumb [ctb]
Maintainer: Carter T. Butts <buttsc@uci.edu>

Diff between network versions 1.19.0 dated 2024-12-09 and 1.20.0 dated 2026-02-07

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Package MOSAlloc updated to version 1.2.4 with previous version 1.2.3 dated 2026-01-27

Title: Constraint Multiobjective Sample Allocation
Description: Provides a framework for multipurpose optimal resource allocation in survey sampling, extending the classical optimal allocation principles introduced by Tschuprow (1923) and Neyman (1934) to multidomain and multivariate allocation problems. The primary method mosalloc() allows for the consideration of precision and cost constraints at the subpopulation level while minimizing either a vector of sampling errors or survey costs across a broad range of optimal sample allocation problems. The approach supports both single- and multistage designs. For single-stage stratified random sampling, the mosallocSTRS() function offers a user- friendly interface. Sensitivity analysis is supported through the problem's dual variables, which are naturally obtained via the internal use of the Embedded Conic Solver from the 'ECOSolveR' package. See Willems (2025, <doi:10.25353/ubtr-9200-484c-5c89>) for a detailed description of the theory behind 'MOSAlloc'.
Author: Felix Willems [aut, cre] , Ralf Muennich [ths]
Maintainer: Felix Willems <mail.willemsf+MOSAlloc@gmail.com>

Diff between MOSAlloc versions 1.2.3 dated 2026-01-27 and 1.2.4 dated 2026-02-07

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Package garma updated to version 1.0.1 with previous version 1.0.0 dated 2026-02-05

Title: Fitting and Forecasting Gegenbauer ARMA Time Series Models
Description: Methods for estimating univariate long memory-seasonal/cyclical Gegenbauer time series processes. See for example (2022) <doi:10.1007/s00362-022-01290-3>. Refer to the vignette for details of fitting these processes.
Author: Richard Hunt [aut, cre]
Maintainer: Richard Hunt <maint@huntemail.id.au>

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Package tdigest (with last version 0.4.2) was removed from CRAN

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

2024-06-19 0.4.2
2022-10-04 0.4.1
2019-08-01 0.3.0

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Package vazul updated to version 1.1.0 with previous version 1.0.0 dated 2026-01-28

Title: Analysis Blinding Tools
Description: Provides tools for analysis blinding in confirmatory research contexts by masking and scrambling test-relevant aspects of data. Vector-, data frame-, and row-wise operations support blinding for hierarchical and repeated-measures designs. For more details see MacCoun and Perlmutter (2015) <doi:10.1038/526187a> and Dutilh, Sarafoglou, and Wagenmakers (2019) <doi:10.1007/s11229-019-02456-7>.
Author: Tamas Nagy [aut, cre] , Alexandra Sarafoglou [aut, dtc] , Marton Kovacs [aut]
Maintainer: Tamas Nagy <nagytamas.hungary@gmail.com>

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Package scorecard updated to version 0.4.6 with previous version 0.4.5 dated 2025-08-20

Title: Credit Risk Scorecard
Description: The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring.
Author: Shichen Xie [aut, cre]
Maintainer: Shichen Xie <xie@shichen.name>

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Package reporter updated to version 1.4.6 with previous version 1.4.5 dated 2025-11-22

Title: Creates Statistical Reports
Description: Contains functions to create regulatory-style statistical reports. Originally designed to create tables, listings, and figures for the pharmaceutical, biotechnology, and medical device industries, these reports are generalized enough that they could be used in any industry. Generates text, rich-text, PDF, HTML, and Microsoft Word file formats. The package specializes in printing wide and long tables with automatic page wrapping and splitting. Reports can be produced with a minimum of function calls, and without relying on other table packages. The package supports titles, footnotes, page header, page footers, spanning headers, page by variables, and automatic page numbering.
Author: David Bosak [aut, cre], Bill Huang [aut], Kevin Kramer [ctb], Duong Tran [ctb], Raphael Huang [ctb], Archytas Clinical Solutions [cph]
Maintainer: David Bosak <dbosak01@gmail.com>

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Package partykit updated to version 1.2-25 with previous version 1.2-24 dated 2025-05-02

Title: A Toolkit for Recursive Partytioning
Description: A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources ('rpart', 'RWeka', 'PMML') yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the 'party' package are provided based on the new infrastructure. A description of this package was published by Hothorn and Zeileis (2015) <https://jmlr.org/papers/v16/hothorn15a.html>.
Author: Torsten Hothorn [aut, cre] , Heidi Seibold [ctb] , Achim Zeileis [aut]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

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 partykit-1.2-25/partykit/tests/regtest-party.R           |    3 
 partykit-1.2-25/partykit/tests/regtest-party.Rout.save   |   91 ++-
 partykit-1.2-25/partykit/tests/regtest-split.Rout.save   |   10 
 partykit-1.2-25/partykit/tests/regtest-weights.Rout.save |   10 
 partykit-1.2-25/partykit/vignettes/constparty.Rnw        |  206 ++++----
 partykit-1.2-25/partykit/vignettes/ctree.Rnw             |   94 ++-
 partykit-1.2-25/partykit/vignettes/mob.Rnw               |   86 ++-
 partykit-1.2-25/partykit/vignettes/packages.bib          |only
 partykit-1.2-25/partykit/vignettes/party.bib             |  118 ----
 partykit-1.2-25/partykit/vignettes/partykit.Rnw          |   34 -
 42 files changed, 958 insertions(+), 1608 deletions(-)

More information about partykit at CRAN
Permanent link

Package mlpack updated to version 4.7.0 with previous version 4.6.3 dated 2025-09-01

Title: 'Rcpp' Integration for the 'mlpack' Library
Description: A fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. See also Curtin et al. (2023) <doi:10.21105/joss.05026>.
Author: Yashwant Singh Parihar [aut, ctb, cph], Ryan Curtin [aut, ctb, cph, cre], Dirk Eddelbuettel [aut, ctb, cph], James Balamuta [aut, ctb, cph], Bill March [ctb, cph], Dongryeol Lee [ctb, cph], Nishant Mehta [ctb, cph], Parikshit Ram [ctb, cph], James Cl [...truncated...]
Maintainer: Ryan Curtin <ryan@ratml.org>

Diff between mlpack versions 4.6.3 dated 2025-09-01 and 4.7.0 dated 2026-02-07

 mlpack-4.6.3/mlpack/inst/include/mlpack/core/data/format.hpp                                                   |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/core/data/image_info.hpp                                               |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/core/data/image_info_impl.hpp                                          |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/core/data/load_image_impl.hpp                                          |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/core/data/load_model_impl.hpp                                          |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/core/data/load_vec_impl.hpp                                            |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/core/data/map_policies/missing_policy.hpp                              |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/core/data/save_image_impl.hpp                                          |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/core/data/types.hpp                                                    |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/core/data/types_impl.hpp                                               |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/core/data/utilities.hpp                                                |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/core/util/gitversion.hpp                                               |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/methods/ann/convolution_rules/fft_convolution.hpp                      |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/methods/ann/convolution_rules/svd_convolution.hpp                      |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/methods/ann/layer/not_adapted/gru.hpp                                  |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/methods/ann/layer/not_adapted/gru_impl.hpp                             |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/methods/ann/layer/not_adapted/lookup.hpp                               |only
 mlpack-4.6.3/mlpack/inst/include/mlpack/methods/ann/layer/not_adapted/lookup_impl.hpp                          |only
 mlpack-4.7.0/mlpack/DESCRIPTION                                                                                |   10 
 mlpack-4.7.0/mlpack/LICENSE                                                                                    |    2 
 mlpack-4.7.0/mlpack/MD5                                                                                        |  875 +++++-----
 mlpack-4.7.0/mlpack/R/adaboost.R                                                                               |    4 
 mlpack-4.7.0/mlpack/R/approx_kfn.R                                                                             |    4 
 mlpack-4.7.0/mlpack/R/bayesian_linear_regression.R                                                             |    4 
 mlpack-4.7.0/mlpack/R/cf.R                                                                                     |    4 
 mlpack-4.7.0/mlpack/R/dbscan.R                                                                                 |    4 
 mlpack-4.7.0/mlpack/R/decision_tree.R                                                                          |    4 
 mlpack-4.7.0/mlpack/R/det.R                                                                                    |    4 
 mlpack-4.7.0/mlpack/R/emst.R                                                                                   |    4 
 mlpack-4.7.0/mlpack/R/fastmks.R                                                                                |    4 
 mlpack-4.7.0/mlpack/R/gmm_generate.R                                                                           |    4 
 mlpack-4.7.0/mlpack/R/gmm_probability.R                                                                        |    4 
 mlpack-4.7.0/mlpack/R/gmm_train.R                                                                              |    4 
 mlpack-4.7.0/mlpack/R/hmm_generate.R                                                                           |    4 
 mlpack-4.7.0/mlpack/R/hmm_loglik.R                                                                             |    4 
 mlpack-4.7.0/mlpack/R/hmm_train.R                                                                              |    4 
 mlpack-4.7.0/mlpack/R/hmm_viterbi.R                                                                            |    4 
 mlpack-4.7.0/mlpack/R/hoeffding_tree.R                                                                         |    4 
 mlpack-4.7.0/mlpack/R/image_converter.R                                                                        |    4 
 mlpack-4.7.0/mlpack/R/kde.R                                                                                    |    4 
 mlpack-4.7.0/mlpack/R/kernel_pca.R                                                                             |    4 
 mlpack-4.7.0/mlpack/R/kfn.R                                                                                    |    4 
 mlpack-4.7.0/mlpack/R/kmeans.R                                                                                 |    4 
 mlpack-4.7.0/mlpack/R/knn.R                                                                                    |    4 
 mlpack-4.7.0/mlpack/R/krann.R                                                                                  |    4 
 mlpack-4.7.0/mlpack/R/lars.R                                                                                   |    4 
 mlpack-4.7.0/mlpack/R/linear_regression.R                                                                      |    4 
 mlpack-4.7.0/mlpack/R/linear_svm.R                                                                             |    4 
 mlpack-4.7.0/mlpack/R/lmnn.R                                                                                   |    4 
 mlpack-4.7.0/mlpack/R/local_coordinate_coding.R                                                                |    4 
 mlpack-4.7.0/mlpack/R/logistic_regression.R                                                                    |    4 
 mlpack-4.7.0/mlpack/R/lsh.R                                                                                    |    4 
 mlpack-4.7.0/mlpack/R/mean_shift.R                                                                             |    4 
 mlpack-4.7.0/mlpack/R/nbc.R                                                                                    |    4 
 mlpack-4.7.0/mlpack/R/nca.R                                                                                    |    4 
 mlpack-4.7.0/mlpack/R/nmf.R                                                                                    |    4 
 mlpack-4.7.0/mlpack/R/pca.R                                                                                    |    4 
 mlpack-4.7.0/mlpack/R/perceptron.R                                                                             |    4 
 mlpack-4.7.0/mlpack/R/preprocess_binarize.R                                                                    |    4 
 mlpack-4.7.0/mlpack/R/preprocess_describe.R                                                                    |    2 
 mlpack-4.7.0/mlpack/R/preprocess_one_hot_encoding.R                                                            |    4 
 mlpack-4.7.0/mlpack/R/preprocess_scale.R                                                                       |    8 
 mlpack-4.7.0/mlpack/R/preprocess_split.R                                                                       |    4 
 mlpack-4.7.0/mlpack/R/radical.R                                                                                |    4 
 mlpack-4.7.0/mlpack/R/random_forest.R                                                                          |    4 
 mlpack-4.7.0/mlpack/R/softmax_regression.R                                                                     |    4 
 mlpack-4.7.0/mlpack/R/sparse_coding.R                                                                          |    4 
 mlpack-4.7.0/mlpack/R/test_r_binding.R                                                                         |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack.h                                                                      |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack.hpp                                                                    |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/base.hpp                                                               |    1 
 mlpack-4.7.0/mlpack/inst/include/mlpack/config.hpp                                                             |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/arma_extend/find_nan.hpp                                          |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/cereal/low_precision.hpp                                          |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/cv/cv_base.hpp                                                    |   22 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/cv/cv_base_impl.hpp                                               |   14 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/cv/k_fold_cv.hpp                                                  |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/cv/k_fold_cv_impl.hpp                                             |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/cv/meta_info_extractor.hpp                                        |   20 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/cv/metrics/f1_impl.hpp                                            |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/cv/metrics/facilities.hpp                                         |    3 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/cv/metrics/precision_impl.hpp                                     |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/cv/metrics/r2_score_impl.hpp                                      |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/cv/metrics/silhouette_score_impl.hpp                              |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/cv/simple_cv.hpp                                                  |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/cv/simple_cv_impl.hpp                                             |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/binarize.hpp                                                 |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/check_categorical_param.hpp                                  |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/combine_options.hpp                                          |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/confusion_matrix.hpp                                         |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/confusion_matrix_impl.hpp                                    |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/data.hpp                                                     |   10 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/data_options.hpp                                             |  359 +++-
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/dataset_mapper.hpp                                           |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/dataset_mapper_impl.hpp                                      |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/detect_file_type.hpp                                         |   39 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/detect_file_type_impl.hpp                                    |  196 ++
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/extension.hpp                                                |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/font8x8_basic.h                                              |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/has_serialize.hpp                                            |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/image_bounding_box.hpp                                       |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/image_bounding_box_impl.hpp                                  |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/image_layout.hpp                                             |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/image_layout_impl.hpp                                        |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/image_letterbox.hpp                                          |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/image_options.hpp                                            |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/image_resize_crop.hpp                                        |  161 +
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/imputation_methods/custom_imputation.hpp                     |   48 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/imputation_methods/listwise_deletion.hpp                     |   48 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/imputation_methods/mean_imputation.hpp                       |  157 +
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/imputation_methods/median_imputation.hpp                     |   89 -
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/imputer.hpp                                                  |   90 -
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/is_naninf.hpp                                                |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/load.hpp                                                     |  288 ---
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/load_arff.hpp                                                |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/load_arff_impl.hpp                                           |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/load_categorical.hpp                                         |    5 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/load_categorical_impl.hpp                                    |   36 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/load_dense.hpp                                               |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/load_deprecated.hpp                                          |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/load_image.hpp                                               |  118 -
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/load_impl.hpp                                                |  369 +---
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/load_model.hpp                                               |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/load_numeric.hpp                                             |  211 +-
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/load_sparse.hpp                                              |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/map_policies/datatype.hpp                                    |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/map_policies/increment_policy.hpp                            |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/map_policies/map_policies.hpp                                |    1 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/matrix_options.hpp                                           |  172 +
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/normalize_labels.hpp                                         |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/normalize_labels_impl.hpp                                    |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/one_hot_encoding.hpp                                         |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/one_hot_encoding_impl.hpp                                    |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/save.hpp                                                     |  146 -
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/save_dense.hpp                                               |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/save_deprecated.hpp                                          |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/save_image.hpp                                               |  128 -
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/save_impl.hpp                                                |  303 ---
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/save_matrix.hpp                                              |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/save_model.hpp                                               |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/save_numeric.hpp                                             |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/save_sparse.hpp                                              |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/scaler_methods/max_abs_scaler.hpp                            |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/scaler_methods/mean_normalization.hpp                        |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/scaler_methods/min_max_scaler.hpp                            |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/scaler_methods/pca_whitening.hpp                             |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/scaler_methods/standard_scaler.hpp                           |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/scaler_methods/zca_whitening.hpp                             |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/split_data.hpp                                               |   14 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/string_algorithms.hpp                                        |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/string_encoding.hpp                                          |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/string_encoding_dictionary.hpp                               |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/string_encoding_impl.hpp                                     |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/string_encoding_policies/bag_of_words_encoding_policy.hpp    |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/string_encoding_policies/dictionary_encoding_policy.hpp      |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/string_encoding_policies/policy_traits.hpp                   |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/string_encoding_policies/tf_idf_encoding_policy.hpp          |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/text_options.hpp                                             |  144 +
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/tokenizers/char_extract.hpp                                  |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/data/tokenizers/split_by_any_of.hpp                               |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/distributions/gamma_distribution_impl.hpp                         |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/distributions/laplace_distribution.hpp                            |   18 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/distributions/laplace_distribution_impl.hpp                       |   14 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/hpt/cv_function.hpp                                               |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/hpt/cv_function_impl.hpp                                          |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/hpt/hpt.hpp                                                       |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/hpt/hpt_impl.hpp                                                  |   18 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/math/make_alias.hpp                                               |   12 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/math/random.hpp                                                   |   24 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/math/shuffle_data.hpp                                             |  324 ---
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/metrics/non_maximal_suppression_impl.hpp                          |   19 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/stb/bundled/stb_image.h                                           |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/stb/bundled/stb_image_resize2.h                                   |  530 +++---
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/stb/bundled/stb_image_write.h                                     |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/tree/binary_space_tree/rp_tree_mean_split_impl.hpp                |   14 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/tree/cellbound.hpp                                                |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/tree/cosine_tree/cosine_tree_impl.hpp                             |   20 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/tree/octree/octree.hpp                                            |   10 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/tree/octree/octree_impl.hpp                                       |   18 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/util/arma_traits.hpp                                              |   63 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/util/coot_traits.hpp                                              |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/util/forward.hpp                                                  |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/util/param.hpp                                                    |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/util/params_impl.hpp                                              |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/util/using.hpp                                                    |   31 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/util/version.hpp                                                  |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/core/util/version_impl.hpp                                             |    9 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/adaboost/adaboost_classify_main.cpp                            |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/adaboost/adaboost_main.cpp                                     |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/adaboost/adaboost_train_main.cpp                               |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/activation_functions.hpp              |    1 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/bipolar_sigmoid_function.hpp          |   10 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/elish_function.hpp                    |   29 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/elliot_function.hpp                   |   16 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/gaussian_function.hpp                 |   13 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/gelu_exact_function.hpp               |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/gelu_function.hpp                     |   43 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/hard_sigmoid_function.hpp             |   14 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/hard_swish_function.hpp               |   10 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/hyper_sinh_function.hpp               |   21 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/identity_function.hpp                 |   10 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/inverse_quadratic_function.hpp        |   14 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/lisht_function.hpp                    |   12 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/logistic_function.hpp                 |   26 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/mish_function.hpp                     |   12 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/multi_quadratic_function.hpp          |   10 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/poisson1_function.hpp                 |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/quadratic_function.hpp                |   10 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/rectifier_function.hpp                |   20 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/silu_function.hpp                     |   18 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/softplus_function.hpp                 |   21 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/softsign_function.hpp                 |   38 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/spline_function.hpp                   |   16 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/swish_function.hpp                    |   20 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/tanh_exponential_function.hpp         |   16 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/activation_functions/tanh_function.hpp                     |   17 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/ann.hpp                                                    |    3 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/convolution_rules/base_convolution.hpp                     |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/convolution_rules/convolution_rules.hpp                    |    3 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/convolution_rules/im2col_convolution.hpp                   |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/convolution_rules/naive_convolution.hpp                    |  263 +--
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/dag_network.hpp                                            |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/dag_network_impl.hpp                                       |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/dists/bernoulli_distribution_impl.hpp                      |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/dists/normal_distribution_impl.hpp                         |    9 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/ffn.hpp                                                    |   42 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/ffn_impl.hpp                                               |   46 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/init_rules/const_init.hpp                                  |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/init_rules/gaussian_init.hpp                               |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/init_rules/he_init.hpp                                     |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/init_rules/kathirvalavakumar_subavathi_init.hpp            |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/init_rules/lecun_normal_init.hpp                           |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/init_rules/nguyen_widrow_init.hpp                          |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/init_rules/oivs_init.hpp                                   |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/init_rules/orthogonal_init.hpp                             |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/init_rules/random_init.hpp                                 |   12 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/adaptive_max_pooling.hpp                             |   44 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/adaptive_max_pooling_impl.hpp                        |   30 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/adaptive_mean_pooling.hpp                            |   44 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/adaptive_mean_pooling_impl.hpp                       |   32 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/add.hpp                                              |   36 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/add_impl.hpp                                         |   26 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/add_merge.hpp                                        |   37 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/add_merge_impl.hpp                                   |   26 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/alpha_dropout.hpp                                    |   33 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/alpha_dropout_impl.hpp                               |   27 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/base_layer.hpp                                       |   79 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/batch_norm.hpp                                       |   34 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/batch_norm_impl.hpp                                  |  109 -
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/c_relu.hpp                                           |   38 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/c_relu_impl.hpp                                      |   45 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/celu.hpp                                             |   33 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/celu_impl.hpp                                        |   59 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/concat.hpp                                           |   36 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/concat_impl.hpp                                      |   26 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/concatenate.hpp                                      |   36 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/concatenate_impl.hpp                                 |   28 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/convolution.hpp                                      |   93 -
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/convolution_impl.hpp                                 |  327 +--
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/dropconnect.hpp                                      |   38 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/dropconnect_impl.hpp                                 |   40 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/dropout.hpp                                          |   36 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/dropout_impl.hpp                                     |   35 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/elu.hpp                                              |   48 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/elu_impl.hpp                                         |   47 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/embedding.hpp                                        |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/embedding_impl.hpp                                   |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/flexible_relu.hpp                                    |   36 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/flexible_relu_impl.hpp                               |   40 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/ftswish.hpp                                          |   35 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/ftswish_impl.hpp                                     |   52 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/grouped_convolution.hpp                              |   60 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/grouped_convolution_impl.hpp                         |  333 +--
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/gru.hpp                                              |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/gru_impl.hpp                                         |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/hard_tanh.hpp                                        |   28 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/hard_tanh_impl.hpp                                   |   24 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/identity.hpp                                         |   39 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/identity_impl.hpp                                    |   24 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/layer.hpp                                            |   70 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/layer_norm.hpp                                       |   24 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/layer_norm_impl.hpp                                  |   32 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/layer_types.hpp                                      |    5 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/leaky_relu.hpp                                       |   43 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/leaky_relu_impl.hpp                                  |   25 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/linear.hpp                                           |   34 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/linear3d.hpp                                         |   36 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/linear3d_impl.hpp                                    |   36 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/linear_impl.hpp                                      |   30 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/linear_no_bias.hpp                                   |   32 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/linear_no_bias_impl.hpp                              |   40 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/linear_recurrent.hpp                                 |   39 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/linear_recurrent_impl.hpp                            |   91 -
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/log_softmax.hpp                                      |   53 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/log_softmax_impl.hpp                                 |  130 -
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/lstm.hpp                                             |   55 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/lstm_impl.hpp                                        |  258 +-
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/max_pooling.hpp                                      |   38 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/max_pooling_impl.hpp                                 |   28 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/mean_pooling.hpp                                     |   48 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/mean_pooling_impl.hpp                                |   32 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/multi_layer.hpp                                      |   42 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/multi_layer_impl.hpp                                 |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/multihead_attention.hpp                              |   48 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/multihead_attention_impl.hpp                         |  225 +-
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/nearest_interpolation.hpp                            |   53 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/nearest_interpolation_impl.hpp                       |   73 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/noisylinear.hpp                                      |   77 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/noisylinear_impl.hpp                                 |   36 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/padding.hpp                                          |   38 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/padding_impl.hpp                                     |   52 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/parametric_relu.hpp                                  |   50 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/parametric_relu_impl.hpp                             |   36 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/radial_basis_function.hpp                            |   69 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/radial_basis_function_impl.hpp                       |   59 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/recurrent_layer.hpp                                  |   13 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/relu6.hpp                                            |   40 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/relu6_impl.hpp                                       |   28 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/repeat.hpp                                           |   49 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/repeat_impl.hpp                                      |   20 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/serialization.hpp                                    |  118 -
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/softmax.hpp                                          |   40 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/softmax_impl.hpp                                     |   20 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/softmin.hpp                                          |   43 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/softmin_impl.hpp                                     |   20 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/sum_reduce.hpp                                       |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/layer/sum_reduce_impl.hpp                                  |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/loss_functions/cosine_embedding_loss_impl.hpp              |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/loss_functions/mean_bias_error_impl.hpp                    |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss_impl.hpp         |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/loss_functions/negative_log_likelihood.hpp                 |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/loss_functions/negative_log_likelihood_impl.hpp            |   44 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/loss_functions/poisson_nll_loss_impl.hpp                   |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/models                                                     |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/regularizer/orthogonal_regularizer_impl.hpp                |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/rnn.hpp                                                    |  178 +-
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/ann/rnn_impl.hpp                                               |  268 ++-
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/approx_kfn/drusilla_select_impl.hpp                            |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_main.cpp |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/bias_svd/bias_svd_function_impl.hpp                            |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/cf/cf_model.hpp                                                |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/decision_tree/decision_tree.hpp                                |   12 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/decision_tree/decision_tree_impl.hpp                           |   24 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/decision_tree/decision_tree_main.cpp                           |    1 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/decision_tree/decision_tree_regressor.hpp                      |   12 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp                 |   24 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/det/det_main.cpp                                               |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/hmm/hmm_train_main.cpp                                         |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/hmm/hmm_util_impl.hpp                                          |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/hoeffding_trees/hoeffding_tree.hpp                             |   12 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/hoeffding_trees/hoeffding_tree_impl.hpp                        |   62 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/hoeffding_trees/hoeffding_tree_main.cpp                        |    3 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/hoeffding_trees/hoeffding_tree_model.hpp                       |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/hoeffding_trees/hoeffding_tree_model_impl.hpp                  |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/kde/kde_rules_impl.hpp                                         |   12 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/lars/lars_impl.hpp                                             |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/linear_svm/linear_svm_function_impl.hpp                        |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/linear_svm/linear_svm_main.cpp                                 |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/lmnn/lmnn_main.cpp                                             |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/lsh/lsh_main.cpp                                               |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/matrix_completion/matrix_completion_impl.hpp                   |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/naive_bayes/naive_bayes_classifier_impl.hpp                    |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/naive_bayes/nbc_main.cpp                                       |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/nca/nca_main.cpp                                               |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/neighbor_search/kfn_main.cpp                                   |   16 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/neighbor_search/knn_main.cpp                                   |   16 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/neighbor_search/neighbor_search.hpp                            |  188 +-
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/neighbor_search/neighbor_search_impl.hpp                       |  241 ++
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/neighbor_search/neighbor_search_stat.hpp                       |   10 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/neighbor_search/ns_model.hpp                                   |   30 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/neighbor_search/ns_model_impl.hpp                              |  101 -
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/neighbor_search/typedef.hpp                                    |   44 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/pca/pca_impl.hpp                                               |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/perceptron/perceptron.hpp                                      |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/perceptron/perceptron_impl.hpp                                 |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/perceptron/perceptron_main.cpp                                 |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/preprocess/image_converter_main.cpp                            |    5 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/preprocess/preprocess_binarize_main.cpp                        |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/preprocess/preprocess_describe_main.cpp                        |    1 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/preprocess/preprocess_imputer_main.cpp                         |  183 --
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/preprocess/preprocess_one_hot_encoding_main.cpp                |   12 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/preprocess/preprocess_scale_main.cpp                           |    5 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/preprocess/preprocess_split_main.cpp                           |    7 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/preprocess/scaling_model.hpp                                   |   14 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/preprocess/scaling_model_impl.hpp                              |   38 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/random_forest/random_forest.hpp                                |   10 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/random_forest/random_forest_impl.hpp                           |   18 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/range_search/range_search_main.cpp                             |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/rann/krann_main.cpp                                            |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/regularized_svd/regularized_svd_function_impl.hpp              |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/reinforcement_learning/async_learning_impl.hpp                 |   16 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/reinforcement_learning/ddpg_impl.hpp                           |   32 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/reinforcement_learning/environment/acrobot.hpp                 |    8 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/reinforcement_learning/environment/cart_pole.hpp               |    6 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/reinforcement_learning/environment/cont_double_pole_cart.hpp   |   11 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/reinforcement_learning/environment/pendulum.hpp                |   11 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/reinforcement_learning/policy/aggregated_policy.hpp            |    4 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/reinforcement_learning/q_learning_impl.hpp                     |   20 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/reinforcement_learning/q_networks/categorical_dqn.hpp          |   38 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/reinforcement_learning/q_networks/dueling_dqn.hpp              |  150 -
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/reinforcement_learning/q_networks/simple_dqn.hpp               |   18 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/reinforcement_learning/sac_impl.hpp                            |   28 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/reinforcement_learning/td3_impl.hpp                            |   28 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/softmax_regression/softmax_regression_function_impl.hpp        |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/methods/svdplusplus/svdplusplus_function_impl.hpp                      |    2 
 mlpack-4.7.0/mlpack/inst/include/mlpack/namespace_compat.hpp                                                   |only
 mlpack-4.7.0/mlpack/inst/include/mlpack/prereqs.hpp                                                            |    1 
 mlpack-4.7.0/mlpack/man/adaboost.Rd                                                                            |    2 
 mlpack-4.7.0/mlpack/man/approx_kfn.Rd                                                                          |    2 
 mlpack-4.7.0/mlpack/man/bayesian_linear_regression.Rd                                                          |    2 
 mlpack-4.7.0/mlpack/man/cf.Rd                                                                                  |    2 
 mlpack-4.7.0/mlpack/man/dbscan.Rd                                                                              |    2 
 mlpack-4.7.0/mlpack/man/decision_tree.Rd                                                                       |    2 
 mlpack-4.7.0/mlpack/man/det.Rd                                                                                 |    2 
 mlpack-4.7.0/mlpack/man/emst.Rd                                                                                |    2 
 mlpack-4.7.0/mlpack/man/fastmks.Rd                                                                             |    2 
 mlpack-4.7.0/mlpack/man/gmm_generate.Rd                                                                        |    2 
 mlpack-4.7.0/mlpack/man/gmm_probability.Rd                                                                     |    2 
 mlpack-4.7.0/mlpack/man/gmm_train.Rd                                                                           |    2 
 mlpack-4.7.0/mlpack/man/hmm_generate.Rd                                                                        |    2 
 mlpack-4.7.0/mlpack/man/hmm_loglik.Rd                                                                          |    2 
 mlpack-4.7.0/mlpack/man/hmm_train.Rd                                                                           |    2 
 mlpack-4.7.0/mlpack/man/hmm_viterbi.Rd                                                                         |    2 
 mlpack-4.7.0/mlpack/man/hoeffding_tree.Rd                                                                      |    2 
 mlpack-4.7.0/mlpack/man/image_converter.Rd                                                                     |    2 
 mlpack-4.7.0/mlpack/man/kde.Rd                                                                                 |    2 
 mlpack-4.7.0/mlpack/man/kernel_pca.Rd                                                                          |    2 
 mlpack-4.7.0/mlpack/man/kfn.Rd                                                                                 |    2 
 mlpack-4.7.0/mlpack/man/kmeans.Rd                                                                              |    2 
 mlpack-4.7.0/mlpack/man/knn.Rd                                                                                 |    2 
 mlpack-4.7.0/mlpack/man/krann.Rd                                                                               |    2 
 mlpack-4.7.0/mlpack/man/lars.Rd                                                                                |    2 
 mlpack-4.7.0/mlpack/man/linear_regression.Rd                                                                   |    2 
 mlpack-4.7.0/mlpack/man/linear_svm.Rd                                                                          |    2 
 mlpack-4.7.0/mlpack/man/lmnn.Rd                                                                                |    2 
 mlpack-4.7.0/mlpack/man/local_coordinate_coding.Rd                                                             |    2 
 mlpack-4.7.0/mlpack/man/logistic_regression.Rd                                                                 |    2 
 mlpack-4.7.0/mlpack/man/lsh.Rd                                                                                 |    2 
 mlpack-4.7.0/mlpack/man/mean_shift.Rd                                                                          |    2 
 mlpack-4.7.0/mlpack/man/nbc.Rd                                                                                 |    2 
 mlpack-4.7.0/mlpack/man/nca.Rd                                                                                 |    2 
 mlpack-4.7.0/mlpack/man/nmf.Rd                                                                                 |    2 
 mlpack-4.7.0/mlpack/man/pca.Rd                                                                                 |    2 
 mlpack-4.7.0/mlpack/man/perceptron.Rd                                                                          |    2 
 mlpack-4.7.0/mlpack/man/preprocess_binarize.Rd                                                                 |    2 
 mlpack-4.7.0/mlpack/man/preprocess_one_hot_encoding.Rd                                                         |    2 
 mlpack-4.7.0/mlpack/man/preprocess_scale.Rd                                                                    |    6 
 mlpack-4.7.0/mlpack/man/preprocess_split.Rd                                                                    |    2 
 mlpack-4.7.0/mlpack/man/radical.Rd                                                                             |    2 
 mlpack-4.7.0/mlpack/man/random_forest.Rd                                                                       |    2 
 mlpack-4.7.0/mlpack/man/softmax_regression.Rd                                                                  |    2 
 mlpack-4.7.0/mlpack/man/sparse_coding.Rd                                                                       |    2 
 mlpack-4.7.0/mlpack/man/test_r_binding.Rd                                                                      |    2 
 mlpack-4.7.0/mlpack/src/Makevars                                                                               |    4 
 mlpack-4.7.0/mlpack/src/mlpack/bindings/R/get_printable_param.hpp                                              |    8 
 mlpack-4.7.0/mlpack/src/mlpack/bindings/R/get_r_type.hpp                                                       |   30 
 mlpack-4.7.0/mlpack/src/mlpack/bindings/R/get_type.hpp                                                         |   32 
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 mlpack-4.7.0/mlpack/src/mlpack/bindings/R/print_serialize_util.hpp                                             |    4 
 mlpack-4.7.0/mlpack/src/mlpack/bindings/R/tests/test_r_binding_main.cpp                                        |    6 
 mlpack-4.7.0/mlpack/src/r_util.cpp                                                                             |   22 
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Package ife updated to version 0.2.3 with previous version 0.2.1 dated 2025-11-28

Title: Autodiff for Influence Function Based Estimates
Description: Implements an S7 class for estimates based on influence functions, with forward mode automatic differentiation defined for standard arithmetic operations.
Author: Nicholas Williams [aut, cre, cph]
Maintainer: Nicholas Williams <ntwilliams.personal@gmail.com>

Diff between ife versions 0.2.1 dated 2025-11-28 and 0.2.3 dated 2026-02-07

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Package gratia updated to version 0.11.2 with previous version 0.11.1 dated 2025-08-25

Title: Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'
Description: Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.
Author: Gavin L. Simpson [aut, cre, cph] , Henrik Singmann [ctb]
Maintainer: Gavin L. Simpson <ucfagls@gmail.com>

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 gratia-0.11.2/gratia/DESCRIPTION                                                                            |   18 
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 gratia-0.11.2/gratia/NAMESPACE                                                                              |   10 
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 gratia-0.11.2/gratia/R/too_far.R                                                                            |   18 
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Package edgemodelr updated to version 0.1.6 with previous version 0.1.5 dated 2026-01-28

Title: Local Large Language Model Inference Engine
Description: Enables R users to run large language models locally using 'GGUF' model files and the 'llama.cpp' inference engine. Provides a complete R interface for loading models, generating text completions, and streaming responses in real-time. Supports local inference without requiring cloud APIs or internet connectivity, ensuring complete data privacy and control. Based on the 'llama.cpp' project by Georgi Gerganov (2023) <https://github.com/ggml-org/llama.cpp>.
Author: Pawan Rama Mali [aut, cre, cph], Georgi Gerganov [aut, cph] , The ggml authors [cph] , Jeffrey Quesnelle [ctb, cph] , Bowen Peng [ctb, cph] , pi6am [ctb] , Ivan Yurchenko [ctb] , Dirk Eddelbuettel [ctb, rev]
Maintainer: Pawan Rama Mali <prm@outlook.in>

Diff between edgemodelr versions 0.1.5 dated 2026-01-28 and 0.1.6 dated 2026-02-07

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Package dwp updated to version 1.2 with previous version 1.1 dated 2023-07-01

Title: Density-Weighted Proportion
Description: Fit a Poisson regression to carcass distance data and integrate over the searched area at a wind farm to estimate the fraction of carcasses falling in the searched area and format the output for use as the dwp parameter in the 'GenEst' or 'eoa' package for estimating bird and bat mortality, following Dalthorp, et al. (2024) <doi:10.3133/tm7A3>.
Author: Daniel Dalthorp [aut, cre], Manuela Huso [aut]
Maintainer: Daniel Dalthorp <ddalthorp@protonmail.com>

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Package cisp updated to version 0.2.0 with previous version 0.1.0 dated 2024-11-21

Title: A Correlation Indicator Based on Spatial Patterns
Description: Utilizes spatial association marginal contributions derived from spatial stratified heterogeneity to capture the degree of correlation between spatial patterns.
Author: Wenbo Lv [aut, cre, cph] , Yongze Song [aut] , Rui Qu [aut] , Wufan Zhao [aut] , Nan Jia [aut]
Maintainer: Wenbo Lv <lyu.geosocial@gmail.com>

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Package bootnet updated to version 1.7.1 with previous version 1.6 dated 2024-02-21

Title: Bootstrap Methods for Various Network Estimation Routines
Description: Bootstrap methods to assess accuracy and stability of estimated network structures and centrality indices <doi:10.3758/s13428-017-0862-1>. Allows for flexible specification of any undirected network estimation procedure in R, and offers default sets for various estimation routines.
Author: Sacha Epskamp [aut, cre], Eiko I. Fried [ctb]
Maintainer: Sacha Epskamp <mail@sachaepskamp.com>

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Package bibliorefer updated to version 0.1.3 with previous version 0.1.2 dated 2025-10-31

Title: Generator of Main Scientific References
Description: Generates a list, with a size defined by the user, containing the main scientific references and the frequency distribution of authors and journals in the list obtained. The database is a dataframe with academic production metadata made available by bibliographic collections such as Scopus, Web of Science, etc. The temporal evolution of scientific production on a given topic is presented and ordered lists of articles are constructed by number of citations and of authors and journals by level of productivity. Massimo Aria, Corrado Cuccurullo. (2017) <doi:10.1016/j.joi.2017.08.007>. Caibo Zhou, Wenyan Song. (2021) <doi:10.1016/j.jclepro.2021.126943>.
Author: Marcio Eustaquio [aut, cre]
Maintainer: Marcio Eustaquio <marcioeustaquio@id.uff.br>

Diff between bibliorefer versions 0.1.2 dated 2025-10-31 and 0.1.3 dated 2026-02-07

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Package bcdata updated to version 0.5.2 with previous version 0.5.1 dated 2025-03-26

Title: Search and Retrieve Data from the BC Data Catalogue
Description: Search, query, and download tabular and 'geospatial' data from the British Columbia Data Catalogue (<https://catalogue.data.gov.bc.ca/>). Search catalogue data records based on keywords, data licence, sector, data format, and B.C. government organization. View metadata directly in R, download many data formats, and query 'geospatial' data available via the B.C. government Web Feature Service ('WFS') using 'dplyr' syntax.
Author: Andy Teucher [aut, cre] , Sam Albers [aut, ctb] , Stephanie Hazlitt [aut, ctb] , Province of British Columbia [cph]
Maintainer: Andy Teucher <andy.teucher@gmail.com>

Diff between bcdata versions 0.5.1 dated 2025-03-26 and 0.5.2 dated 2026-02-07

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Package arrow updated to version 23.0.0.1 with previous version 23.0.0 dated 2026-01-23

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

Diff between arrow versions 23.0.0 dated 2026-01-23 and 23.0.0.1 dated 2026-02-07

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Fri, 06 Feb 2026

Package plotthis updated to version 0.10.0 with previous version 0.9.3 dated 2026-01-09

Title: High-Level Plotting Built Upon 'ggplot2' and Other Plotting Packages
Description: Provides high-level API and a wide range of options to create stunning, publication-quality plots effortlessly. It is built upon 'ggplot2' and other plotting packages, and is designed to be easy to use and to work seamlessly with 'ggplot2' objects. It is particularly useful for creating complex plots with multiple layers, facets, and annotations. It also provides a set of functions to create plots for specific types of data, such as Venn diagrams, alluvial diagrams, and phylogenetic trees. The package is designed to be flexible and customizable, and to work well with the 'ggplot2' ecosystem. The API can be found at <https://pwwang.github.io/plotthis/reference/index.html>.
Author: Panwen Wang [aut, cre]
Maintainer: Panwen Wang <pwwang@pwwang.com>

Diff between plotthis versions 0.9.3 dated 2026-01-09 and 0.10.0 dated 2026-02-06

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Package prome updated to version 3.1.0.1 with previous version 3.0.1.5 dated 2025-11-08

Title: Patient-Reported Outcome Data Analysis with Stan
Description: Algorithms and subroutines for patient-reported outcome data analysis.
Author: Bin Wang [aut, cre]
Maintainer: Bin Wang <bwang831@gmail.com>

Diff between prome versions 3.0.1.5 dated 2025-11-08 and 3.1.0.1 dated 2026-02-06

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Package osrm.backend updated to version 0.2.0 with previous version 0.1.1 dated 2025-12-06

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 0.1.1 dated 2025-12-06 and 0.2.0 dated 2026-02-06

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Package crosswalkr updated to version 0.4.0 with previous version 0.3.0 dated 2025-04-09

Title: Rename and Encode Data Frames Using External Crosswalk Files
Description: A pair of functions for renaming and encoding data frames using external crosswalk files. It is especially useful when constructing master data sets from multiple smaller data sets that do not name or encode variables consistently across files. Based on similar commands in 'Stata'.
Author: Benjamin Skinner [aut, cre]
Maintainer: Benjamin Skinner <ben@btskinner.io>

Diff between crosswalkr versions 0.3.0 dated 2025-04-09 and 0.4.0 dated 2026-02-06

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New package whisper with initial version 0.1.0
Package: whisper
Title: Native R 'torch' Implementation of 'OpenAI' 'Whisper'
Version: 0.1.0
Description: Speech-to-text transcription using a native R 'torch' implementation of 'OpenAI' 'Whisper' model <https://github.com/openai/whisper>. Supports multiple model sizes from tiny (39M parameters) to large-v3 (1.5B parameters) with integrated download from 'HuggingFace' <https://huggingface.co/> via the 'hfhub' package. Provides automatic speech recognition with optional language detection and translation to English. Audio preprocessing, mel spectrogram computation, and transformer-based encoder-decoder inference are all implemented in R using the 'torch' package.
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/cornball-ai/whisper
BugReports: https://github.com/cornball-ai/whisper/issues
Imports: torch, av, jsonlite, hfhub, safetensors, stats, utils
Suggests: tinytest
NeedsCompilation: no
Packaged: 2026-02-04 01:07:15 UTC; troy
Author: Troy Hernandez [aut, cre], cornball.ai [cph], OpenAI [cph] )
Maintainer: Troy Hernandez <troy@cornball.ai>
Repository: CRAN
Date/Publication: 2026-02-06 20:00:02 UTC

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New package vascr with initial version 0.1.4
Package: vascr
Title: Process Biological Impedance Sensing Data
Version: 0.1.4
Description: Process complex impedance sensing datasets, including those generated by ECIS, xCELLigence and cellZscope instruments. Data can be imported to a standardised tidy format and then plotted. Support for conducting and plotting the outputs of ANOVA (with appropriate tests of statistical assumptions) and cross-correlation analysis. For data processed using this package see Hucklesby et al. (2020) <doi:10.3390/bios11050159>.
License: GPL (>= 3)
URL: https://github.com/JamesHucklesby/vascr
BugReports: https://github.com/JamesHucklesby/vascr/issues
Depends: R (>= 3.6)
Imports: cli, dplyr, doFuture, foreach, furrr, ggnewscale, ggplot2, ggpubr, progressr, ggtext, glue, memoise, multcomp, patchwork, rlang, rstatix, stringr, tidyr, utils, ggrepel, nlme
Suggests: bslib, DBI, DT, data.table, future, odbc, purrr, readr, readxl, shiny, shinyjs, testthat (>= 3.0.0), vdiffr, writexl, testthat, diffviewer
Encoding: UTF-8
LazyData: TRUE
NeedsCompilation: no
Packaged: 2026-02-04 00:31:12 UTC; James
Author: James JW Hucklesby [cre, aut, cph] , Charles Unsworth [aut] , E Scott Graham [aut] , Catherine E angel [aut]
Maintainer: James JW Hucklesby <james.hucklesby@auckland.ac.nz>
Repository: CRAN
Date/Publication: 2026-02-06 19:50:02 UTC

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New package tirt with initial version 0.1.3
Package: tirt
Title: Testlet Item Response Theory
Version: 0.1.3
Description: Implementation of Testlet Item Response Theory (tirt). A light-version yet comprehensive and streamlined framework for psychometric analysis using unidimensional Item Response Theory (IRT; Baker & Kim (2004) <doi:10.1201/9781482276725>) and Testlet Response Theory (TRT; Wainer et al., (2007) <doi:10.1017/CBO9780511618765>). Designed for researchers, this package supports the estimation of item and person parameters for a wide variety of models, including binary (i.e., Rasch, 2-Parameter Logistic, 3-Parameter Logistic) and polytomous (Partial Credit Model, Generalized Partial Credit Model, Graded Response Model) formats. It also supports the estimation of Testlet models (Rasch Testlet, 2-Parameter Logistic Testlet, 3-Parameter Logistic Testlet, Bifactor, Partial Credit Model Testlet, Graded Response), allowing users to account for local item dependence in bundled items. A key feature is the specialized support for combination use and joint estimation of item response mod [...truncated...]
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 4.1.0)
Imports: dplyr, tidyr, purrr, gtools, stats, utils
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-04 02:39:09 UTC; jx
Author: Jiawei Xiong [aut, cre], Cheng Tang [ctb], Qidi Liu [ctb]
Maintainer: Jiawei Xiong <jiawei.xiong@uga.edu>
Repository: CRAN
Date/Publication: 2026-02-06 20:00:08 UTC

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Package RcmdrPlugin.RiskDemo updated to version 3.3 with previous version 3.2 dated 2024-02-06

Title: R Commander Plug-in for Risk Demonstration
Description: R Commander plug-in to demonstrate various actuarial and financial risks. It includes valuation of bonds and stocks, portfolio optimization, classical ruin theory, demography and epidemic.
Author: Arto Luoma [aut, cre]
Maintainer: Arto Luoma <arto.luoma@wippies.com>

Diff between RcmdrPlugin.RiskDemo versions 3.2 dated 2024-02-06 and 3.3 dated 2026-02-06

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New package nlmixr2auto with initial version 1.0.0
Package: nlmixr2auto
Title: Automated Population Pharmacokinetic Modeling
Version: 1.0.0
Description: Automated population pharmacokinetic modeling framework for data-driven initialisation, model evaluation, and metaheuristic optimization. Supports genetic algorithms, ant colony optimization, tabu search, and stepwise procedures for automated model selection and parameter estimation within the nlmixr2 ecosystem.
License: GPL (>= 3)
URL: https://github.com/ucl-pharmacometrics/nlmixr2auto
BugReports: https://github.com/ucl-pharmacometrics/nlmixr2auto/issues
Depends: R (>= 4.1.0), nlmixr2data
Imports: nlmixr2, nlmixr2est, nlmixr2autoinit, rxode2, dplyr, progressr, processx (> 3.8.0), withr, crayon
Encoding: UTF-8
Language: en-US
NeedsCompilation: no
Packaged: 2026-02-03 20:08:03 UTC; zhonghuihuang
Author: Zhonghui Huang [aut, cre], Joseph Standing [ctb], Matthew Fidler [ctb], Frank Kloprogge [ctb]
Maintainer: Zhonghui Huang <huangzhonghui22@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-06 19:40:02 UTC

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New package keyed with initial version 0.1.3
Package: keyed
Title: Explicit Key Assumptions for Flat-File Data
Version: 0.1.3
Language: en-US
Description: Helps make implicit data assumptions explicit by attaching keys to flat-file data that error when those assumptions are violated. Designed for CSV-first workflows without database infrastructure or version control. Provides key definition, assumption checks, join diagnostics, and optional drift detection against reference snapshots.
License: MIT + file LICENSE
URL: https://github.com/gcol33/keyed, https://gillescolling.com/keyed/
BugReports: https://github.com/gcol33/keyed/issues
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: cli, dplyr (>= 1.0.0), digest, pillar, rlang (>= 1.0.0), tibble, vctrs (>= 0.5.0)
Suggests: joinspy, knitr, rmarkdown, testthat (>= 3.0.0), uuid
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-03 21:34:34 UTC; Gilles Colling
Author: Gilles Colling [aut, cre, cph]
Maintainer: Gilles Colling <gilles.colling051@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-06 19:50:08 UTC

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New package indonesiaFootballScoutR with initial version 0.1.3
Package: indonesiaFootballScoutR
Title: Tools for Football Player Scouting in Indonesia
Version: 0.1.3
Description: Provides tools to scrape, clean, and analyze football player data from Indonesian leagues and perform similarity-based scouting analysis using standardized numeric features. The similarity approach follows common vector-space methods as described in Manning et al. (2008, ISBN:9780521865715) and Salton et al. (1975, <doi:10.1145/361219.361220>).
License: MIT + file LICENSE
Encoding: UTF-8
Imports: dplyr, rvest, purrr, tibble, stringr, readr, proxy
URL: https://github.com/tioanta/indonesiaFootballScoutR
BugReports: https://github.com/tioanta/indonesiaFootballScoutR/issues
NeedsCompilation: no
Packaged: 2026-02-04 02:38:20 UTC; BRI
Author: Tio Anta Wibawa [aut, cre]
Maintainer: Tio Anta Wibawa <tio158@gmail.com>
Depends: R (>= 4.1.0)
Repository: CRAN
Date/Publication: 2026-02-06 20:00:13 UTC

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New package bioLeak with initial version 0.1.0
Package: bioLeak
Title: Leakage-Safe Modeling and Auditing for Genomic and Clinical Data
Version: 0.1.0
Description: Prevents and detects information leakage in biomedical machine learning. Provides leakage-resistant split policies (subject-grouped, batch-blocked, study leave-out, time-ordered), guarded preprocessing (train-only imputation, normalization, filtering, feature selection), cross-validated fitting with common learners, permutation-gap auditing, batch and fold association tests, and duplicate detection.
License: MIT + file LICENSE
URL: https://github.com/selcukorkmaz/bioLeak
BugReports: https://github.com/selcukorkmaz/bioLeak/issues
Encoding: UTF-8
Depends: R (>= 4.3)
Imports: digest, methods, stats, utils, SummarizedExperiment, graphics, hardhat, parsnip
Suggests: BiocParallel, cli, dials, FNN, future, future.apply, ggplot2, glmnet, mice, missForest, pkgload, ranger, randomForest, recipes, RANN, rsample, tune, VIM, workflows, xgboost, yardstick, pROC, PRROC, survival, knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-03 22:05:49 UTC; selcuk
Author: Selcuk Korkmaz [aut, cre]
Maintainer: Selcuk Korkmaz <selcukorkmaz@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-06 19:50:14 UTC

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New package bayesics with initial version 2.0.2
Package: bayesics
Title: Bayesian Analyses for One- and Two-Sample Inference and Regression Methods
Version: 2.0.2
Maintainer: Daniel K. Sewell <daniel-sewell@uiowa.edu>
Description: Perform fundamental analyses using Bayesian parametric and non-parametric inference (regression, anova, 1 and 2 sample inference, non-parametric tests, etc.). (Practically) no Markov chain Monte Carlo (MCMC) is used; all exact finite sample inference is completed via closed form solutions or else through posterior sampling automated to ensure precision in interval estimate bounds. Diagnostic plots for model assessment, and key inferential quantities (point and interval estimates, probability of direction, region of practical equivalence, and Bayes factors) and model visualizations are provided. Bayes factors are computed either by the Savage Dickey ratio given in Dickey (1971) <doi:10.1214/aoms/1177693507> or by Chib's method as given in xxx. Interpretations are from Kass and Raftery (1995) <doi:10.1080/01621459.1995.10476572>. ROPE bounds are based on discussions in Kruschke (2018) <doi:10.1177/2515245918771304>. Methods for determining the number of posterior samp [...truncated...]
License: GPL (>= 3)
Encoding: UTF-8
Depends: R (>= 4.1.0)
Suggests: datasets, rstanarm, knitr, splines, testthat (>= 3.0.0)
Imports: tidyr, dplyr, rlang, janitor, extraDistr, mvtnorm, Matrix, future, future.apply, ggplot2, patchwork, BMS, cluster, DFBA, tibble, survival
URL: https://github.com/dksewell/bayesics
BugReports: https://github.com/dksewell/bayesics/issues
NeedsCompilation: no
Packaged: 2026-02-03 17:35:13 UTC; dksewell
Author: Daniel K. Sewell [aut, cre, cph] , Alan Arakkal [aut]
Repository: CRAN
Date/Publication: 2026-02-06 19:10:02 UTC

More information about bayesics at CRAN
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New package BayesianHybridDesign with initial version 0.1.0
Package: BayesianHybridDesign
Title: Bayesian Hybrid Design and Analysis
Version: 0.1.0
Description: Implements Bayesian hybrid designs that incorporate historical control data into a current clinical trial. The package uses a dynamic power prior method to determine the degree of borrowing from the historical data, creating a 'hybrid' control arm. This approach is primarily designed for studies with a binary primary endpoint, such as the overall response rate (ORR). Functions are provided for design calibration, sample size calculation, power evaluation, and final analysis. Additionally, it includes functions adapted from the 'SAMprior' package (v1.1.1) by Yang et al. (2023) <https://academic.oup.com/biometrics/article/79/4/2857/7587575> to support the Self-Adapting Mixture (SAM) prior framework for comparison.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 4.1)
Imports: assertthat, checkmate, doParallel, foreach, ggplot2, Metrics, parallel, RBesT
Suggests: broom, knitr, purrr, rmarkdown, rstanarm, scales, testthat (>= 3.0.0), tidyr, tools
NeedsCompilation: no
Packaged: 2026-02-04 03:18:27 UTC; zhaoh
Author: Philip He [aut, cph], Zhaohua Lu [aut, cre, cph]
Maintainer: Zhaohua Lu <zhaohua.lu@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-06 20:00:24 UTC

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Package autotab updated to version 0.1.2 with previous version 0.1.1 dated 2025-11-24

Title: Variational Autoencoders for Heterogeneous Tabular Data
Description: Build and train a variational autoencoder (VAE) for mixed-type tabular data (continuous, binary, categorical). Models are implemented using 'TensorFlow' and 'Keras' via the 'reticulate' interface, enabling reproducible VAE training for heterogeneous tabular datasets.
Author: Sarah Milligan [aut, cre]
Maintainer: Sarah Milligan <slm1999@bu.edu>

Diff between autotab versions 0.1.1 dated 2025-11-24 and 0.1.2 dated 2026-02-06

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Permanent link

New package areaOfEffect with initial version 0.2.4
Package: areaOfEffect
Title: Spatial Support at Scale
Version: 0.2.4
Description: Formalizes spatial support at scale for ecological and geographical analysis. Given points and support polygons, classifies points as "core" (inside original support) or "halo" (inside scaled support but outside original), pruning all others. The default scale produces equal core and halo areas - a geometrically derived choice requiring no tuning. An optional mask enforces hard boundaries such as coastlines. Political borders are treated as soft boundaries with no ecological meaning.
License: MIT + file LICENSE
URL: https://gillescolling.com/areaOfEffect/, https://github.com/gcol33/areaOfEffect
BugReports: https://github.com/gcol33/areaOfEffect/issues
Encoding: UTF-8
Language: en-US
Imports: methods, sf
Suggests: lwgeom, testthat (>= 3.0.0), knitr, rmarkdown, svglite
VignetteBuilder: knitr
Depends: R (>= 3.5)
LazyData: true
NeedsCompilation: no
Packaged: 2026-02-03 20:41:48 UTC; Gilles Colling
Author: Gilles Colling [aut, cre, cph]
Maintainer: Gilles Colling <gilles.colling051@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-06 19:40:14 UTC

More information about areaOfEffect at CRAN
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Package UCSCXenaTools updated to version 1.7.0 with previous version 1.6.1 dated 2025-08-21

Title: Download and Explore Datasets from UCSC Xena Data Hubs
Description: Download and explore datasets from UCSC Xena data hubs, which are a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. Databases are normalized so they can be combined, linked, filtered, explored and downloaded.
Author: Shixiang Wang [aut, cre] , Xue-Song Liu [aut] , Martin Morgan [ctb], Christine Stawitz [rev] , Carl Ganz [rev]
Maintainer: Shixiang Wang <w_shixiang@163.com>

Diff between UCSCXenaTools versions 1.6.1 dated 2025-08-21 and 1.7.0 dated 2026-02-06

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Package SomaDataIO updated to version 6.5.0 with previous version 6.4.0 dated 2025-08-29

Title: Input/Output 'SomaScan' Data
Description: Load and export 'SomaScan' data via the 'SomaLogic Operating Co., Inc.' structured text file called an ADAT ('*.adat'). For file format see <https://github.com/SomaLogic/SomaLogic-Data/blob/main/README.md>. The package also exports auxiliary functions for manipulating, wrangling, and extracting relevant information from an ADAT object once in memory.
Author: Stu Field [aut] , Caleb Scheidel [cre], SomaLogic Operating Co., Inc. [cph, fnd]
Maintainer: Caleb Scheidel <calebjscheidel@gmail.com>

Diff between SomaDataIO versions 6.4.0 dated 2025-08-29 and 6.5.0 dated 2026-02-06

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Package cpfa updated to version 1.2-6 with previous version 1.2-5 dated 2026-01-14

Title: Classification with Parallel Factor Analysis
Description: Classification using Richard A. Harshman's Parallel Factor Analysis-1 (Parafac) model or Parallel Factor Analysis-2 (Parafac2) model fit to a three-way or four-way data array. See Harshman and Lundy (1994): <doi:10.1016/0167-9473(94)90132-5>. Uses component weights from one mode of a Parafac or Parafac2 model as features to tune parameters for one or more classification methods via a k-fold cross-validation procedure. Allows for constraints on different tensor modes. Supports penalized logistic regression, support vector machine, random forest, feed-forward neural network, regularized discriminant analysis, and gradient boosting machine. Supports binary and multiclass classification. Predicts class labels or class probabilities and calculates multiple classification performance measures. Implements parallel computing via the 'parallel', 'doParallel', and 'doRNG' packages.
Author: Matthew A. Asisgress [aut, cre]
Maintainer: Matthew A. Asisgress <mattgress@protonmail.ch>

Diff between cpfa versions 1.2-5 dated 2026-01-14 and 1.2-6 dated 2026-02-06

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Package shinyscholar updated to version 0.4.4 with previous version 0.4.3 dated 2025-09-05

Title: A Template for Creating Reproducible 'shiny' Applications
Description: Create a skeleton 'shiny' application with create_template() that is reproducible, can be saved and meets academic standards for attribution. Forked from 'wallace'. Code is split into modules that are loaded and linked together automatically and each call one function. Guidance pages explain modules to users and flexible logging informs them of any errors. Options enable asynchronous operations, viewing of source code, interactive maps and data tables. Use to create complex analytical applications, following best practices in open science and software development. Includes functions for automating repetitive development tasks and an example application at run_shinyscholar() that requires install.packages("shinyscholar", dependencies = TRUE). A guide to developing applications can be found on the package website.
Author: Simon E. H. Smart [aut, cre, cph], Tim Lucas [aut] , Jamie M. Kass [ctb], Gonzalo E. Pinilla-Buitrago [ctb], Andrea Paz [ctb], Bethany A. Johnson [ctb], Valentina Grisales-Betancur [ctb], Dean Attali [ctb], Matthew E. Aiello-Lammens [ctb], Cory Merow [...truncated...]
Maintainer: Simon E. H. Smart <simon.smart@cantab.net>

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Package OmopSketch updated to version 1.0.1 with previous version 1.0.0 dated 2025-11-19

Title: Characterise Tables of an OMOP Common Data Model Instance
Description: Summarises key information in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Assess suitability to perform specific epidemiological studies and explore the different domains to obtain feasibility counts and trends.
Author: Marta Alcalde-Herraiz [aut] , Kim Lopez-Guell [aut] , Elin Rowlands [aut] , Cecilia Campanile [aut, cre] , Edward Burn [aut] , Marti Catala [aut]
Maintainer: Cecilia Campanile <cecilia.campanile@ndorms.ox.ac.uk>

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Package ifo updated to version 0.2.3 with previous version 0.2.2 dated 2025-08-31

Title: Client for the Ifo Institute Time Series
Description: Download ifo business survey data and more time series from ifo institute <https://www.ifo.de/en/ifo-time-series>.
Author: Maximilian Muecke [aut, cre]
Maintainer: Maximilian Muecke <muecke.maximilian@gmail.com>

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Package feisr updated to version 1.3.1 with previous version 1.3.0 dated 2022-04-01

Title: Estimating Fixed Effects Individual Slope Models
Description: Provides the function feis() to estimate fixed effects individual slope (FEIS) models. The FEIS model constitutes a more general version of the often-used fixed effects (FE) panel model, as implemented in the package 'plm' by Croissant and Millo (2008) <doi:10.18637/jss.v027.i02>. In FEIS models, data are not only person demeaned like in conventional FE models, but detrended by the predicted individual slope of each person or group. Estimation is performed by applying least squares lm() to the transformed data. For more details on FEIS models see Bruederl and Ludwig (2015, ISBN:1446252442); Frees (2001) <doi:10.2307/3316008>; Polachek and Kim (1994) <doi:10.1016/0304-4076(94)90075-2>; Ruettenauer and Ludwig (2020) <doi:10.1177/0049124120926211>; Wooldridge (2010, ISBN:0262294354). To test consistency of conventional FE and random effects estimators against heterogeneous slopes, the package also provides the functions feistest() for an artificial regression test [...truncated...]
Author: Tobias Ruettenauer [aut, cre] , Volker Ludwig [aut]
Maintainer: Tobias Ruettenauer <ruettenauer@sowi.uni-kl.de>

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Package eurlex updated to version 0.4.9 with previous version 0.4.8 dated 2024-07-03

Title: Retrieve Data on European Union Law
Description: Access to data on European Union laws and court decisions made easy with pre-defined 'SPARQL' queries and 'GET' requests. See Ovadek (2021) <doi:10.1080/2474736X.2020.1870150> .
Author: Michal Ovadek [aut, cre, cph]
Maintainer: Michal Ovadek <michal.ovadek@gmail.com>

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Package BioCro updated to version 3.3.1 with previous version 3.3.0 dated 2025-12-13

Title: Modular Crop Growth Simulations
Description: A cross-platform representation of models as sets of equations that facilitates modularity in model building and allows users to harness modern techniques for numerical integration and data visualization. Documentation is provided by several vignettes included in this package; also see Lochocki et al. (2022) <doi:10.1093/insilicoplants/diac003>.
Author: Justin M. McGrath [cre, aut] , Edward B. Lochocki [aut] , Yufeng He [aut] , Scott W. Oswald [aut] , Scott Rohde [aut] , Deepak Jaiswal [aut] , Megan L. Matthews [aut] , Fernando E. Miguez [aut] , Stephen P. Long [aut] , Dan Wang [ctb], David LeBauer [...truncated...]
Maintainer: Justin M. McGrath <jmcgrath@illinois.edu>

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Package profileModel updated to version 0.6.2 with previous version 0.6.1 dated 2021-01-08

Title: Profiling Inference Functions for Various Model Classes
Description: Provides tools that can be used to calculate, evaluate, plot and use for inference the profiles of *arbitrary* inference functions for *arbitrary* 'glm'-like fitted models with linear predictors. More information on the methods that are implemented can be found in Kosmidis (2008) <https://www.r-project.org/doc/Rnews/Rnews_2008-2.pdf>.
Author: Ioannis Kosmidis [aut, cre]
Maintainer: Ioannis Kosmidis <ioannis.kosmidis@warwick.ac.uk>

Diff between profileModel versions 0.6.1 dated 2021-01-08 and 0.6.2 dated 2026-02-06

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Package ipeaplot updated to version 0.5.1 with previous version 0.5.0 dated 2025-10-07

Title: Add Ipea Editorial Standards to 'ggplot2' Graphics
Description: Convenient functions to create 'ggplot2' graphics following the editorial guidelines of the Institute for Applied Economic Research (Ipea).
Author: Pedro Ferreira [aut, cre], Pedro Jorge [aut], Daniel Lima [aut], Gustavo Coelho [aut], Rafael H. M. Pereira [aut], Lucas Mation [aut], Fabio Vaz [ctb], Ipea - Institue for Applied Economic Research [cph, fnd]
Maintainer: Pedro Ferreira <pedro.ferreira2@ipea.gov.br>

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Package gooseR updated to version 0.1.2 with previous version 0.1.1 dated 2025-12-22

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

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Package vigicaen updated to version 1.0.0 with previous version 0.16.1 dated 2025-07-25

Title: 'VigiBase' Pharmacovigilance Database Toolbox
Description: Perform the analysis of the World Health Organization (WHO) Pharmacovigilance database 'VigiBase' (Extract Case Level version), <https://who-umc.org/> e.g., load data, perform data management, disproportionality analysis, and descriptive statistics. Intended for pharmacovigilance routine use or studies. This package is NOT supported nor reflect the opinion of the WHO, or the Uppsala Monitoring Centre. Disproportionality methods are described by Norén et al (2013) <doi:10.1177/0962280211403604>.
Author: Charles Dolladille [aut, cre] , Basile Chretien [aut] , Universite de Caen Normandie [cph] , Unite de pharmaco-epidemiologie [cph]
Maintainer: Charles Dolladille <cdolladille@hotmail.com>

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Package micar updated to version 1.2.0 with previous version 1.1.2 dated 2021-04-16

Title: 'Mica' Data Web Portal Client
Description: 'Mica' is a server application used to create data web portals for large-scale epidemiological studies or multiple-study consortia. 'Mica' helps studies to provide scientifically robust data visibility and web presence without significant information technology effort. 'Mica' provides a structured description of consortia, studies, annotated and searchable data dictionaries, and data access request management. This 'Mica' client allows to perform data extraction for reporting purposes.
Author: Yannick Marcon [aut, cre] , OBiBa group [cph]
Maintainer: Yannick Marcon <yannick.marcon@obiba.org>

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Package AutoPlots updated to version 1.5.0 with previous version 1.0.0 dated 2024-01-23

Title: Creating Echarts Visualizations as Easy as Possible
Description: Create beautiful and interactive visualizations in a single function call. The 'data.table' package is utilized to perform the data wrangling necessary to prepare your data for the plot types you wish to build, along with allowing fast processing for big data. There are two broad classes of plots available: standard plots and machine learning evaluation plots. There are lots of parameters available in each plot type function for customizing the plots (such as faceting) and data wrangling (such as variable transformations and aggregation).
Author: Adrian Antico [aut, cre, cph]
Maintainer: Adrian Antico <adrianantico@gmail.com>

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New package wired with initial version 1.0.0
Package: wired
Title: Weighted Adaptive Prediction with Structured Dependence
Version: 1.0.0
Maintainer: Giancarlo Vercellino <giancarlo.vercellino@gmail.com>
Description: Builds a joint probabilistic forecast across series and horizons using adaptive copulas (Gaussian/t) with shrinkage-repaired correlations. At the low level it calls a probabilistic mixer per series and horizon, which backtests several simple predictors, predicts next-window Continuous Ranked Probability Score (CRPS), and converts those scores into softmax weights to form a calibrated mixture (r/q/p/dfun). The mixer blends eight simple predictors: a naive predictor that wraps the last move in a PERT distribution; an arima predictor using auto.arima for one-step forecasts; an Exponentially Weighted Moving Average (EWMA) gaussian predictor with mean/variance under a Gaussian; a historical bootstrap predictor that resamples past horizon-aligned moves; a drift residual bootstrap predictor combining linear trend with bootstrapped residuals; a volatility-scaled naive predictor centering on the last move and scaling by recent volatility; a robust median mad predictor using median/MAD with Lapl [...truncated...]
License: GPL-3
Imports: mc2d (>= 0.2.1), forecast (>= 8.24.0), quantreg (>= 6.1), MASS (>= 7.3-65), imputeTS (>= 3.4)
Encoding: UTF-8
URL: https://rpubs.com/giancarlo_vercellino/wired
Suggests: knitr, testthat (>= 3.0.0)
Depends: R (>= 4.1.0)
NeedsCompilation: no
Packaged: 2026-02-03 16:42:12 UTC; gianc
Author: Giancarlo Vercellino [aut, cre, cph]
Repository: CRAN
Date/Publication: 2026-02-06 14:20:03 UTC

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Package tidydfidx updated to version 0.0-3 with previous version 0.0-1 dated 2025-06-16

Title: Indexed 'tibble' and Methods for 'dplyr'
Description: Provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure, and support for tibbles and methods for 'dplyr'.
Author: Yves Croissant [aut, cre]
Maintainer: Yves Croissant <yves.croissant@univ-reunion.fr>

Diff between tidydfidx versions 0.0-1 dated 2025-06-16 and 0.0-3 dated 2026-02-06

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New package snreg with initial version 1.2.0
Package: snreg
Title: Regression with Skew-Normally Distributed Error Term
Version: 1.2.0
Date: 2026-01-31
Maintainer: Oleg Badunenko <Oleg.Badunenko@brunel.ac.uk>
Description: Models with skew‑normally distributed and thus asymmetric error terms, implementing the methods developed in Badunenko and Henderson (2023) "Production analysis with asymmetric noise" <doi:10.1007/s11123-023-00680-5>. The package provides tools to estimate regression models with skew‑normal error terms, allowing both the variance and skewness parameters to be heteroskedastic. It also includes a stochastic frontier framework that accommodates both i.i.d. and heteroskedastic inefficiency terms.
URL: https://olegbadunenko.github.io/snreg/
Imports: Formula, npsf
License: GPL-3
Encoding: UTF-8
LazyData: TRUE
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-02-03 16:41:02 UTC; boo
Author: Oleg Badunenko [aut, cre] , John Burkardt [ctb, cph]
Repository: CRAN
Date/Publication: 2026-02-06 14:20:13 UTC

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New package resLIK with initial version 0.1.2
Package: resLIK
Title: Representation-Level Control Surfaces for Reliability Sensing
Version: 0.1.2
Description: Implements the Representation-Level Control Surfaces (RLCS) paradigm for ensuring the reliability of autonomous systems and AI models. It provides three deterministic sensors: Residual Likelihood (ResLik) for population-level anomaly detection, Temporal Consistency Sensor (TCS) for drift and shock detection, and Agreement Sensor for multi-modal redundancy checks. These sensors feed into a standardized control surface that issues 'PROCEED', 'DEFER', or 'ABSTAIN' signals based on strict safety invariants, allowing systems to detect and react to out-of-distribution states, sensor failures, and environmental shifts before they propagate to decision-making layers.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: utils
Suggests: testthat, stats, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-03 14:10:32 UTC; sulky
Author: MD. Arshad [aut, cre]
Maintainer: MD. Arshad <arshad10867c@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-06 14:10:02 UTC

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New package PPtreeExt with initial version 0.1.0
Package: PPtreeExt
Version: 0.1.0
Title: Projection Pursuit Classification Tree Extensions
Maintainer: Natalia da Silva <natalia.dasilva@fcea.edu.uy>
Description: Implements extensions to the projection pursuit tree algorithm for supervised classification, see Lee, Y. (2013), <doi:10.1214/13-EJS810> and Lee, E-K. (2018) <doi:10.18637/jss.v083.i08>. The algorithm is changed in two ways: improving prediction boundaries by modifying the choice of split points-through class subsetting; and increasing flexibility by allowing multiple splits per group.
License: GPL (>= 2)
URL: https://github.com/natydasilva/PPtreeExt
LazyData: yes
Depends: R (>= 4.3.0)
Imports: Rcpp (>= 1.1.0), ggplot2, shiny, MASS, gridExtra, MixSim, PPtreeViz
Suggests: knitr, randomForest, rpart, GGally, RColorBrewer, roxygen2 (>= 7.3.0), rmarkdown, rsample
Encoding: UTF-8
LinkingTo: Rcpp,RcppArmadillo
NeedsCompilation: yes
Packaged: 2026-02-03 16:44:46 UTC; nataliadasilva
Author: Natalia da Silva [aut, cre] , Dianne Cook [aut], Eun-Kyung Lee [aut]
Repository: CRAN
Date/Publication: 2026-02-06 14:20:18 UTC

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New package mortSOA with initial version 0.1.0
Package: mortSOA
Title: Obtain Data from the Society of Actuaries 'Mortality and Other Rate Tables' Site
Version: 0.1.0
Maintainer: Matt Heaphy <mattrmattrs@gmail.com>
Description: The Society of Actuaries (SOA) provides an extensive online database called 'Mortality and Other Rate Tables' ('MORT') at <https://mort.soa.org/>. This database contains mortality, lapse, and valuation tables that cover a variety of product types and nations. Users of the database can download any tables in 'Excel', 'CSV', or 'XML' formats. This package provides convenience functions that read 'XML' formats from the database and return R objects.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: cli, dplyr, httr2, purrr, rlang, tidyr, xml2
Depends: R (>= 4.1)
LazyData: true
URL: https://github.com/mattheaphy/mortSOA/, https://mattheaphy.github.io/mortSOA/
BugReports: https://github.com/mattheaphy/mortSOA/issues
NeedsCompilation: no
Packaged: 2026-02-03 13:41:50 UTC; Matt
Author: Matt Heaphy [aut, cre, cph]
Repository: CRAN
Date/Publication: 2026-02-06 14:10:08 UTC

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New package tidylearn with initial version 0.1.0
Package: tidylearn
Title: A Unified Tidy Interface to R's Machine Learning Ecosystem
Version: 0.1.0
Description: Provides a unified tidyverse-compatible interface to R's machine learning packages. Wraps established implementations from 'glmnet', 'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster', 'dbscan', and others - providing consistent function signatures, tidy tibble output, and unified 'ggplot2'-based visualization. The underlying algorithms are unchanged; 'tidylearn' simply makes them easier to use together. Access raw model objects via the $fit slot for package-specific functionality. Methods include random forests Breiman (2001) <doi:10.1023/A:1010933404324>, LASSO regression Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, elastic net Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, support vector machines Cortes and Vapnik (1995) <doi:10.1007/BF00994018>, and gradient boosting Friedman (2001) <doi:10.1214/aos/1013203451>.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 3.6.0)
Imports: dplyr (>= 1.0.0), ggplot2 (>= 3.3.0), tibble (>= 3.0.0), tidyr (>= 1.0.0), purrr (>= 0.3.0), rlang (>= 0.4.0), magrittr, stats, e1071, gbm, glmnet, nnet, randomForest, rpart, rsample, ROCR, yardstick, cluster (>= 2.1.0), dbscan (>= 1.1.0), MASS, smacof (>= 2.1.0)
Suggests: arules, arulesViz, car, caret, DT, GGally, ggforce, gridExtra, keras, knitr, lmtest, mclust, moments, NeuralNetTools, onnx, parsnip, recipes, reticulate, rmarkdown, rpart.plot, scales, shiny, shinydashboard, tensorflow, testthat (>= 3.0.0), workflows, xgboost
URL: https://github.com/ces0491/tidylearn
BugReports: https://github.com/ces0491/tidylearn/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-03 09:52:28 UTC; cesai_b8mratk
Author: Cesaire Tobias [aut, cre]
Maintainer: Cesaire Tobias <cesaire@sheetsolved.com>
Repository: CRAN
Date/Publication: 2026-02-06 13:50:02 UTC

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Package resourcecode updated to version 0.5.3 with previous version 0.5.2 dated 2026-01-09

Title: Access to the 'RESOURCECODE' Hindcast Database
Description: Utility functions to download data from the 'RESOURCECODE' hindcast database of sea-states, time series of sea-state parameters and time series of 1D and 2D wave spectra. See <https://resourcecode.ifremer.fr> for more details about the available data. Also provides facilities to plot and analyse downloaded data, such as computing the sea-state parameters from both the 1D and 2D surface elevation variance spectral density.
Author: Nicolas Raillard [aut, cre]
Maintainer: Nicolas Raillard <nicolas.raillard@ifremer.fr>

Diff between resourcecode versions 0.5.2 dated 2026-01-09 and 0.5.3 dated 2026-02-06

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 resourcecode-0.5.3/resourcecode/NEWS.md                                      |    8 
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New package quickSentiment with initial version 0.1.0
Package: quickSentiment
Title: A Fast and Flexible Pipeline for Text Classification
Version: 0.1.0
Description: A high-level wrapper that simplifies text classification into three streamlined steps: preprocessing, model training, and prediction. It unifies the interface for multiple algorithms (including 'glmnet', 'ranger', and 'xgboost') and vectorization methods (Bag-of-Words, Term Frequency-Inverse Document Frequency (TF-IDF)), allowing users to go from raw text to a trained sentiment model in two function calls. The resulting model artifact automatically handles preprocessing for new datasets in the third step, ensuring consistent prediction pipelines.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: quanteda, stopwords, foreach, stringr, textstem, glmnet, ranger, xgboost, caret, Matrix, magrittr, doParallel
VignetteBuilder: knitr
Suggests: knitr, rmarkdown, spelling
Language: en-US
NeedsCompilation: no
Packaged: 2026-02-03 03:01:22 UTC; meala
Author: Alabhya Dahal [aut, cre]
Maintainer: Alabhya Dahal <alabhya.dahal@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-06 13:30:02 UTC

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New package numspellR with initial version 0.1.0
Package: numspellR
Title: Detection of Numeric Persistence and Rigidity Patterns
Version: 0.1.0
Description: Tools for detecting numeric persistence ("spells") and rigidity patterns in time-ordered numeric data. The package identifies periods of stability, computes spell-based rigidity metrics, and provides plain-language interpretations suitable for policy and applied analysis.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 3.6.0)
Imports: stats
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-03 07:48:07 UTC; BPS Prov Jawa Timur
Author: Joko Ade Nursiyono [aut, cre]
Maintainer: Joko Ade Nursiyono <jokoadenursiyono@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-06 13:40:02 UTC

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Package gifti updated to version 0.9.0 with previous version 0.8.0 dated 2020-11-11

Title: Reads in 'Neuroimaging' 'GIFTI' Files with Geometry Information
Description: Functions to read in the geometry format under the 'Neuroimaging' 'Informatics' Technology Initiative ('NIfTI'), called 'GIFTI' <https://www.nitrc.org/projects/gifti/>. These files contain surfaces of brain imaging data.
Author: John Muschelli [aut, cre]
Maintainer: John Muschelli <muschellij2@gmail.com>

Diff between gifti versions 0.8.0 dated 2020-11-11 and 0.9.0 dated 2026-02-06

 DESCRIPTION                       |   16 +-
 MD5                               |   22 +--
 NEWS.md                           |    6 
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 R/read_gifti.R                    |    4 
 README.md                         |   10 -
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New package climenu with initial version 0.1.3
Package: climenu
Title: Interactive Command-Line Menus
Version: 0.1.3
Author: Petr ÄŒala [aut, cre]
Maintainer: Petr ÄŒala <61505008@fsv.cuni.cz>
Description: Provides interactive command-line menu functionality with single and multiple selection menus, keyboard navigation (arrow keys or vi-style j/k), preselection, and graceful fallback for non-interactive environments. Inspired by tools such as 'inquirer.js' <https://github.com/SBoudrias/Inquirer.js>, 'pick' <https://github.com/aisk/pick>, and 'survey' <https://github.com/AlecAivazis/survey>. Designed to be lightweight and easy to integrate into 'R' packages and scripts.
License: MIT + file LICENSE
URL: https://github.com/PetrCala/climenu
BugReports: https://github.com/PetrCala/climenu/issues
Depends: R (>= 4.0.0)
Imports: cli (>= 3.6.0)
Suggests: covr, devtools, keypress, knitr, rmarkdown, roxygen2 (>= 7.0.0), testthat (>= 3.0.0)
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-02-03 07:07:04 UTC; runner
Repository: CRAN
Date/Publication: 2026-02-06 13:40:06 UTC

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Package resourcer updated to version 1.5.0 with previous version 1.4.0 dated 2023-04-25

Title: Resource Resolver
Description: A resource represents some data or a computation unit. It is described by a URL and credentials. This package proposes a Resource model with "resolver" and "client" classes to facilitate the access and the usage of the resources.
Author: Yannick Marcon [aut, cre] , OBiBa group [cph]
Maintainer: Yannick Marcon <yannick.marcon@obiba.org>

Diff between resourcer versions 1.4.0 dated 2023-04-25 and 1.5.0 dated 2026-02-06

 DESCRIPTION                          |   18 +
 MD5                                  |   20 +-
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 R/PostgresResourceConnector.R        |   19 +-
 README.md                            |   14 -
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 inst/doc/resources-introduction.Rmd  |    6 
 inst/doc/resources-introduction.html |  323 ++++++++++++++++++++---------------
 inst/resources/resource.js           |  108 +++++------
 vignettes/resources-introduction.Rmd |    6 
 11 files changed, 330 insertions(+), 251 deletions(-)

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Package tealeaves updated to version 1.0.7 with previous version 1.0.6.1 dated 2025-06-23

Title: Solve for Leaf Temperature Using Energy Balance
Description: Implements models of leaf temperature using energy balance. It uses units to ensure that parameters are properly specified and transformed before calculations. It allows separate lower and upper surface conductances to heat and water vapour, so sensible and latent heat loss are calculated for each surface separately as in Foster and Smith (1986) <doi:10.1111/j.1365-3040.1986.tb02108.x>. It's straightforward to model leaf temperature over environmental gradients such as light, air temperature, humidity, and wind. It can also model leaf temperature over trait gradients such as leaf size or stomatal conductance. Other references are Monteith and Unsworth (2013, ISBN:9780123869104), Nobel (2009, ISBN:9780123741431), and Okajima et al. (2012) <doi:10.1007/s11284-011-0905-5>.
Author: Chris Muir [aut, cre]
Maintainer: Chris Muir <cdmuir@wisc.edu>

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Package simulist updated to version 0.7.0 with previous version 0.6.0 dated 2025-08-28

Title: Simulate Disease Outbreak Line List and Contacts Data
Description: Tools to simulate realistic raw case data for an epidemic in the form of line lists and contacts using a branching process. Simulated outbreaks are parameterised with epidemiological parameters and can have age-structured populations, age-stratified hospitalisation and death risk and time-varying case fatality risk.
Author: Joshua W. Lambert [aut, cre, cph] , Carmen Tamayo Cuartero [aut] , Hugo Gruson [ctb, rev] , Pratik R. Gupte [ctb, rev] , Adam Kucharski [rev] , Chris Hartgerink [rev] , Sebastian Funk [ctb] , London School of Hygiene and Tropical Medicine, LSHTM [cph [...truncated...]
Maintainer: Joshua W. Lambert <joshua.lambert@lshtm.ac.uk>

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Package SBMTrees readmission to version 1.4 with previous version 1.2 dated 2024-12-11

Title: Longitudinal Sequential Imputation and Prediction with Bayesian Trees Mixed-Effects Models for Longitudinal Data
Description: Implements a sequential imputation framework using Bayesian Mixed-Effects Trees ('SBMTrees') for handling missing data in longitudinal studies. The package supports a variety of models, including non-linear relationships and non-normal random effects and residuals, leveraging Dirichlet Process priors for increased flexibility. Key features include handling Missing at Random (MAR) longitudinal data, imputation of both covariates and outcomes, and generating posterior predictive samples for further analysis. The methodology is designed for applications in epidemiology, biostatistics, and other fields requiring robust handling of missing data in longitudinal settings.
Author: Jungang Zou [aut, cre], Liangyuan Hu [aut], Robert McCulloch [ctb], Rodney Sparapani [ctb], Charles Spanbauer [ctb], Robert Gramacy [ctb], Jean-Sebastien Roy [ctb]
Maintainer: Jungang Zou <jungang.zou@gmail.com>

This is a re-admission after prior archival of version 1.2 dated 2024-12-11

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Package permute updated to version 0.9-10 with previous version 0.9-8 dated 2025-06-25

Title: Functions for Generating Restricted Permutations of Data
Description: A set of restricted permutation designs for freely exchangeable, line transects (time series), and spatial grid designs plus permutation of blocks (groups of samples) is provided. 'permute' also allows split-plot designs, in which the whole-plots or split-plots or both can be freely-exchangeable or one of the restricted designs. The 'permute' package is modelled after the permutation schemes of 'Canoco 3.1' (and later) by Cajo ter Braak.
Author: Gavin L. Simpson [aut, cph, cre] , R Core Team [cph], Douglas M. Bates [ctb], Jari Oksanen [ctb]
Maintainer: Gavin L. Simpson <ucfagls@gmail.com>

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Package RSQLite updated to version 2.4.6 with previous version 2.4.5 dated 2025-11-30

Title: SQLite Interface for R
Description: Embeds the SQLite database engine in R and provides an interface compliant with the DBI package. The source for the SQLite engine (version 3.51.2) and for various extensions is included. System libraries will never be consulted because this package relies on static linking for the plugins it includes; this also ensures a consistent experience across all installations.
Author: Kirill Mueller [aut, cre] , Hadley Wickham [aut], David A. James [aut], Seth Falcon [aut], D. Richard Hipp [ctb] , Dan Kennedy [ctb] , Joe Mistachkin [ctb] , SQLite Authors [ctb] , Liam Healy [ctb] , R Consortium [fnd], RStudio [cph]
Maintainer: Kirill Mueller <kirill@cynkra.com>

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Package bindrcpp updated to version 0.2.4 with previous version 0.2.3 dated 2023-12-11

Title: An 'Rcpp' Interface to Active Bindings
Description: Provides an easy way to fill an environment with active bindings that call a C++ function.
Author: Kirill Mueller [aut, cre] , RStudio [cph]
Maintainer: Kirill Mueller <kirill@cynkra.com>

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Package mapycusmaximus updated to version 1.0.7 with previous version 1.0.0 dated 2025-12-19

Title: Focus-Glue-Context Fisheye Transformations for Spatial Visualization
Description: Focus-glue-context (FGC) fisheye transformations to two-dimensional coordinates and spatial vector geometries. Implements a smooth radial distortion that enlarges a focal region, transitions through a glue ring, and preserves outside context. Methods build on generalized fisheye views and focus+context mapping. For more details see Furnas (1986) <doi:10.1145/22339.22342>, Furnas (2006) <doi:10.1145/1124772.1124921> and Yamamoto et al. (2009) <doi:10.1145/1653771.1653788>.
Author: Alex Nguyen [aut, cre, cph], Dianne Cook [aut] , Michael Lydeamore [aut]
Maintainer: Alex Nguyen <thanhcuong10091992@gmail.com>

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Package tweedie updated to version 3.0.10 with previous version 2.3.5 dated 2022-08-17

Title: Evaluation of Tweedie Exponential Family Models
Description: Maximum likelihood computations for Tweedie families, including the series expansion (Dunn and Smyth, 2005; <doi:10.1007/s11222-005-4070-y>) and the Fourier inversion (Dunn and Smyth, 2008; <doi:10.1007/s11222-007-9039-6>), and related methods.
Author: Peter K. Dunn [cre, aut]
Maintainer: Peter K. Dunn <pdunn2@usc.edu.au>

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Package deSolve updated to version 1.41 with previous version 1.40 dated 2023-11-27

Title: Solvers for Initial Value Problems of Differential Equations ('ODE', 'DAE', 'DDE')
Description: Functions that solve initial value problems of a system of first-order ordinary differential equations ('ODE'), of partial differential equations ('PDE'), of differential algebraic equations ('DAE'), and of delay differential equations. The functions provide an interface to the FORTRAN functions 'lsoda', 'lsodar', 'lsode', 'lsodes' of the 'ODEPACK' collection, to the FORTRAN functions 'dvode', 'zvode' and 'daspk' and a C-implementation of solvers of the 'Runge-Kutta' family with fixed or variable time steps. The package contains routines designed for solving 'ODEs' resulting from 1-D, 2-D and 3-D partial differential equations ('PDE') that have been converted to 'ODEs' by numerical differencing.
Author: Karline Soetaert [aut] , Thomas Petzoldt [aut, cre] , R. Woodrow Setzer [aut] , Peter N. Brown [ctb] , George D. Byrne [ctb] , Ernst Hairer [ctb] , Alan C. Hindmarsh [ctb] , Cleve Moler [ctb] , Linda R. Petzold [ctb] , Youcef Saad [ctb] , Clement W. [...truncated...]
Maintainer: Thomas Petzoldt <thomas.petzoldt@tu-dresden.de>

Diff between deSolve versions 1.40 dated 2023-11-27 and 1.41 dated 2026-02-06

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Package simPH updated to version 1.3.15 with previous version 1.3.14 dated 2025-04-18

Title: Simulate and Plot Estimates from Cox Proportional Hazards Models
Description: Simulates and plots quantities of interest (relative hazards, first differences, and hazard ratios) for linear coefficients, multiplicative interactions, polynomials, penalised splines, and non-proportional hazards, as well as stratified survival curves from Cox Proportional Hazard models. It also simulates and plots marginal effects for multiplicative interactions. Methods described in Gandrud (2015) <doi:10.18637/jss.v065.i03>.
Author: Christopher Gandrud [aut, cre]
Maintainer: Christopher Gandrud <christopher.gandrud@gmail.com>

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Package GWSDAT updated to version 3.3.0 with previous version 3.2.1 dated 2024-03-04

Title: GroundWater Spatiotemporal Data Analysis Tool (GWSDAT)
Description: Shiny application for the analysis of groundwater monitoring data, designed to work with simple time-series data for solute concentration and ground water elevation, but can also plot non-aqueous phase liquid (NAPL) thickness if required. Also provides the import of a site basemap in GIS shapefile format.
Author: Wayne Jones [aut, cre]
Maintainer: Wayne Jones <wayne.w.jones@shell.com>

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Package DATAstudio updated to version 1.2.2 with previous version 1.2.1 dated 2025-03-29

Title: The Research Data Warehouse of Miguel de Carvalho
Description: Pulls together a collection of datasets from Miguel de Carvalho research articles and books. Including, for example: - de Carvalho (2012) <doi:10.1016/j.jspi.2011.08.016>; - de Carvalho et al (2012) <doi:10.1080/03610926.2012.709905>; - de Carvalho et al (2012) <doi:10.1016/j.econlet.2011.09.007>); - de Carvalho and Davison (2014) <doi:10.1080/01621459.2013.872651>; - de Carvalho and Rua (2017) <doi:10.1016/j.ijforecast.2015.09.004>; - de Carvalho et al (2023) <doi:10.1002/sta4.560>; - de Carvalho et al (2022) <doi:10.1007/s13253-021-00469-9>; - Palacios et al (2025) <doi:10.1214/24-BA1420>.
Author: Miguel de Carvalho [aut, cre]
Maintainer: Miguel de Carvalho <Miguel.deCarvalho@ed.ac.uk>

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Package toOrdinal updated to version 1.4-0.0 with previous version 1.3-0.0 dated 2022-02-24

Title: Cardinal to Ordinal Number & Date Conversion
Description: Language specific cardinal to ordinal number conversion.
Author: Damian W. Betebenner [aut, cre], Andrew Martin [ctb], Jeff Erickson [ctb]
Maintainer: Damian W. Betebenner <dbetebenner@nciea.org>

Diff between toOrdinal versions 1.3-0.0 dated 2022-02-24 and 1.4-0.0 dated 2026-02-06

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Package simStateSpace updated to version 1.2.15 with previous version 1.2.14 dated 2026-01-10

Title: Simulate Data from State Space Models
Description: Provides a streamlined and user-friendly framework for simulating data in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. This package was designed to generate data for the simulations performed in Pesigan, Russell, and Chow (2025) <doi:10.1037/met0000779>.
Author: Ivan Jacob Agaloos Pesigan [aut, cre, cph] , Michael A. Russell [ctb] , Sy-Miin Chow [ctb]
Maintainer: Ivan Jacob Agaloos Pesigan <r.jeksterslab@gmail.com>

Diff between simStateSpace versions 1.2.14 dated 2026-01-10 and 1.2.15 dated 2026-02-06

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Package RPostgres updated to version 1.4.9 with previous version 1.4.8 dated 2025-02-25

Title: C++ Interface to PostgreSQL
Description: Fully DBI-compliant C++-backed interface to PostgreSQL <https://www.postgresql.org/>, an open-source relational database.
Author: Hadley Wickham [aut], Jeroen Ooms [aut], Kirill Mueller [aut, cre] , RStudio [cph], R Consortium [fnd], Tomoaki Nishiyama [ctb]
Maintainer: Kirill Mueller <kirill@cynkra.com>

Diff between RPostgres versions 1.4.8 dated 2025-02-25 and 1.4.9 dated 2026-02-06

 RPostgres-1.4.8/RPostgres/src/logging.cpp                                    |only
 RPostgres-1.4.9/RPostgres/DESCRIPTION                                        |   15 
 RPostgres-1.4.9/RPostgres/MD5                                                |  186 +++---
 RPostgres-1.4.9/RPostgres/NAMESPACE                                          |    1 
 RPostgres-1.4.9/RPostgres/NEWS.md                                            |   21 
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 RPostgres-1.4.9/RPostgres/src/DbConnection.h                                 |   11 
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 RPostgres-1.4.9/RPostgres/src/DbResult.cpp                                   |   19 
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 RPostgres-1.4.9/RPostgres/src/PqResultImpl.h                                 |   15 
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 RPostgres-1.4.9/RPostgres/src/RPostgres-init.c                               |    4 
 RPostgres-1.4.9/RPostgres/src/RPostgres_types.h                              |   16 
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 RPostgres-1.4.9/RPostgres/src/encode.h                                       |   10 
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 RPostgres-1.4.9/RPostgres/tools/winlibs.R                                    |    8 
 RPostgres-1.4.9/RPostgres/vignettes/work-queue.Rmd                           |    2 
 95 files changed, 1469 insertions(+), 813 deletions(-)

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Package RMariaDB updated to version 1.3.5 with previous version 1.3.4 dated 2025-02-24

Title: Database Interface and MariaDB Driver
Description: Implements a DBI-compliant interface to MariaDB (<https://mariadb.org/>) and MySQL (<https://www.mysql.com/>) databases.
Author: Kirill Mueller [aut, cre] , Jeroen Ooms [aut] , David James [aut], Saikat DebRoy [aut], Hadley Wickham [aut], Jeffrey Horner [aut], R Consortium [fnd], RStudio [cph]
Maintainer: Kirill Mueller <kirill@cynkra.com>

Diff between RMariaDB versions 1.3.4 dated 2025-02-24 and 1.3.5 dated 2026-02-06

 DESCRIPTION               |   15 +--
 MD5                       |   60 ++++++------
 NEWS.md                   |   15 ++-
 R/cpp11.R                 |    4 
 configure                 |    7 -
 src/CMakeLists.txt        |    2 
 src/DbConnection.cpp      |  217 +++++++++++++++++++++++++++++-----------------
 src/DbConnection.h        |   27 +++--
 src/DbResult.cpp          |   22 ++--
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Package partools updated to version 1.1.7 with previous version 1.1.6 dated 2017-04-10

Title: Tools for the 'Parallel' Package
Description: Miscellaneous utilities for parallelizing large computations. Alternative to MapReduce. File splitting and distributed operations such as sort and aggregate. "Software Alchemy" method for parallelizing most statistical methods, presented in N. Matloff, Parallel Computation for Data Science, Chapman and Hall, 2015. Includes a debugging aid.
Author: Norm Matloff [cre, aut], Clark Fitzgerald [aut], Reed Davis [aut], Robin Yancey [aut], Shunxu Huang [aut], Alex Rumbaugh [aut], Hadley Wickham [aut]
Maintainer: Norm Matloff <normmatloff@gmail.com>

Diff between partools versions 1.1.6 dated 2017-04-10 and 1.1.7 dated 2026-02-06

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 19 files changed, 85 insertions(+), 25 deletions(-)

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Package mlVAR updated to version 0.5.5 with previous version 0.5.2 dated 2024-02-01

Title: Multi-Level Vector Autoregression
Description: Estimates the multi-level vector autoregression model on time-series data. Three network structures are obtained: temporal networks, contemporaneous networks and between-subjects networks.
Author: Sacha Epskamp [aut, cre], Marie K. Deserno [aut], Laura F. Bringmann [aut], Myrthe Veenman [ctb]
Maintainer: Sacha Epskamp <mail@sachaepskamp.com>

Diff between mlVAR versions 0.5.2 dated 2024-02-01 and 0.5.5 dated 2026-02-06

 DESCRIPTION      |   14 +++++-----
 MD5              |   30 +++++++++++-----------
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Package ggstatsplot updated to version 0.13.5 with previous version 0.13.4 dated 2025-12-09

Title: 'ggplot2' Based Plots with Statistical Details
Description: Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) <doi:10.21105/joss.03236>.
Author: Indrajeet Patil [cre, aut, cph]
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

Diff between ggstatsplot versions 0.13.4 dated 2025-12-09 and 0.13.5 dated 2026-02-06

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Package gap updated to version 1.9 with previous version 1.6 dated 2024-08-27

Title: Genetic Analysis Package
Description: As first reported [Zhao, J. H. 2007. "gap: Genetic Analysis Package". J Stat Soft 23(8):1-18. <doi:10.18637/jss.v023.i08>], it is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates. Over years, the package has been developed in-between many projects hence also in line with the name (gap).
Author: Jing Hua Zhao [aut, cre] , Kurt Hornik [ctb], Brian Ripley [ctb], Uwe Ligges [ctb], Achim Zeileis [ctb]
Maintainer: Jing Hua Zhao <jinghuazhao@hotmail.com>

Diff between gap versions 1.6 dated 2024-08-27 and 1.9 dated 2026-02-06

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Package BeeBDC updated to version 1.3.3 with previous version 1.3.2 dated 2026-01-16

Title: Occurrence Data Cleaning
Description: Flags and checks occurrence data that are in Darwin Core format. The package includes generic functions and data as well as some that are specific to bees. This package is meant to build upon and be complimentary to other excellent occurrence cleaning packages, including 'bdc' and 'CoordinateCleaner'. This package uses datasets from several sources and particularly from the Discover Life Website, created by Ascher and Pickering (2020). For further information, please see the original publication and package website. Publication - Dorey et al. (2023) <doi:10.1101/2023.06.30.547152> and package website - Dorey et al. (2023) <https://github.com/jbdorey/BeeBDC>.
Author: James B. Dorey [aut, cre, cph] , Robert L. O'Reilly [aut] , Silas Bossert [aut] , Erica E. Fischer [aut]
Maintainer: James B. Dorey <jbdorey@me.com>

Diff between BeeBDC versions 1.3.2 dated 2026-01-16 and 1.3.3 dated 2026-02-06

 DESCRIPTION                              |    6 ++--
 MD5                                      |   40 +++++++++++++++----------------
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