Mon, 12 Oct 2026

Package piar updated to version 0.11.0 with previous version 0.10.1 dated 2026-07-30

Title: Price Index Aggregation
Description: Most price indexes are made with a two-step procedure, where period-over-period elementary indexes are first calculated for a collection of elementary aggregates at each point in time, and then aggregated according to a price index aggregation structure. These indexes can then be chained together to form a time series that gives the evolution of prices with respect to a fixed base period. This package contains a collection of functions that revolve around this work flow, making it easy to build standard price indexes, and implement the methods described by Balk (2008, <doi:10.1017/CBO9780511720758>), von der Lippe (2007, <doi:10.3726/978-3-653-01120-3>), and the CPI manual (2020, <doi:10.5089/9781484354841.069>) and PPI manual (2004, <doi:10.5089/9781589063044.069>) for bilateral price indexes.
Author: Steve Martin [aut, cre, cph]
Maintainer: Steve Martin <marberts@protonmail.com>

Diff between piar versions 0.10.1 dated 2026-07-30 and 0.11.0 dated 2026-10-12

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Package icebergr updated to version 0.2.0 with previous version 0.1.0 dated 2026-09-10

Title: Read and Write 'Apache Iceberg' Tables
Description: A native client for 'Apache Iceberg', the open table format used by 'Snowflake', 'Databricks', 'BigQuery', 'AWS' and 'Dremio'. Rather than reaching 'Iceberg' through a query engine such as 'DuckDB', this package talks to it directly: it connects to REST and 'AWS Glue' catalogs, lists namespaces and tables, reads the schema and partition specification of a table, scans data with predicates and projections pushed down, travels back through snapshot history, and appends new data. 'Apache Arrow' is the interchange layer throughout, so scan results arrive in R without a serialisation round trip. Built on 'iceberg-rust', the Apache-governed 'Rust' implementation, via 'extendr'. Supports table spec versions 1 and 2; see the 'README' for the full matrix of supported and unsupported features. This is a community package, not affiliated with or endorsed by The Apache Software Foundation; 'Apache', 'Apache Iceberg' and 'Iceberg' are trademarks of The Apache Software Foundation.
Author: Youzhi Yu [aut, cre], The Apache Software Foundation [cph]
Maintainer: Youzhi Yu <yuyouzhi666@icloud.com>

Diff between icebergr versions 0.1.0 dated 2026-09-10 and 0.2.0 dated 2026-10-12

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Package HACSim updated to version 1.0.7-2 with previous version 1.0.7-1 dated 2025-09-19

Title: Iterative Extrapolation of Species' Haplotype Accumulation Curves for Genetic Diversity Assessment
Description: Performs iterative extrapolation of species' haplotype accumulation curves using a nonparametric stochastic (Monte Carlo) optimization method for assessment of specimen sampling completeness based on the approach of Phillips et al. (2015) <doi:10.1515/dna-2015-0008>, Phillips et al. (2019) <doi:10.1002/ece3.4757> and Phillips et al. (2020) <doi: 10.7717/peerj-cs.243>. 'HACSim' outputs a number of useful summary statistics of sampling coverage ("Measures of Sampling Closeness"), including an estimate of the likely required sample size (along with desired level confidence intervals) necessary to recover a given number/proportion of observed unique species' haplotypes. Any genomic marker can be targeted to assess likely required specimen sample sizes for genetic diversity assessment. The method is particularly well-suited to assess sampling sufficiency for DNA barcoding initiatives. Users can also simulate their own DNA sequences according to various models of nucleotide [...truncated...]
Author: Jarrett D. Phillips [aut, cre], Steven H. French [ctb], Navdeep Singh [ctb]
Maintainer: Jarrett D. Phillips <phillipsjarrett1@gmail.com>

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Package GeoThinneR updated to version 2.1.3 with previous version 2.1.2 dated 2026-08-25

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

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Package edgemodelr updated to version 0.4.5 with previous version 0.4.3 dated 2026-09-08

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, extracting vector representations, and streaming responses in real-time. Includes grammar-constrained generation for structured output, text classification, and retrieval-augmented generation (RAG) pipelines. 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>

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Package DSIR updated to version 0.11.0 with previous version 0.10.0 dated 2026-10-01

Title: Data Science Infrastructure for Global Health
Description: Supports global health data analysis, including a publication-ready 'ggplot2' theme, a 'flextable' defaults helper, a thin pie chart wrapper, built-in regional country-code datasets with a WHO region lookup helper, a geometric mean function for indicator aggregation, an average annual rate of reduction function for indicator progress tracking, direct age standardization against the bundled WHO 2026 Standard Population, period life-table construction, a snapshot helper for reproducible data pulls, and convenience clients for the World Health Organization Global Health Observatory (GHO) through its legacy OData API <https://ghoapi.azureedge.net/api/> or the public WHO xMart OData API <https://xmart-api-public.who.int/DATA_>, Global Health Estimates (GHE) through WHO xMart, and the United Nations Sustainable Development Goals (SDG) API <https://unstats.un.org/SDGAPI/swagger/>.
Author: Shanlong Ding [aut, cre]
Maintainer: Shanlong Ding <dings@who.int>

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Package dparser updated to version 1.3.1-14 with previous version 1.3.1-13 dated 2024-10-22

Title: Port of 'Dparser' Package
Description: A Scannerless GLR parser/parser generator. Note that GLR standing for "generalized LR", where L stands for "left-to-right" and R stands for "rightmost (derivation)". For more information see <https://en.wikipedia.org/wiki/GLR_parser>. This parser is based on the Tomita (1987) algorithm. (Paper can be found at <https://aclanthology.org/P84-1073.pdf>). The original 'dparser' package documentation can be found at <https://dparser.sourceforge.net/>. This allows you to add mini-languages to R (like rxode2's ODE mini-language Wang, Hallow, and James 2015 <DOI:10.1002/psp4.12052>) or to parse other languages like 'NONMEM' to automatically translate them to R code. To use this in your code, add a LinkingTo dparser in your DESCRIPTION file and instead of using #include <dparse.h> use #include <dparser.h>. This also provides a R-based port of the make_dparser <https://dparser.sourceforge.net/d/make_dparser.cat> command called mkdparser(). Addition [...truncated...]
Author: Matthew Fidler [aut, cre], John Plevyak [aut, cph]
Maintainer: Matthew Fidler <matthew.fidler@gmail.com>

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Package AV1R readmission to version 0.1.4 with previous version 0.1.3 dated 2026-03-23

Title: 'AV1' Video Encoding for Biological Microscopy Data
Description: Converts microscopy video and image data (H.264/H.265, AVI/MJPEG, TIFF stacks) to the 'AV1' codec for compact storage, and archives 16-bit image stacks and videos losslessly in the 'FFV1' format. Encoding runs on the GPU via 'Vulkan' or 'VAAPI' when available and falls back to the CPU through 'FFmpeg' and 'SVT-AV1'. Whole experiment folders can be converted at once.
Author: Yuri Baramykov [aut, cre] , The Khronos Group Inc. [cph], FFmpeg developers [cph]
Maintainer: Yuri Baramykov <lbsbmsu@mail.ru>

This is a re-admission after prior archival of version 0.1.3 dated 2026-03-23

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Package arcstat updated to version 0.3.1 with previous version 0.3.0 dated 2026-09-14

Title: Arc-Length Statistics: Goodness of Fit, Distributions and a Bayesian Test
Description: Inference from the arc length of statistical functions. Three tools share one pure-C back-end: a goodness-of-fit test based on the arc length of the probability plot, with an analytic saddlepoint null and sensitivity to local density structure that the empirical-distribution tests miss; two constructions that build a distribution from the arc length of its defining curve, the arc-length generator and the quantile arc-length family estimated by L-moments; and a Bayesian nonparametric arc-length goodness-of-fit test on the Dirichlet-process posterior. The same C sources back the 'Python' package 'arcstat'.
Author: M. Theodor Loots [aut, cre]
Maintainer: M. Theodor Loots <theo.loots@gmail.com>

Diff between arcstat versions 0.3.0 dated 2026-09-14 and 0.3.1 dated 2026-10-12

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Sun, 11 Oct 2026

Package tidycjk updated to version 0.2.0 with previous version 0.1.0 dated 2026-09-09

Title: Tidy Tools for Chinese, Japanese and Korean Text
Description: A tidy toolkit for text that is written in Chinese, Japanese or Korean. Most text tooling in R assumes that words are separated by whitespace, which CJK writing does not use, so ordinary summaries of a text column either treat a sentence as one undifferentiated blob or split it into isolated characters. Word segmentation is a pluggable engine, with dictionary-based segmentation for Chinese and Japanese supplied through 'ICU' and further engines registrable by the caller, because where a word ends is a fact about a language and not about Unicode. 'tidycjk' classifies characters by Unicode block, reports which script and which language a text is written in, measures how much of a text is CJK, and turns those measurements into tibbles that slot straight into a 'tidyverse' workflow. It also measures display width in terminal columns, pads, truncates and wraps to a width rather than to a character count, and normalises fullwidth and halfwidth forms surgically -- including composing halfwidt [...truncated...]
Author: Youzhi Yu [aut, cre]
Maintainer: Youzhi Yu <yuyouzhi666@icloud.com>

Diff between tidycjk versions 0.1.0 dated 2026-09-09 and 0.2.0 dated 2026-10-11

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Package sp updated to version 2.2-4 with previous version 2.2-3 dated 2026-07-19

Title: Classes and Methods for Spatial Data
Description: Classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc. From this version, 'rgdal', 'maptools', and 'rgeos' are no longer used at all, see <https://r-spatial.org/r/2023/05/15/evolution4.html> for details.
Author: Edzer Pebesma [aut, cre], Roger Bivand [aut], Barry Rowlingson [ctb], Virgilio Gomez-Rubio [ctb], Robert Hijmans [ctb], Michael Sumner [ctb], Don MacQueen [ctb], Jim Lemon [ctb], Finn Lindgren [ctb], Josh O'Brien [ctb], Joseph O'Rourke [ctb], Patrick [...truncated...]
Maintainer: Edzer Pebesma <edzer.pebesma@uni-muenster.de>

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Package shidashi updated to version 0.3.0 with previous version 0.2.0 dated 2026-04-10

Title: A Shiny Dashboard Template Modular System with Chat Bot Support
Description: A template dashboard system with AI agent integrated. Comes with default themes that can be customized. Developers can upload modified templates on 'Github', and users can easily download templates with 'RStudio' project wizard. The key features of the default template include light and dark theme switcher, resizing graphs, synchronizing inputs across sessions, new notification system, fancy progress bars, and card-like flip panels with back sides, as well as various of 'HTML' tool widgets.
Author: Zhengjia Wang [cph, aut, cre] , ColorlibHQ [cph] , Bootstrap contributors [ctb] , Twitter, Inc [cph] , Ivan Sagalaev [ctb, cph] , Rene Haas [ctb, cph] , Zeno Rocha [ctb, cph]
Maintainer: Zhengjia Wang <dipterix.wang@gmail.com>

Diff between shidashi versions 0.2.0 dated 2026-04-10 and 0.3.0 dated 2026-10-11

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Package rbridge readmission to version 1.0.3 with previous version 1.0.2 dated 2020-02-29

Title: Restricted Bridge Estimation
Description: Implements bridge regression with linear coefficient restrictions using the local quadratic approximation described by Yuzbasi, Arashi and Akdeniz (2021) <doi:10.1007/s00500-021-05763-9>. Provides coefficient paths, cross-validation, prediction and coefficient extraction for restricted and unrestricted linear models.
Author: Bahadir Yuzbasi [aut, cre], Mohammad Arashi [aut], Fikri Akdeniz [aut], Jerome Friedman [ctb] , Trevor Hastie [ctb] , Robert Tibshirani [ctb]
Maintainer: Bahadir Yuzbasi <b.yzb@hotmail.com>

This is a re-admission after prior archival of version 1.0.2 dated 2020-02-29

Diff between rbridge versions 1.0.2 dated 2020-02-29 and 1.0.3 dated 2026-10-11

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Package PLMIX readmission to version 2.2.1 with previous version 2.2.0 dated 2025-06-30

Title: Bayesian Analysis of Finite Mixtures of Plackett-Luce Models for Partial Rankings/Orderings
Description: Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) <doi:10.1007/s11336-016-9530-0> and Mollica and Tardella (2014) <doi:10.1002/sim.6224>.
Author: Cristina Mollica [aut, cre], Luca Tardella [aut]
Maintainer: Cristina Mollica <cristina.mollica@uniroma1.it>

This is a re-admission after prior archival of version 2.2.0 dated 2025-06-30

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Package motifcluster updated to version 0.2.4 with previous version 0.2.3 dated 2022-11-18

Title: Motif-Based Spectral Clustering of Weighted Directed Networks
Description: Tools for spectral clustering of weighted directed networks using motif adjacency matrices. Methods perform well on large and sparse networks, and random sampling methods for generating weighted directed networks are also provided. Based on methodology detailed in Underwood, Elliott and Cucuringu (2020) <doi:10.48550/arXiv.2004.01293>.
Author: William George Underwood [aut, cre]
Maintainer: William George Underwood <william.underwood@warwick.ac.uk>

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Package globals updated to version 0.20.0 with previous version 0.19.1 dated 2026-03-13

Title: Identify Global Objects in R Expressions
Description: Identifies global ("unknown" or "free") objects in R expressions by code inspection using various strategies (ordered, liberal, conservative, or deep-first search). The objective of this package is to make it as simple as possible to identify global objects for the purpose of exporting them in parallel, distributed compute environments.
Author: Henrik Bengtsson [aut, cre, cph] , Davis Vaughan [ctb]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

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Package svrep updated to version 0.9.2 with previous version 0.9.1 dated 2025-10-08

Title: Tools for Creating, Updating, and Analyzing Survey Replicate Weights
Description: Provides tools for creating and working with survey replicate weights, extending functionality of the 'survey' package from Lumley (2004) <doi:10.18637/jss.v009.i08>. Implements bootstrap methods for complex surveys, including the generalized survey bootstrap as described by Beaumont and Patak (2012) <doi:10.1111/j.1751-5823.2011.00166.x>. Methods are provided for applying nonresponse adjustments to both full-sample and replicate weights as described by Rust and Rao (1996) <doi:10.1177/096228029600500305>. Implements methods for sample-based calibration described by Opsomer and Erciulescu (2021) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021002/article/00006-eng.htm>. Diagnostic functions are included to compare weights and weighted estimates from different sets of replicate weights.
Author: Ben Schneider [aut, cre]
Maintainer: Ben Schneider <benjamin.julius.schneider@gmail.com>

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Package rolleigen updated to version 1.0.1 with previous version 1.0.0 dated 2026-06-19

Title: Rolling Eigenanalysis
Description: Fast and efficient computation of rolling and expanding eigenanalysis for time-series data. The 'rolleigen' package decomposes the covariance matrix of the explanatory variables into eigenvalues and eigenvectors to perform principal component analysis (Pearson, 1901, <doi:10.1080/14786440109462720>; Hotelling, 1933, <doi:10.1037/h0071325>) and principal component regression (Massy, 1965, <doi:10.1080/01621459.1965.10480787>) over rolling and expanding windows. For each window, the eigenvalues and eigenvectors are computed from the covariance matrix and, optionally, ordered from the largest to smallest eigenvalue to summarize the directions of greatest variation in the data. A subset of leading components is then used to fit a regression that mitigates collinearity in the explanatory variables. Use cases include dimensionality reduction, factor extraction, and regression on collinear explanatory variables. The package supports rolling and expanding windows, weights, a [...truncated...]
Author: Jason Foster [aut, cre]
Maintainer: Jason Foster <jason.j.foster@gmail.com>

Diff between rolleigen versions 1.0.0 dated 2026-06-19 and 1.0.1 dated 2026-10-11

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Package future.callr updated to version 1.0.1 with previous version 1.0.0 dated 2026-06-22

Title: A Future API for Parallel Processing using 'callr'
Description: Implementation of the Future API <doi:10.32614/RJ-2021-048> on top of the 'callr' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local (Linux, macOS, Windows, ...) machine. Contrary to backends relying on the 'parallel' package (e.g. 'future::multisession') and socket connections, the 'callr' backend provided here can run more than 125 parallel R processes.
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

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Package filearray updated to version 0.2.4 with previous version 0.2.3 dated 2026-08-21

Title: File-Backed Array for Out-of-Memory Computation
Description: Stores large arrays in files to avoid occupying large memories. Implemented with super fast gigabyte-level multi-threaded reading/writing via 'OpenMP'. Supports multiple non-character data types (double, float, complex, integer, logical, and raw).
Author: Zhengjia Wang [aut, cre, cph]
Maintainer: Zhengjia Wang <dipterix.wang@gmail.com>

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Package mlr updated to version 2.19.4 with previous version 2.19.3 dated 2025-08-22

Title: Machine Learning in R
Description: Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.
Author: Bernd Bischl [aut] , Michel Lang [aut] , Lars Kotthoff [aut], Patrick Schratz [aut] , Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio [aut] , Mason Gallo [aut], Jakob Bossek [ctb] , Erich Studerus [ctb] , Leonard [...truncated...]
Maintainer: Martin Binder <mlr.developer@mb706.com>

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Package kernreg updated to version 1.1 with previous version 1.0 dated 2026-02-16

Title: Nadaraya-Watson Kernel Regression
Description: Fast implementation of Nadaraya-Watson kernel regression for either univariate or multivariate responses, with one or more bandwidths. K-fold cross-validation is also performed.
Author: Michail Tsagris [aut, cre], Christos Adam [aut]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>

Diff between kernreg versions 1.0 dated 2026-02-16 and 1.1 dated 2026-10-11

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Package groupedHyperframe updated to version 0.4.6 with previous version 0.4.5 dated 2026-08-25

Title: Grouped Hyper Data Frame
Description: Extensions of "hyper data frame" (from package 'spatstat.geom') with a grouping structure. The author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications. Backward compatibility with user-code published in <doi:10.1093/bioinformatics/btaf430> is not maintained in versions >= 0.4.0 (June 2026) of this package. The authors of those publications are the appropriate contacts for reproducibility inquiries.
Author: Tingting Zhan [aut, cre]
Maintainer: Tingting Zhan <tingtingzhan@gmail.com>

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Package crmPack updated to version 2.3.0 with previous version 2.2.1 dated 2026-07-27

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

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More information about crmPack at CRAN
Permanent link

Package umx updated to version 4.80.0 with previous version 4.65.0 dated 2026-05-18

Title: Structural Equation Modeling and Twin Modeling in R
Description: Quickly create, run, and report structural equation and twin models. See '?umx' for help, and umx_open_CRAN_page("umx") for NEWS. Citations are: * Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R. Twin Research and Human Genetics, 22, 27-41. <doi:10.1017/thg.2019.2>. * Luis F.S. Castro-de-Araujo, Nathan A. Gillespie, Michael C. Neale, and Timothy Charles Bates (2026). umx version 4.5: Extending Twin and Path-Based SEM in R with CLPM, MR-DoC, Definition Variables, Onyx Integration, and Censored Distributions. Twin Research and Human Genetics, 1-6. <doi:10.1017/thg.2026.10056>.
Author: Timothy C. Bates [aut, cre] , Luis De Araujo [ctb], Nathan Gillespie [wit], Hermine Maes [ctb], Michael C. Neale [ctb], Joshua N. Pritikin [ctb], Brenton Wiernik [ctb], Michael Zakharin [wit]
Maintainer: Timothy C. Bates <timothy.c.bates@gmail.com>

Diff between umx versions 4.65.0 dated 2026-05-18 and 4.80.0 dated 2026-10-11

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 umx-4.80.0/umx/man/umxSummaryIP.Rd                                |   16 
 umx-4.80.0/umx/man/umxSummaryMRDoC.Rd                             |   12 
 umx-4.80.0/umx/man/umxSummarySexLim.Rd                            |   66 
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 umx-4.80.0/umx/man/umxSuperModel.Rd                               |   26 
 umx-4.80.0/umx/man/umxThresholdMatrix.Rd                          |   21 
 umx-4.80.0/umx/man/umxTwinMaker.Rd                                |   81 
 umx-4.80.0/umx/man/umxTwoStage.Rd                                 |   21 
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 umx-4.80.0/umx/man/umxWeightedAIC.Rd                              |   42 
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 umx-4.80.0/umx/man/umx_apply.Rd                                   |   43 
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 umx-4.80.0/umx/man/umx_cor.Rd                                     |   51 
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 umx-4.80.0/umx/man/umx_file_load_pseudo.Rd                        |   14 
 umx-4.80.0/umx/man/umx_find_object.Rd                             |   42 
 umx-4.80.0/umx/man/umx_fun_mean_sd.Rd                             |  189 
 umx-4.80.0/umx/man/umx_get_bracket_addresses.Rd                   |  189 
 umx-4.80.0/umx/man/umx_get_checkpoint.Rd                          |   40 
 umx-4.80.0/umx/man/umx_get_options.Rd                             |   39 
 umx-4.80.0/umx/man/umx_grep.Rd                                    |   22 
 umx-4.80.0/umx/man/umx_has_CIs.Rd                                 |   33 
 umx-4.80.0/umx/man/umx_has_been_run.Rd                            |   33 
 umx-4.80.0/umx/man/umx_has_means.Rd                               |   33 
 umx-4.80.0/umx/man/umx_has_square_brackets.Rd                     |   34 
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 umx-4.80.0/umx/man/umx_is_MxMatrix.Rd                             |   33 
 umx-4.80.0/umx/man/umx_is_MxModel.Rd                              |   33 
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 umx-4.80.0/umx/man/umx_is_class.Rd                                |   16 
 umx-4.80.0/umx/man/umx_is_cov.Rd                                  |   35 
 umx-4.80.0/umx/man/umx_is_endogenous.Rd                           |   16 
 umx-4.80.0/umx/man/umx_is_exogenous.Rd                            |   16 
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 umx-4.80.0/umx/man/umx_long2wide.Rd                               |   18 
 umx-4.80.0/umx/man/umx_lower.tri.Rd                               |   35 
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 umx-4.80.0/umx/man/umx_make_sql_from_excel.Rd                     |   18 
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 umx-4.80.0/umx/man/umx_means.Rd                                   |   38 
 umx-4.80.0/umx/man/umx_merge_randomized_columns.Rd                |   51 
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 umx-4.80.0/umx/man/umx_polychoric.Rd                              |   57 
 umx-4.80.0/umx/man/umx_polypairwise.Rd                            |   66 
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 umx-4.80.0/umx/man/umx_read_lower.Rd                              |   55 
 umx-4.80.0/umx/man/umx_rename.Rd                                  |   51 
 umx-4.80.0/umx/man/umx_rename_file.Rd                             |   14 
 umx-4.80.0/umx/man/umx_reorder.Rd                                 |   57 
 umx-4.80.0/umx/man/umx_residualize.Rd                             |   18 
 umx-4.80.0/umx/man/umx_rot.Rd                                     |   23 
 umx-4.80.0/umx/man/umx_round.Rd                                   |   38 
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 umx-4.80.0/umx/man/umx_score_scale.Rd                             |   59 
 umx-4.80.0/umx/man/umx_select_valid.Rd                            |   51 
 umx-4.80.0/umx/man/umx_set_auto_plot.Rd                           |   39 
 umx-4.80.0/umx/man/umx_set_auto_run.Rd                            |   35 
 umx-4.80.0/umx/man/umx_set_checkpoint.Rd                          |   50 
 umx-4.80.0/umx/man/umx_set_condensed_slots.Rd                     |   39 
 umx-4.80.0/umx/man/umx_set_cores.Rd                               |   68 
 umx-4.80.0/umx/man/umx_set_data_variance_check.Rd                 |   39 
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 umx-4.80.0/umx/man/umx_set_optimization_options.Rd                |   39 
 umx-4.80.0/umx/man/umx_set_optimizer.Rd                           |   58 
 umx-4.80.0/umx/man/umx_set_plot_file_suffix.Rd                    |   39 
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 umx-4.80.0/umx/man/umx_set_separator.Rd                           |   41 
 umx-4.80.0/umx/man/umx_set_silent.Rd                              |   39 
 umx-4.80.0/umx/man/umx_set_table_format.Rd                        |   39 
 umx-4.80.0/umx/man/umx_stack.Rd                                   |   57 
 umx-4.80.0/umx/man/umx_standardize.Rd                             |  185 
 umx-4.80.0/umx/man/umx_str_chars.Rd                               |   22 
 umx-4.80.0/umx/man/umx_str_from_object.Rd                         |   22 
 umx-4.80.0/umx/man/umx_string_to_algebra.Rd                       |  189 
 umx-4.80.0/umx/man/umx_strings2numeric.Rd                         |   51 
 umx-4.80.0/umx/man/umx_tail.Rd                                    |only
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 umx-4.80.0/umx/man/umx_wide2long.Rd                               |   35 
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 umx-4.80.0/umx/man/umx_wide4lmer.Rd                               |   40 
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Package tidymedia updated to version 0.2.2 with previous version 0.2.1 dated 2026-10-10

Title: Media File Preprocessing and Metadata for the 'tidyverse'
Description: Batch preprocessing and metadata extraction for audio, video and image files, built on the command-line programs 'FFmpeg' (<https://ffmpeg.org/>) and 'MediaInfo' (<https://mediaarea.net/en/MediaInfo>). Trim, crop, scale, convert and standardize files one at a time or across a whole directory, and read container and stream metadata back as tibbles for use with the 'tidyverse'.
Author: Jeffrey Girard [aut, cre]
Maintainer: Jeffrey Girard <me@jmgirard.com>

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Package secr updated to version 5.5.1 with previous version 5.4.3 dated 2026-05-27

Title: Spatially Explicit Capture-Recapture
Description: Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.
Author: Murray Efford [aut, cre] , Philipp Jund [ctb] , David Fletcher [ctb] , Yan Ru Choo [ctb]
Maintainer: Murray Efford <murray.efford@otago.ac.nz>

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Package rupturesRcpp updated to version 2.0.0 with previous version 1.0.3 dated 2026-09-18

Title: Object-Oriented Interface for Offline Change-Point Detection
Description: A collection of efficient implementations of popular offline change-point detection algorithms, featuring a consistent, object-oriented interface for practical use.
Author: Minh Long Nguyen [aut, cre], Toby Hocking [aut], Charles Truong [aut], Huy Nhat Minh Nguyen [ctb]
Maintainer: Minh Long Nguyen <edelweiss611428@gmail.com>

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Package Qindex updated to version 0.2.2 with previous version 0.2.1 dated 2026-08-26

Title: A Legacy Package
Description: The author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications. Backward compatibility with user-code published in <doi:10.1186/s12859-023-05408-8>, <doi:10.1016/j.labinv.2023.100158> and <doi:10.1093/bioinformatics/btaf182> is not maintained in versions >= 0.4.0 (June 2026) of this package. The authors of those publications are the appropriate contacts for reproducibility inquiries.
Author: Tingting Zhan [aut, cre]
Maintainer: Tingting Zhan <tingtingzhan@gmail.com>

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Package hyper.gam updated to version 0.3.4 with previous version 0.3.3 dated 2026-08-25

Title: Generalized Additive Models with Hyper Column
Description: An interactive HTML widget of the perspective plot for generalized additive models; an alternative solution of the function mgcv::vis.gam(). This R package author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications. Backward compatibility with user-code published in <doi:10.1093/bioinformatics/btaf182> and <doi:10.1093/bioinformatics/btaf430> is not maintained in versions >= 0.3.0 (June 2026) of this package. The authors of those publications are the appropriate contacts for reproducibility inquiries.
Author: Tingting Zhan [aut, cre], Erjia Cui [ctb]
Maintainer: Tingting Zhan <tingtingzhan@gmail.com>

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Package glmtree readmission to version 0.4.0 with previous version 0.3.1 dated 2024-03-06

Title: Logistic Regression Trees
Description: A logistic regression tree is a decision tree with logistic regressions at its leaves. A particular stochastic expectation maximization algorithm is used to draw a few good trees, that are then assessed via the user's criterion of choice among BIC / AIC / test set Gini. The formal development is given in a PhD chapter, see Ehrhardt (2019) <https://github.com/adimajo/manuscrit_these/releases/>.
Author: Adrien Ehrhardt [aut, cre]
Maintainer: Adrien Ehrhardt <adrien.ehrhardt@polytechnique.edu>

This is a re-admission after prior archival of version 0.3.1 dated 2024-03-06

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Package countries updated to version 1.2.5 with previous version 1.2.4 dated 2026-07-18

Title: Deal with Country Data in an Easy Way
Description: Wrangle country data more effectively and quickly. This package contains functions to easily identify and convert country names, download country information, merge country data from different sources, and make quick world maps.
Author: Francesco Saverio Bellelli [aut, cre, cph]
Maintainer: Francesco Saverio Bellelli <fsabellelli@gmail.com>

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Package clampSeg updated to version 1.3-1 with previous version 1.3-0 dated 2026-10-08

Title: Idealisation of Patch Clamp Recordings
Description: Implements the model-free multiscale idealisation approaches: Jump-Segmentation by MUltiResolution Filter (JSMURF), Hotz et al. (2013) <doi:10.1109/TNB.2013.2284063>, JUmp Local dEconvolution Segmentation filter (JULES), Pein et al. (2018) <doi:10.1109/TNB.2018.2845126>, and Heterogeneous Idealization by Local testing and DEconvolution (HILDE), Pein et al. (2021) <doi:10.1109/TNB.2020.3031202>. Further details on how to use them are given in Pein, Eltzner and Munk (2021) <doi:10.1007/s00249-021-01506-8>.
Author: Florian Pein [aut, cre], Timo Aspelmeier [ctb]
Maintainer: Florian Pein <f.pein@lancaster.ac.uk>

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Package swash updated to version 3.0.1 with previous version 3.0.0 dated 2026-10-02

Title: Health Geography Toolbox for Model-Based Analysis of Infections Panel Data
Description: Within epidemic outbreaks, infections grow and decline differently between regions, and the velocity of spatial spread differs between countries. The swash library offers a set of model-based analyses for these topics. Spread velocity may be analysed with the Swash-Backwash Model for the Single Epidemic Wave by Cliff and Haggett (2006) <doi:10.1007/s10109-006-0027-8> and corresponding functions for bootstrap confidence intervals, country comparison, and visualization of results. Differences in epidemic growth between regions may be analysed using logistic growth models, exponential growth models, Hawkes processes and breakpoint analyses. Cluster and hotspot analyses can be conducted using neighborhood matrices. See Wieland (2020) <doi:10.18335/region.v7i2.324> for an application of some of the mentioned analysis methods. All functionalities are accessed by the class "infpan" for infections panel data defined in this package, which is built from a data.frame provided by the [...truncated...]
Author: Thomas Wieland [aut, cre]
Maintainer: Thomas Wieland <geowieland@googlemail.com>

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Package spacc updated to version 0.11.0 with previous version 0.8.3 dated 2026-06-20

Title: Fast Spatial Species Accumulation Curves
Description: High-performance spatial species accumulation curves using fixed-focus spatially constrained rarefaction, nearest-centroid expansion, and nearest-neighbor walks with a C++ backend for speed. Supports Hill numbers (q=0,1,2), beta diversity partitioning (turnover/nestedness) at site and pooled resolution, coverage-based rarefaction and extrapolation of alpha and beta diversity, phylogenetic diversity (PD; Faith's PD, EvoHeritage, mean pairwise distance, mean nearest taxon distance), functional diversity accumulation including aggregated functional diversity and intraspecific trait variation, phylogenetic and functional redundancy, interaction network accumulation, diversity-area relationships (DAR), endemism-area curves, sampling-effort correction and fragmentation analysis, species-area relationship (SAR) models based on extreme value theory (EVT), and area-based richness extrapolation via the total-species curve. Total richness is also estimated from frequency counts, including the acc [...truncated...]
Author: Gilles Colling [aut, cre, cph] , Marius Muja [ctb, cph] ), David G. Lowe [ctb, cph] ), Jose Luis Blanco [ctb, cph] )
Maintainer: Gilles Colling <gilles.colling051@gmail.com>

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Package rglobi readmission to version 0.3.6 with previous version 0.3.4 dated 2023-10-06

Title: Interface to Global Biotic Interactions
Description: A programmatic interface to the web service methods provided by Global Biotic Interactions ('GloBI') (<https://www.globalbioticinteractions.org/>). 'GloBI' provides access to spatial-temporal species interaction records from sources all over the world. 'rglobi' provides methods to search species interactions by location, interaction type, and taxonomic name.
Author: Jorrit Poelen [aut, cre], Stephen Gosnell [aut], Sergey Slyusarev [aut], Helen Waters [aut]
Maintainer: Jorrit Poelen <jhpoelen@jhpoelen.nl>

This is a re-admission after prior archival of version 0.3.4 dated 2023-10-06

Diff between rglobi versions 0.3.4 dated 2023-10-06 and 0.3.6 dated 2026-10-11

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Package repo.data updated to version 0.2.6 with previous version 0.2.2 dated 2026-06-20

Title: R Repositories Data
Description: Retrieve metadata about packages from repositories to explore package dependencies, links between help pages, aliases, package availability on a given date, and other repository-dependent outcome. In addition, it provides access to information about the processes at CRAN. This metadata can be used to help package maintainers and users navigate changes to dependencies and with reproducibility.
Author: Lluis Revilla Sancho [aut, cre, cph]
Maintainer: Lluis Revilla Sancho <lluis.revilla@gmail.com>

Diff between repo.data versions 0.2.2 dated 2026-06-20 and 0.2.6 dated 2026-10-11

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Package polle updated to version 1.6.7 with previous version 1.6.6 dated 2026-10-10

Title: Policy Learning
Description: Package for learning and evaluating (subgroup) policies via doubly robust loss functions. Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2026) <doi:10.18637/jss.v116.i04> for documentation and references.
Author: Andreas Nordland [aut, cre], Klaus Holst [aut]
Maintainer: Andreas Nordland <andreasnordland@gmail.com>

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Package OmicFlow updated to version 1.7.0 with previous version 1.6.0 dated 2026-05-19

Title: Fast and Efficient (Automated) Analysis of Sparse Omics Data
Description: A generalised data structure for fast and efficient loading and data munching of sparse omics data. The 'OmicFlow' requires an up-front validated metadata template from the user, which serves as a guide to connect all the pieces together by aligning them into a single object that is defined as an 'omics' class. Once this unified structure is established, users can perform manual subsetting, visualisation, and statistical analysis, or leverage the automated 'autoFlow' method to generate a comprehensive report.
Author: Alem Gusinac [aut, cre] , Thomas Ederveen [aut] , Annemarie Boleij [aut, fnd]
Maintainer: Alem Gusinac <alem.gusinac@gmail.com>

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Package MultiLevelOptimalBayes updated to version 0.1.0.0 with previous version 0.0.4.0 dated 2025-09-23

Title: Regularized Bayesian Estimator for Two-Level Latent Variable Models
Description: Implements a regularized Bayesian estimator that optimizes the estimation of between-group coefficients for multilevel latent variable models by minimizing mean squared error (MSE) and balancing variance and bias. The package provides more reliable estimates in scenarios with limited data, offering a robust solution for accurate parameter estimation in two-level latent variable models. It is designed for researchers in psychology, education, and related fields who face challenges in estimating between-group effects under small sample sizes and low intraclass correlation coefficients. The package includes comprehensive S3 methods for result objects: print(), summary(), coef(), se(), vcov(), confint(), as.data.frame(), dim(), length(), names(), and update() for enhanced usability and integration with standard R workflows. Dashuk et al. (2025a) <doi:10.1017/psy.2025.10045> derived the optimal regularized Bayesian estimator; Dashuk et al. (2025b) <doi:10.1007/s41237-025-00264-7> [...truncated...]
Author: Valerii Dashuk [aut, cre], Binayak Timilsina [aut], Martin Hecht [aut], Steffen Zitzmann [aut]
Maintainer: Valerii Dashuk <vadashuk@gmail.com>

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Package MoNAn updated to version 2.0.0 with previous version 1.1.0 dated 2024-09-12

Title: Mobility Network Analysis
Description: Implements the method to analyse weighted mobility networks or distribution networks as outlined in: Block, P., Stadtfeld, C., & Robins, G. (2022) <doi:10.1016/j.socnet.2021.08.003>. The purpose of the model is to analyse the structure of mobility, incorporating exogenous predictors pertaining to individuals and locations known from classical mobility analyses, as well as modelling emergent mobility patterns akin to structural patterns known from the statistical analysis of social networks. Version 2 runs the simulations in 'C++' (using 'Rcpp') and, besides stochastic approximation (Robbins-Monro), can estimate parameters with Geyer-Thompson MCMC maximum likelihood estimation.
Author: Per Block [cre, aut, cph] , Christoph Stadtfeld [aut] , Nico Keiser [aut] , Marion Hoffman [aut] , Jingying Wang [aut]
Maintainer: Per Block <block@soziologie.uzh.ch>

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Package lfstat updated to version 0.9.15 with previous version 0.9.13 dated 2025-09-04

Title: Calculation of Low Flow Statistics for Daily Stream Flow Data
Description: The "Manual on Low-flow Estimation and Prediction" (Gustard & Demuth (2009, ISBN:978-92-63-11029-9)), published by the World Meteorological Organisation, gives a comprehensive summary on how to analyse stream flow data focusing on low-flows. This packages provides functions to compute the described statistics and produces plots similar to the ones in the manual.
Author: Tobias Gauster [ctb, cre], Gregor Laaha [aut] , Daniel Koffler [aut]
Maintainer: Tobias Gauster <t.gauster@gmail.com>

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Package ebrahim.gof updated to version 2.10.0 with previous version 2.9.0 dated 2026-10-01

Title: Goodness-of-Fit and Calibration Tests for Logistic Regression
Description: Provides a unified battery of goodness-of-fit and calibration tests for binary logistic regression, runnable in a single call via 'run.all.gof()'. Around twenty-five tests spanning five decades of literature are aggregated and grouped by the departure each is built to detect: global and standardized statistics, partition tests such as Hosmer-Lemeshow, directed and covariate-space tests, smoothing and resampling tests, and calibration tests. Each is obtained from its own package where installed and attributed to its authors. The package also implements the author's own procedures for sparse data, where the Hosmer-Lemeshow test loses power: the omnibus Ebrahim-Farrington test 'ef.gof()', the directed 'edge.gof()' and its covariate-space variant 'cdef.gof()', the Cauchy-combination ensemble 'edges.gof()', 'DeepGOF-1' (a pretrained convolutional statistic whose level comes from the analyst's own parametric bootstrap rather than from the network), and 'legoft()' (a frozen-weight combination [...truncated...]
Author: Ebrahim Khaled Ebrahim [aut, cre] , Jiawei Zhang [ctb, cph] , Jie Ding [ctb, cph] , Yuhong Yang [ctb, cph]
Maintainer: Ebrahim Khaled Ebrahim <ebrahimkhaled@alexu.edu.eg>

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Package deseats updated to version 1.1.3 with previous version 1.1.2 dated 2026-03-16

Title: Data-Driven Locally Weighted Regression for Trend and Seasonality in TS
Description: Various methods for the identification of trend and seasonal components in time series (TS) are provided. Among them is a data-driven locally weighted regression approach with automatically selected bandwidth for equidistant short-memory time series. The approach is a combination / extension of the algorithms by Feng (2013) <doi:10.1080/02664763.2012.740626> and Feng, Y., Gries, T., and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598> and a brief description of this new method is provided in the package documentation and in more detail in Schulz (2026) <doi:10.32614/RJ-2026-025>. Furthermore, the package allows its users to apply the base model of the Berlin procedure, version 4.1, as described in Speth (2004) <https://www.destatis.de/DE/Methoden/Saisonbereinigung/BV41-methodenbericht-Heft3_2004.pdf?__blob=publicationFile>. Permission to include this procedure was kindly provided by the Federal Statistical Office of Germany.
Author: Yuanhua Feng [aut] , Dominik Schulz [aut, cre]
Maintainer: Dominik Schulz <dominik.schulz.r@gmail.com>

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Package coresynth updated to version 1.0.1 with previous version 0.5.0 dated 2026-08-25

Title: Fast and Unified Synthetic Control Methods
Description: A unified 'Formula' interface to the Synthetic Control Method (SCM) and related panel-data causal inference estimators: Synthetic Difference-in-Differences (SDID), Generalized Synthetic Control (GSC), Matrix Completion (MC), Time-Aware Synthetic Control (TASC), and Synthetic Interventions (SI), together with an experimental-design variant. Computational bottlenecks (quadratic programming, singular value decomposition, and Kalman filtering) are implemented in 'C++' via 'RcppArmadillo'. Methods are described in Abadie, Diamond and Hainmueller (2010) <doi:10.1198/jasa.2009.ap08746>, Arkhangelsky, Athey, Hirshberg, Imbens and Wager (2021) <doi:10.1257/aer.20190159>, Xu (2017) <doi:10.1017/pan.2016.2>, Athey, Bayati, Doudchenko, Imbens and Khosravi (2021) <doi:10.1080/01621459.2021.1891924>, and Agarwal, Shah and Shen (2025) <doi:10.1287/opre.2025.1590>.
Author: Yosuke Abe [aut, cre]
Maintainer: Yosuke Abe <yosuke.abe0507@gmail.com>

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Package openxlsx2 updated to version 1.30 with previous version 1.29 dated 2026-08-20

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

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Package pkgstats updated to version 0.2.5 with previous version 0.2.4 dated 2026-07-21

Title: Metrics of R Packages
Description: Static code analyses for R packages using the external code-tagging libraries 'ctags' and 'gtags'. Static analyses enable packages to be analysed very quickly, generally a couple of seconds at most. The package also provides access to a database generating by applying the main function to the full 'CRAN' archive, enabling the statistical properties of any package to be compared with all other 'CRAN' packages.
Author: Mark Padgham [aut, cre] , Michael Sumner [ctb] , Jeffrey Hollister [ctb] , Egor Kotov [ctb]
Maintainer: Mark Padgham <mark.padgham@email.com>

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Package pkgdiff updated to version 1.0.5 with previous version 1.0.4 dated 2026-07-14

Title: Identifies Package Differences
Description: Identifies differences between versions of a package. Specifically, the functions help determine if there are breaking changes from one package version to the next. The package also includes a stability assessment, to help you determine the overall stability of a package, or even an entire repository.
Author: David Bosak [aut, cre], Brian Varney [ctb], Kevin Putschko [ctb]
Maintainer: David Bosak <dbosak01@gmail.com>

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Package skmeans updated to version 0.2-21 with previous version 0.2-20 dated 2026-03-26

Title: Spherical k-Means Clustering
Description: Algorithms to compute spherical k-means partitions. Features several methods, including a genetic and a fixed-point algorithm and an interface to the CLUTO vcluster program.
Author: Kurt Hornik [aut, cre] , Ingo Feinerer [aut] , Martin Kober [aut]
Maintainer: Kurt Hornik <Kurt.Hornik@R-project.org>

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Package shinyglass updated to version 0.5.0 with previous version 0.4.0 dated 2026-09-20

Title: Liquid Glass Design Themes for 'shiny' Applications
Description: Provides drop-in Liquid Glass themes for 'shiny'. Call glass_theme() and pass the result as theme = to fluidPage(), navbarPage(), or any 'bslib'-aware page function to get translucent surfaces, backdrop blur, and system typography on 'Bootstrap' components. Includes light and dark presets with runtime switching and an OS-following 'auto' mode, an iOS-style intensity control from Ultra Clear to Tinted, optional persistence of the look, named wallpaper scenes, helpers to match 'ggplot2', 'plotly', 'gt', and 'DT' output to the glass pack, a flatten mode for print and screenshots, and documented CSS tokens.
Author: Eric Anderson [aut, cre, cph]
Maintainer: Eric Anderson <eric.ray.anderson@gmail.com>

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Package duckh3 updated to version 0.2.0 with previous version 0.1.0 dated 2026-04-24

Title: H3 Extension of 'DuckDB'
Description: Fast & memory-efficient functions to analyze and manipulate large data sets. It leverages the fast analytical capabilities of 'DuckDB' and its spatial extension (see <https://duckdb.org/community_extensions/extensions/h3>) while maintaining compatibility with R’s spatial data ecosystem to work with spatial vector data.
Author: Adrian Cidre Gonzalez [aut, cre] , Egor Kotov [aut] , Rafael H. M. Pereira [aut]
Maintainer: Adrian Cidre Gonzalez <adrian.cidre@gmail.com>

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Package MFF updated to version 0.2.5 with previous version 0.2.4 dated 2026-08-31

Title: Meta Fuzzy Functions
Description: Implements Meta Fuzzy Functions (MFFs) for regression Tak and Ucan (2026) <doi:10.1016/j.asoc.2026.114592> by aggregating predictions from multiple base learners using membership weights learned in the prediction space of validation set. The package supports fuzzy and crisp meta-ensemble structures via Fuzzy C-Means (FCM) Tak (2018) <doi:10.1016/j.asoc.2018.08.009>, Possibilistic FCM (PFCM) Tak (2021) <doi:10.1016/j.ins.2021.01.024>, Gustafson–Kessel (GK) clustering, and k-means, and provides a workflow to (i) generate validation/test prediction matrices from common regression learners (linear and penalized regression via 'glmnet', random forests, gradient boosting with 'xgboost' and 'lightgbm'), (ii) fit cluster-wise meta fuzzy functions and compute membership-based weights, (iii) tune clustering-related hyperparameters (number of clusters/functions, fuzziness exponent, possibilistic regularization) via grid search on validation loss, and (iv) predict on new/test pre [...truncated...]
Author: Nihat Tak [aut, cre], Sadik Coban [ctb]
Maintainer: Nihat Tak <nihattak@gmail.com>

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Package lambdastar updated to version 0.8.2 with previous version 0.8.1 dated 2026-10-07

Title: Measurement and Linear Hypothesis Models for Lambda Star
Description: Estimates intrinsic and captured noncentrality from parallel measurements and evaluates the numerical parsimony functional for an explicitly encoded hypothesis matrix, formula, or compatible linear model. Includes common-case model comparisons, quantized entropy capacity, controlled temperature integration, and explicit singularity diagnostics. Uses manuscript projection estimates by default, with an explicit alternative population estimator. Supports common nuisance adjustment and ordinary case or cluster percentile bootstrap intervals, with diagnostics for undefined estimates and preserved model coding. Provides reusable row-bound design specifications and explicit conditional term blocks, named linear restrictions, explicit predictor-grid contrasts, and fixed or reevaluated basis recipes under a homogeneous isotropic measurement-fluctuation assumption. Supports explicit known-reference mean hypotheses and paired differences from parallel measurement pairs. Encodes fixed person-by-oc [...truncated...]
Author: Mike Hammes [aut, cre, cph]
Maintainer: Mike Hammes <mike.hammes@mikehammes.name>

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Package fable.prophet updated to version 1.0.0 with previous version 0.1.1 dated 2026-10-02

Title: Prophet Modelling Interface for 'fable'
Description: Allows prophet models from the 'prophet' package to be used in a tidy workflow with the modelling interface of 'fabletools'. This extends 'prophet' to provide enhanced model specification and management, performance evaluation methods, and model combination tools.
Author: Mitchell O'Hara-Wild [aut, cre], Sean Taylor [ctb] , Ben Letham [ctb]
Maintainer: Mitchell O'Hara-Wild <mail@mitchelloharawild.com>

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Sat, 10 Oct 2026

Package Gtheory4LLM updated to version 0.5.0 with previous version 0.2.0 dated 2026-10-04

Title: Generalizability Theory for LLM Subjective Tasks
Description: Studies the reliability and generalizability of subjective judgments produced by large language models (LLMs), including annotation, rating, and structured LLM-as-a-judge tasks. Specifies evaluator, prompt, generation, and repeated-run facets through crossed or explicitly nested random sources with configurable item interactions. Fits univariate models, joint Gaussian models, and joint discrete models for binary, ordinal, and unordered categorical outcomes, with source-specific covariance. Gaussian models use exact balanced likelihood; discrete models use a dense first-order Laplace approximation with Gaussian latent random effects. Supported balanced decision studies compare evaluator, prompt, and replication allocations using observed Gaussian or explicitly requested latent binary and ordinal reliability, with random or fixed facets after Brennan (2001). Gaussian fits report asymptotic Wald standard errors for their variance components and delta-method intervals for the coefficients; [...truncated...]
Author: Jin Liu [aut, cre, cph]
Maintainer: Jin Liu <Veronica.Liu0206@gmail.com>

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Package silicate updated to version 0.7.2 with previous version 0.7.1 dated 2023-01-06

Title: Common Forms for Complex Hierarchical and Relational Data Structures
Description: Generate common data forms for complex data suitable for conversions and transmission by decomposition as paths or primitives. Paths are sequentially-linked records, primitives are basic atomic elements and both can model many forms and be grouped into hierarchical structures. The universal models 'SC0' (structural) and 'SC' (labelled, relational) are composed of edges and can represent any hierarchical form. Specialist models 'PATH', 'ARC' and 'TRI' provide the most common intermediate forms used for converting from one form to another. The methods are inspired by the simplicial complex <https://en.wikipedia.org/wiki/Simplicial_complex> and provide intermediate forms that relate spatial data structures to this mathematical construct.
Author: Michael D. Sumner [aut, cre], John Corbett [ctb] , Simon Wotherspoon [ctb], Kent Johnson [dtc], Mark Padgham [aut]
Maintainer: Michael D. Sumner <mdsumner@gmail.com>

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Package semFromKeys updated to version 0.5.6 with previous version 0.5.5 dated 2026-09-13

Title: Run 'lavaan' Models from Keys Lists
Description: Specifying 'lavaan' models manually can be time consuming when multiple similar models are required. The 'semFromKeys' package streamlines the process of running 'lavaan' models by generating model code from simple keys lists and running entire collections of models at once. The package was inspired by the process used in the code for Bainbridge, T. F., Ludeke, S. G., & Smillie, L. D. (2022) <doi:10.1037/pspp0000395>. The package also optionally checks that identical models have not been run on the same data, which saves time when code needs to be run again.
Author: Timothy F. Bainbridge [aut, cre, cph]
Maintainer: Timothy F. Bainbridge <tfbainbridge@gmail.com>

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Package MultiSpline updated to version 0.3.0 with previous version 0.2.0 dated 2026-04-16

Title: Spline-Based Nonlinear Modeling for Multilevel and Longitudinal Data
Description: Provides a unified framework for fitting, predicting, and interpreting nonlinear relationships in single-level, multilevel, and longitudinal regression models. Flexible functional forms are supported using natural cubic splines ('splines'), B-splines ('splines'), and GAM smooths ('mgcv'). Supports two-way and nested clustering via 'lme4', automatic knot selection by AIC or BIC, multilevel R-squared decomposition (marginal and conditional R-squared of Nakagawa and Schielzeth (2013) <doi:10.1111/j.2041-210x.2012.00261.x>, extended to random slopes as in Johnson (2014) <doi:10.1111/2041-210X.12225>, with level-specific variance partitioning), a postestimation suite returning first and second derivatives with confidence bands, turning points and inflection regions, and a model comparison workflow contrasting linear, polynomial, and spline fits by AIC, BIC, and likelihood-ratio tests. Cluster heterogeneity in nonlinear effects is supported via random-slope spline terms. Any 'lme [...truncated...]
Author: Subir Hait [aut, cre]
Maintainer: Subir Hait <haitsubi@msu.edu>

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Package radiant.multivariate updated to version 1.6.9 with previous version 1.6.8 dated 2026-01-10

Title: Multivariate Menu for Radiant: Business Analytics using R and Shiny
Description: The Radiant Multivariate menu includes interfaces for perceptual mapping, factor analysis, cluster analysis, and conjoint analysis. The application extends the functionality in 'radiant.data'.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

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Package radiant.model updated to version 1.6.13 with previous version 1.6.12 dated 2026-09-20

Title: Model Menu for Radiant: Business Analytics using R and Shiny
Description: The Radiant Model menu includes interfaces for linear and logistic regression, naive Bayes, neural networks, classification and regression trees, model evaluation, collaborative filtering, decision analysis, and simulation. The application extends the functionality in 'radiant.data'.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

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Package sd2R updated to version 0.2.1.1 with previous version 0.2.1 dated 2026-06-19

Title: Stable Diffusion Image Generation
Description: Provides Stable Diffusion image generation using the 'ggmlR' library, with no 'Python' or external API dependencies. Supports text-to-image and image-to-image generation for SD 1.x, SD 2.x, 'SDXL', Flux, and 'FLUX.2'. A single sd_generate() function handles the entire pipeline, including sampling and high-resolution output. Features multi-GPU support, a 'Shiny' GUI, and runs on CPU or 'Vulkan' GPU across Linux, macOS, and Windows.
Author: Yuri Baramykov [aut, cre] , Georgi Gerganov [ctb, cph] , leejet [ctb, cph] , stduhpf [ctb] , Green-Sky [ctb] , wbruna [ctb] , akleine [ctb] , Martin Raiber [cph] , Rich Geldreich [cph] , RAD Game Tools [cph] , Valve Software [cph] , Alex Evans [cph] [...truncated...]
Maintainer: Yuri Baramykov <lbsbmsu@mail.ru>

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Package bioregion updated to version 1.5.0 with previous version 1.4.0 dated 2026-03-29

Title: Comparison of Bioregionalization Methods
Description: The main purpose of this package is to propose a transparent methodological framework to compare bioregionalization methods based on hierarchical and non-hierarchical clustering algorithms (Kreft & Jetz (2010) <doi:10.1111/j.1365-2699.2010.02375.x>) and network algorithms (Lenormand et al. (2019) <doi:10.1002/ece3.4718> and Leroy et al. (2019) <doi:10.1111/jbi.13674>).
Author: Maxime Lenormand [aut, cre] , Boris Leroy [aut] , Pierre Denelle [aut]
Maintainer: Maxime Lenormand <maxime.lenormand@inrae.fr>

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Package wrappedtools updated to version 0.10.0 with previous version 0.9.9 dated 2025-09-04

Title: Useful Wrappers Around Commonly Used Functions
Description: The main functionalities of 'wrappedtools' are: adding backticks to variable names; rounding to desired precision with special case for p-values; selecting columns based on pattern and/or class and storing their position, name, and backticked name; computing and formatting of descriptive statistics (e.g. mean±SD), comparing groups and creating publication-ready tables with descriptive statistics and p-values; creating specialized plots for correlation matrices. Functions were mainly written for my own daily work or teaching, but may be of use to others as well.
Author: Andreas Busjahn [cre, aut] , Franziska Eidloth [aut], Bilal Asser [aut]
Maintainer: Andreas Busjahn <andreas@busjahn.net>

Diff between wrappedtools versions 0.9.9 dated 2025-09-04 and 0.10.0 dated 2026-10-10

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Package scorecraft updated to version 0.3.2 with previous version 0.3.0 dated 2026-10-06

Title: Scorecard Development and Internal Ratings-Based Risk Parameters
Description: Builds points scorecards for binary targets (credit risk, fraud, propensity) on the optimal binning and weight of evidence engine of 'OptimalBinningWoE', and takes them to the risk parameters of the internal ratings-based (IRB) approach. Variables are selected through optimal binning, eight admission rules, hold-out revalidation with frozen bins and a consensus of 'glmnet', 'xgboost', 'lightgbm' and 'ranger' models weighted by out-of-sample performance; the audit funnel never drops a candidate from the report. The scorecard is fitted with an explicit, auditable scale alignment (a log-odds regression on the raw score composed with the points-to-double-the-odds map); cut-offs are swept with frozen cuts; reject inference is reported as a sensitivity band; the population and characteristic stability indices (PSI and CSI) are monitored with both the fixed and the sample-size-adjusted threshold; and production SQL is generated in fourteen dialects, with the agreement between R and SQL verifi [...truncated...]
Author: Jose Evandeilton Lopes [aut, cre, cph]
Maintainer: Jose Evandeilton Lopes <evandeilton@gmail.com>

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Package fdb updated to version 0.2.2 with previous version 0.2.0 dated 2026-10-04

Title: Frequentist Dynamic Borrowing for Hybrid-Control Survival Trials
Description: Implements a class of likelihood-informed frequentist dynamic borrowing methods for hybrid-control survival trials based on penalized Cox partial likelihood estimation. Implements four likelihood-informed penalty structures (precision-weighted L1, smoothed integrated-gate, information-adaptive minimax concave penalty (MCP), and likelihood-ratio-weighted L1), together with the adaptive lasso borrowing approach of Li et al. (2023, <doi:10.1002/bimj.202100406>). Provides conditional model-based standard errors and local plug-in sandwich variance approximations, with smoothed penalties. Tools for design-stage lambda calibration via simulation, including a two-stage coarse-fine grid search, drift-level early stopping, and per-method tuning under both inference types, are also provided. A simulation harness for evaluating type I error and statistical power across population drift scenarios is included.
Author: Yusuke Yamaguchi [aut, cre]
Maintainer: Yusuke Yamaguchi <yamagubed@gmail.com>

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Package neuralnetwork updated to version 0.1.4 with previous version 0.1.1 dated 2026-09-06

Title: Fast Compact Multilayer Perceptrons
Description: A small multilayer perceptron implementation for 'R'. It supports regression and classification, multiple hidden layers, mini-batch training, adaptive moment estimation, stochastic gradient descent, momentum, Nesterov acceleration, resilient backpropagation and limited-memory quasi-Newton optimization, dropout, squared-weight regularization, early stopping, convergence thresholds, gradient clipping, sample and class weights, callback hooks, target scaling and robust Huber loss for regression, 'Rcpp' forward-pass kernels, formula interfaces, model evaluation with balanced classification metrics, cross-validation, compact tuning, permutation importance, model persistence helpers, and standard prediction methods. Methods follow Rumelhart, Hinton and Williams (1986) <doi:10.1038/323533a0>, with optimizers including Riedmiller and Braun (1993) <doi:10.1109/ICNN.1993.298623>, Nocedal (1980) <doi:10.1090/S0025-5718-1980-0572855-7>, and Kingma and Ba (2014) <doi:10.48550/a [...truncated...]
Author: Feng Ji [aut, cre]
Maintainer: Feng Ji <f.ji@utoronto.ca>

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Package iSTATS updated to version 1.8 with previous version 1.7 dated 2023-12-05

Title: A Graphical Interface to Perform STOCSY Analyses on NMR Data
Description: Launches a 'shiny' based application for Nuclear Magnetic Resonance (NMR) data importation and Statistical TOtal Correlation SpectroscopY (STOCSY) analyses in a full interactive approach. The theoretical background and applications of the STOCSY method are described by Cloarec, O., Dumas, M. E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., Blancher, C., Gauguier, D., Lindon, J. C., Holmes, E. & Nicholson, J. (2005) <doi:10.1021/ac048630x>. Spectral alignment follows the interval correlation optimized shifting method of Savorani, F., Tomasi, G. & Engelsen, S. B. (2010) <doi:10.1016/j.jmr.2009.11.012>.
Author: Luiz Henrique Keng Queiroz Junior [aut, cre], Vitor Mendes de Oliveira [aut], Renan Ziemann Wilhelms [aut]
Maintainer: Luiz Henrique Keng Queiroz Junior <keng@ufg.br>

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Package essential8 updated to version 0.2.0 with previous version 0.1.0 dated 2026-09-03

Title: Reproducible Cardiovascular Health Metric Scoring
Description: Provides a reproducible R implementation of the American Heart Association Life's Essential 8 cardiovascular health scoring framework for adult records from people aged 20 years or older. Calculates the eight component scores and their unweighted composite score from available component data using the 2022 adult definition described by Lloyd-Jones et al. (2022) <doi:10.1161/CIR.0000000000001078>.
Author: Carson Richardson [aut, cre, cph] , Amrik Khalsa [ctb] , Sara Conroy [ctb]
Maintainer: Carson Richardson <carson.richardson@outlook.com>

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Package duckspatial updated to version 1.3.0 with previous version 1.2.1 dated 2026-07-04

Title: R Interface to 'DuckDB' Database with Spatial Extension
Description: Fast and memory-efficient functions to analyze and manipulate large spatial datasets. It leverages the fast analytical capabilities of ‘DuckDB’ and its spatial extension (see <https://duckdb.org/docs/stable/core_extensions/spatial/overview>) while maintaining compatibility with R’s spatial data ecosystem to work with spatial vector data.
Author: Adrian Cidre Gonzalez [aut, cre] , Egor Kotov [aut] , Rafael H. M. Pereira [aut]
Maintainer: Adrian Cidre Gonzalez <adrian.cidre@gmail.com>

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Package denstest updated to version 1.0.2 with previous version 1.0.1 dated 2026-06-25

Title: Density Equality Testing
Description: Methods for testing the equality between groups of estimated density functions. The package implements FDET (Fourier-based Density Equality Testing) and MDET (Moment-based Density Equality Testing), two new approaches introduced by the author. Both methods extend an earlier testing approach by Delicado (2007), "Functional k-sample problem when data are density functions" <doi:10.1007/s00180-007-0047-y>, which is referred to as DET (Density Equality Testing) in this package for clarity. FDET compares groups of densities based on their global shape using Fourier transforms, while MDET tests for differences in distributional moments. All methods are described in Anarat, Krutmann and Schwender (2026), "Testing for Differences in Extrinsic Skin Aging Based on Density Functions" (Under revision).
Author: Akin Anarat [aut, cre]
Maintainer: Akin Anarat <akin.anarat@hhu.de>

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Package bayprior updated to version 0.4.0 with previous version 0.3.2 dated 2026-08-19

Title: Bayesian Prior Elicitation, Diagnostics, and Regulatory Reporting
Description: A toolkit for constructing, validating, and justifying Bayesian priors in clinical trial settings. Implements expert elicitation via quantile matching, the roulette method, and moment matching across six distribution families, linear and logarithmic expert pooling, meta-analytic-predictive (MAP) priors derived from historical trials, prior-data conflict diagnostics including the Box p-value (with an exact prior predictive option for selected prior and data combinations), an exploratory surprisal (S-value) companion to it, surprise index, information divergence, and Mahalanobis distance, sensitivity analyses with tornado and influence heatmap plots, sceptical, robust, and power priors, and automated prior justification reports. Includes a fully modular 'Shiny' application for interactive use. Methods based on O'Hagan et al. (2006, ISBN:9780470029886), Box (1980) <doi:10.2307/2982063>, Greenland (2023) <doi:10.1111/sjos.12625>, Oakley and O'Hagan (2010) <https://tonyohagan [...truncated...]
Author: Ndoh Penn [aut, cre]
Maintainer: Ndoh Penn <ndohpenn9@gmail.com>

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Package enviGCMS updated to version 0.9.1 with previous version 0.9.0 dated 2026-10-07

Title: GC/LC-MS Data Analysis for Environmental Science
Description: Gas/Liquid Chromatography-Mass Spectrometer(GC/LC-MS) Data Analysis for Environmental Science. This package covered topics such molecular isotope ratio, matrix effects and Short-Chain Chlorinated Paraffins analysis etc. in environmental analysis.
Author: Miao YU [aut, cre] , Thanh Wang [ctb]
Maintainer: Miao YU <yufreecas@gmail.com>

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Package BGVAR updated to version 2.7.0 with previous version 2.5.9 dated 2025-09-22

Title: Bayesian Global Vector Autoregressions
Description: Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available. The package has a companion paper: Boeck, M., Feldkircher, M. and F. Huber (2022) "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R", Journal of Statistical Software, Vol. 104(9), pp. 1-28 <doi:10.18 [...truncated...]
Author: Maximilian Boeck [aut, cre] , Martin Feldkircher [aut] , Florian Huber [aut] , Darjus Hosszejni [ctb]
Maintainer: Maximilian Boeck <maximilian.boeck@wu.ac.at>

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Package polle updated to version 1.6.6 with previous version 1.6.5 dated 2026-09-30

Title: Policy Learning
Description: Package for learning and evaluating (subgroup) policies via doubly robust loss functions. Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2026) <doi:10.18637/jss.v116.i04> for documentation and references.
Author: Andreas Nordland [aut, cre], Klaus Holst [aut]
Maintainer: Andreas Nordland <andreasnordland@gmail.com>

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Package wordbankr readmission to version 2.0.0 with previous version 1.0.3 dated 2024-03-01

Title: Accessing the 'Wordbank' Database
Description: Connecting to 'Wordbank' <https://wordbank.stanford.edu>, an open repository for developmental vocabulary data from the MacArthur-Bates Communicative Development Inventories (Frank et al. 2017 <doi:10.1017/S0305000916000209>). Data are read from a versioned dataset hosted on 'Redivis', so analyses can be pinned to a specific release.
Author: Mika Braginsky [aut, cre, cph], Daniel Yurovsky [ctb], Michael Frank [ctb], Danielle Kellier [ctb], Alvin Tan [ctb]
Maintainer: Mika Braginsky <mika.br@gmail.com>

This is a re-admission after prior archival of version 1.0.3 dated 2024-03-01

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Package SeqNet readmission to version 1.1.4 with previous version 1.1.3 dated 2021-07-09

Title: Generate RNA-Seq Data from Gene-Gene Association Networks
Description: Methods to generate random gene-gene association networks and simulate RNA-seq data from them, as described in Grimes and Datta (2021) <doi:10.18637/jss.v098.i12>. Includes functions to generate random networks of any size and perturb them to obtain differential networks. Network objects are built from individual, overlapping modules that represent pathways. The resulting network has various topological properties that are characteristic of gene regulatory networks. RNA-seq data can be generated such that the association among gene expression profiles reflect the underlying network. A reference RNA-seq dataset can be provided to model realistic marginal distributions. Plotting functions are available to visualize a network, compare two networks, and compare the expression of two genes across multiple networks.
Author: Tyler Grimes [aut, cre], Somnath Datta [aut]
Maintainer: Tyler Grimes <tyler.grimes@unf.edu>

This is a re-admission after prior archival of version 1.1.3 dated 2021-07-09

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Package markovchain updated to version 1.2 with previous version 1.1.1 dated 2026-09-18

Title: Easy Handling Discrete Time Markov Chains
Description: Description: Functions and S4 classes to create, manage and analyse discrete time Markov chains. Includes probabilistic analysis of chain structure (state classification, hitting times, stationary distributions, spectral and mixing diagnostics), statistical inference (maximum likelihood and Bayesian estimation, tests of the Markov property, homogeneity, stationarity and order, simulation), higher-order and multivariate chains, state aggregation, and import/export to JSON, YAML and CSV. Basic support for continuous time Markov chains is also provided; some of these functions depend on the suggested 'ctmcd' package. See Spedicato (2017) <doi:10.32614/RJ-2017-036>.
Author: Giorgio Alfredo Spedicato [aut, cre] , Tae Seung Kang [aut], Sai Bhargav Yalamanchi [aut], Mildenberger Thoralf [ctb] , Deepak Yadav [aut], Ignacio Cordon [aut] , Vandit Jain [ctb], Toni Giorgino [ctb] , Richel J.C. Bilderbeek [ctb] , Daniel Ebbert [ [...truncated...]
Maintainer: Giorgio Alfredo Spedicato <spedicato_giorgio@yahoo.it>

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Permanent link

Package fqardl updated to version 1.1.0 with previous version 1.0.6 dated 2026-09-30

Title: Fourier ARDL Methods: Quantile, Nonlinear, Multi-Threshold & Unit Root Tests
Description: Implementation of ARDL methods for cointegration analysis with structural breaks and asymmetric effects. Includes: (1) Fourier Quantile ARDL (FQARDL), quantile regression with Fourier approximation following the quantile ARDL of Cho, Kim and Shin (2015) <doi:10.1016/j.jeconom.2015.05.003>; (2) Fourier Nonlinear ARDL (FNARDL), asymmetric cointegration with partial sum decomposition following Shin, Yu and Greenwood-Nimmo (2014) <doi:10.1007/978-1-4899-8008-3_9>; (3) a deprecated Multi-Threshold NARDL (MTNARDL) function; (4) Fourier unit root tests, ADF and KPSS tests with Fourier terms following Enders and Lee (2012) <doi:10.1016/j.econlet.2012.04.081> and Becker, Enders and Lee (2006) <doi:10.1111/j.1467-9892.2006.00478.x>. Features automatic lag and frequency selection, Wald bounds statistics with the tables of Pesaran, Shin and Smith (2001) <doi:10.1002/jae.616> (no verdict with Fourier terms, for which the tables are not valid), recursive bootstrap bound [...truncated...]
Author: Muhammad Alkhalaf [aut, cre, cph]
Maintainer: Muhammad Alkhalaf <muhammedalkhalaf@gmail.com>

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Package fbardl updated to version 1.2.0 with previous version 1.1.0 dated 2026-09-30

Title: Fourier Bootstrap ARDL Cointegration Test
Description: Implements the Fourier Bootstrap Autoregressive Distributed Lag (FBARDL) bounds testing approach for cointegration analysis. Combines the Pesaran, Shin and Smith (2001) <doi:10.1002/jae.616> ARDL bounds testing framework with Fourier terms to capture smooth structural breaks, as in Yilanci, Bozoklu and Gorus (2020) <doi:10.1016/j.scs.2020.102035>, and recursive bootstrap critical values following McNown, Sam and Goh (2018) <doi:10.1080/00036846.2017.1366643> and Bertelli, Vacca and Zoia (2022) <doi:10.1016/j.econmod.2022.105987>, with the Fourier frequency and the lags selected again in every bootstrap replication (a package choice); finite-sample bounds test critical values from Kripfganz and Schneider (2020) <doi:10.1111/obes.12377> for models without Fourier terms. Features include lag selection via AIC/BIC, Fourier frequency selection by minimum SSR, long-run and short-run coefficient estimation and diagnostic tests.
Author: Muhammad Alkhalaf [aut, cre, cph]
Maintainer: Muhammad Alkhalaf <muhammedalkhalaf@gmail.com>

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Package estimatr updated to version 2.0.1 with previous version 2.0.0 dated 2026-09-16

Title: Fast Estimators for Design-Based Inference
Description: Fast procedures for a small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>. Fixed effects are absorbed by alternating projections rather than by dummy expansion, and Horvitz-Thompson variance is computed from the randomization declaration.
Author: Alexander Coppock [aut, cre], Graeme Blair [aut], Jasper Cooper [aut], Luke Sonnet [aut], Macartan Humphreys [ctb], Neal Fultz [ctb], Lily Medina [ctb], Russell Lenth [ctb], Molly Offer-Westort [ctb]
Maintainer: Alexander Coppock <acoppock@gmail.com>

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Package ardlverse updated to version 2.1.0 with previous version 2.0.3 dated 2026-09-30

Title: Comprehensive ARDL: Panel, Bootstrap and Fourier Methods
Description: A unified framework for Autoregressive Distributed Lag (ARDL) modeling and cointegration analysis. Implements Panel ARDL with Pooled Mean Group (PMG), Mean Group (MG), and Dynamic Fixed Effects (DFE) estimators following Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>. Provides the bounds test of Pesaran, Shin and Smith (2001) <doi:10.1002/jae.616> with the critical values of Kripfganz and Schneider (2020) <doi:10.1111/obes.12377> and with recursive bootstrap critical values following Bertelli, Vacca and Zoia (2022) <doi:10.1016/j.econmod.2022.105987> and McNown, Sam and Goh (2018) <doi:10.1080/00036846.2017.1366643>. Includes Quantile Nonlinear ARDL (QNARDL) combining distributional and asymmetric effects based on Shin, Yu and Greenwood-Nimmo (2014) <doi:10.1007/978-1-4899-8008-3_9>, Fourier ARDL for modeling smooth structural breaks following Enders and Lee (2012) <doi:10.1016/j.econlet.2012.04.081>, and Fourier (bootstrap [...truncated...]
Author: Muhammad Abdullah Alkhalaf [aut, cre] , Yeleazar Levchenko [ctb] )
Maintainer: Muhammad Abdullah Alkhalaf <muhammedalkhalaf@gmail.com>

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Package steinsampling updated to version 0.1.3 with previous version 0.1.2 dated 2026-09-07

Title: Kernelized Stein Discrepancy for Goodness-of-Fit Tests and Stein Sampling
Description: Provides Stein-discrepancy goodness-of-fit tests and Stein-method-based sampling tools. The tests include kernel Stein discrepancy U- and V-statistics following Liu et al. (2016) <doi:10.48550/arXiv.1602.03253> and Chwialkowski et al. (2016) <doi:10.48550/arXiv.1602.02964>, plus the finite set Stein discrepancy test of Jitkrittum et al. (2017) <doi:10.48550/arXiv.1705.07673>. The sampling tools include Stein thinning, Stein Points, Stein Point Markov chain Monte Carlo, and Stein variational gradient descent following Riabiz et al. (2022) <doi:10.48550/arXiv.2005.03952>, Chen et al. (2018) <doi:10.48550/arXiv.1803.10161>, Chen et al. (2019) <doi:10.48550/arXiv.1905.03673>, and Liu and Wang (2016) <doi:10.48550/arXiv.1608.04471>. Gaussian mixture utilities are included for constructing example targets, simulation, density evaluation, and score callbacks.
Author: Junhao Gao [aut, cre], Ery Arias-Castro [aut]
Maintainer: Junhao Gao <jug049@ucsd.edu>

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Package mathr updated to version 0.1.5 with previous version 0.1.3 dated 2026-09-10

Title: Scientific Computation Using R
Description: A collection of undergraduate level mathematical routines for quantitative work, covering calculus, distribution functions, random variate generation, linear algebra, differential equations and optimization, sized for one semester. The package is best studied with its source open: every routine is short and written to be read.
Author: Kyun-Seop Bae [aut, cre]
Maintainer: Kyun-Seop Bae <k@acr.kr>

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Package lifecontingencies updated to version 1.6.3 with previous version 1.6.0 dated 2026-08-23

Title: Financial and Actuarial Mathematics for Life Contingencies
Description: Classes and methods that allow the user to manage life table, actuarial tables (also multiple decrements tables). Moreover, functions to easily perform demographic, financial and actuarial mathematics on life contingencies insurances calculations are contained therein. See Spedicato (2013) <doi:10.18637/jss.v055.i10>.
Author: Giorgio Alfredo Spedicato [aut, cre] , Christophe Dutang [ctb] , Reinhold Kainhofer [ctb] , Kevin J Owens [ctb], Ernesto Schirmacher [ctb], Gian Paolo Clemente [ctb] , Ivan Williams [ctb]
Maintainer: Giorgio Alfredo Spedicato <spedicato_giorgio@yahoo.it>

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Package Romney updated to version 0.1.1 with previous version 0.1.0 dated 2026-05-20

Title: Classical Cultural Consensus Analysis
Description: Implements classical cultural consensus analysis with formal, informal, and covariance agreement models, 'UCINET'-aligned minimum-residual factor extraction, competence estimation, and answer-key estimation. Based on the classical framework of Romney, Weller, and Batchelder (1986) <doi:10.1525/aa.1986.88.2.02a00020>, Romney, Batchelder, and Weller (1987) <doi:10.1177/000276487031002003>, and Weller (2007) <doi:10.1177/1525822X07303502>.
Author: Werner Hertzog [aut, cre]
Maintainer: Werner Hertzog <werner.hertzog@isek.uzh.ch>

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Package pc updated to version 0.3 with previous version 0.2 dated 2026-04-10

Title: Pattern Causality Analysis
Description: Infer causation from observational data through pattern causality analysis (PC), with original algorithm for time series data from Stavroglou et al. (2020) <doi:10.1073/pnas.1918269117>, as well as methodological extensions for spatial cross-sectional data introduced by Zhang & Wang (2025) <doi:10.1080/13658816.2025.2581207>, together with a systematic description proposed in Lyu et al. (2026) <doi:10.1016/j.compenvurbsys.2026.102435>.
Author: Wenbo Lyu [aut, cre, cph]
Maintainer: Wenbo Lyu <lyu.geosocial@gmail.com>

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Package forestsearch updated to version 0.4.0 with previous version 0.1.0 dated 2026-03-23

Title: Exploratory Subgroup Identification in Survival and GLM Outcomes
Description: Implements statistical methods for exploratory subgroup identification in clinical trials. Provides tools for identifying patient subgroups with differential treatment effects using machine learning approaches including Generalized Random Forests (GRF), LASSO regularization, and exhaustive combinatorial search algorithms. Supports survival endpoints (Cox proportional hazards), binary outcomes (log odds ratio, log relative risk, risk difference), continuous outcomes (mean difference), and count / rate outcomes (log incidence rate ratio via Poisson, quasi-Poisson, or negative-binomial GLMs with optional person-time offset). Features bootstrap bias correction using infinitesimal jackknife methods to address selection bias in post-hoc analyses. Designed for clinical researchers conducting exploratory subgroup analyses in randomized controlled trials, particularly for multi-regional clinical trials (MRCT) requiring regional consistency evaluation. Methods are described in Leon et al. (2024) [...truncated...]
Author: Larry Leon [aut, cre]
Maintainer: Larry Leon <larry.leon.05@post.harvard.edu>

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Fri, 09 Oct 2026

Package ggtangle updated to version 0.1.4 with previous version 0.1.3 dated 2026-09-24

Title: Draw Network with Data
Description: Extends the 'ggplot2' plotting system to support network visualization. Inspired by the 'Method 1' in 'ggtree' (G Yu (2018) <doi:10.1093/molbev/msy194>), 'ggtangle' is designed to work with network associated data.
Author: Guangchuang Yu [aut, cre]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>

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Package teal.modules.general updated to version 0.9.0 with previous version 0.8.0 dated 2026-08-01

Title: General Modules for 'teal' Applications
Description: Prebuilt 'shiny' modules containing tools for viewing data, visualizing data, understanding missing and outlier values within your data and performing simple data analysis. This extends 'teal' framework that supports reproducible research and analysis.
Author: Dony Unardi [aut, cre], Dawid Kaledkowski [aut], Pawel Rucki [aut], Mahmoud Hallal [aut], Ondrej Slama [ctb], Maciej Nasinski [aut], Konrad Pagacz [aut], Nikolas Burkoff [aut], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Dony Unardi <unardid@gene.com>

Diff between teal.modules.general versions 0.8.0 dated 2026-08-01 and 0.9.0 dated 2026-10-09

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Package SimtablR updated to version 3.1.1 with previous version 1.2.0 dated 2026-02-21

Title: Easy Publication-Ready Tables and Regression Analysis
Description: Streamlines the creation of descriptive frequency tables ('Table 1'), diagnostic test accuracy evaluations (sensitivity, specificity, predictive values), and multi-outcome regression summaries. Features a grammar for publication-ready epidemiological tables built on design-aware effect measures, prevalence and odds ratio calculations, and seamless integration with 'flextable' for exporting results to 'Microsoft Word' and 'PowerPoint'.
Author: Matheus Trabuco Gonzalez [aut, cre]
Maintainer: Matheus Trabuco Gonzalez <matheustrabucogonzalez@gmail.com>

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Package GulFM updated to version 0.5.1 with previous version 0.5.0 dated 2026-01-07

Title: General Unilateral Load Estimator for Two-Layer Latent Factor Models
Description: Implements general unilateral loading estimator for two-layer latent factor models with smooth, element-wise factor transformations. We provide data simulation, loading estimation,finite-sample error bounds, and diagnostic tools for zero-mean and sub-Gaussian assumptions. A unified interface is given for evaluating estimation accuracy and cosine similarity. The philosophy of the package is described in Guo G. (2026) <doi:10.1016/j.apm.2025.116280>.
Author: Guangbao Guo [aut, cre]
Maintainer: Guangbao Guo <ggb11111111@163.com>

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Package ham updated to version 1.4.0 with previous version 1.3.0 dated 2026-07-11

Title: Healthcare Analysis Methods
Description: Conducts analyses for healthcare program evaluations or intervention studies. Calculates regression analyses for standard ordinary least squares (OLS or linear) or logistic models. Performs regression models used for causal modeling such as differences-in-differences (DID) and interrupted time series (ITS) models. Provides limited interpretations of model results and a ranking of variable importance in models. Performs propensity score models, top-coding of model outcome variables, and can return new data with the newly formed variables. Conducts Bayesian analysis summaries and graphs, decision curve analysis, and produces some Shewhart control charts. Also performs Cronbach's alpha for various scale items (e.g., survey questions). See Github URL for examples in the README file. For more details on the statistical methods, see Allen & Yen (1979, ISBN:0-8185-0283-5), Angrist & Pischke (2009, ISBN:9780691120355), Cohen (1988, ISBN:0-8058-0283-5), Gebski (2012) <doi:10.1017/S0 [...truncated...]
Author: Stephen Zuniga [aut, cre, cph]
Maintainer: Stephen Zuniga <rms.shiny@gmail.com>

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Package bbqr updated to version 0.2.0 with previous version 0.1.0 dated 2026-09-08

Title: Bayesian Quantile Regression with Lasso and Adaptive Lasso
Description: Markov chain Monte Carlo samplers for Bayesian quantile regression, based on the asymmetric Laplace distribution and the location-scale mixture representation of Kozumi and Kobayashi (2011) <doi:10.1080/00949655.2010.496117>. A binary response and an observed continuous response are both supported, each with three penalty layers behind one interface: no penalty, following Benoit and Van den Poel (2012) <doi:10.1002/jae.1216>; the Bayesian lasso, following Benoit, Al-Hamzawi and Yu (2013) <doi:10.1007/s00180-013-0439-0>; and the Bayesian adaptive lasso of Rubio Garcia (2023) <https://soar.wichita.edu/entities/publication/a2f86232-4704-4ec2-b685-751e7b04ec42>. In the binary family each is available as published and in a corrected form, the default, in which every improper prior component is replaced by a proper one so that the posterior exists unconditionally; the continuous family ships the corrected form only. The continuous adaptive-lasso layer at its default r [...truncated...]
Author: Fernando Rubio Garcia [aut, cre] , Mai Dao [ths] , Dries F. Benoit [ctb, cph] , Rahim Al-Hamzawi [ctb], Keming Yu [ctb], Dirk Van den Poel [ctb]
Maintainer: Fernando Rubio Garcia <j332v755@wichita.edu>

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Package TrialSimulator updated to version 1.39.0 with previous version 1.35.8 dated 2026-09-04

Title: Clinical Trial Simulator
Description: Simulate phase II and/or phase III clinical trials. It supports various types of endpoints and adaptive strategies. Tools for carrying out graphical testing procedure and combination test under group sequential design are also provided.
Author: Han Zhang [cre, aut]
Maintainer: Han Zhang <zhangh.ustc@gmail.com>

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Permanent link

Package teal.modules.clinical updated to version 0.14.0 with previous version 0.13.0 dated 2026-07-02

Title: 'teal' Modules for Standard Clinical Outputs
Description: Provides user-friendly tools for creating and customizing clinical trial reports. By leveraging the 'teal' framework, this package provides 'teal' modules to easily create an interactive panel that allows for seamless adjustments to data presentation, thereby streamlining the creation of detailed and accurate reports.
Author: Dony Unardi [aut, cre], Joe Zhu [aut] , Jana Stoilova [aut], Davide Garolini [aut], Emily de la Rua [aut], Abinaya Yogasekaram [aut], Mahmoud Hallal [aut], Dawid Kaledkowski [aut], Rosemary Li [aut], Heng Wang [aut], Pawel Rucki [aut], Nikolas Burkof [...truncated...]
Maintainer: Dony Unardi <unardid@gene.com>

Diff between teal.modules.clinical versions 0.13.0 dated 2026-07-02 and 0.14.0 dated 2026-10-09

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Permanent link

Package rqualify updated to version 1.2.0 with previous version 1.1.0 dated 2026-08-28

Title: Qualification of R Software Installations
Description: Qualify R software installations using R Markdown as the foundation for the Installation Qualification (IQ) and Operational Qualification (OQ) when used in environments (such as regulated clinical trials) where such processes may be required.
Author: Donnie Musgrove [aut, cre] , Graeme L. Hickey [aut] , Marc Schwartz [aut] , Alan Haynes [ctb], Bryan Martin [ctb] , Medtronic Inc. [cph]
Maintainer: Donnie Musgrove <donniemusgrove@gmail.com>

Diff between rqualify versions 1.1.0 dated 2026-08-28 and 1.2.0 dated 2026-10-09

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More information about rqualify at CRAN
Permanent link

Package fcall updated to version 0.1.7 with previous version 0.1.6 dated 2025-12-10

Title: Parse Farm Credit Administration Call Report Data into Tidy Data Frames
Description: Parses financial condition and performance data (Call Reports) for institutions in the United States Farm Credit System. Contains functions for downloading files from the Farm Credit Administration (FCA) Call Report archive website and reading the files into tidy data frame format. The archive website can be found at <https://www.fca.gov/bank-oversight/call-report-data-for-download>.
Author: Michael Thomas [aut, cre], Ivan Millanes [aut], Ketchbrook Analytics [cph, fnd]
Maintainer: Michael Thomas <mthomas@ketchbrookanalytics.com>

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Package statuser updated to version 0.4.0 with previous version 0.3.1 dated 2026-06-24

Title: Statistical Tools Designed for End Users
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()).
Author: Uri Simonsohn [aut, cre]
Maintainer: Uri Simonsohn <urisohn@gmail.com>

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Package MVNGmod updated to version 0.1.3 with previous version 0.1.2 dated 2026-07-16

Title: Matrix-Variate Non-Gaussian Linear Regression Models
Description: Fits matrix-variate variance-gamma (MVVG) and matrix-variate normal-inverse-Gaussian (MVNIG) linear regression models using expectation-conditional maximization (ECM) algorithms. The models accommodate clustered matrix-valued responses, with unequal numbers of observations across subjects, correlated responses, skewness, and within-subject dependence. Functions are provided for model fitting, prediction, and subject-level influence assessment using approximate generalized Cook's distances. The package also includes motivating periodontal data from Gullah-speaking African Americans with Type-II diabetes. For details on the underlying matrix-variate distributions (MVVG and MVNIG), see Gallaugher and McNicholas (2019, <doi:10.1016/j.spl.2018.08.012>).
Author: Samuel Soon [aut, cre], Dipankar Bandyopadhyay [aut], Qingyang Liu [aut]
Maintainer: Samuel Soon <samksoon2@gmail.com>

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Package blme updated to version 1.0-8 with previous version 1.0-7 dated 2026-01-10

Title: Bayesian Linear Mixed-Effects Models
Description: Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting, implementing the methods of Chung, et al. (2013) <doi:10.1007/s11336-013-9328-2>. Extends package 'lme4' (Bates, Maechler, Bolker, and Walker (2015) <doi:10.18637/jss.v067.i01>).
Author: Vincent Dorie [aut, cre] , Douglas Bates [ctb] , Martin Maechler [ctb] , Ben Bolker [ctb] , Steven Walker [ctb]
Maintainer: Vincent Dorie <vdorie@gmail.com>

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Package teal.picks updated to version 0.3.1 with previous version 0.3.0 dated 2026-07-30

Title: Dataset and Variable Picker and Merge Module for 'teal' Applications
Description: Allows users to interactively select datasets, variables, and values within 'teal' applications using a 'tidyselect'-style interface. Selected picks can be merged and transformed into analysis-ready data within 'teal' modules.
Author: Dawid Kaledkowski [aut] , Andre Verissimo [aut] , Marcin Kosinski [aut], Lluis Revilla Sancho [aut] , Oriol Senan [aut] , Dony Unardi [rev, cre], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Dony Unardi <unardid@gene.com>

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Package surreal updated to version 0.0.3 with previous version 0.0.2 dated 2026-01-11

Title: Create Datasets with Hidden Images in Residual Plots
Description: Implements the "Residual (Sur)Realism" algorithm described by Stefanski (2007) <doi:10.1198/000313007X190079> to generate datasets that reveal hidden images or messages in their residual plots. It offers both predefined datasets and tools to embed custom text or images into residual structures. Allowing users to create intriguing visual demonstrations for teaching model diagnostics.
Author: James Joseph Balamuta [aut, cre, cph]
Maintainer: James Joseph Balamuta <james.balamuta@gmail.com>

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Package OlinkAnalyzeVignettes updated to version 1.1.0 with previous version 1.0.1 dated 2026-06-08

Title: Vignettes for Analyzing Data using 'OlinkAnalyze'
Description: Exemplifying analysis of large-scale protein data from the 'Olink platform', primarily relative protein expression data that has been exported from 'Olink NPX Software', as well as QUANT data from 'Olink'. QUANT data is log-transformed. Materials focus on reading data, demonstrating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to guide users extract biological insights from large-scale protein data run on the 'Olink platform'. More information on 'Olink' data can be found at <https://olink.com/>.
Author: Kathleen Nevola [aut, cre] , Marianne Sandin [aut] , Jamey Guess [aut] , Simon Forsberg [aut] , Christoffer Cambronero [aut] , Pascal Pucholt [aut] , Boxi Zhang [aut] , Masoumeh Sheikhi [aut] , Klev Diamanti [aut] , Amrita Kar [aut] , Lei Conze [aut] [...truncated...]
Maintainer: Kathleen Nevola <biostattools@olink.com>

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Package circhelp updated to version 1.4.0 with previous version 1.1 dated 2024-07-04

Title: Circular Analyses Helper Functions
Description: Light-weight functions for computing descriptive statistics in different circular spaces (e.g., 2pi, 180, or 360 degrees), to handle angle-dependent biases, pad circular data, and more. Specifically aimed for psychologists and neuroscientists analyzing circular data. Basic methods are based on Jammalamadaka and SenGupta (2001) <doi:10.1142/4031>, removal of cardinal biases is based on the approach introduced in van Bergen, Ma, Pratte, & Jehee (2015) <doi:10.1038/nn.4150> and Chetverikov and Jehee (2023) <doi:10.1038/s41467-023-43251-w>.
Author: Andrey Chetverikov [aut, cre] , Eline Van Geert [ctb]
Maintainer: Andrey Chetverikov <andrey.chetverikov@uib.no>

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Package wintime updated to version 1.0.1 with previous version 1.0.0 dated 2026-09-11

Title: Win Time Methods for Time-to-Event Data in Clinical Trials
Description: Performs an analysis of time-to-event clinical trial data using various "win time" methods, including 'ewt', 'ewtr', 'rmt', 'ewtp', 'rewtp', 'ewtpr', 'rewtpr', 'max', 'wtr', 'rwtr', 'pwt', and 'rpwt'. These methods are used to calculate and compare treatment effects on ordered composite endpoints. The package handles event times, event indicators, and treatment arm indicators and supports calculations on observed and resampled data. Detailed explanations of each method and usage examples are provided in "Use of win time for ordered composite endpoints in clinical trials," by Troendle et al. (2024)<doi:10.1002/sim.10045>. For more information, see the package documentation or the vignette titled "Introduction to wintime."
Author: James Troendle [aut, cre], Samuel Lawrence [aut]
Maintainer: James Troendle <james.troendle@nih.gov>

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Package SimTOST updated to version 1.1.0 with previous version 1.0.1 dated 2025-02-18

Title: Sample Size Estimation for Bio-Equivalence Trials Through Simulation
Description: Sample size estimation for bio-equivalence trials is supported through a simulation-based approach that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints. Users can model complex trial scenarios, including parallel and crossover designs, intra-subject variability, and different equivalence margins. Monte Carlo simulations enable accurate estimation of power and type I error rates, ensuring well-calibrated study designs. The statistical framework builds on established methods for equivalence testing and multiple hypothesis testing in bio-equivalence studies, as described in Schuirmann (1987) <doi:10.1007/BF01068419>, Mielke et al. (2018) <doi:10.1080/19466315.2017.1371071>, Shieh (2022) <doi:10.1371/journal.pone.0269128>, and Sozu et al. (2015) <doi:10.1007/978-3-319-22005-5>. Comprehensive documentation and vignettes guid [...truncated...]
Author: Thomas Debray [aut, cre], Tim Friede [ctb], Johanna Munoz [ctb], Dewi Amaliah [ctb], Wei Wei [ctb], Marian Mitroiu [ctb], Scott McDonald [ctb], Biogen Inc [cph, fnd] , Smart Data Analysis and Statistics B.V. [cph, fnd]
Maintainer: Thomas Debray <tdebray@fromdatatowisdom.com>

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Package pQTLdata updated to version 0.7 with previous version 0.6 dated 2026-03-09

Title: A Collection of Proteome Panels and Metadata
Description: It aggregates protein panel data and metadata for protein quantitative trait locus (pQTL) analysis using 'pQTLtools' (<https://jinghuazhao.github.io/pQTLtools/>). The package includes data from affinity-based panels such as 'Olink' (<https://olink.com/>) and 'SomaScan' (<https://somalogic.com/>), as well as mass spectrometry-based panels from 'CellCarta' (<https://cellcarta.com/>), 'Seer' (<https://seer.bio/>) and 'SWATH-MS' (<doi:10.15252/msb.20178126>). The metadata encompasses updated annotations and publication details.
Author: Jing Hua Zhao [aut, cre] , Uwe Ligges [ctb], Benjamin Altmann [ctb], Brian Ripley [ctb]
Maintainer: Jing Hua Zhao <jinghuazhao@hotmail.com>

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Package drimmR updated to version 1.0.4 with previous version 1.0.3 dated 2026-02-12

Title: Estimation, Simulation and Reliability of Drifting Markov Models
Description: Performs the drifting Markov models (DMM) which are non-homogeneous Markov models designed for modeling the heterogeneities of sequences in a more flexible way than homogeneous Markov chains or even hidden Markov models. In this context, we developed an R package dedicated to the estimation, simulation and the exact computation of associated reliability of drifting Markov models. The implemented methods are described in Vergne, N. (2008), <doi:10.2202/1544-6115.1326> and Barbu, V.S., Vergne, N. (2019) <doi:10.1007/s11009-018-9682-8> .
Author: Nicolas Vergne [aut, cre] , Corentin Lothode [aut] , Alexandre Seiller [aut], Victor Mataigne [ctb], Arnaud Lefebvre [ctb], Annthomy Gilles [ctb], Vlad Stefan Barbu [aut]
Maintainer: Nicolas Vergne <nicolas.vergne@univ-rouen.fr>

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Package pharmaverseadamjnj updated to version 0.0.8 with previous version 0.0.7 dated 2026-09-28

Title: J&J Innovative Medicine ADaM Test Data
Description: A set of Analysis Data Model (ADaM) datasets constructed by modifying the ADaM datasets in the 'pharmaverseadam' package to meet J&J Innovative Medicine's standard data structure for Clinical and Statistical Programming.
Author: David Munoz Tord [aut, cre], Nicholas Masel [aut], Joe Kovach [aut], Mahesh Divakaran [ctb], Selvaraj Shanmugam [ctb], Renfei Mao [ctb], J&J Innovative Medicine [cph, fnd]
Maintainer: David Munoz Tord <david.munoztord@mailbox.org>

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Package ggstatsplot updated to version 1.1.2 with previous version 1.1.1 dated 2026-08-25

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.03167>.
Author: Indrajeet Patil [cre, aut, cph]
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

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Package freedom updated to version 1.1.1 with previous version 1.0.1 dated 2020-09-08

Title: Demonstration of Disease Freedom (DDF)
Description: Implements the formulae required to calculate freedom from disease according to Cameron and Baldock (1998) <doi:10.1016/S0167-5877(97)00081-0>. These are the methods used at the Swedish national veterinary institute (SVA) to evaluate the performance of our nation animal disease surveillance programmes.
Author: Thomas Rosendal [aut, cre] , Petter Hopp [ctb]
Maintainer: Thomas Rosendal <trosendal@gmail.com>

Diff between freedom versions 1.0.1 dated 2020-09-08 and 1.1.1 dated 2026-10-09

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Package scTenifoldKnk updated to version 2.0.0 with previous version 1.1 dated 2026-09-02

Title: In-Silico Knockout Experiments from Single-Cell Gene Regulatory Networks
Description: A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells. It also predicts the direction (up or down) of the response of each gene from the WT expression, and reads all knockouts of a network from a single heat kernel, which makes transcriptome-wide knockout screens practical.
Author: Daniel Osorio [aut, cre] , Yan Zhong [aut, ctb], Guanxun Li [aut, ctb], Qian Xu [aut, ctb], Yongjian Yang [aut, ctb], Yanan Tian [aut, ctb], Robert Chapkin [aut, ctb], Jianhua Huang [aut, ctb], James J. Cai [aut, ctb, ths]
Maintainer: Daniel Osorio <dcosorioh@gmail.com>

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Package RcppSimdJson updated to version 0.1.16 with previous version 0.1.15 dated 2026-01-14

Title: 'Rcpp' Bindings for the 'simdjson' Header-Only Library for 'JSON' Parsing
Description: The 'JSON' format is ubiquitous for data interchange, and the 'simdjson' library written by Daniel Lemire (and many contributors) provides a high-performance parser for these files which by relying on parallel 'SIMD' instruction manages to parse these files as faster than disk speed. See the <doi:10.48550/arXiv.1902.08318> paper for more details about 'simdjson'. This package parses 'JSON' from string, file, or remote URLs under a variety of settings.
Author: Dirk Eddelbuettel [aut, cre] , Brendan Knapp [aut] , Daniel Lemire [aut]
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package locuszoomr updated to version 1.2.0 with previous version 1.1.0 dated 2026-09-17

Title: Gene Locus Plot with Gene Annotations
Description: Publication-ready regional gene locus plots similar to those produced by the web interface 'LocusZoom' <https://my.locuszoom.org>, but running locally in R. Genetic or genomic data with gene annotation tracks are plotted via R base graphics, 'ggplot2' or 'plotly', allowing flexibility and easy customisation including laying out multiple locus plots on the same page. It uses the 'LDlink' API <https://ldlink.nih.gov/?tab=apiaccess> to query linkage disequilibrium data from the 1000 Genomes Project and can overlay this on plots <doi:10.1093/bioadv/vbaf006>.
Author: Myles Lewis [aut, cre] , Tom Willis [ctb]
Maintainer: Myles Lewis <myles.lewis@qmul.ac.uk>

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Package GTFShift updated to version 1.1.0 with previous version 1.0.0 dated 2026-09-27

Title: Explore and Analyse General Transit Feed Specification (GTFS) Files with a Focus on Urban Mobility
Description: A bundle of methods to harmonize GTFS and OSM data, enabling the integration and exploration of different layers of transit data, starting with the planned operations (GTFS), but also the infrastructure topology (OSM) and real-time information (GTFS-RT).
Author: Goncalo F. Matos [aut, cre] , Rosa Felix [aut] , Miguel Relvas Pires [ctb]
Maintainer: Goncalo F. Matos <goncaloafmatos@tecnico.pt>

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Package FuzzyClass updated to version 0.2.0 with previous version 0.1.7 dated 2025-09-03

Title: Fuzzy and Non-Fuzzy Classifiers
Description: It provides classifiers which can be used for discrete variables and for continuous variables based on the Naive Bayes and Fuzzy Naive Bayes hypothesis. Those methods were developed by researchers belong to the 'Laboratory of Technologies for Virtual Teaching and Statistics (LabTEVE)' and 'Laboratory of Applied Statistics to Image Processing and Geoprocessing (LEAPIG)' at 'Federal University of Paraiba, Brazil'. They considered some statistical distributions and their papers were published in the scientific literature, as for instance, the Gaussian classifier using fuzzy parameters, proposed by 'Moraes, Ferreira and Machado' (2021) <doi:10.1007/s40815-020-00936-4>.
Author: Jodavid Ferreira [aut, cre] , Ronei Moraes [ctb] , Liliane Machado [ctb] , Arthur Ricardo [ctb], Isaac Araujo [ctb]
Maintainer: Jodavid Ferreira <jodavid@protonmail.com>

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Package climate updated to version 1.4.2 with previous version 1.4.1 dated 2026-09-18

Title: Interface to Download Meteorological (and Hydrological) Datasets
Description: Automatize downloading of meteorological and hydrological data from publicly available repositories: OGIMET (<http://ogimet.com/index.phtml.en>), University of Wyoming - atmospheric vertical profiling data (<http://weather.uwyo.edu/upperair/>), Polish Institute of Meteorology and Water Management - National Research Institute (<https://danepubliczne.imgw.pl>), and National Oceanic & Atmospheric Administration (NOAA). This package also allows for searching geographical coordinates for each observation and calculate distances to the nearest stations.
Author: Bartosz Czernecki [aut, cre] , Arkadiusz Glogowski [aut] , Jakub Nowosad [aut] , IMGW-PIB [ctb]
Maintainer: Bartosz Czernecki <nwp@amu.edu.pl>

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Package tidydr updated to version 0.0.7 with previous version 0.0.6 dated 2025-07-25

Title: Unify Dimensionality Reduction Results
Description: Dimensionality reduction is widely used in many domains for analyzing and visualizing high-dimensional data. 'tidydr' provides uniform output and is compatible with multiple methods, including 'prcomp', 'cmdscale', 'Rtsne', 'umap' and 'metaMDS'. Any function returning a numeric matrix can also be used. The unified result can be visualized directly with 'ggplot2', and several methods can be run and compared in a single call.
Author: Guangchuang Yu [aut, cre, cph] , Shuangbin Xu [aut]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>

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Package spaAlign updated to version 0.0.7 with previous version 0.0.6 dated 2026-01-07

Title: Stratigraphic Plug Alignment for Integrating Plug-Based and XRF Data
Description: Implements the Stratigraphic Plug Alignment (SPA) procedure for integrating sparsely sampled plug-based measurements (e.g., total organic carbon, porosity, mineralogy) with high-resolution X-ray fluorescence (XRF) geochemical data. SPA uses linear interpolation via the base approx() function with constrained extrapolation (rule = 1) to preserve stratigraphic order and avoid estimation beyond observed depths. The method aligns all datasets to a common depth grid, enabling high-resolution multivariate analysis and stratigraphic interpretation of core-based datasets such as those from the Utica and Point Pleasant formations. See R Core Team (2025) <https://stat.ethz.ch/R-manual/R-devel/library/stats/html/stats-package.html> and Omodolor (2025) <http://rave.ohiolink.edu/etdc/view?acc_num=case175262671767524> for methodological background and geological context.
Author: Hope E. Omodolor [aut] , Jeffrey M. Yarus [aut], Roger H. French [cre]
Maintainer: Roger H. French <rxf131@case.edu>

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Package riskweightedassets updated to version 1.2.4 with previous version 1.1.1 dated 2026-09-24

Title: Reproducible Risk-Weighted Asset Calculations
Description: Provides transparent, deterministic and auditable calculations of risk-weighted assets, own-funds requirements, interest-rate risk in the banking book and related capital metrics. It supports canonical in-memory tables and versioned spreadsheet datasets, strict validation, synthetic reference profiles, bitemporal snapshots, calculation controls and traceable regulatory source metadata. Methods are parameterised against the European Parliament and Council (2013) Capital Requirements Regulation <https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32013R0575> and its amending Regulation (EU) 2024/1623 <https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1623>. A granular analyst API exposes individual formulae, domain views, controls, schemas and auditable parameter overrides. The implementation is intended for analytical, educational and model-validation use and does not constitute legal or supervisory advice.
Author: Dimitrios Geromichalos [cre], RiskDataScience GmbH [aut, cph]
Maintainer: Dimitrios Geromichalos <riskdatascience@web.de>

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Package mapsf updated to version 1.3.0 with previous version 1.2.2 dated 2026-09-02

Title: Thematic Cartography
Description: Create and integrate thematic maps in your workflow. This package helps to design various cartographic representations such as proportional symbols, choropleth or typology maps. It also offers several functions to display layout elements that improve the graphic presentation of maps (e.g. scale bar, north arrow, title, labels). 'mapsf' maps 'sf' objects on 'base' graphics.
Author: Timothee Giraud [cre, aut] , Hugues Pecout [ctb] , Ronan Ysebaert [ctb] , Elina Marveaux [ctb] , Ian Fellows [cph] , Danielle Navarro [cph]
Maintainer: Timothee Giraud <timothee.giraud@cnrs.fr>

Diff between mapsf versions 1.2.2 dated 2026-09-02 and 1.3.0 dated 2026-10-09

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Permanent link

Package gson updated to version 0.2.2 with previous version 0.2.1 dated 2026-08-04

Title: Base Class and Methods for 'gson' Format
Description: Provides a lightweight container and exchange format for gene set collections. A 'GSON' object stores which genes belong to which gene set, together with gene set and gene names, the identifier types in use, species, versions and source metadata. A collection can be built from data frames, read from and written to the 'gson' JavaScript Object Notation (JSON) format and the 'GMT' format, subset by gene set, merged across sources, validated, and resolved to the web addresses of the databases it comes from, so that a collection gathered by one package can be analysed by another.
Author: Guangchuang Yu [aut, cre, cph]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>

Diff between gson versions 0.2.1 dated 2026-08-04 and 0.2.2 dated 2026-10-09

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Package ggfun updated to version 0.2.2 with previous version 0.2.1 dated 2026-07-02

Title: Miscellaneous Functions for 'ggplot2'
Description: Provides a collection of 'ggplot2' extensions and utilities for creating geometric layers, applying themes, working with legends, and modifying plot objects.
Author: Guangchuang Yu [aut, cre, cph] , Shuangbin Xu [aut]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>

Diff between ggfun versions 0.2.1 dated 2026-07-02 and 0.2.2 dated 2026-10-09

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Package SelectionTools updated to version 26.5 with previous version 26.4 dated 2026-09-21

Title: Simulation and Data Analysis for Plant Breeders
Description: Provides tools for simulation of plant breeding programs as described, for example, by Melchinger and Frisch (2023) <doi:10.1007/s00122-023-04446-3>, prediction of segregation variance (Osthushenrich, Frisch and Herzog (2017) <doi:10.1371/journal.pone.0188839>), genomic prediction (Hofheinz and Frisch (2014) <doi:10.1534/g3.113.010025>), linkage disequilibrium based haplotype construction, and planning of marker assisted back crossing programs. It provides an integrated framework for simulation and analysis of plant breeding programs.
Author: Matthias Frisch [aut, cre], Hans Peter Maurer [ctb], Philipp Heilmann [ctb]
Maintainer: Matthias Frisch <matthias.frisch@uni-giessen.de>

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Package tsgarch updated to version 1.0.5 with previous version 1.0.4 dated 2026-05-23

Title: Univariate GARCH Models
Description: Multiple flavors of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a large choice of conditional distributions. Methods for specification, estimation, prediction, filtering, simulation, statistical testing and more. Represents a partial re-write and re-think of 'rugarch', making use of automatic differentiation for estimation.
Author: Alexios Galanos [aut, cre, cph]
Maintainer: Alexios Galanos <alexios@4dscape.com>

Diff between tsgarch versions 1.0.4 dated 2026-05-23 and 1.0.5 dated 2026-10-09

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Package statsExpressions updated to version 2.1.2 with previous version 2.1.1 dated 2026-08-24

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

Diff between statsExpressions versions 2.1.1 dated 2026-08-24 and 2.1.2 dated 2026-10-09

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Package rankinPlot updated to version 1.2.0 with previous version 1.1.0 dated 2023-01-30

Title: Convenient Plotting for the Modified Rankin Scale and Other Ordinal Outcome Data
Description: Provides convenient tools for visualising ordinal outcome data following conventions within stroke research literature. It currently supports the "Grotta Bar" approach pioneered by The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group (1995) <doi:10.1056/NEJM199512143332401> and Probability-Probability plots for visualising Desirability of Outcome Ranking (DOOR) scales with large numbers of categories proposed by Johns et al. (2026) <doi:10.1177/17474930261475853>.
Author: Hannah Johns [aut, cre] , Andreas Gammelgaard Damsbo [aut] , Florian van Leeuwen [aut]
Maintainer: Hannah Johns <dr.hannah.johns@gmail.com>

Diff between rankinPlot versions 1.1.0 dated 2023-01-30 and 1.2.0 dated 2026-10-09

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Package OptHoldoutSize updated to version 0.1.0.3 with previous version 0.1.0.2 dated 2026-04-07

Title: Estimation of Optimal Size for a Holdout Set for Updating a Predictive Score
Description: Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is 'held-out' from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) <doi:10.48550/arXiv.2202.06374> (in Annals of Applied Statistics) for details of methods.
Author: Sami Haidar-Wehbe [aut], Sam Emerson [aut] , Louis Aslett [aut] , James Liley [cre, aut]
Maintainer: James Liley <james.liley@durham.ac.uk>

Diff between OptHoldoutSize versions 0.1.0.2 dated 2026-04-07 and 0.1.0.3 dated 2026-10-09

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Package haplo.stats readmission to version 1.9.9.1 with previous version 1.9.9.1 dated 2026-09-10

Title: Statistical Analysis of Haplotypes with Traits and Covariates when Linkage Phase is Ambiguous
Description: Routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em(), haplo.glm(), haplo.score(), and haplo.power(); all of which have detailed examples in the vignette.
Author: Schaid Daniel [aut], Jason P. Sinnwell [aut, cre]
Maintainer: Jason P. Sinnwell <sinnwell.jason@mayo.edu>

This is a re-admission after prior archival of version 1.9.9.1 dated 2026-09-10

Diff between haplo.stats versions 1.9.9.1 dated 2026-09-10 and 1.9.9.1 dated 2026-10-09

 0 files changed

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Package brickster updated to version 0.2.15 with previous version 0.2.14 dated 2026-07-24

Title: R Toolkit for 'Databricks'
Description: Collection of utilities that improve using 'Databricks' from R. Primarily functions that wrap specific 'Databricks' APIs (<https://docs.databricks.com/api>), 'RStudio' connection pane support, quality of life functions to make 'Databricks' simpler to use.
Author: Zac Davies [aut, cre], Rafi Kurlansik [aut], Databricks [cph, fnd]
Maintainer: Zac Davies <zac@databricks.com>

Diff between brickster versions 0.2.14 dated 2026-07-24 and 0.2.15 dated 2026-10-09

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Package CommonDataModel updated to version 1.1.0 with previous version 1.0.1 dated 2024-10-01

Title: OMOP CDM DDL and Documentation Generator
Description: Generates the scripts required to create an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) database and associated documentation for supported database platforms. Leverages the 'SqlRender' package to convert the Data Definition Language (DDL) script written in parameterized Structured Query Language (SQL) to the other supported dialects.
Author: Clair Blacketer [aut, cre]
Maintainer: Clair Blacketer <mblacke@its.jnj.com>

Diff between CommonDataModel versions 1.0.1 dated 2024-10-01 and 1.1.0 dated 2026-10-09

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More information about CommonDataModel at CRAN
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Package aedseo updated to version 1.2.0 with previous version 1.1.0 dated 2026-01-23

Title: Automated and Early Detection of Seasonal Epidemic Onset and Burden Levels
Description: A powerful tool for automating the early detection of seasonal epidemic onsets in time series data. It offers the ability to estimate growth rates across consecutive time intervals, calculate the sum of cases (SoC) within those intervals, and estimate seasonal onsets within user defined seasons. With use of a disease-specific threshold it also offers the possibility to estimate seasonal onset of epidemics. Additionally it offers the ability to estimate burden levels for seasons based on historical data. It is aimed towards epidemiologists, public health professionals, and researchers seeking to identify and respond to seasonal epidemics in a timely fashion.
Author: Sofia Myrup Otero [aut] , Kasper Schou Telkamp [aut] , Lasse Engbo Christiansen [aut, cre] , Rasmus Skytte Randloev [rev] , Statens Serum Institut, SSI [cph, fnd]
Maintainer: Lasse Engbo Christiansen <lsec@ssi.dk>

Diff between aedseo versions 1.1.0 dated 2026-01-23 and 1.2.0 dated 2026-10-09

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More information about aedseo at CRAN
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Package tern updated to version 0.9.13 with previous version 0.9.12 dated 2026-09-29

Title: Create Common TLGs Used in Clinical Trials
Description: Table, Listings, and Graphs (TLG) library for common outputs used in clinical trials.
Author: Joe Zhu [aut, cre] , Daniel Sabanes Bove [aut], Jana Stoilova [aut], Davide Garolini [aut] , Emily de la Rua [aut] , Abinaya Yogasekaram [aut] , Heng Wang [aut], Francois Collin [aut], Adrian Waddell [aut], Pawel Rucki [aut], Chendi Liao [aut], Jenni [...truncated...]
Maintainer: Joe Zhu <joe.zhu@roche.com>

Diff between tern versions 0.9.12 dated 2026-09-29 and 0.9.13 dated 2026-10-09

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Package restatapi updated to version 0.30.0 with previous version 0.25.0 dated 2026-03-30

Title: Search and Retrieve Data from Eurostat Database
Description: Eurostat is the statistical office of the European Union and provides high quality statistics for Europe. Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/web/main/data/database>). The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX) Web Services (<https://ec.europa.eu/eurostat/web/user-guides/data-browser/api-data-access/api-detailed-guidelines/sdmx2-1>) to search and download data from the Eurostat database using the SDMX standard.
Author: Matyas Meszaros [aut, cre], Sebastian Weinand [ctb]
Maintainer: Matyas Meszaros <matyas.meszaros@ec.europa.eu>

Diff between restatapi versions 0.25.0 dated 2026-03-30 and 0.30.0 dated 2026-10-09

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More information about restatapi at CRAN
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Package openalexR updated to version 3.1.1 with previous version 3.1.0 dated 2026-07-02

Title: Getting Bibliographic Records from 'OpenAlex' Database Using 'DSL' API
Description: A set of tools to extract bibliographic content from 'OpenAlex' database using API <https://help.openalex.org/>.
Author: Massimo Aria [aut, cre, cph] , Corrado Cuccurullo [ctb] , Trang Le [aut] , June Choe [aut]
Maintainer: Massimo Aria <aria@unina.it>

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Package ICESat2VegR updated to version 0.0.6 with previous version 0.0.4 dated 2026-10-06

Title: ICESat-2 Data Analysis for Land and Vegetation
Description: Provides tools for downloading, reading, processing, visualizing, and exporting NASA's ICESat-2 ATL03 (Global Geolocated Photon Data) and ATL08 (Land and Vegetation Height) products. Supports photon- and segment-level analysis, spatial sampling, gridding, statistical and machine-learning modeling, and integration with 'Google Earth Engine' (<https://earthengine.google.com/>) for wall-to-wall mapping of vegetation structure and other land attributes.
Author: Carlos Alberto Silva [aut, cph, cre], Caio Hamamura [aut, cph], Cesar Alvites [aut, ctb], Alexander J. Gaskins [aut, ctb], Sunil Arya [ctb, cph] ), David Mount [ctb, cph] ), University of Maryland [cph] ), Chuck Gantz [ctb] , Cole Krehbiel [ctb]
Maintainer: Carlos Alberto Silva <c.silva@ufl.edu>

Diff between ICESat2VegR versions 0.0.4 dated 2026-10-06 and 0.0.6 dated 2026-10-09

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More information about ICESat2VegR at CRAN
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Package grouprar updated to version 0.2.0 with previous version 0.1.0 dated 2024-03-04

Title: Group Response Adaptive Randomization for Clinical Trials
Description: Implements group response-adaptive randomization procedures, which include standard (non-group) response-adaptive randomization methods as special cases. The package also handles delayed and missing responses, which broadens its use in real-world trials. It offers functions for simulating a variety of response-adaptive randomization procedures, to help guide the choice of design for a clinical trial, including the doubly adaptive biased coin design and the multi-arm efficient randomized adaptive design (ERADE), k-arm optimal target allocations, group sequential monitoring, and a function that computes allocation probabilities for an ongoing trial. For details of the methods and algorithms, see the following references: Wei, L. J. (1979) <doi:10.1214/aos/1176344614>; Wei, L. J. and Durham, S. (1978) <doi:10.1080/01621459.1978.10480109>; Durham, S. D., Flournoy, N. and Li, W. (1998) <doi:10.2307/3315771>; Ivanova, A., Rosenberger, W. F., Durham, S. D. and Flournoy, N. ( [...truncated...]
Author: Guannan Zhai [aut, cre], Feifang Hu [aut, ths]
Maintainer: Guannan Zhai <guannanzhai1996@gmail.com>

Diff between grouprar versions 0.1.0 dated 2024-03-04 and 0.2.0 dated 2026-10-09

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Package controlcharts updated to version 0.0.23 with previous version 0.0.19 dated 2026-08-04

Title: Interactive Plotting for Funnel, Sigma, and Statistical Process Control Charts
Description: Generate interactive funnel plots, multi-indicator sigma charts, and statistical process control ('SPC') charts. Chart calculation and plotting use JavaScript, allowing dynamic charts without a Shiny server. For more details see Spiegelhalter (2004) <doi:10.1002/sim.1970> and Pfadt & Wheeler (1995) <doi:10.1901/jaba.1995.28-349>.
Author: Andrew R. Johnson [aut, cre] , Healthcare Quality Intelligence Unit [aut]
Maintainer: Andrew R. Johnson <andrew.johnson@arjohnsonau.com>

Diff between controlcharts versions 0.0.19 dated 2026-08-04 and 0.0.23 dated 2026-10-09

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Package bgev updated to version 0.3 with previous version 0.2 dated 2025-11-05

Title: Bimodal GEV Distribution with Location Parameter
Description: Density, distribution function, quantile function random generation and estimation of bimodal GEV distribution given in Otiniano et al. (2023) <doi:10.1007/s10651-023-00566-7>. This new generalization of the well-known GEV (Generalized Extreme Value) distribution is useful for modeling heterogeneous bimodal data from different areas.
Author: Thiago do Rego Sousa [aut, cre], Yasmin Lirio [aut], Cira Etheowalda Guevara Otiniano [aut]
Maintainer: Thiago do Rego Sousa <thiagodoregosousa@gmail.com>

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Package baRulho readmission to version 2.2.0 with previous version 2.1.7 dated 2026-07-24

Title: Quantifying (Animal) Sound Degradation
Description: Intended to facilitate acoustic analysis of (animal) sound propagation experiments, which typically aim to quantify changes in signal structure when transmitted in a given habitat by broadcasting and re-recording animal sounds at increasing distances. The package offers a workflow with functions to prepare the data set for analysis as well as to calculate and visualize several degradation metrics, including blur ratio, signal-to-noise ratio, excess attenuation and envelope correlation among others (Dabelsteen et al 1993 <doi:10.1121/1.406682>).
Author: Marcelo Araya-Salas [aut, cre] , Michael Mahoney [rev] , Dena Clink [rev]
Maintainer: Marcelo Araya-Salas <marcelo.araya@ucr.ac.cr>

This is a re-admission after prior archival of version 2.1.7 dated 2026-07-24

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Package ardea updated to version 0.1.1 with previous version 0.0.9 dated 2026-10-08

Title: Infrastructure for Interacting with GPUs
Description: Compiling, managing, and dispatching functions to GPUs. Will compile tooling for successfully detected frameworks, currently limited to: 'OpenCL' (<https://www.khronos.org/opencl/resources>), 'CUDA' (<https://docs.nvidia.com/cuda/>), and 'Metal' (<https://developer.apple.com/documentation/metal>).
Author: Nicholas Cooley [aut, cre]
Maintainer: Nicholas Cooley <npcooley@gmail.com>

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Package STATcubeR updated to version 1.0.1 with previous version 1.0.0 dated 2024-11-29

Title: R Interface for the 'STATcube' REST API and Open Government Data
Description: Import data from the 'STATcube' REST API or from the open data portal of Statistics Austria. This package includes a client for API requests as well as parsing utilities for data which originates from 'STATcube'. Documentation about 'STATcubeR' is provided by several vignettes included in the package as well as on the public 'pkgdown' page at <https://statistikat.github.io/STATcubeR/>.
Author: Bernhard Meindl [ctb, cre], Alexander Kowarik [ctb] , Gregor de Cillia [aut]
Maintainer: Bernhard Meindl <Bernhard.Meindl@statistik.gv.at>

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Permanent link

Package genekitr updated to version 1.3.0 with previous version 1.2.8 dated 2024-09-06

Title: Gene Analysis Toolkit
Description: Provides features for searching, converting, analyzing, plotting, and exporting data effortlessly by inputting feature IDs. Enables easy retrieval of feature information, conversion of ID types, gene enrichment analysis, publication-level figures, group interaction plotting, and result export in one Excel file for seamless sharing and communication.
Author: Yunze Liu [aut, cre]
Maintainer: Yunze Liu <jieandze1314@gmail.com>

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Package SuessR updated to version 0.1.7 with previous version 0.1.6 dated 2025-02-20

Title: Suess and Laws Corrections for Marine Stable Carbon Isotope Data
Description: Generates region-specific Suess and Laws corrections for stable carbon isotope data from marine organisms collected between 1850 and 2025. Version 0.1.7 of 'SuessR' contains four built-in regions: the Bering Sea ('Bering Sea'), the Aleutian archipelago ('Aleutian Islands'), the Gulf of Alaska ('Gulf of Alaska'), and the subpolar North Atlantic ('Subpolar North Atlantic'). Users can supply their own environmental data for regions currently not built into the package to generate corrections for those regions.
Author: Casey Clark [cre, aut], Mattias Cape [aut], Mark Shapley [aut], Franz Mueter [aut], Bruce Finney [aut], Nicole Misarti [aut]
Maintainer: Casey Clark <casey.t.clark@gmail.com>

Diff between SuessR versions 0.1.6 dated 2025-02-20 and 0.1.7 dated 2026-10-09

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Thu, 08 Oct 2026

Package forestat updated to version 1.2.0 with previous version 1.1.0 dated 2023-10-10

Title: Forest Carbon Sequestration and Potential Productivity Calculation
Description: Include assessing site classes based on the stand height growth and establishing a nonlinear mixed-effect biomass model under different site classes based on the whole stand model to achieve more accurate estimation of carbon sequestration. In particular, a carbon sequestration potential productivity calculation method based on the potential mean annual increment is proposed. This package is applicable to both natural forests and plantations. It can quantitatively assess stand’s potential productivity, realized productivity, and possible improvement under certain site, and can be used in many aspects such as site quality assessment, tree species suitability evaluation, and forest degradation evaluation. Reference: Lei X, Fu L, Li H, et al (2018) <doi:10.11707/j.1001-7488.20181213>. Fu L, Sharma R P, Zhu G, et al (2017) <doi:10.3390/f8040119>.
Author: Liyong Fu [aut], Shirong Liu [aut], Shouzheng Tang [aut], Guangshuang Duan [aut], Zhong Wang [aut], Linyan Feng [aut], Dongbo Xie [aut], Yuanyuan Han [aut, cre]
Maintainer: Yuanyuan Han <jackhanyuan@foxmail.com>

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Package betaStability updated to version 0.1.0 with previous version 0.0.4 dated 2026-06-05

Title: Quantify the Compositional Stability of Each Community Based on a Single Sampling Event
Description: Quantify the stability of each community based on the beta diversity between communities gathered in a single sampling event rather than a series of continuous sampling activities.
Author: Yu Gao [aut, cre, cph, fnd]
Maintainer: Yu Gao <gaoyu19920914@gmail.com>

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Package spaMM updated to version 4.7.17 with previous version 4.7.0 dated 2026-09-09

Title: Mixed-Effect Models, with or without Spatial Random Effects
Description: Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.
Author: Francois Rousset [aut, cre, cph] , Jean-Baptiste Ferdy [aut, cph], Alexandre Courtiol [aut]
Maintainer: Francois Rousset <francois.rousset@umontpellier.fr>

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Package RGraphSpace updated to version 1.5.7 with previous version 1.5.2 dated 2026-08-24

Title: Rendering Graphs as Coherent Spatial Objects in 'ggplot2'
Description: An interface for rendering 'igraph' objects as 'ggplot2' graphics within a normalized coordinate space. 'RGraphSpace' implements new geometries that treat a graph as a single coherent object, synchronizing node and edge layers under standard aesthetic mappings. Node features are resolved on demand, supporting high-dimensional data without expanding node tables. Spatial alignment is available at the pixel level, with node coordinates anchored to pixel centers through a half-pixel offset, enabling precise node positioning over external reference frames such as images and maps. Core functionality builds on 'igraph', 'ggplot2', and 'tidygraph'; optional geometry and large raster-background images use 'sf' and 'terra' when installed.
Author: Flavio Gabriel Carazza-Kessler [aut] , Jonathan Andre Back [aut] , Lana Bazan Peters Querne [aut] , Victor Henrique Apolonio dos Santos [aut] , Vinicius Chagas [ctb], Mauro Antonio Alves Castro [aut, cre]
Maintainer: Mauro Antonio Alves Castro <mauro.a.castro@gmail.com>

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Package lakefetch updated to version 0.1.14 with previous version 0.1.3 dated 2026-03-20

Title: Calculate Fetch and Wave Exposure for Lake Sampling Points
Description: Calculates fetch (open water distance) and wave exposure metrics for lake sampling points. Downloads lake boundaries from 'OpenStreetMap', calculates directional fetch using a ray-casting approach, and optionally integrates National Hydrography Dataset ('NHD') data <https://www.usgs.gov/national-hydrography> for hydrological context including outlet and inlet locations. Can estimate lake depth from surface area using empirical relationships, and integrate historical weather data for cumulative wave energy calculations. Includes an optional interactive 'shiny' application for visualization.
Author: Jeremy Lynch Farrell [aut, cre]
Maintainer: Jeremy Lynch Farrell <farrej2@rpi.edu>

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Package roxygen2 updated to version 8.1.1 with previous version 8.1.0 dated 2026-08-04

Title: In-Line Documentation for R
Description: Generate your Rd documentation, 'NAMESPACE' file, and collation field using specially formatted comments. Writing documentation in-line with code makes it easier to keep your documentation up-to-date as your requirements change. 'roxygen2' is inspired by the 'Doxygen' system for C++.
Author: Hadley Wickham [aut, cre, cph] , Peter Danenberg [aut, cph], Gabor Csardi [aut], Manuel Eugster [aut, cph], Posit Software, PBC [cph, fnd]
Maintainer: Hadley Wickham <hadley@posit.co>

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Package overtureR updated to version 0.3.1 with previous version 0.2.5 dated 2025-04-21

Title: Load 'Overture' Datasets as 'dbplyr' and 'sf'-Ready Data Frames
Description: An integrated R interface to the 'Overture' API (<https://docs.overturemaps.org/>). Allows R users to return 'Overture' data as 'dbplyr' data frames or materialized 'sf' spatial data frames.
Author: Arthur Gailes [aut, cre, cph]
Maintainer: Arthur Gailes <agailes1@gmail.com>

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Package sketchy updated to version 1.0.6 with previous version 1.0.5 dated 2025-01-16

Title: Create Custom Research Compendiums
Description: Provides functions to create and manage research compendiums for data analysis. Research compendiums are a standard and intuitive folder structure for organizing the digital materials of a research project, which can significantly improve reproducibility. The package offers several compendium structure options that fit different research project as well as the ability of duplicating the folder structure of existing projects or implementing custom structures. It also simplifies the use of version control.
Author: Marcelo Araya-Salas [aut, cre] , Andrea Yure Arriaga Madrigal [aut]
Maintainer: Marcelo Araya-Salas <marcelo.araya@ucr.ac.cr>

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Permanent link

Package wnpmle updated to version 0.1.3 with previous version 0.1.2 dated 2026-06-18

Title: Weighted NPMLE for Recurrent Events with a Competing Terminal Event
Description: Provides regression modeling and prediction for the marginal mean of recurrent events in the presence of a competing terminal event using the weighted nonparametric maximum likelihood estimator (wNPMLE) of Bellach and Kosorok (2026) <doi:10.48550/arXiv.2605.25934>. Two classes of transformation models are implemented: Box-Cox transformation models and logarithmic transformation models. These extend the proportional means model of Ghosh and Lin (2002) <doi:10.17615/pt0g-y207> and the transformation model framework of Zeng and Lin (2006) <doi:10.1093/biomet/93.3.627>. Parameter estimation is performed using automatic differentiation through the Template Model Builder (TMB) framework. Standard errors are computed using sandwich variance estimators that account for estimation of the inverse-probability censoring weights following Bellach, Kosorok, Rüschendorf and Fine (2019) <doi:10.1080/01621459.2017.1401540>.
Author: Anna Bellach [aut, cre]
Maintainer: Anna Bellach <abellach.biostat@gmail.com>

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Package tidylearn updated to version 0.6.0 with previous version 0.5.0 dated 2026-09-02

Title: A Unified Tidy Interface to R's Machine Learning Ecosystem
Description: Provides a unified tidyverse-compatible interface to R's machine learning ecosystem - from data ingestion to model publishing. The tl_read() family reads data from files ('CSV', 'Excel', 'Parquet', 'JSON'), databases ('SQLite', 'PostgreSQL', 'MySQL', 'BigQuery'), and cloud sources ('S3', 'GitHub', 'Kaggle'). The tl_model() function wraps established implementations from 'glmnet', 'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster', 'dbscan', and others with consistent function signatures and tidy tibble output. Results flow into unified 'ggplot2'-based visualization and optional formatted 'gt' tables via the tl_table() family. The underlying algorithms are unchanged; 'tidylearn' simply makes them easier to use together. Access raw model objects via the $fit slot for a supervised method, or $fit$model for an unsupervised one. Methods include random forests Breiman (2001) <doi:10.1023/A:1010933404324>, LASSO regression Tibshirani (1996) <doi:10.1111/j.2517-6161 [...truncated...]
Author: Cesaire Tobias [aut, cre]
Maintainer: Cesaire Tobias <cesaire@sheetsolved.com>

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Permanent link

Package rdborrow updated to version 0.0.5.0 with previous version 0.0.4.2 dated 2026-10-01

Title: External Control Borrowing for Rare Disease Trials
Description: Implements causal inference methods for incorporating external control data into randomized controlled trials (RCTs) with longitudinal outcomes. Provides an analysis module supporting weighting-based methods such as inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW), difference-in-differences (DID), and synthetic control approaches for borrowing external control information, as well as a simulation module for generating trial and external control data, evaluating estimator performance via Monte Carlo studies, and conducting power analyses for sample size determination. Methods are based on Zhou et al. (2025) <doi:10.1093/jrsssa/qnae075> and Zhou et al. (2024) <doi:10.1080/10543406.2024.2330209>.
Author: Lei Shi [aut], Matt Secrest [cre, aut] , Herbert Pang [aut], Chen Chen [aut], Jiawen Zhu [aut], Genentech, Inc. [cph]
Maintainer: Matt Secrest <secrmatt@gmail.com>

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Permanent link

Package pdp updated to version 0.10.0 with previous version 0.8.3 dated 2026-01-23

Title: Partial Dependence Plots
Description: A general framework for constructing partial dependence (i.e., marginal effect) plots from various types of machine learning models in R.
Author: Brandon M. Greenwell [aut, cre]
Maintainer: Brandon M. Greenwell <greenwell.brandon@gmail.com>

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Package crypto2 updated to version 3.0.0 with previous version 2.0.5 dated 2025-09-11

Title: Download Crypto Currency Data from 'CoinMarketCap' and 'CoinGecko'
Description: Retrieves crypto currency information and historical prices as well as information on the exchanges they are listed on. Historical data contains daily open, high, low and close values for all crypto currencies. The package draws on two complementary sources: 'CoinMarketCap' <https://coinmarketcap.com> (primary, via the 'crypto_*' functions) and 'CoinGecko' <https://www.coingecko.com> (secondary, via the 'cg_*' functions). Both sources are queried without an 'API' key; the two function families return tibbles with identical column conventions so downstream pipelines work on either source.
Author: Sebastian Stoeckl [aut, cre] , Jesse Vent [ctb]
Maintainer: Sebastian Stoeckl <sebastian.stoeckl@uni.li>

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Package scTenifoldNet updated to version 1.4.3 with previous version 1.4 dated 2026-08-04

Title: Construct and Compare scGRN from Single-Cell Transcriptomic Data
Description: A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See <doi:10.1016/j.patter.2020.100139> for more details.
Author: Daniel Osorio [aut, cre] , Yan Zhong [aut, ctb], Guanxun Li [aut, ctb], Jianhua Huang [aut, ctb], James Cai [aut, ctb, ths]
Maintainer: Daniel Osorio <dcosorioh@gmail.com>

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Package polyglotSQL updated to version 0.1.2 with previous version 0.1.1 dated 2026-09-27

Title: SQL Parsing, Analysis and Dialect Translation
Description: Parse, tokenize, validate, format, analyze and translate SQL between more than 30 dialects ('PostgreSQL', 'MySQL', 'BigQuery', 'Snowflake', 'DuckDB', 'T-SQL', and others) using the 'polyglot-sql' Rust crate <https://github.com/tobilg/polyglot>, a Rust port of the 'SQLGlot' 'Python' library. All processing happens locally in the R session; no database connection, 'Python' runtime or external service is required. Includes column-level lineage, structural query analysis, query optimization, 'AST' diffing and 'OpenLineage' facet generation.
Author: Andre Leite [aut, cre] , Marcos Wasiliew [aut] , Hugo Vasconcelos [aut] , Carlos Amorim [aut] , Diogo Bezerra [aut] , Julia Nascimento Barreto [aut] , Tobias Mueller [cph] ), Toby Mao [cph] , The authors of the vendored Rust dependencies [cph]
Maintainer: Andre Leite <leite@castlab.org>

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Package LMest updated to version 4.0.0 with previous version 3.2.8 dated 2026-01-14

Title: Generalized Latent Markov Models
Description: Latent Markov models for longitudinal continuous and categorical data. See Bartolucci, Pandolfi, Pennoni (2017)<doi:10.18637/jss.v081.i04>.
Author: Francesco Bartolucci [aut, cre], Silvia Pandolfi [aut], Fulvia Pennoni [aut], Luca Brusa [ctb], Alessio Farcomeni [ctb], Alessio Serafini [ctb]
Maintainer: Francesco Bartolucci <francesco.bartolucci@unipg.it>

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Package FastSurvival updated to version 1.1.0 with previous version 1.0.0 dated 2026-09-29

Title: Fast Survival Analysis and Simulation for Clinical Trials
Description: Provides fast alternatives to standard survival analysis functions in the 'survival' package, together with tools for time-to-event trial simulation and sequential analysis. The estimation and testing functions cover a single-time-point Kaplan-Meier estimator (survfit_fast()), log-rank tests including weighted and stratified variants (survdiff_fast()), a closed-form hazard ratio estimator based on the Pike-Halley Estimator method (coxph_fast()), restricted mean survival time (rmst_fast()), window mean survival time (wmst_fast()), milestone survival comparison (milestone_fast()), median survival time (medsurv_fast()), the max-combo test (maxcombo_fast()), the robust modestly-weighted log-rank test (rmw_fast()), the weighted Kaplan-Meier (Pepe-Fleming) test (wkm_fast()), the average hazard with survival weight (ahsw_fast()), and the Kalbfleisch-Prentice average hazard ratio (ahr_fast()). The simulation layer generates individual patient data (simdata_fast()), determines the calendar time [...truncated...]
Author: Gosuke Homma [aut, cre]
Maintainer: Gosuke Homma <my.name.is.gosuke@gmail.com>

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Package subtools updated to version 1.1.1 with previous version 1.1.0 dated 2026-03-24

Title: Read and Manipulate Video Subtitles
Description: A collection of functions to read, write and manipulate video subtitles. Supported formats include 'srt', 'subrip', 'sub', 'subviewer', 'microdvd', 'ssa', 'ass', 'substation', 'vtt', and 'webvtt'.
Author: Francois Keck [aut, cre, cph] , Bob Rudis [ctb] , Alban Sagouis [ctb]
Maintainer: Francois Keck <francois.keck@gmail.com>

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Package joinpointR updated to version 2.0.0 with previous version 1.1.0 dated 2026-07-20

Title: Tidy Tools for Joinpoint Regression Models
Description: Provides tools to fit joinpoint regression models with a log-linear specification by levels of one or two categorical variable(s) using the grid-search method. It includes functions to estimate the Annual Percent Change (APC) and the Average Annual Percent Change (AAPC), along with their 95% confidence intervals, and to generate formatted summary tables and plots of results.
Author: Tamara Ricardo [aut, cre]
Maintainer: Tamara Ricardo <tamararicardo83@gmail.com>

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More information about joinpointR at CRAN
Permanent link

Package paws updated to version 0.11.0 with previous version 0.10.0 dated 2026-06-01

Title: Amazon Web Services Software Development Kit
Description: Interface to Amazon Web Services <https://aws.amazon.com>, including storage, database, and compute services, such as 'Simple Storage Service' ('S3'), 'DynamoDB' 'NoSQL' database, and 'Lambda' functions-as-a-service.
Author: David Kretch [aut], Adam Banker [aut], Dyfan Jones [cre], Amazon.com, Inc. [cph]
Maintainer: Dyfan Jones <dyfan.r.jones@gmail.com>

Diff between paws versions 0.10.0 dated 2026-06-01 and 0.11.0 dated 2026-10-08

 paws-0.10.0/paws/man/panorama.Rd                             |only
 paws-0.11.0/paws/DESCRIPTION                                 |   25 
 paws-0.11.0/paws/MD5                                         |  565 +-
 paws-0.11.0/paws/NAMESPACE                                   |   19 
 paws-0.11.0/paws/R/paws.R                                    | 2315 ++++++++---
 paws-0.11.0/paws/man/accessanalyzer.Rd                       |    4 
 paws-0.11.0/paws/man/account.Rd                              |    9 
 paws-0.11.0/paws/man/acm.Rd                                  |   29 
 paws-0.11.0/paws/man/acmpca.Rd                               |    4 
 paws-0.11.0/paws/man/apigateway.Rd                           |    4 
 paws-0.11.0/paws/man/apigatewaymanagementapi.Rd              |    4 
 paws-0.11.0/paws/man/apigatewayv2.Rd                         |    4 
 paws-0.11.0/paws/man/appfabric.Rd                            |    4 
 paws-0.11.0/paws/man/applicationautoscaling.Rd               |    4 
 paws-0.11.0/paws/man/applicationcostprofiler.Rd              |    4 
 paws-0.11.0/paws/man/applicationinsights.Rd                  |    4 
 paws-0.11.0/paws/man/appmesh.Rd                              |    4 
 paws-0.11.0/paws/man/appregistry.Rd                          |    4 
 paws-0.11.0/paws/man/apprunner.Rd                            |    4 
 paws-0.11.0/paws/man/appstream.Rd                            |    4 
 paws-0.11.0/paws/man/arczonalshift.Rd                        |    4 
 paws-0.11.0/paws/man/athena.Rd                               |    4 
 paws-0.11.0/paws/man/auditmanager.Rd                         |    4 
 paws-0.11.0/paws/man/augmentedairuntime.Rd                   |    4 
 paws-0.11.0/paws/man/autoscaling.Rd                          |    4 
 paws-0.11.0/paws/man/autoscalingplans.Rd                     |    4 
 paws-0.11.0/paws/man/backup.Rd                               |   20 
 paws-0.11.0/paws/man/backupgateway.Rd                        |    4 
 paws-0.11.0/paws/man/batch.Rd                                |    7 
 paws-0.11.0/paws/man/bedrock.Rd                              |   16 
 paws-0.11.0/paws/man/bedrockagent.Rd                         |   15 
 paws-0.11.0/paws/man/bedrockagentcore.Rd                     |    6 
 paws-0.11.0/paws/man/bedrockagentcorecontrol.Rd              |   48 
 paws-0.11.0/paws/man/bedrockagentruntime.Rd                  |   14 
 paws-0.11.0/paws/man/bedrockdataautomation.Rd                |    4 
 paws-0.11.0/paws/man/bedrockdataautomationruntime.Rd         |    4 
 paws-0.11.0/paws/man/bedrockruntime.Rd                       |    5 
 paws-0.11.0/paws/man/billing.Rd                              |   15 
 paws-0.11.0/paws/man/billingconductor.Rd                     |    6 
 paws-0.11.0/paws/man/braket.Rd                               |    6 
 paws-0.11.0/paws/man/budgets.Rd                              |    4 
 paws-0.11.0/paws/man/chatbot.Rd                              |    4 
 paws-0.11.0/paws/man/cleanroomsml.Rd                         |    4 
 paws-0.11.0/paws/man/cloud9.Rd                               |    4 
 paws-0.11.0/paws/man/cloudcontrolapi.Rd                      |    4 
 paws-0.11.0/paws/man/clouddirectory.Rd                       |    6 
 paws-0.11.0/paws/man/cloudformation.Rd                       |    6 
 paws-0.11.0/paws/man/cloudfront.Rd                           |    4 
 paws-0.11.0/paws/man/cloudfrontkeyvaluestore.Rd              |    4 
 paws-0.11.0/paws/man/cloudhsm.Rd                             |    4 
 paws-0.11.0/paws/man/cloudhsmv2.Rd                           |    4 
 paws-0.11.0/paws/man/cloudsearch.Rd                          |    4 
 paws-0.11.0/paws/man/cloudsearchdomain.Rd                    |    4 
 paws-0.11.0/paws/man/cloudtrail.Rd                           |   70 
 paws-0.11.0/paws/man/cloudtraildataservice.Rd                |    4 
 paws-0.11.0/paws/man/cloudwatch.Rd                           |   51 
 paws-0.11.0/paws/man/cloudwatchapplicationsignals.Rd         |   13 
 paws-0.11.0/paws/man/cloudwatchinternetmonitor.Rd            |    4 
 paws-0.11.0/paws/man/cloudwatchlogs.Rd                       |   19 
 paws-0.11.0/paws/man/cloudwatchobservabilityaccessmanager.Rd |    4 
 paws-0.11.0/paws/man/cloudwatchrum.Rd                        |    4 
 paws-0.11.0/paws/man/codeartifact.Rd                         |    4 
 paws-0.11.0/paws/man/codebuild.Rd                            |    4 
 paws-0.11.0/paws/man/codecatalyst.Rd                         |    4 
 paws-0.11.0/paws/man/codecommit.Rd                           |    6 
 paws-0.11.0/paws/man/codeconnections.Rd                      |    4 
 paws-0.11.0/paws/man/codedeploy.Rd                           |    4 
 paws-0.11.0/paws/man/codeguruprofiler.Rd                     |    4 
 paws-0.11.0/paws/man/codegurureviewer.Rd                     |    4 
 paws-0.11.0/paws/man/codegurusecurity.Rd                     |    4 
 paws-0.11.0/paws/man/codepipeline.Rd                         |    4 
 paws-0.11.0/paws/man/codestarconnections.Rd                  |    4 
 paws-0.11.0/paws/man/codestarnotifications.Rd                |    4 
 paws-0.11.0/paws/man/cognitoidentity.Rd                      |    4 
 paws-0.11.0/paws/man/cognitoidentityprovider.Rd              |   14 
 paws-0.11.0/paws/man/cognitosync.Rd                          |    4 
 paws-0.11.0/paws/man/comprehend.Rd                           |    4 
 paws-0.11.0/paws/man/comprehendmedical.Rd                    |    4 
 paws-0.11.0/paws/man/computeoptimizer.Rd                     |    4 
 paws-0.11.0/paws/man/configservice.Rd                        |    9 
 paws-0.11.0/paws/man/connect.Rd                              |   40 
 paws-0.11.0/paws/man/connectcampaignservice.Rd               |    4 
 paws-0.11.0/paws/man/connectcampaignservicev2.Rd             |    4 
 paws-0.11.0/paws/man/connectcases.Rd                         |    4 
 paws-0.11.0/paws/man/connectcontactlens.Rd                   |    6 
 paws-0.11.0/paws/man/connectparticipant.Rd                   |    8 
 paws-0.11.0/paws/man/connectwisdomservice.Rd                 |    4 
 paws-0.11.0/paws/man/controltower.Rd                         |    4 
 paws-0.11.0/paws/man/costandusagereportservice.Rd            |    4 
 paws-0.11.0/paws/man/costexplorer.Rd                         |    4 
 paws-0.11.0/paws/man/customerprofiles.Rd                     |   13 
 paws-0.11.0/paws/man/datapipeline.Rd                         |    4 
 paws-0.11.0/paws/man/datazone.Rd                             |    6 
 paws-0.11.0/paws/man/dax.Rd                                  |    4 
 paws-0.11.0/paws/man/detective.Rd                            |    4 
 paws-0.11.0/paws/man/devopsguru.Rd                           |    4 
 paws-0.11.0/paws/man/directconnect.Rd                        |   14 
 paws-0.11.0/paws/man/directoryservice.Rd                     |    4 
 paws-0.11.0/paws/man/dlm.Rd                                  |    4 
 paws-0.11.0/paws/man/docdb.Rd                                |    4 
 paws-0.11.0/paws/man/docdbelastic.Rd                         |    4 
 paws-0.11.0/paws/man/drs.Rd                                  |   24 
 paws-0.11.0/paws/man/dynamodb.Rd                             |    5 
 paws-0.11.0/paws/man/dynamodbstreams.Rd                      |    4 
 paws-0.11.0/paws/man/ebs.Rd                                  |    4 
 paws-0.11.0/paws/man/ec2.Rd                                  |   50 
 paws-0.11.0/paws/man/ec2instanceconnect.Rd                   |    4 
 paws-0.11.0/paws/man/ecr.Rd                                  |    4 
 paws-0.11.0/paws/man/ecrpublic.Rd                            |    4 
 paws-0.11.0/paws/man/ecs.Rd                                  |    4 
 paws-0.11.0/paws/man/efs.Rd                                  |    4 
 paws-0.11.0/paws/man/eks.Rd                                  |   10 
 paws-0.11.0/paws/man/elasticache.Rd                          |    4 
 paws-0.11.0/paws/man/elasticbeanstalk.Rd                     |   36 
 paws-0.11.0/paws/man/elasticsearchservice.Rd                 |    4 
 paws-0.11.0/paws/man/elb.Rd                                  |    6 
 paws-0.11.0/paws/man/elbv2.Rd                                |    6 
 paws-0.11.0/paws/man/emr.Rd                                  |    9 
 paws-0.11.0/paws/man/emrcontainers.Rd                        |    8 
 paws-0.11.0/paws/man/emrserverless.Rd                        |    4 
 paws-0.11.0/paws/man/entityresolution.Rd                     |    4 
 paws-0.11.0/paws/man/eventbridge.Rd                          |    4 
 paws-0.11.0/paws/man/eventbridgepipes.Rd                     |    4 
 paws-0.11.0/paws/man/eventbridgescheduler.Rd                 |    4 
 paws-0.11.0/paws/man/finspace.Rd                             |    4 
 paws-0.11.0/paws/man/finspacedata.Rd                         |    4 
 paws-0.11.0/paws/man/firehose.Rd                             |    4 
 paws-0.11.0/paws/man/fis.Rd                                  |    4 
 paws-0.11.0/paws/man/fms.Rd                                  |    4 
 paws-0.11.0/paws/man/forecastqueryservice.Rd                 |    4 
 paws-0.11.0/paws/man/forecastservice.Rd                      |    4 
 paws-0.11.0/paws/man/frauddetector.Rd                        |    4 
 paws-0.11.0/paws/man/fsx.Rd                                  |    4 
 paws-0.11.0/paws/man/glacier.Rd                              |    4 
 paws-0.11.0/paws/man/globalaccelerator.Rd                    |    4 
 paws-0.11.0/paws/man/glue.Rd                                 |   45 
 paws-0.11.0/paws/man/gluedatabrew.Rd                         |    4 
 paws-0.11.0/paws/man/guardduty.Rd                            |   19 
 paws-0.11.0/paws/man/health.Rd                               |    5 
 paws-0.11.0/paws/man/healthlake.Rd                           |   23 
 paws-0.11.0/paws/man/iam.Rd                                  |    8 
 paws-0.11.0/paws/man/iamrolesanywhere.Rd                     |    4 
 paws-0.11.0/paws/man/identitystore.Rd                        |   17 
 paws-0.11.0/paws/man/imagebuilder.Rd                         |   86 
 paws-0.11.0/paws/man/inspector.Rd                            |    6 
 paws-0.11.0/paws/man/inspector2.Rd                           |   14 
 paws-0.11.0/paws/man/ivs.Rd                                  |    7 
 paws-0.11.0/paws/man/ivschat.Rd                              |    6 
 paws-0.11.0/paws/man/ivsrealtime.Rd                          |    4 
 paws-0.11.0/paws/man/kafka.Rd                                |    9 
 paws-0.11.0/paws/man/kafkaconnect.Rd                         |    5 
 paws-0.11.0/paws/man/kendra.Rd                               |    4 
 paws-0.11.0/paws/man/kendraranking.Rd                        |    4 
 paws-0.11.0/paws/man/keyspaces.Rd                            |    4 
 paws-0.11.0/paws/man/kinesis.Rd                              |   10 
 paws-0.11.0/paws/man/kinesisanalytics.Rd                     |    4 
 paws-0.11.0/paws/man/kinesisanalyticsv2.Rd                   |    4 
 paws-0.11.0/paws/man/kms.Rd                                  |    4 
 paws-0.11.0/paws/man/lakeformation.Rd                        |    4 
 paws-0.11.0/paws/man/lambda.Rd                               |    7 
 paws-0.11.0/paws/man/lexmodelbuildingservice.Rd              |    4 
 paws-0.11.0/paws/man/lexmodelsv2.Rd                          |   33 
 paws-0.11.0/paws/man/lexruntimeservice.Rd                    |    4 
 paws-0.11.0/paws/man/lexruntimev2.Rd                         |    4 
 paws-0.11.0/paws/man/licensemanager.Rd                       |    4 
 paws-0.11.0/paws/man/licensemanagerlinuxsubscriptions.Rd     |    4 
 paws-0.11.0/paws/man/licensemanagerusersubscriptions.Rd      |    4 
 paws-0.11.0/paws/man/lightsail.Rd                            |    5 
 paws-0.11.0/paws/man/locationservice.Rd                      |    4 
 paws-0.11.0/paws/man/lookoutequipment.Rd                     |    4 
 paws-0.11.0/paws/man/machinelearning.Rd                      |    4 
 paws-0.11.0/paws/man/macie2.Rd                               |    4 
 paws-0.11.0/paws/man/managedgrafana.Rd                       |    4 
 paws-0.11.0/paws/man/marketplacecatalog.Rd                   |    6 
 paws-0.11.0/paws/man/marketplacecommerceanalytics.Rd         |    4 
 paws-0.11.0/paws/man/marketplaceentitlementservice.Rd        |    4 
 paws-0.11.0/paws/man/marketplacemetering.Rd                  |    8 
 paws-0.11.0/paws/man/memorydb.Rd                             |    4 
 paws-0.11.0/paws/man/mq.Rd                                   |    5 
 paws-0.11.0/paws/man/mturk.Rd                                |    4 
 paws-0.11.0/paws/man/mwaa.Rd                                 |    4 
 paws-0.11.0/paws/man/neptune.Rd                              |    4 
 paws-0.11.0/paws/man/neptunedata.Rd                          |    4 
 paws-0.11.0/paws/man/networkfirewall.Rd                      |   12 
 paws-0.11.0/paws/man/networkmanager.Rd                       |    4 
 paws-0.11.0/paws/man/omics.Rd                                |    4 
 paws-0.11.0/paws/man/opensearchingestion.Rd                  |    4 
 paws-0.11.0/paws/man/opensearchservice.Rd                    |   12 
 paws-0.11.0/paws/man/opensearchserviceserverless.Rd          |    4 
 paws-0.11.0/paws/man/organizations.Rd                        |   30 
 paws-0.11.0/paws/man/paymentcryptographycontrolplane.Rd      |    4 
 paws-0.11.0/paws/man/paymentcryptographydataplane.Rd         |    6 
 paws-0.11.0/paws/man/pcaconnectorad.Rd                       |    4 
 paws-0.11.0/paws/man/personalize.Rd                          |    4 
 paws-0.11.0/paws/man/personalizeevents.Rd                    |    4 
 paws-0.11.0/paws/man/personalizeruntime.Rd                   |    4 
 paws-0.11.0/paws/man/pi.Rd                                   |    4 
 paws-0.11.0/paws/man/pinpoint.Rd                             |    4 
 paws-0.11.0/paws/man/pinpointemail.Rd                        |    4 
 paws-0.11.0/paws/man/pinpointsmsvoice.Rd                     |    4 
 paws-0.11.0/paws/man/pinpointsmsvoicev2.Rd                   |    8 
 paws-0.11.0/paws/man/polly.Rd                                |    4 
 paws-0.11.0/paws/man/pricing.Rd                              |    4 
 paws-0.11.0/paws/man/prometheusservice.Rd                    |    6 
 paws-0.11.0/paws/man/proton.Rd                               |    4 
 paws-0.11.0/paws/man/quicksight.Rd                           |   71 
 paws-0.11.0/paws/man/ram.Rd                                  |    4 
 paws-0.11.0/paws/man/rds.Rd                                  |    4 
 paws-0.11.0/paws/man/rdsdataservice.Rd                       |    8 
 paws-0.11.0/paws/man/recyclebin.Rd                           |    4 
 paws-0.11.0/paws/man/redshift.Rd                             |    8 
 paws-0.11.0/paws/man/redshiftdataapiservice.Rd               |    5 
 paws-0.11.0/paws/man/redshiftserverless.Rd                   |    4 
 paws-0.11.0/paws/man/rekognition.Rd                          |   45 
 paws-0.11.0/paws/man/resiliencehub.Rd                        |    4 
 paws-0.11.0/paws/man/resourceexplorer.Rd                     |    4 
 paws-0.11.0/paws/man/resourcegroups.Rd                       |    4 
 paws-0.11.0/paws/man/resourcegroupstaggingapi.Rd             |    4 
 paws-0.11.0/paws/man/route53.Rd                              |    4 
 paws-0.11.0/paws/man/route53domains.Rd                       |    4 
 paws-0.11.0/paws/man/route53profiles.Rd                      |    4 
 paws-0.11.0/paws/man/route53recoverycluster.Rd               |    4 
 paws-0.11.0/paws/man/route53recoverycontrolconfig.Rd         |    4 
 paws-0.11.0/paws/man/route53recoveryreadiness.Rd             |    4 
 paws-0.11.0/paws/man/route53resolver.Rd                      |   10 
 paws-0.11.0/paws/man/s3.Rd                                   |   26 
 paws-0.11.0/paws/man/s3control.Rd                            |    4 
 paws-0.11.0/paws/man/s3outposts.Rd                           |    4 
 paws-0.11.0/paws/man/s3tables.Rd                             |    4 
 paws-0.11.0/paws/man/sagemaker.Rd                            |   12 
 paws-0.11.0/paws/man/sagemakeredgemanager.Rd                 |    4 
 paws-0.11.0/paws/man/sagemakerfeaturestoreruntime.Rd         |    9 
 paws-0.11.0/paws/man/sagemakergeospatialcapabilities.Rd      |    4 
 paws-0.11.0/paws/man/sagemakermetrics.Rd                     |    4 
 paws-0.11.0/paws/man/sagemakerruntime.Rd                     |    4 
 paws-0.11.0/paws/man/savingsplans.Rd                         |    4 
 paws-0.11.0/paws/man/schemas.Rd                              |    4 
 paws-0.11.0/paws/man/secretsmanager.Rd                       |    4 
 paws-0.11.0/paws/man/securityhub.Rd                          |   14 
 paws-0.11.0/paws/man/securitylake.Rd                         |    4 
 paws-0.11.0/paws/man/serverlessapplicationrepository.Rd      |    4 
 paws-0.11.0/paws/man/servicecatalog.Rd                       |    4 
 paws-0.11.0/paws/man/servicediscovery.Rd                     |    4 
 paws-0.11.0/paws/man/servicequotas.Rd                        |    4 
 paws-0.11.0/paws/man/ses.Rd                                  |    4 
 paws-0.11.0/paws/man/sesv2.Rd                                |   24 
 paws-0.11.0/paws/man/sfn.Rd                                  |    4 
 paws-0.11.0/paws/man/shield.Rd                               |    4 
 paws-0.11.0/paws/man/simpledb.Rd                             |    4 
 paws-0.11.0/paws/man/sns.Rd                                  |    8 
 paws-0.11.0/paws/man/sqs.Rd                                  |    4 
 paws-0.11.0/paws/man/ssm.Rd                                  |   12 
 paws-0.11.0/paws/man/ssmcontacts.Rd                          |    4 
 paws-0.11.0/paws/man/ssmincidents.Rd                         |    4 
 paws-0.11.0/paws/man/ssmsap.Rd                               |    4 
 paws-0.11.0/paws/man/sso.Rd                                  |    4 
 paws-0.11.0/paws/man/ssoadmin.Rd                             |    4 
 paws-0.11.0/paws/man/ssooidc.Rd                              |    6 
 paws-0.11.0/paws/man/storagegateway.Rd                       |    4 
 paws-0.11.0/paws/man/sts.Rd                                  |   12 
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Package ohun updated to version 1.0.5 with previous version 1.0.4 dated 2025-10-22

Title: Optimizing Acoustic Signal Detection
Description: Facilitates the automatic detection of acoustic signals, providing functions to diagnose and optimize the performance of detection routines. Detections from other software can also be explored and optimized. This package has been peer-reviewed by rOpenSci. Araya-Salas et al. (2022) <doi:10.1101/2022.12.13.520253>.
Author: Marcelo Araya-Salas [aut, cre] , Alec L. Robitaille [rev] , Sam Lapp [rev]
Maintainer: Marcelo Araya-Salas <marcelo.araya@ucr.ac.cr>

Diff between ohun versions 1.0.4 dated 2025-10-22 and 1.0.5 dated 2026-10-08

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 NAMESPACE                              |    3 
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More information about ohun at CRAN
Permanent link

Package irtQ updated to version 1.3.1 with previous version 1.3.0 dated 2026-10-05

Title: Unidimensional Item Response Theory Modeling
Description: Fit unidimensional item response theory (IRT) models to test data, which includes both dichotomous and polytomous items, calibrate pretest item parameters, estimate examinees' abilities, and examine the IRT model-data fit on item-level in different ways as well as provide useful functions related to IRT analyses such as differential item functioning analysis. In addition, the package provides a set of classical test theory functions for computing item- and test-level statistics (e.g., item difficulty, item-total correlation, and coefficient alpha) and for scoring and analyzing selected-response item data. The bring.flexmirt() and write.flexmirt() functions were written by modifying the read.flexmirt() function (Pritikin & Falk (2020) <doi:10.1177/0146621620929431>). The bring.bilog() and bring.parscale() functions were written by modifying the read.bilog() and read.parscale() functions, respectively (Weeks (2010) <doi:10.18637/jss.v035.i12>). The bisection() function wa [...truncated...]
Author: Hwanggyu Lim [aut, cre], Craig S. Wells [ctb], James Howard [ctb], Joshua Pritikin [ctb], Jonathan P Weeks [ctb], Jorge Gonzalez [ctb], David Magis [ctb]
Maintainer: Hwanggyu Lim <hglim83@gmail.com>

Diff between irtQ versions 1.3.0 dated 2026-10-05 and 1.3.1 dated 2026-10-08

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More information about irtQ at CRAN
Permanent link

Package insectecol updated to version 1.1.2 with previous version 1.1.1 dated 2026-10-05

Title: Insect Ecology Data Analysis Toolkit
Description: A collection of analytical tools for insect ecology research, currently covering age-stage, two-sex life table analysis, dose-response bioassays, temperature-dependent development and insect phenology prediction. The life table module follows the age-stage, two-sex life table theory of Chi (1988) <doi:10.1093/ee/17.1.26> and Chi et al. (2020) <doi:10.1127/entomologia/2020/0936>. It supports fast batch processing of multi-group datasets, validates raw 'csv' data, computes cohort size, mean fecundity, age-stage survival rates, age-specific survival, age-specific fecundity, life expectancy, and derived population parameters (net reproductive rate, intrinsic and finite rates of increase, mean generation time), simultaneously generates age-stage survival curves for all groups, and exports all tabular results and plots to 'Excel' in a single run. The bioassay module estimates lethal concentrations by the traditional and the weighted (improved) linear regression methods and by pro [...truncated...]
Author: Wangyao Li [aut, cre, cph], Yongsheng Zhang [aut], Huan Yu [aut]
Maintainer: Wangyao Li <1561941342@qq.com>

Diff between insectecol versions 1.1.1 dated 2026-10-05 and 1.1.2 dated 2026-10-08

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Permanent link

Package effectcheck updated to version 0.7.17 with previous version 0.2.3 dated 2026-03-25

Title: Statistical Consistency Checker for Published Research Results
Description: A conservative, assumption-aware statistical consistency checker for already-extracted research-results text. Parses test statistics, effect sizes, and confidence intervals across multiple citation styles including American Psychological Association (APA), Harvard, Frontiers, PLOS ONE, Scientific Reports, Nature Human Behaviour, PeerJ, eLife, PNAS, and others. Recomputes effect sizes using all plausible variants when design is ambiguous, and validates internal consistency. Supports t-tests, F-tests/ANOVA, correlations, chi-square, z-tests, regression, and nonparametric tests. Explicitly tracks all assumptions and uncertainty in output. Detects decision errors (significance reversals) similar to 'statcheck'. From v0.4.0 file extraction is no longer part of the package — pair with an external extractor (e.g., 'docpluck' at <https://docpluck.app>) and pass the resulting text to check_text(). Note: this package is under active development and results should be independently verified. [...truncated...]
Author: Gilad Feldman [aut, cre]
Maintainer: Gilad Feldman <giladfel@gmail.com>

Diff between effectcheck versions 0.2.3 dated 2026-03-25 and 0.7.17 dated 2026-10-08

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Package toml updated to version 1.2.0 with previous version 1.1.0 dated 2026-04-13

Title: Read, Write, and Modify TOML Files
Description: Simple toolkit for working with TOML text. Based on tomledit which allows for modifying TOML while preserving order, comments,and whitespace.
Author: Jeroen Ooms [aut, cre]
Maintainer: Jeroen Ooms <jeroenooms@gmail.com>

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Package ParCC updated to version 1.4.1 with previous version 1.4.0 dated 2026-03-30

Title: Parameter Converter and Calculator for Health Technology Assessment
Description: An interactive 'shiny' application for Health Technology Assessment (HTA) parameter estimation. Converts between rates, probabilities, odds, and hazard ratios (HR); extrapolates survival curves (Exponential, Weibull, Log-Logistic); fits probabilistic sensitivity analysis (PSA) distributions (Beta, Gamma, LogNormal, Dirichlet) via the method of moments; calculates incremental cost-effectiveness ratios (ICERs), net monetary benefit (NMB), value-based pricing, and budget impact; adjusts costs for inflation, discounting, and purchasing power parity (PPP) across 30 countries; and adjusts background mortality using life-table methods. Designed for researchers building cost-effectiveness and budget-impact models who need auditable, formula-documented parameter transformations. Methods include Zhang and Yu (1998) <doi:10.1001/jama.280.19.1690> for odds ratio (OR) to relative risk (RR) conversion and Chinn (2000, Statistics in Medicine, 19, 3127-3131) for effect-size transformations.
Author: Abhijit Pakhare [aut, cre], Soumya Jain [aut], Anvita Malhotra [aut], Shivansh Verma [aut], Biju Somen [aut], Oshima Sachin [aut], Beena Joshi [aut], Ankur Joshi [aut]
Maintainer: Abhijit Pakhare <drpakhare@gmail.com>

Diff between ParCC versions 1.4.0 dated 2026-03-30 and 1.4.1 dated 2026-10-08

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Package biomod2 updated to version 4.3-4-7 with previous version 4.3-4-6 dated 2026-05-23

Title: Ensemble Platform for Species Distribution Modeling
Description: Functions for species distribution modelling, to calibrate, evaluate, and project species-environment relationships across space and time using multiple modelling algorithms and ensemble forecasting. It accommodates diverse ecological data types (presence-only, presence-absence, counts, multi-class abundance, or relative/absolute abundance) within a unified modelling workflow which includes cross-validation schemes, pseudo-absence selection strategies, expanded model parametrization options, a dozen of algorithms, and tools for exploring and visualizing outputs.
Author: Maya Gueguen [aut, cre] , Helene Blancheteau [aut], Remi Lemaire-Patin [aut], Wilfried Thuiller [aut]
Maintainer: Maya Gueguen <maya.gueguen@univ-grenoble-alpes.fr>

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More information about biomod2 at CRAN
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Package myman updated to version 0.10.0 with previous version 0.1.0 dated 2026-09-27

Title: Draw from Sequence of 'My Man' Posts by Kevin Kruse
Description: Starting on the afternoon of July 17, 2026, Kevin Kruse fired off an astonishing array of 'BlueSky' replies to an initial post of his featuring a certain government figure: <https://bsky.app/profile/did:plc:cnpe7qvcyjrhm6w7w7e4atur/post/3mqum4mxsuk2g>. This lasted a week and generated nearly seven hundred posts. A second wave started on August 12, 2026, with this post: <https://bsky.app/profile/kevinmkruse.bsky.social/post/3mstvbjpagc2a>. A third wave started on August 17, 2026, with <https://bsky.app/profile/kevinmkruse.bsky.social/post/3mtcpiw7gi22j>. A fourth wave started on August 27, 2026, with <https://bsky.app/profile/kevinmkruse.bsky.social/post/3mu3pugs2yk2f>. A fifth wave ran on August 30, 2026, beginning with <https://bsky.app/profile/kevinmkruse.bsky.social/post/3mudbzy5ksk25>. A sixth wave started September 4, 2026, with <https://bsky.app/profile/kevinmkruse.bsky.social/post/3muparqtdkk2w>. A seventh wave started September 12, 2026, wi [...truncated...]
Author: Dirk Eddelbuettel [aut, cre]
Maintainer: Dirk Eddelbuettel <edd@debian.org>

Diff between myman versions 0.1.0 dated 2026-09-27 and 0.10.0 dated 2026-10-08

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

Title: Microdata Infrastructure Tools for Firm-Level Microdata Research
Description: Supports the full analysis pipeline for researchers working with firm-level microdata. Provides data tools for panel preparation (import, outlier detection, classification harmonization), analytical methods (production function estimation, capital stock measurement, markups, intensity measures, distributions, regression, clustering), and disclosure tools for tagging outputs with dominance and observation counts before aggregation and publication. Production function estimation implements methods by Ackerberg, Caves and Frazer (2015) <doi:10.3982/ECTA13408>, Levinsohn and Petrin (2003) <doi:10.1111/1467-937X.00246>, Wooldridge (2009) <doi:10.1016/j.econlet.2009.04.026>, Petrin, Poi and Levinsohn (2004) <doi:10.1177/1536867X0400400202>, and Arellano and Bond (1991) <doi:10.2307/2297968> with the "too many instruments" correction by Roodman (2009) <doi:10.1111/j.1468-0084.2008.00542.x>. Markup estimation follows De Loecker and Warzynski (2012) <doi:1 [...truncated...]
Author: Daniele Aglio [aut], Eric Bartelsman [aut], Mirja Haelbig [aut], Marco Miorandi [aut], Johanna Weiss [aut, cre], Alessandro Zona Mattioli [aut], Julian Diaz-Acosta [ctb], Alberto Ferreira [ctb], Javier Miranda [ctb], Marcelo Piemonte Ribeiro [ctb], Re [...truncated...]
Maintainer: Johanna Weiss <johanna.weiss@iwhesl.onmicrosoft.com>

Diff between mditools versions 0.1.0 dated 2026-08-20 and 0.1.1 dated 2026-10-08

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Package gtfsio updated to version 1.2.2 with previous version 1.2.1 dated 2026-05-20

Title: Read and Write General Transit Feed Specification (GTFS) Files
Description: Tools for the development of packages related to General Transit Feed Specification (GTFS) files. Establishes a standard for representing GTFS feeds using R data types. Provides fast and flexible functions to read and write GTFS feeds while sticking to this standard. Defines a basic 'gtfs' class which is meant to be extended by packages that depend on it. And offers utility functions that support checking the structure of GTFS objects.
Author: Daniel Herszenhut [aut] , Flavio Poletti [aut, cre], Mark Padgham [aut], Rafael H. M. Pereira [rev] , Tom Buckley [rev], Ipea - Institute for Applied Economic Research [cph, fnd]
Maintainer: Flavio Poletti <flavio.poletti@hotmail.ch>

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Package ggRandomForests updated to version 3.5.4 with previous version 3.5.3 dated 2026-09-11

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

Diff between ggRandomForests versions 3.5.3 dated 2026-09-11 and 3.5.4 dated 2026-10-08

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 R/gg_partial_varpro.R                                                             |    4 
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Package paws.compute updated to version 0.11.0 with previous version 0.10.0 dated 2026-05-31

Title: 'Amazon Web Services' Compute Services
Description: Interface to 'Amazon Web Services' compute services, including 'Elastic Compute Cloud' ('EC2'), 'Lambda' functions-as-a-service, containers, batch processing, and more <https://aws.amazon.com/>.
Author: David Kretch [aut], Adam Banker [aut], Dyfan Jones [cre], Amazon.com, Inc. [cph]
Maintainer: Dyfan Jones <dyfan.r.jones@gmail.com>

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 316 files changed, 12014 insertions(+), 9639 deletions(-)

More information about paws.compute at CRAN
Permanent link

Package clampSeg readmission to version 1.3-0 with previous version 1.2-0 dated 2025-06-27

Title: Idealisation of Patch Clamp Recordings
Description: Implements the model-free multiscale idealisation approaches: Jump-Segmentation by MUltiResolution Filter (JSMURF), Hotz et al. (2013) <doi:10.1109/TNB.2013.2284063>, JUmp Local dEconvolution Segmentation filter (JULES), Pein et al. (2018) <doi:10.1109/TNB.2018.2845126>, and Heterogeneous Idealization by Local testing and DEconvolution (HILDE), Pein et al. (2021) <doi:10.1109/TNB.2020.3031202>. Further details on how to use them are given in Pein, Eltzner and Munk (2021) <doi:10.1007/s00249-021-01506-8>.
Author: Florian Pein [aut, cre], Timo Aspelmeier [ctb]
Maintainer: Florian Pein <f.pein@lancaster.ac.uk>

This is a re-admission after prior archival of version 1.2-0 dated 2025-06-27

Diff between clampSeg versions 1.2-0 dated 2025-06-27 and 1.3-0 dated 2026-10-08

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More information about clampSeg at CRAN
Permanent link

Package wordvector updated to version 0.6.5 with previous version 0.6.4 dated 2026-09-10

Title: Word and Document Vector Models
Description: Create dense vector representation of words and documents using 'quanteda'. Implements Word2vec (Mikolov et al., 2013) <doi:10.48550/arXiv.1310.4546>, Doc2vec (Le & Mikolov, 2014) <doi:10.48550/arXiv.1405.4053> and Latent Semantic Analysis (Deerwester et al., 1990) <doi:10.1002/(SICI)1097-4571(199009)41:6%3C391::AID-ASI1%3E3.0.CO;2-9>.
Author: Kohei Watanabe [aut, cre, cph] , Jan Wijffels [aut] , BNOSAC [cph] , Max Fomichev [ctb, cph]
Maintainer: Kohei Watanabe <watanabe.kohei@gmail.com>

Diff between wordvector versions 0.6.4 dated 2026-09-10 and 0.6.5 dated 2026-10-08

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Permanent link

Package suwo updated to version 0.2.2 with previous version 0.2.1 dated 2026-04-20

Title: Access Nature Media Repositories
Description: Streamline searching, downloading and formatting of nature media files (e.g. audios, photos) from online repositories. The package offers functions for obtaining media metadata from online repositories, downloading associated media files and updating data sets with new records.
Author: Marcelo Araya-Salas [aut, cre] , Jorge Elizondo-Calvo [aut] , Alejandro Rico-Guevara [aut] , Hugo Gruson [rev] , Eric R Scott [rev]
Maintainer: Marcelo Araya-Salas <marcelo.araya@ucr.ac.cr>

Diff between suwo versions 0.2.1 dated 2026-04-20 and 0.2.2 dated 2026-10-08

 DESCRIPTION                                 |   25 
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 NAMESPACE                                   |    1 
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More information about suwo at CRAN
Permanent link

Package suppr updated to version 1.1.0 with previous version 1.0.1 dated 2026-09-07

Title: Supplementary Idiomatic Utilities and Extensions
Description: Miscellaneous supplementary functions designed to follow idiomatic 'R' conventions. Some functions are simple wrappers that reduce repetitive code, while others address common tasks or extend existing 'R' functions.
Author: Luke Jenkins [aut, cre, cph]
Maintainer: Luke Jenkins <luke-jenkins-dev@outlook.com>

Diff between suppr versions 1.0.1 dated 2026-09-07 and 1.1.0 dated 2026-10-08

 DESCRIPTION                            |    6 
 MD5                                    |   15 +
 NAMESPACE                              |  254 ++++++++++++++++-----------------
 NEWS.md                                |   24 +--
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More information about suppr at CRAN
Permanent link

Package qapproach updated to version 0.1.2 with previous version 0.1.1 dated 2026-09-22

Title: The Q Approach to Consensus Building
Description: Implements a workflow based on Q method to support consensus-building processes. It prepares participant rankings, selects and fits group perspectives, calculates consensus priority scores, validates results by bootstrap resampling, and produces publication-ready figures. The underlying method is described by Geschke et al. (2022) "The Q approach to consensus building: integrating diverse perspectives to guide decision-making" <doi:10.32942/X2F59S>.
Author: Jonas Geschke [aut, cre]
Maintainer: Jonas Geschke <hallo@qapproach.app>

Diff between qapproach versions 0.1.1 dated 2026-09-22 and 0.1.2 dated 2026-10-08

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 NAMESPACE                                 |    4 
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Permanent link

Package paws.security.identity updated to version 0.11.0 with previous version 0.10.0 dated 2026-05-31

Title: 'Amazon Web Services' Security, Identity, & Compliance Services
Description: Interface to 'Amazon Web Services' security, identity, and compliance services, including the 'Identity & Access Management' ('IAM') service for managing access to services and resources, and more <https://aws.amazon.com/>.
Author: David Kretch [aut], Adam Banker [aut], Dyfan Jones [cre], Amazon.com, Inc. [cph]
Maintainer: Dyfan Jones <dyfan.r.jones@gmail.com>

Diff between paws.security.identity versions 0.10.0 dated 2026-05-31 and 0.11.0 dated 2026-10-08

 DESCRIPTION                                                     |   11 
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 R/account_service.R                                             |    9 
 R/acm_interfaces.R                                              |  299 ++-
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 R/clouddirectory_interfaces.R                                   |  393 +---
 R/clouddirectory_operations.R                                   |  198 +-
 R/clouddirectory_service.R                                      |    6 
 R/cloudhsm_interfaces.R                                         |  120 -
 R/cloudhsm_operations.R                                         |   60 
 R/cloudhsm_service.R                                            |    4 
 R/cloudhsmv2_interfaces.R                                       |  108 -
 R/cloudhsmv2_operations.R                                       |   54 
 R/cloudhsmv2_service.R                                          |    6 
 R/cognitoidentity_interfaces.R                                  |  126 -
 R/cognitoidentity_operations.R                                  |   69 
 R/cognitoidentity_service.R                                     |    6 
 R/cognitoidentityprovider_interfaces.R                          |  793 +++------
 R/cognitoidentityprovider_operations.R                          |  799 +++++++--
 R/cognitoidentityprovider_service.R                             |   16 
 R/cognitosync_interfaces.R                                      |   99 -
 R/cognitosync_operations.R                                      |   51 
 R/cognitosync_service.R                                         |    4 
 R/detective_interfaces.R                                        |  141 -
 R/detective_operations.R                                        |   87 -
 R/detective_service.R                                           |    6 
 R/directoryservice_interfaces.R                                 |  480 +----
 R/directoryservice_operations.R                                 |  240 +-
 R/directoryservice_service.R                                    |    4 
 R/fms_interfaces.R                                              |  225 --
 R/fms_operations.R                                              |  126 -
 R/fms_service.R                                                 |    4 
 R/guardduty_interfaces.R                                        |  663 +++----
 R/guardduty_operations.R                                        |  814 ++++++++-
 R/guardduty_service.R                                           |   21 
 R/iam_interfaces.R                                              |  835 +++------
 R/iam_operations.R                                              |  703 ++++++--
 R/iam_service.R                                                 |   10 
 R/iamrolesanywhere_interfaces.R                                 |  180 --
 R/iamrolesanywhere_operations.R                                 |   90 -
 R/iamrolesanywhere_service.R                                    |    4 
 R/identitystore_interfaces.R                                    |  144 -
 R/identitystore_operations.R                                    |  259 ++-
 R/identitystore_service.R                                       |   17 
 R/inspector2_interfaces.R                                       |  510 ++----
 R/inspector2_operations.R                                       |  452 ++++-
 R/inspector2_service.R                                          |   14 
 R/inspector_interfaces.R                                        |  192 --
 R/inspector_operations.R                                        |  119 -
 R/inspector_service.R                                           |    6 
 R/kms_interfaces.R                                              |  282 +--
 R/kms_operations.R                                              |  164 +
 R/kms_service.R                                                 |    4 
 R/macie2_interfaces.R                                           |  486 +----
 R/macie2_operations.R                                           |  243 +-
 R/macie2_service.R                                              |    6 
 R/pcaconnectorad_interfaces.R                                   |  117 -
 R/pcaconnectorad_operations.R                                   |   75 
 R/pcaconnectorad_service.R                                      |    4 
 R/ram_interfaces.R                                              |  210 --
 R/ram_operations.R                                              |  105 -
 R/ram_service.R                                                 |    4 
 R/secretsmanager_interfaces.R                                   |  132 -
 R/secretsmanager_operations.R                                   |   69 
 R/secretsmanager_service.R                                      |    4 
 R/securityhub_interfaces.R                                      |  754 +++-----
 R/securityhub_operations.R                                      |  698 ++++++--
 R/securityhub_service.R                                         |   16 
 R/securitylake_interfaces.R                                     |  186 --
 R/securitylake_operations.R                                     |   93 -
 R/securitylake_service.R                                        |    4 
 R/shield_interfaces.R                                           |  216 --
 R/shield_operations.R                                           |  108 -
 R/shield_service.R                                              |    4 
 R/sso_interfaces.R                                              |   21 
 R/sso_operations.R                                              |   12 
 R/sso_service.R                                                 |    4 
 R/ssoadmin_interfaces.R                                         |  456 +----
 R/ssoadmin_operations.R                                         |  244 +-
 R/ssoadmin_service.R                                            |    4 
 R/ssooidc_interfaces.R                                          |   24 
 R/ssooidc_operations.R                                          |   12 
 R/ssooidc_service.R                                             |    6 
 R/sts_interfaces.R                                              |   66 
 R/sts_operations.R                                              |   63 
 R/sts_service.R                                                 |   12 
 R/sysdata.rda                                                   |only
 R/verifiedpermissions_interfaces.R                              |  204 --
 R/verifiedpermissions_operations.R                              |  102 -
 R/verifiedpermissions_service.R                                 |    4 
 R/waf_interfaces.R                                              |  462 +----
 R/waf_operations.R                                              |  231 +-
 R/waf_service.R                                                 |    4 
 R/wafregional_interfaces.R                                      |  486 +----
 R/wafregional_operations.R                                      |  243 +-
 R/wafregional_service.R                                         |    4 
 R/wafv2_interfaces.R                                            |  370 +---
 R/wafv2_operations.R                                            |  347 +++-
 R/wafv2_service.R                                               |    8 
 man/accessanalyzer.Rd                                           |    4 
 man/account.Rd                                                  |    9 
 man/account_get_primary_email_update_status.Rd                  |only
 man/account_send_phone_number_verification.Rd                   |only
 man/account_verify_phone_number.Rd                              |only
 man/acm.Rd                                                      |   29 
 man/acm_create_acme_domain_validation.Rd                        |only
 man/acm_create_acme_endpoint.Rd                                 |only
 man/acm_create_acme_external_account_binding.Rd                 |only
 man/acm_delete_acme_domain_validation.Rd                        |only
 man/acm_delete_acme_endpoint.Rd                                 |only
 man/acm_delete_acme_external_account_binding.Rd                 |only
 man/acm_describe_acme_account.Rd                                |only
 man/acm_describe_acme_domain_validation.Rd                      |only
 man/acm_describe_acme_endpoint.Rd                               |only
 man/acm_describe_acme_external_account_binding.Rd               |only
 man/acm_get_acme_external_account_binding_credentials.Rd        |only
 man/acm_list_acme_accounts.Rd                                   |only
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 man/acm_list_acme_endpoints.Rd                                  |only
 man/acm_list_acme_external_account_bindings.Rd                  |only
 man/acm_list_certificate_domain_validations.Rd                  |only
 man/acm_list_certificates.Rd                                    |    3 
 man/acm_list_tags_for_resource.Rd                               |only
 man/acm_request_certificate.Rd                                  |    4 
 man/acm_revoke_acme_account.Rd                                  |only
 man/acm_revoke_acme_external_account_binding.Rd                 |only
 man/acm_tag_resource.Rd                                         |only
 man/acm_untag_resource.Rd                                       |only
 man/acm_update_acme_domain_validation.Rd                        |only
 man/acm_update_acme_endpoint.Rd                                 |only
 man/acm_update_certificate_options.Rd                           |    6 
 man/acmpca.Rd                                                   |    4 
 man/acmpca_create_certificate_authority.Rd                      |    2 
 man/acmpca_update_certificate_authority.Rd                      |    2 
 man/cleanroomsml.Rd                                             |    4 
 man/clouddirectory.Rd                                           |    6 
 man/cloudhsm.Rd                                                 |    4 
 man/cloudhsmv2.Rd                                               |    4 
 man/cognitoidentity.Rd                                          |    4 
 man/cognitoidentityprovider.Rd                                  |   14 
 man/cognitoidentityprovider_admin_delete_software_token.Rd      |only
 man/cognitoidentityprovider_admin_get_user_auth_factors.Rd      |only
 man/cognitoidentityprovider_admin_initiate_auth.Rd              |    6 
 man/cognitoidentityprovider_admin_link_provider_for_user.Rd     |    4 
 man/cognitoidentityprovider_create_identity_provider.Rd         |    7 
 man/cognitoidentityprovider_create_user_import_job.Rd           |    7 
 man/cognitoidentityprovider_create_user_pool.Rd                 |   25 
 man/cognitoidentityprovider_create_user_pool_client.Rd          |    2 
 man/cognitoidentityprovider_create_user_pool_domain.Rd          |    9 
 man/cognitoidentityprovider_create_user_pool_replica.Rd         |only
 man/cognitoidentityprovider_delete_user_pool_replica.Rd         |only
 man/cognitoidentityprovider_describe_terms_by_client.Rd         |only
 man/cognitoidentityprovider_get_client_token.Rd                 |only
 man/cognitoidentityprovider_get_provisioned_limit.Rd            |only
 man/cognitoidentityprovider_initiate_auth.Rd                    |    6 
 man/cognitoidentityprovider_list_user_pool_replicas.Rd          |only
 man/cognitoidentityprovider_update_identity_provider.Rd         |    7 
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 man/cognitoidentityprovider_update_user_pool.Rd                 |   13 
 man/cognitoidentityprovider_update_user_pool_client.Rd          |    2 
 man/cognitoidentityprovider_update_user_pool_domain.Rd          |    5 
 man/cognitoidentityprovider_update_user_pool_replica.Rd         |only
 man/cognitosync.Rd                                              |    4 
 man/detective.Rd                                                |    4 
 man/directoryservice.Rd                                         |    4 
 man/fms.Rd                                                      |    4 
 man/guardduty.Rd                                                |   19 
 man/guardduty_create_custom_detection_rule_association.Rd       |only
 man/guardduty_create_custom_detection_rule_org_configuration.Rd |only
 man/guardduty_create_filter.Rd                                  |    7 
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 man/guardduty_get_custom_detection_rule.Rd                      |only
 man/guardduty_get_custom_detection_rule_association.Rd          |only
 man/guardduty_get_custom_detection_rule_org_configuration.Rd    |only
 man/guardduty_get_investigation.Rd                              |only
 man/guardduty_list_custom_detection_rule_associations.Rd        |only
 man/guardduty_list_custom_detection_rule_org_configurations.Rd  |only
 man/guardduty_list_custom_detection_rules.Rd                    |only
 man/guardduty_list_investigations.Rd                            |only
 man/guardduty_update_custom_detection_rule_association.Rd       |only
 man/guardduty_update_custom_detection_rule_org_configuration.Rd |only
 man/guardduty_update_filter.Rd                                  |    7 
 man/iam.Rd                                                      |    8 
 man/iam_acquire_role.Rd                                         |only
 man/iam_create_service_specific_credential.Rd                   |    4 
 man/iam_get_account_properties.Rd                               |only
 man/iam_get_role_template_version.Rd                            |only
 man/iam_list_service_specific_credentials.Rd                    |    2 
 man/iam_put_account_properties.Rd                               |only
 man/iam_simulate_custom_policy.Rd                               |   11 
 man/iam_simulate_principal_policy.Rd                            |   13 
 man/iamrolesanywhere.Rd                                         |    4 
 man/identitystore.Rd                                            |   17 
 man/identitystore_create_group.Rd                               |    4 
 man/identitystore_create_group_membership.Rd                    |    8 
 man/identitystore_create_user.Rd                                |    4 
 man/identitystore_delete_group.Rd                               |   12 
 man/identitystore_delete_group_membership.Rd                    |    8 
 man/identitystore_delete_user.Rd                                |   12 
 man/identitystore_describe_group.Rd                             |    8 
 man/identitystore_describe_group_membership.Rd                  |    8 
 man/identitystore_describe_identity_store.Rd                    |only
 man/identitystore_describe_user.Rd                              |    8 
 man/identitystore_get_group_id.Rd                               |    4 
 man/identitystore_get_group_membership_id.Rd                    |    8 
 man/identitystore_get_user_id.Rd                                |    4 
 man/identitystore_is_member_in_groups.Rd                        |    8 
 man/identitystore_list_group_memberships.Rd                     |   10 
 man/identitystore_list_group_memberships_for_member.Rd          |    8 
 man/identitystore_list_groups.Rd                                |    8 
 man/identitystore_list_identity_stores.Rd                       |only
 man/identitystore_list_users.Rd                                 |    8 
 man/identitystore_update_group.Rd                               |   17 
 man/identitystore_update_identity_store.Rd                      |only
 man/identitystore_update_user.Rd                                |   12 
 man/inspector.Rd                                                |    6 
 man/inspector2.Rd                                               |   14 
 man/inspector2_create_connector.Rd                              |only
 man/inspector2_delete_connector.Rd                              |only
 man/inspector2_get_configuration.Rd                             |    9 
 man/inspector2_list_connector_scan_configurations.Rd            |only
 man/inspector2_list_connectors.Rd                               |only
 man/inspector2_update_configuration.Rd                          |   12 
 man/inspector2_update_connector.Rd                              |only
 man/inspector2_update_connector_scan_configuration.Rd           |only
 man/inspector_create_assessment_target.Rd                       |    2 
 man/inspector_create_assessment_template.Rd                     |    2 
 man/inspector_get_assessment_report.Rd                          |    2 
 man/inspector_list_assessment_targets.Rd                        |    2 
 man/kms.Rd                                                      |    4 
 man/kms_derive_shared_secret.Rd                                 |    2 
 man/macie2.Rd                                                   |    4 
 man/pcaconnectorad.Rd                                           |    4 
 man/ram.Rd                                                      |    4 
 man/secretsmanager.Rd                                           |    4 
 man/securityhub.Rd                                              |   14 
 man/securityhub_create_connector.Rd                             |only
 man/securityhub_delete_connector.Rd                             |only
 man/securityhub_describe_standards.Rd                           |    8 
 man/securityhub_disable_security_hub_feature_v2.Rd              |only
 man/securityhub_disable_security_hub_v2.Rd                      |    2 
 man/securityhub_enable_security_hub_feature_v2.Rd               |only
 man/securityhub_get_connector.Rd                                |only
 man/securityhub_get_enabled_standards.Rd                        |    5 
 man/securityhub_get_remediations_v2.Rd                          |only
 man/securityhub_list_connectors.Rd                              |only
 man/securityhub_list_connectors_v2.Rd                           |    5 
 man/securityhub_list_exposures_by_remediation_v2.Rd             |only
 man/securityhub_list_free_trial_statuses_v2.Rd                  |only
 man/securityhub_list_security_control_definitions.Rd            |    5 
 man/securityhub_update_connector.Rd                             |only
 man/securitylake.Rd                                             |    4 
 man/shield.Rd                                                   |    4 
 man/sso.Rd                                                      |    4 
 man/ssoadmin.Rd                                                 |    4 
 man/ssoadmin_update_instance.Rd                                 |    7 
 man/ssooidc.Rd                                                  |    6 
 man/sts.Rd                                                      |   12 
 man/sts_assume_role.Rd                                          |    5 
 man/sts_assume_role_with_saml.Rd                                |    5 
 man/sts_assume_role_with_web_identity.Rd                        |    5 
 man/sts_assume_root.Rd                                          |    9 
 man/sts_get_federation_token.Rd                                 |    5 
 man/sts_get_session_token.Rd                                    |    5 
 man/verifiedpermissions.Rd                                      |    4 
 man/waf.Rd                                                      |    4 
 man/wafregional.Rd                                              |    4 
 man/wafv2.Rd                                                    |    8 
 man/wafv2_associate_web_acl.Rd                                  |    1 
 man/wafv2_create_rule_group.Rd                                  |    5 
 man/wafv2_create_web_acl.Rd                                     |    5 
 man/wafv2_disassociate_web_acl.Rd                               |    1 
 man/wafv2_get_revenue_statistics.Rd                             |only
 man/wafv2_get_revenue_statistics_summary.Rd                     |only
 man/wafv2_get_revenue_statistics_time_series.Rd                 |only
 man/wafv2_get_web_acl_for_resource.Rd                           |    1 
 man/wafv2_list_settlement_records.Rd                            |only
 man/wafv2_update_rule_group.Rd                                  |    5 
 man/wafv2_update_web_acl.Rd                                     |    5 
 tests/testthat.R                                                |only
 tests/testthat/test_accessanalyzer.R                            |    4 
 tests/testthat/test_account.R                                   |    4 
 tests/testthat/test_acm.R                                       |   13 
 tests/testthat/test_acmpca.R                                    |    4 
 tests/testthat/test_cleanroomsml.R                              |    8 
 tests/testthat/test_clouddirectory.R                            |   10 
 tests/testthat/test_cloudhsm.R                                  |    2 
 tests/testthat/test_cloudhsmv2.R                                |    6 
 tests/testthat/test_cognitoidentity.R                           |    2 
 tests/testthat/test_cognitoidentityprovider.R                   |    2 
 tests/testthat/test_cognitosync.R                               |    4 
 tests/testthat/test_detective.R                                 |    8 
 tests/testthat/test_directoryservice.R                          |    8 
 tests/testthat/test_fms.R                                       |    2 
 tests/testthat/test_guardduty.R                                 |   41 
 tests/testthat/test_iam.R                                       |   19 
 tests/testthat/test_iamrolesanywhere.R                          |    6 
 tests/testthat/test_identitystore.R                             |   10 
 tests/testthat/test_inspector.R                                 |    9 
 tests/testthat/test_inspector2.R                                |   25 
 tests/testthat/test_kms.R                                       |    6 
 tests/testthat/test_macie2.R                                    |   16 
 tests/testthat/test_pcaconnectorad.R                            |    6 
 tests/testthat/test_ram.R                                       |    7 
 tests/testthat/test_secretsmanager.R                            |    4 
 tests/testthat/test_securityhub.R                               |   57 
 tests/testthat/test_securitylake.R                              |    6 
 tests/testthat/test_shield.R                                    |    7 
 tests/testthat/test_sso.R                                       |    2 
 tests/testthat/test_ssoadmin.R                                  |    6 
 tests/testthat/test_ssooidc.R                                   |    2 
 tests/testthat/test_sts.R                                       |    2 
 tests/testthat/test_verifiedpermissions.R                       |    4 
 tests/testthat/test_waf.R                                       |   15 
 tests/testthat/test_wafregional.R                               |   15 
 tests/testthat/test_wafv2.R                                     |    2 
 331 files changed, 11874 insertions(+), 8867 deletions(-)

More information about paws.security.identity at CRAN
Permanent link

Package paws.machine.learning updated to version 0.11.0 with previous version 0.10.0 dated 2026-05-30

Title: 'Amazon Web Services' Machine Learning Services
Description: Interface to 'Amazon Web Services' machine learning services, including 'SageMaker' managed machine learning service, natural language processing, speech recognition, translation, and more <https://aws.amazon.com/machine-learning/>.
Author: David Kretch [aut], Adam Banker [aut], Dyfan Jones [cre], Amazon.com, Inc. [cph]
Maintainer: Dyfan Jones <dyfan.r.jones@gmail.com>

Diff between paws.machine.learning versions 0.10.0 dated 2026-05-30 and 0.11.0 dated 2026-10-08

 paws.machine.learning-0.10.0/paws.machine.learning/R/panorama_interfaces.R                                             |only
 paws.machine.learning-0.10.0/paws.machine.learning/R/panorama_operations.R                                             |only
 paws.machine.learning-0.10.0/paws.machine.learning/R/panorama_service.R                                                |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama.Rd                                                     |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_create_application_instance.Rd                         |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_create_job_for_devices.Rd                              |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_create_node_from_template_job.Rd                       |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_create_package.Rd                                      |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_create_package_import_job.Rd                           |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_delete_device.Rd                                       |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_delete_package.Rd                                      |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_deregister_package_version.Rd                          |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_describe_application_instance.Rd                       |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_describe_application_instance_details.Rd               |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_describe_device.Rd                                     |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_describe_device_job.Rd                                 |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_describe_node.Rd                                       |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_describe_node_from_template_job.Rd                     |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_describe_package.Rd                                    |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_describe_package_import_job.Rd                         |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_describe_package_version.Rd                            |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_list_application_instance_dependencies.Rd              |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_list_application_instance_node_instances.Rd            |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_list_application_instances.Rd                          |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_list_devices.Rd                                        |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_list_devices_jobs.Rd                                   |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_list_node_from_template_jobs.Rd                        |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_list_nodes.Rd                                          |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_list_package_import_jobs.Rd                            |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_list_packages.Rd                                       |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_list_tags_for_resource.Rd                              |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_provision_device.Rd                                    |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_register_package_version.Rd                            |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_remove_application_instance.Rd                         |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_signal_application_instance_node_instances.Rd          |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_tag_resource.Rd                                        |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_untag_resource.Rd                                      |only
 paws.machine.learning-0.10.0/paws.machine.learning/man/panorama_update_device_metadata.Rd                              |only
 paws.machine.learning-0.10.0/paws.machine.learning/tests/testthat/test_panorama.R                                      |only
 paws.machine.learning-0.11.0/paws.machine.learning/DESCRIPTION                                                         |   18 
 paws.machine.learning-0.11.0/paws.machine.learning/MD5                                                                 |  643 +-
 paws.machine.learning-0.11.0/paws.machine.learning/NAMESPACE                                                           |   37 
 paws.machine.learning-0.11.0/paws.machine.learning/R/augmentedairuntime_interfaces.R                                   |   30 
 paws.machine.learning-0.11.0/paws.machine.learning/R/augmentedairuntime_operations.R                                   |   15 
 paws.machine.learning-0.11.0/paws.machine.learning/R/augmentedairuntime_service.R                                      |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrock_interfaces.R                                              |  656 +-
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrock_operations.R                                              |  443 +
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrock_service.R                                                 |   16 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockagent_interfaces.R                                         |  502 --
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockagent_operations.R                                         |  500 +-
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockagent_service.R                                            |   15 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockagentcore_interfaces.R                                     |  410 -
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockagentcore_operations.R                                     |  338 +
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockagentcore_service.R                                        |    6 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockagentcorecontrol_interfaces.R                              | 1122 ++--
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockagentcorecontrol_operations.R                              | 1506 +++++-
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockagentcorecontrol_service.R                                 |   48 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockagentruntime_interfaces.R                                  |  226 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockagentruntime_operations.R                                  |  260 -
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockagentruntime_service.R                                     |   14 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockdataautomation_interfaces.R                                |  162 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockdataautomation_operations.R                                |   81 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockdataautomation_service.R                                   |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockdataautomationruntime_interfaces.R                         |   36 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockdataautomationruntime_operations.R                         |   18 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockdataautomationruntime_service.R                            |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockruntime_interfaces.R                                       |   70 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockruntime_operations.R                                       |   63 
 paws.machine.learning-0.11.0/paws.machine.learning/R/bedrockruntime_service.R                                          |    5 
 paws.machine.learning-0.11.0/paws.machine.learning/R/comprehend_interfaces.R                                           |  510 --
 paws.machine.learning-0.11.0/paws.machine.learning/R/comprehend_operations.R                                           |  255 -
 paws.machine.learning-0.11.0/paws.machine.learning/R/comprehend_service.R                                              |    6 
 paws.machine.learning-0.11.0/paws.machine.learning/R/comprehendmedical_interfaces.R                                    |  156 
 paws.machine.learning-0.11.0/paws.machine.learning/R/comprehendmedical_operations.R                                    |   78 
 paws.machine.learning-0.11.0/paws.machine.learning/R/comprehendmedical_service.R                                       |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/forecastqueryservice_interfaces.R                                 |   12 
 paws.machine.learning-0.11.0/paws.machine.learning/R/forecastqueryservice_operations.R                                 |    6 
 paws.machine.learning-0.11.0/paws.machine.learning/R/forecastqueryservice_service.R                                    |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/forecastservice_interfaces.R                                      |  330 -
 paws.machine.learning-0.11.0/paws.machine.learning/R/forecastservice_operations.R                                      |  189 
 paws.machine.learning-0.11.0/paws.machine.learning/R/forecastservice_service.R                                         |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/frauddetector_interfaces.R                                        |  435 -
 paws.machine.learning-0.11.0/paws.machine.learning/R/frauddetector_operations.R                                        |  219 
 paws.machine.learning-0.11.0/paws.machine.learning/R/frauddetector_service.R                                           |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/lexmodelbuildingservice_interfaces.R                              |  225 
 paws.machine.learning-0.11.0/paws.machine.learning/R/lexmodelbuildingservice_operations.R                              |  126 
 paws.machine.learning-0.11.0/paws.machine.learning/R/lexmodelbuildingservice_service.R                                 |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/lexmodelsv2_interfaces.R                                          |  630 --
 paws.machine.learning-0.11.0/paws.machine.learning/R/lexmodelsv2_operations.R                                          |  333 -
 paws.machine.learning-0.11.0/paws.machine.learning/R/lexmodelsv2_service.R                                             |   32 
 paws.machine.learning-0.11.0/paws.machine.learning/R/lexruntimeservice_interfaces.R                                    |   30 
 paws.machine.learning-0.11.0/paws.machine.learning/R/lexruntimeservice_operations.R                                    |   15 
 paws.machine.learning-0.11.0/paws.machine.learning/R/lexruntimeservice_service.R                                       |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/lexruntimev2_interfaces.R                                         |   36 
 paws.machine.learning-0.11.0/paws.machine.learning/R/lexruntimev2_operations.R                                         |   18 
 paws.machine.learning-0.11.0/paws.machine.learning/R/lexruntimev2_service.R                                            |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/lookoutequipment_interfaces.R                                     |  261 -
 paws.machine.learning-0.11.0/paws.machine.learning/R/lookoutequipment_operations.R                                     |  147 
 paws.machine.learning-0.11.0/paws.machine.learning/R/lookoutequipment_service.R                                        |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/machinelearning_interfaces.R                                      |  168 
 paws.machine.learning-0.11.0/paws.machine.learning/R/machinelearning_operations.R                                      |   84 
 paws.machine.learning-0.11.0/paws.machine.learning/R/machinelearning_service.R                                         |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/personalize_interfaces.R                                          |  396 -
 paws.machine.learning-0.11.0/paws.machine.learning/R/personalize_operations.R                                          |  213 
 paws.machine.learning-0.11.0/paws.machine.learning/R/personalize_service.R                                             |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/personalizeevents_interfaces.R                                    |   15 
 paws.machine.learning-0.11.0/paws.machine.learning/R/personalizeevents_operations.R                                    |   15 
 paws.machine.learning-0.11.0/paws.machine.learning/R/personalizeevents_service.R                                       |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/personalizeruntime_interfaces.R                                   |   18 
 paws.machine.learning-0.11.0/paws.machine.learning/R/personalizeruntime_operations.R                                   |    9 
 paws.machine.learning-0.11.0/paws.machine.learning/R/personalizeruntime_service.R                                      |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/polly_interfaces.R                                                |   60 
 paws.machine.learning-0.11.0/paws.machine.learning/R/polly_operations.R                                                |   30 
 paws.machine.learning-0.11.0/paws.machine.learning/R/polly_service.R                                                   |    6 
 paws.machine.learning-0.11.0/paws.machine.learning/R/rekognition_interfaces.R                                          |  450 -
 paws.machine.learning-0.11.0/paws.machine.learning/R/rekognition_operations.R                                          |  279 -
 paws.machine.learning-0.11.0/paws.machine.learning/R/rekognition_service.R                                             |   50 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemaker_interfaces.R                                            | 2270 +++-------
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemaker_operations.R                                            | 1538 ++++--
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemaker_service.R                                               |   12 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakeredgemanager_interfaces.R                                 |   15 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakeredgemanager_operations.R                                 |    9 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakeredgemanager_service.R                                    |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakerfeaturestoreruntime_interfaces.R                         |   47 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakerfeaturestoreruntime_operations.R                         |  122 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakerfeaturestoreruntime_service.R                            |    9 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakergeospatialcapabilities_interfaces.R                      |  114 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakergeospatialcapabilities_operations.R                      |   57 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakergeospatialcapabilities_service.R                         |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakermetrics_interfaces.R                                     |   12 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakermetrics_operations.R                                     |    6 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakermetrics_service.R                                        |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakerruntime_interfaces.R                                     |   18 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakerruntime_operations.R                                     |   34 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sagemakerruntime_service.R                                        |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/R/sysdata.rda                                                       |only
 paws.machine.learning-0.11.0/paws.machine.learning/R/textract_interfaces.R                                             |  150 
 paws.machine.learning-0.11.0/paws.machine.learning/R/textract_operations.R                                             |   77 
 paws.machine.learning-0.11.0/paws.machine.learning/R/textract_service.R                                                |    6 
 paws.machine.learning-0.11.0/paws.machine.learning/R/transcribeservice_interfaces.R                                    |  247 -
 paws.machine.learning-0.11.0/paws.machine.learning/R/transcribeservice_operations.R                                    |  201 
 paws.machine.learning-0.11.0/paws.machine.learning/R/transcribeservice_service.R                                       |    7 
 paws.machine.learning-0.11.0/paws.machine.learning/R/translate_interfaces.R                                            |  111 
 paws.machine.learning-0.11.0/paws.machine.learning/R/translate_operations.R                                            |   57 
 paws.machine.learning-0.11.0/paws.machine.learning/R/translate_service.R                                               |    6 
 paws.machine.learning-0.11.0/paws.machine.learning/R/voiceid_interfaces.R                                              |  162 
 paws.machine.learning-0.11.0/paws.machine.learning/R/voiceid_operations.R                                              |   87 
 paws.machine.learning-0.11.0/paws.machine.learning/R/voiceid_service.R                                                 |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/man/augmentedairuntime.Rd                                           |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrock.Rd                                                      |   16 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrock_batch_delete_advanced_prompt_optimization_job.Rd        |    6 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrock_create_advanced_prompt_optimization_job.Rd              |   20 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrock_create_custom_model.Rd                                  |   13 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrock_get_account_data_retention.Rd                           |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrock_get_advanced_prompt_optimization_job.Rd                 |    6 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrock_list_advanced_prompt_optimization_jobs.Rd               |   12 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrock_put_account_data_retention.Rd                           |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrock_stop_advanced_prompt_optimization_job.Rd                |    6 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagent.Rd                                                 |   15 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagent_create_agent.Rd                                    |    7 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagent_create_data_source.Rd                              |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagent_create_vpc_configuration.Rd                        |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagent_delete_resource_policy.Rd                          |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagent_delete_vpc_configuration.Rd                        |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagent_get_flow.Rd                                        |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagent_get_flow_version.Rd                                |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagent_get_prompt.Rd                                      |    8 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagent_get_resource_policy.Rd                             |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagent_get_vpc_configuration.Rd                           |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagent_list_vpc_configurations.Rd                         |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagent_put_resource_policy.Rd                             |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcore.Rd                                             |    6 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcore_create_ab_test.Rd                              |    5 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcore_create_event.Rd                                |   10 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcore_delete_capacity_provider_session.Rd            |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcore_delete_memory_record.Rd                        |    8 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcore_get_memory_record.Rd                           |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcore_ingest_data.Rd                                 |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcore_invoke_agent_runtime.Rd                        |   11 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcore_invoke_harness.Rd                              |   18 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcore_start_batch_evaluation.Rd                      |   14 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcore_start_browser_session.Rd                       |    3 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcore_start_code_interpreter_session.Rd              |    3 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcore_start_recommendation.Rd                        |    8 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol.Rd                                      |   48 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_add_dataset_examples.Rd                 |    2 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_batch_put_gateway_rate_limits.Rd        |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_agent_runtime.Rd                 |   12 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_api_key_credential_provider.Rd   |   10 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_browser.Rd                       |    3 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_capacity_provider.Rd             |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_code_interpreter.Rd              |    3 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_configuration_bundle.Rd          |    3 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_consent_portal.Rd                |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_dataset.Rd                       |   10 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_dataset_version.Rd               |    2 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_gateway_rate_limit.Rd            |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_gateway_target.Rd                |    9 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_harness.Rd                       |    7 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_harness_endpoint.Rd              |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_memory.Rd                        |    3 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_online_evaluation_config.Rd      |   13 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_payment_connector.Rd             |    7 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_payment_manager.Rd               |    5 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_create_policy.Rd                        |    7 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_delete_agent_runtime.Rd                 |    8 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_delete_agent_runtime_endpoint.Rd        |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_delete_capacity_provider.Rd             |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_delete_consent_portal.Rd                |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_delete_dataset.Rd                       |    7 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_delete_dataset_examples.Rd              |    2 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_delete_gateway_rate_limit.Rd            |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_delete_harness.Rd                       |    8 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_delete_harness_endpoint.Rd              |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_delete_memory.Rd                        |    2 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_get_capacity_provider.Rd                |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_get_consent_portal.Rd                   |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_get_dataset.Rd                          |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_get_gateway_rate_limit.Rd               |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_get_harness.Rd                          |    8 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_get_harness_endpoint.Rd                 |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_get_policy_generation.Rd                |    2 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_list_agen_runt_vers_by_capa_prov.Rd     |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_list_capacity_providers.Rd              |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_list_consent_portals.Rd                 |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_list_dataset_examples.Rd                |    6 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_list_datasets.Rd                        |    2 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_list_gateway_rate_limits.Rd             |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_list_harness_endpoints.Rd               |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_list_harness_versions.Rd                |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_list_harnesses.Rd                       |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_list_policy_generation_assets.Rd        |    2 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_rotate_payment_connector_credentials.Rd |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_start_policy_generation.Rd              |    6 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_agent_runtime.Rd                 |   10 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_api_key_credential_provider.Rd   |   13 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_capacity_provider.Rd             |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_configuration_bundle.Rd          |   13 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_consent_portal.Rd                |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_dataset.Rd                       |    2 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_dataset_examples.Rd              |    4 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_gateway.Rd                       |    8 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_gateway_rate_limit.Rd            |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_gateway_target.Rd                |    9 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_harness.Rd                       |    7 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_harness_endpoint.Rd              |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_memory.Rd                        |    3 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_online_evaluation_config.Rd      |    9 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_payment_manager.Rd               |    5 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentcorecontrol_update_policy.Rd                        |    7 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentruntime.Rd                                          |   14 
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentruntime_agentic_retrieve_stream.Rd                  |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentruntime_check_ingested_document_acl.Rd              |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentruntime_get_document_content.Rd                     |only
 paws.machine.learning-0.11.0/paws.machine.learning/man/bedrockagentruntime_get_ingested_document_acl.Rd                |only
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 paws.machine.learning-0.11.0/paws.machine.learning/tests/testthat/test_comprehendmedical.R                             |   12 
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 paws.machine.learning-0.11.0/paws.machine.learning/tests/testthat/test_rekognition.R                                   |   10 
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More information about paws.machine.learning at CRAN
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Package lavaan updated to version 0.7-3 with previous version 0.7-2 dated 2026-07-16

Title: Latent Variable Analysis
Description: Fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models.
Author: Yves Rosseel [aut, cre] , Terrence D. Jorgensen [aut] , Luc De Wilde [aut], Daniel Oberski [ctb], Jarrett Byrnes [ctb], Leonard Vanbrabant [ctb], Victoria Savalei [ctb], Ed Merkle [ctb], Michael Hallquist [ctb], Mijke Rhemtulla [ctb], Myrsini Katsika [...truncated...]
Maintainer: Yves Rosseel <Yves.Rosseel@UGent.be>

Diff between lavaan versions 0.7-2 dated 2026-07-16 and 0.7-3 dated 2026-10-08

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 lavaan-0.7-3/lavaan/R/lav_cfa_fabin.R                     |   10 
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 lavaan-0.7-3/lavaan/R/lav_constraints.R                   |  169 +
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Package invasible updated to version 1.0.0 with previous version 0.1.1 dated 2026-08-05

Title: Predicting Invasion Probabilities from Phylogenetic Data and Species Traits
Description: A phylogenetic modelling approach for predicting species invasion risk, out of a given pool of local species where a subset is known to be invasive elsewhere. The package uses phylogenetic signal estimation and phylogenetic linear and logistic models to estimate probabilities of being invasive based on phylogeny and any set of additional predictors. A ranking method is implemented to evaluate prioritisation strategies. A manuscript describing these methods, by Shahar Dubiner and Tamar Guy-Haim, is in preparation.
Author: Shahar Dubiner [aut, cre]
Maintainer: Shahar Dubiner <dubiner@mail.tau.ac.il>

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Package hypergeo updated to version 1.2-15 with previous version 1.2-14 dated 2025-03-24

Title: The Gauss Hypergeometric Function
Description: The Gaussian hypergeometric function for complex numbers.
Author: Robin K. S. Hankin [aut, cre] , Richard D. Morey [ctb]
Maintainer: Robin K. S. Hankin <hankin.robin@gmail.com>

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Package ggsql updated to version 0.5.2 with previous version 0.3.3 dated 2026-06-03

Title: Grammar of Graphics for SQL
Description: Allows you to write queries that combine SQL (Structured Query Language) data retrieval with visualization specifications in a single, composable syntax. The 'ggsql' package binds directly with the 'ggsql' 'Rust' library and allows you to set up readers and writers and execute queries against it. The package also offers 'knitr' and 'shiny' integration allowing the user to use 'ggsql' in both frameworks.
Author: Thomas Lin Pedersen [aut, cre] , George Stagg [aut] , Teun van den Brand [aut] , Posit, PBC [cph, fnd]
Maintainer: Thomas Lin Pedersen <thomas.pedersen@posit.co>

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Package exams updated to version 2.4-5 with previous version 2.4-4 dated 2026-07-31

Title: Automatic Generation of Exams in R
Description: Automatic generation of exams based on exercises in Markdown or LaTeX format, possibly including R code for dynamic generation of exercise elements. Exercise types include single-choice and multiple-choice questions, arithmetic problems, string questions, and combinations thereof (cloze). Output formats include standalone files (PDF, HTML, Docx, ODT, ...), Moodle XML, QTI 1.2, QTI 2.1, Blackboard, Canvas, OpenOlat, ILIAS, TestVision, Particify, ARSnova, Kahoot!, Grasple, and TCExam. In addition to fully customizable PDF exams, a standardized PDF format (NOPS) is provided that can be printed, scanned, and automatically evaluated.
Author: Achim Zeileis [aut, cre] , Bettina Gruen [aut] , Friedrich Leisch [aut] , Nikolaus Umlauf [aut] , Niels Smits [aut] , Dominik Ernst [ctb] , Patrik Keller [ctb], Reto Stauffer [ctb] , Kenji Sato [ctb] , Florian Wickelmaier [ctb], Sebastian Bachler [ct [...truncated...]
Maintainer: Achim Zeileis <Achim.Zeileis@R-project.org>

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Package admtools updated to version 0.7.0 with previous version 0.6.0 dated 2025-05-20

Title: Estimate and Manipulate Age-Depth Models
Description: Estimate age-depth models from stratigraphic and sedimentological data, and transform data between the time and stratigraphic domain.
Author: Niklas Hohmann [aut] , Emilia Jarochowska [cre]
Maintainer: Emilia Jarochowska <e.jarochowska@uni-muenster.de>

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Package rfPermute updated to version 2.5.6 with previous version 2.5.5 dated 2025-06-10

Title: Estimate Permutation p-Values for Random Forest Importance Metrics
Description: Estimate significance of importance metrics for a Random Forest model by permuting the response variable. Produces null distribution of importance metrics for each predictor variable and p-value of observed. Provides summary and visualization functions for 'randomForest' results.
Author: Eric Archer [aut, cre]
Maintainer: Eric Archer <eric.ivan.archer@gmail.com>

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Package jgd updated to version 0.2.1 with previous version 0.2.0 dated 2026-09-27

Title: JSON Graphics Device
Description: A graphics device that translates R plotting operations into JSON and streams them over a local connection to an external display application. The device acts as a pure recorder with no rendering dependencies; all rendering occurs in that application (e.g. a 'VS Code' extension or a web browser). Official display applications are available from the project homepage.
Author: Grant McDermott [aut, cre], Tatsuya Shima [aut], Dave Gamble [cph] , cJSON contributors [cph]
Maintainer: Grant McDermott <contact@grantmcdermott.com>

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Package healthyR.data updated to version 1.2.1 with previous version 1.2.0 dated 2025-01-12

Title: Data Only Package to 'healthyR'
Description: Provides data for functions typically used in the 'healthyR' package.
Author: Steven Sanderson [aut, cre, cph]
Maintainer: Steven Sanderson <spsanderson@gmail.com>

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Package fuj updated to version 0.3.0 with previous version 0.2.2 dated 2025-04-23

Title: Functions and Utilities for Jordan
Description: Provides core functions and utilities for packages and other code developed by Jordan Mark Barbone.
Author: Jordan Mark Barbone [aut, cph, cre]
Maintainer: Jordan Mark Barbone <jmbarbone@gmail.com>

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Package colorspace updated to version 2.1-4 with previous version 2.1-3 dated 2026-07-12

Title: A Toolbox for Manipulating and Assessing Colors and Palettes
Description: Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ggplot2 color scales. Color palette choice is aided by an interactive app (with either a Tcl/Tk or a shiny graphical user interface) and shiny apps with an HCL color picker and a color vision deficiency emulator. Plotting functions for displaying and assessing palettes include color swatches, visualizations of the HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation functions include: desaturation, lightening/darkening, mixing, and simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly). Details can be found on the project web page at <https://colorspace.R-Forge.R-project.org/> and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical Software, <doi:10.18637/ [...truncated...]
Author: Ross Ihaka [aut], Paul Murrell [aut] , Kurt Hornik [aut] , Jason C. Fisher [aut] , Reto Stauffer [aut] , Claus O. Wilke [aut] , Claire D. McWhite [aut] , Achim Zeileis [aut, cre]
Maintainer: Achim Zeileis <Achim.Zeileis@R-project.org>

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Package piecemeal updated to version 0.3.0 with previous version 0.2.0 dated 2026-03-11

Title: Wrangle Large Simulation Studies
Description: An 'R6' class to set up, run, monitor, collate, and debug large simulation studies comprising many small independent replications and treatment configurations. Parallel processing, reproducibility, fault- and error-tolerance, and ability to resume an interrupted or timed-out simulation study are built in.
Author: Pavel N. Krivitsky [aut, cre]
Maintainer: Pavel N. Krivitsky <pavel@statnet.org>

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Package DDIwR updated to version 0.22 with previous version 0.21 dated 2026-09-12

Title: DDI with R
Description: Useful functions for various DDI (Data Documentation Initiative) related inputs and outputs. Converts data files to and from DDI, SPSS, Stata, SAS, R and Excel, including user declared missing values.
Author: Adrian Dusa [aut, cre, cph]
Maintainer: Adrian Dusa <dusa.adrian@unibuc.ro>

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Package metR updated to version 0.19.0 with previous version 0.18.3 dated 2025-12-09

Title: Tools for Easier Analysis of Meteorological Fields
Description: Many useful functions and extensions for dealing with meteorological data in the tidy data framework. Extends 'ggplot2' for better plotting of scalar and vector fields and provides commonly used analysis methods in the atmospheric sciences.
Author: Elio Campitelli [cre, aut]
Maintainer: Elio Campitelli <eliocampitelli@gmail.com>

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Wed, 07 Oct 2026

Package randomLCA updated to version 1.1-5 with previous version 1.1-4 dated 2024-09-23

Title: Random Effects Latent Class Analysis
Description: Fits standard and random effects latent class models. The single level random effects model is described in Qu et al <doi:10.2307/2533043> and the two level random effects model in Beath and Heller <doi:10.1177/1471082X0800900302>. Examples are given for their use in diagnostic testing.
Author: Ken Beath [aut, cre]
Maintainer: Ken Beath <ken@kjbeath.id.au>

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Package venny updated to version 0.0.4 with previous version 0.0.3 dated 2026-05-27

Title: Venn Diagram
Description: Generate Venn plots, summary tables, and ellipse paths for polygon clipping. Provides direct access to subsets of interest and offers flexible customization of Venn diagrams. Summary tables are also available when Venn diagram visualization is not suitable.
Author: Joon-Keat Lai [aut, cre, cph]
Maintainer: Joon-Keat Lai <p10911004@gmail.com>

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

Title: Model-Robust Standardization in Cluster-Randomized Trials
Description: Implements model-robust standardization for cluster-randomized trials (CRTs). Provides functions that standardize user-specified regression models to estimate marginal treatment effects. The targets include the cluster-average and individual-average treatment effects, with utilities for variance estimation and example simulation datasets. Methods are described in Li, Tong, Fang, Cheng, Kahan, and Wang (2025) <doi:10.1002/sim.70270>.
Author: Jiaqi Tong [aut], Changjun Li [aut, cre], Xi Fang [aut], Chao Cheng [aut], Bingkai Wang [aut], Fan Li [aut]
Maintainer: Changjun Li <changjun.li@yale.edu>

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