Tue, 03 Nov 2026

New package WSSmod with initial version 0.1.0
Package: WSSmod
Title: Weighted Sum Scores for Protein Network Modules
Version: 0.1.0
Description: Computes Weighted Sum Score (WSS) module-level summaries from proteomic data. Protein-level values are sign-aligned to their association with core biomarkers and combined within 'STRING' network modules, weighted by network connectivity and association strength. Bundled reference modules and a fitted 'joinet' model also allow all module scores to be predicted from age, sex and a small panel of plasma biomarkers, without measuring the full proteomic panel. The method is described in Butler et al. (2026) <doi:10.64898/2026.08.14.26360394>.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-US
Depends: R (>= 3.5.0)
Imports: data.table
Suggests: glmnet, joinet, knitr, rmarkdown, RNOmni, testthat (>= 3.0.0)
VignetteBuilder: knitr
LazyData: true
URL: https://github.com/Longo-Lab/WSSmod, https://Longo-Lab.github.io/WSSmod/
BugReports: https://github.com/Longo-Lab/WSSmod/issues
NeedsCompilation: no
Packaged: 2026-10-22 02:57:36 UTC; rrbutler
Author: Robert Butler [aut, cre] , WSSmod authors [cph]
Maintainer: Robert Butler <rrbutler@stanford.edu>
Repository: CRAN
Date/Publication: 2026-11-03 16:00:38 UTC

More information about WSSmod at CRAN
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Package TernTables updated to version 1.8.0 with previous version 1.7.2 dated 2026-06-04

Title: Automated Statistical Analysis and Table Generation for Biomedical Research
Description: Generates publication-ready summary tables for clinical research, supporting descriptive summaries and comparisons across two or three groups. The package streamlines the analytical workflow by detecting variable types and applying appropriate statistical tests (Welch t-test, Wilcoxon rank-sum, Welch ANOVA, Kruskal-Wallis, Chi-squared, or Fisher's exact test). Results are formatted as 'tibble' objects and can be exported to 'Word' or 'Excel' using the 'officer', 'flextable', and 'writexl' packages. Optional pairwise post-hoc testing for three-group comparisons (Games-Howell and Dunn's test) is available via the 'rstatix' package. Example data are derived from the landmark adjuvant colon cancer trial described in Moertel et al. (1990) <doi:10.1056/NEJM199002083220602>.
Author: Joshua D. Preston [aut, cre] , Helen Abadiotakis [aut] , Ailin Tang [aut] , Clayton J. Rust [aut] , John W. O'Neill [aut] , Michael E. Halkos [aut] , Mani A. Daneshmand [aut] , Joshua L. Chan [aut]
Maintainer: Joshua D. Preston <joshua.preston@emory.edu>

Diff between TernTables versions 1.7.2 dated 2026-06-04 and 1.8.0 dated 2026-11-03

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More information about TernTables at CRAN
Permanent link

New package shapleydecomp with initial version 1.0.0
Package: shapleydecomp
Title: Shapley Value Decomposition for Discrete-Time Simulation Models
Version: 1.0.0
Description: Attributes changes in the outcomes of a discrete-time simulation model to its input factors using Shapley values (Shapley 1953) <doi:10.1515/9781400881970-018>, which for binary factors coincide with the symmetric standardization of Das Gupta (1993) <https://www2.census.gov/library/publications/1993/demographics/p23-186.pdf>. Any calibrated model can be decomposed by implementing four S3 methods that run the model, summarize its output, and construct counterfactual inputs. Counterfactuals either remove a factor entirely or hold it at its value in a baseline year, and a sequential decomposition attributes each year's policy conditional on the past.
License: MIT + file LICENSE
URL: https://github.com/aucarter/shapleydecomp, https://aucarter.github.io/shapleydecomp/
BugReports: https://github.com/aucarter/shapleydecomp/issues
Encoding: UTF-8
Depends: R (>= 4.0.0)
Imports: data.table, parallel, utils
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-21 23:30:55 UTC; aucarter
Author: Austin Carter [aut, cre]
Maintainer: Austin Carter <aucarter@uw.edu>
Repository: CRAN
Date/Publication: 2026-11-03 16:00:02 UTC

More information about shapleydecomp at CRAN
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New package SexSeek with initial version 0.1.0
Package: SexSeek
Title: Estimate Genetic Sex from Expression Counts
Version: 0.1.0
Description: Infers the genetic sex of samples, donors or cells from bulk or single-cell RNA sequencing counts. Accepts dense or sparse matrices, delimited text files, 10x Genomics outputs, and 'Seurat', 'SingleCellExperiment' and 'SummarizedExperiment' objects.
License: MIT + file LICENSE
URL: https://sexseek.saketlab.org, https://github.com/saketlab/SexSeek
BugReports: https://github.com/saketlab/SexSeek/issues
Encoding: UTF-8
Language: en-GB
Depends: R (>= 4.1)
Imports: ggplot2, Matrix, methods, stats, utils
Suggests: genevintage (>= 0.1.3), hdf5r, knitr, rmarkdown, SeuratObject, SingleCellExperiment, SummarizedExperiment, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-21 06:14:54 UTC; mcclintock
Author: Saket Choudhary [aut, cre, cph]
Maintainer: Saket Choudhary <saketc@iitb.ac.in>
Repository: CRAN
Date/Publication: 2026-11-03 15:50:18 UTC

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New package sas2r with initial version 0.5.8
Package: sas2r
Title: Translate 'SAS' Programs to R with Dependency-Aware Migration
Version: 0.5.8
Description: Translates 'SAS' programs and projects into R code, orders connected programs by their dependencies, and executes generated code. Compares generated datasets with supplied reference outputs and reports unresolved translation and validation findings. Rule-based translation works offline without a 'SAS' installation. Optional language model translation, review, and repair use a user-configured provider through 'ellmer' and may require an account and API credentials.
License: Apache License (>= 2)
URL: https://github.com/songyue-chen/sas2r
BugReports: https://github.com/songyue-chen/sas2r/issues
Encoding: UTF-8
VignetteBuilder: knitr
Depends: R (>= 4.1)
Imports: callr, cli (>= 3.4), clue, filelock, haven, jsonlite, stats, tibble, utils, vctrs, yaml
Suggests: diffdf, dplyr (>= 1.1.0), ellmer (>= 0.4.2), ggplot2, knitr, pdftools, pkgload, ps, rmarkdown, S7, testthat (>= 3.1.7), withr
NeedsCompilation: no
Packaged: 2026-10-21 16:33:12 UTC; chuckchen
Author: Songyue Chen [aut, cre]
Maintainer: Songyue Chen <songyue.chen.dev@gmail.com>
Repository: CRAN
Date/Publication: 2026-11-03 15:50:02 UTC

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New package RSYC with initial version 0.2.0
Package: RSYC
Title: Remote Sensing-Derived Yield Curves for Canada
Version: 0.2.0
Description: Provides published remote sensing-based yield curves for aboveground biomass and total volume across Canada, as described in Tompalski et al. (2024) <doi:10.1016/j.foreco.2024.121894> and Tompalski et al. (2025) <doi:10.1093/forestry/cpaf067>. Models are available for 150 km tiles and four levels of Canada's ecological land classification. Functions help users find curves by species and area, estimate yield at a given stand age, calibrate curves to local observations, and build spatial yield-curve datasets.
License: GPL (>= 3)
Encoding: UTF-8
Depends: R (>= 4.1.0)
LazyData: true
Imports: digest, glue, lifecycle, magrittr, tibble
Suggests: dplyr, purrr, sf, testthat (>= 3.0.0)
URL: https://ptompalski.github.io/RSYC/, https://github.com/ptompalski/RSYC
BugReports: https://github.com/ptompalski/RSYC/issues
NeedsCompilation: no
Packaged: 2026-10-21 22:58:11 UTC; ptompals
Author: Piotr Tompalski [aut, cre, cph], His Majesty the King in Right of Canada, as represented by the Minister of Natural Resources Canada [cph]
Maintainer: Piotr Tompalski <piotr.tompalski@NRCan-RNCan.gc.ca>
Repository: CRAN
Date/Publication: 2026-11-03 15:50:24 UTC

More information about RSYC at CRAN
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Package redcas updated to version 0.2.1 with previous version 0.1.1 dated 2024-11-08

Title: An Interface to the Computer Algebra System 'REDUCE'
Description: 'REDUCE' is a portable general-purpose computer algebra system supporting scalar, vector, matrix and tensor algebra, symbolic differential and integral calculus, arbitrary precision numerical calculations and output in 'LaTeX' format. 'REDUCE' is based on 'Lisp' and is available on the two dialects 'Portable Standard Lisp' ('PSL') and 'Codemist Standard Lisp' ('CSL'). The 'redcas' package provides an interface for executing arbitrary 'REDUCE' code interactively from 'R', returning output as character vectors. 'R' code and 'REDUCE' code can be interspersed. It also provides specialized functions for calling 'REDUCE' functions, for example, solving simultaneous or differential equations, differentiation and factorization of polynomials and integers returning the output as an appropriate 'R' object. A further specialized function uses 'REDUCE' features to generate 'LaTeX' output and post-processes this for direct use in 'LaTeX' documents using, e.g., 'Sweave'.
Author: Martin Gregory [aut, cre]
Maintainer: Martin Gregory <mairtin.macghreagoir@gmail.com>

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More information about redcas at CRAN
Permanent link

New package nestedscales with initial version 0.2.0
Package: nestedscales
Title: Nested Scales and Multi-Dimensional Indexes for Modeling
Version: 0.2.0
URL: https://optimal2050.github.io/nestedscales/r/, https://github.com/optimal2050/nestedscales
BugReports: https://github.com/optimal2050/nestedscales/issues
Description: Provides multi-level discrete representations of data dimensions for modeling, data processing and visualization. A nested scale records its finest units (atoms) and their membership in coarser levels (frames), including nested, cross-cutting and partially covered relationships. Recasts tabular data between levels using explicit, direction-aware rules for sums, means, standard deviations, copying and within-parent shares. Also supports labeling, subsetting, combining scales into multi-dimensional indexes, clustering units and storing data with its scale metadata. Members may carry attributes (a calorific value, an emission factor) and aliases, resolved along the hierarchy and applied row by row, and a scale is exchanged as a language-neutral definition file: a 'JSON' header with inline, 'Arrow' or 'CSV' tables ('YAML' accepted), with a catalogue of the definitions installed packages ship. Forms the dimension-agnostic core of 'timescales' <https://github.com/optimal2050/timescales> [...truncated...]
License: Apache License (>= 2)
Encoding: UTF-8
Depends: R (>= 4.2)
Imports: dplyr, jsonlite, rlang, S7, stats, utils
Suggests: arrow, cluster, data.table, dtplyr, ggplot2, knitr, pkgdown, rmarkdown, testthat (>= 3.0.0), tibble, withr, yaml
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-22 01:32:14 UTC; admin
Author: Oleg Lugovoy [aut, cre]
Maintainer: Oleg Lugovoy <olugovoy@optimalsolution.dev>
Repository: CRAN
Date/Publication: 2026-11-03 16:00:16 UTC

More information about nestedscales at CRAN
Permanent link

New package naxp with initial version 0.1.0
Package: naxp
Title: Encoded ASCII Expressions
Version: 0.1.0
Description: Converts alphanumeric codes such as postcodes to whole numbers and back again, following the 'naxp' standard <https://naxp.org/spec/>. A naxp ('encoded ASCII expression') is a pattern, defined using a similar syntax to regular expressions, that maps each text it accepts to a number from 1 to a fixed maximum, and any other text to 0. The same pattern maps text to numbers identically across all 'naxp' libraries in all languages on all platforms (subject only to the availability of types able to store large enough integers). Standard naxps are defined for common code systems and are built into all 'naxp' libraries. Two naxps can be compared to determine, among other things, whether stored values keep their meaning when one replaces the other. Wraps the 'naxp' 'C++' library <https://naxp.org/api/#cpp>.
License: Apache License (== 2.0)
URL: https://naxp.org/, https://github.com/logmu-org/r-naxp, https://r-naxp.logmu.org/
BugReports: https://github.com/logmu-org/r-naxp/issues
Depends: R (>= 4.1.0)
Suggests: jsonlite, knitr, pillar, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
LinkingTo: cpp11
Encoding: UTF-8
Language: en-GB
SystemRequirements: C++17
NeedsCompilation: yes
Packaged: 2026-10-21 22:54:12 UTC; Tim Gordon
Author: Tim Gordon [aut, cre]
Maintainer: Tim Gordon <tim.gordon@btinternet.com>
Repository: CRAN
Date/Publication: 2026-11-03 16:00:26 UTC

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New package imedR with initial version 0.3.3
Package: imedR
Title: Guarded Evidence Synthesis for the Indian Microplastics Evidence Database
Version: 0.3.3
Description: A research-grade, guardrail-first interface to the Indian Microplastics Evidence Database (IMED). It provides release-data access, relational and scientific validation, comparability-aware filtering, descriptive and inferential statistics, evidence-synthesis eligibility, meta-analysis helpers, publication figures, mapping, provenance and citation retrieval, and manuscript-oriented outputs. Unsafe pooling across incompatible matrices, denominators, mass bases and comparability classes is blocked by default.
License: MIT + file LICENSE
URL: https://github.com/ChanikyaNaiduCN7/imedR
BugReports: https://github.com/ChanikyaNaiduCN7/imedR/issues
Encoding: UTF-8
Depends: R (>= 4.2.0)
Imports: dplyr, ggplot2, rlang, tibble, readr
Suggests: testthat (>= 3.0.0), sf, metafor, knitr, rmarkdown, scales
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-22 02:53:00 UTC; root
Author: Chanikya Naidu [aut, cre]
Maintainer: Chanikya Naidu <thefisherieschanikyaneeti@gmail.com>
Repository: CRAN
Date/Publication: 2026-11-03 16:00:32 UTC

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New package glmfsa with initial version 1.0.1
Package: glmfsa
Title: Feature Selection with Annealing for Sparse Generalized Linear Models
Version: 1.0.1
Date: 2026-10-21
Description: Fits sparse generalized linear models under an exact cardinality constraint using Feature Selection with Annealing (FSA), the method of Barbu, She, Ding and Gramajo (2017) <doi:10.1109/TPAMI.2016.2544315>. Rather than inducing sparsity through an L1 (lasso-type) penalty, FSA alternates a gradient step on the loss with a step that removes the lowest-magnitude coefficients according to an annealing schedule, so the user specifies the number of variables directly and the surviving coefficients carry no shrinkage bias. The working design matrix physically shrinks as iterations proceed, which makes the cost of a fit fall over time and the method well suited to problems with very many features. Gaussian, binomial, Poisson, multinomial, multi-response Gaussian and Cox proportional hazards families are supported, with robust Lorenz and smoothed support vector machine (SVM) losses available for classification with noisy labels. Dense and sparse design matrices are both handled, and the in [...truncated...]
License: GPL-3
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
Depends: R (>= 3.5.0)
Imports: Rcpp (>= 1.0.0), Matrix, methods, graphics, grDevices, stats, tools, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, glmnet, survival, foreach, doParallel, MASS
VignetteBuilder: knitr
URL: https://github.com/barbua/FSA
BugReports: https://github.com/barbua/FSA/issues
NeedsCompilation: yes
Packaged: 2026-10-21 19:42:32 UTC; abarbu
Author: Adrian Barbu [aut, cre, cph], Yiyuan She [aut], Liangjing Ding [ctb], Gary Gramajo [ctb]
Maintainer: Adrian Barbu <abarbu@fsu.edu>
Repository: CRAN
Date/Publication: 2026-11-03 15:40:02 UTC

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Package GEVStableGarch readmission to version 2.0.0 with previous version 1.1 dated 2015-08-20

Title: ARMA-GARCH/APARCH Models with Stable, GEV and GAt Innovations
Description: Simulation, estimation and forecasting of autoregressive moving average (ARMA) models with generalized autoregressive conditional heteroskedasticity (GARCH) or asymmetric power ARCH (APARCH, with ARCH the autoregressive conditional heteroskedasticity model) errors, with stable (S0 parametrization), generalized extreme value (GEV) and generalized asymmetric t (GAt) innovations. The innovation distribution is a pluggable object, so user defined densities get the same fitting, simulation, stationarity and forecasting machinery.
Author: Thiago do Rego Sousa [aut, cre], Cira Otiniano [ctb], Silvia Lopes [ctb], Diethelm Wuertz [ctb, cph]
Maintainer: Thiago do Rego Sousa <thiagodoregosousa@gmail.com>

This is a re-admission after prior archival of version 1.1 dated 2015-08-20

Diff between GEVStableGarch versions 1.1 dated 2015-08-20 and 2.0.0 dated 2026-11-03

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New package deepICMGP with initial version 0.1.1
Package: deepICMGP
Title: Deep Intrinsic Coregionalization Gaussian Processes for Multiple Outputs
Version: 0.1.1
Author: Chun-Yi Chang [aut, cre], Chih-Li Sung [aut], Annie S. Booth [ctb]
Maintainer: Chun-Yi Chang <chang103@msu.edu>
Description: Fits two-layer deep intrinsic coregionalization Gaussian process surrogate models for multiple outputs observed at shared input locations. Uses Markov chain Monte Carlo sampling for kernel parameters and latent coordinates, with plug-in coregionalization estimates. Provides burn-in removal and thinning, posterior predictive means and within-location output covariances, and sequential experimental design using an active learning criterion. The method is described by Chang and Sung (2026) <doi:10.1615/Int.J.UncertaintyQuantification.2026062570>. Adapts code from the 'deepgp' package, with underlying deep Gaussian process methodology described by Sauer, Gramacy, and Higdon (2023) <doi:10.1080/00401706.2021.2008505>.
License: LGPL
Encoding: UTF-8
Imports: lhs, parallel, stats
NeedsCompilation: yes
Packaged: 2026-10-21 18:59:35 UTC; chang103
Repository: CRAN
Date/Publication: 2026-11-03 15:30:02 UTC

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New package CompositionalLASSO with initial version 1.0
Package: CompositionalLASSO
Title: LASSO Compositional Data Regression
Version: 1.0
Date: 2026-10-22
Author: Michail Tsagris [aut, cre]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>
Depends: R (>= 4.0)
Imports: Compositional, glmnet, Rfast, stats
Suggests: Rfast2
Description: LASSO (Least Absolute Shrinkage and Selection Operator) regression for compositional responses with and without zero values present and continuous predictors. The choice of the penalty parameter, lambda, takes place using K-fold cross-validation. Two relevant papers are Li Z. et al. (2018) <doi:10.1007/s12561-018-9219-2> and Alenazi A.A. (2022) <doi:10.18187/pjsor.v18i4.3969>.
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2026-10-21 20:55:04 UTC; mtsag
Repository: CRAN
Date/Publication: 2026-11-03 15:50:30 UTC

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New package wavecov with initial version 0.1.0
Package: wavecov
Title: Overlap-Aware Workforce Wave Comparisons
Version: 0.1.0
Description: Tools for comparing workforce metric distributions between panel waves using linked stayers, reliability assumptions, or Cauchy bounds. Its purpose is to provide a low-latency alternative to traditional hierarchical or latent-growth modeling that is better-able to scale in cloud based architectures. The mathematics is an extension of rotating panel formulae from the official statistics literature.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: stats
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-21 17:40:09 UTC; G561631
Author: Jack Kostal [aut, cre]
Maintainer: Jack Kostal <jack.kostal@genmills.com>
Repository: CRAN
Date/Publication: 2026-11-03 14:30:02 UTC

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New package theaitetos with initial version 0.1.0
Package: theaitetos
Title: Ancient Intelligence Geometric Superposition Field Calculations
Version: 0.1.0
Description: Establishes a deterministic geometric extension framework within the 'theaitetos' package, honoring the ancient mathematical foundations of Theaetetus of Athens (400 BC). Incorporates the stereometric proofs on the regular octahedron later recorded in Plato (ca. 369 BC) "Theaetetus" <urn:cts:greekLit:tlg0059.tlg006> and Euclid (ca. 300 BC) "Elements" <urn:cts:greekLit:tlg1799.tlg001>. References the standard Greek text tradition by Heiberg (1885) "Euclidis Elementa" and the analytical translations by Heath (1926) "The Thirteen Books of Euclid's Elements". To achieve operational utility, the architecture delivers a high-level computational core engine that abstracts complex 3D-to-2D vector projections and scalar wave superpositions, eliminating low-level reprogramming. It integrates a standardized visualization layer built upon 'ggplot2' to render high-resolution continuous scalar fields and provides a runtime-configurable selection interface across multiple physical and bio [...truncated...]
License: GPL-3
Encoding: UTF-8
Imports: ggplot2
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-21 18:41:44 UTC; katharinabrecht
Author: Katharina Brecht [aut, cre]
Maintainer: Katharina Brecht <katharina.quantumdata@proton.me>
Repository: CRAN
Date/Publication: 2026-11-03 14:40:02 UTC

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New package superstats with initial version 1.0
Package: superstats
Title: Super Statistics
Version: 1.0
Date: 2026-10-21
Author: Michail Tsagris [aut, cre], Manos Papadakis [aut], Alzeley Omar [aut, cph]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>
Depends: R (>= 4.0)
Imports: graphics, grDevices, Rfast, Rfast2
Description: Superstatistics generally applies to analyzing complex systems where traditional statistical methods are insufficient. This approach is particularly valuable in areas like physics, finance, and environmental science. It steps in when there are underlying variables or parameters in a system that fluctuate significantly over space or time, which traditional statistics might overlook. For more information see: Alzeley O. (2020) <doi:10.1002/cmm4.1085>.
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2026-10-21 14:53:21 UTC; mtsag
Repository: CRAN
Date/Publication: 2026-11-03 14:20:02 UTC

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New package r2sbml with initial version 0.1.0
Package: r2sbml
Title: Read 'SBML' Models and Convert Them to Simulation Code
Version: 0.1.0
Description: Reads models in the Systems Biology Markup Language ('SBML') (Hucka et al. (2003) <doi:10.1093/bioinformatics/btg015>) through the bundled 'libSBML' library (Bornstein et al. (2008) <doi:10.1093/bioinformatics/btn051>). Lists a model's species, parameters, compartments, reactions, rules, events and function definitions, and converts the model into ready-to-run simulation code for 'deSolve', 'mrgsolve', 'rxode2', 'MATLAB', 'Julia' or 'ubiquity'. Models are read, not modified.
URL: https://sn248.github.io/r2sbml/, https://github.com/sn248/r2sbml
BugReports: https://github.com/sn248/r2sbml/issues
Encoding: UTF-8
Language: en-GB
License: GPL (>= 2)
Copyright: See file inst/COPYRIGHTS
Imports: Rcpp (>= 0.12.6)
Suggests: deSolve, knitr, mrgsolve, rmarkdown, rxode2, testthat (>= 3.0.0), ubiquity
VignetteBuilder: knitr
LinkingTo: Rcpp
Biarch: TRUE
SystemRequirements: cmake, libxml2, libbz2
NeedsCompilation: yes
Packaged: 2026-10-21 12:12:09 UTC; quantilogy
Author: Satyaprakash Nayak [aut, cre, cph], Sarah M. Keating [ctb] , Frank T. Bergmann [ctb] , Lucian P. Smith [ctb] , Brett G. Olivier [ctb] , Akiya Jouraku [ctb] , Michael Hucka [ctb] , Ben Bornstein [ctb] , Jeroen Ooms [ctb] , California Institute of Tech [...truncated...]
Maintainer: Satyaprakash Nayak <satyaprakash.nayak@gmail.com>
Repository: CRAN
Date/Publication: 2026-11-03 14:10:02 UTC

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New package nerfplus with initial version 1.0.0
Package: nerfplus
Title: Network-Assisted Random Forest+ (NeRF+)
Version: 1.0.0
Description: Network-assisted Random Forest+ (NeRF+) is a flexible and interpretable machine learning model for incorporating network data alongside node-level covariate information. Briefly, NeRF+ extends a generalization of random forests (RF) called RF+ to the network-assisted regression setting by incorporating both a network cohesion penalty and network embeddings as additional covariates. Using this approach, NeRF+ inherits both the flexibility and interpretability of RFs while allowing researchers to easily incorporate network information in their model to further improve predictive performance. This package provides tools to fit and interpret NeRF+ models, as detailed in Tang, Levina, and Zhu (2025) <doi:10.48550/arXiv.2509.15611>.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 4.1.0)
Suggests: DT, fresh, ggplot2, ggraph, htmltools, igraph, kableExtra, knitr, patchwork, plotly, shiny, shinycssloaders, shinydashboard, shinydashboardPlus, shinyjs, shinyWidgets, testthat (>= 3.0.0), tippy
Imports: abind, caret, dplyr, furrr, glmnet, irlba, Matrix, purrr, ranger, Rcpp, rlang, stringr, tibble, tidyr, tidyselect, yardstick
LinkingTo: Rcpp, RcppArmadillo
LazyData: true
URL: https://tiffanymtang.github.io/nerfplus/
NeedsCompilation: yes
Packaged: 2026-10-21 20:19:18 UTC; ttang4
Author: Tiffany Tang [aut, cre]
Maintainer: Tiffany Tang <ttang4@nd.edu>
Repository: CRAN
Date/Publication: 2026-11-03 14:40:08 UTC

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New package MMEFM with initial version 0.1.1
Package: MMEFM
Title: Multilevel Main Effects Matrix Factor Model Tools
Version: 0.1.1
Description: Provides estimation, rank selection, and simulation for grouped matrix-valued time series using the Multilevel Main Effects Matrix Factor Model. The model separates global and local grand means, row and column main effects, and bilinear interaction components, allowing nonstationary main effects under suitable conditions. A circular-shift bootstrap is used to detect a shared global common component and screen participating groups on a stationary segment. The underlying methodology is developed in Liu, Lam and Cen (2026) <doi:10.48550/arXiv.2610.18335>.
License: MIT + file LICENSE
URL: https://github.com/kx-liu/MMEFM
BugReports: https://github.com/kx-liu/MMEFM/issues
Depends: R (>= 4.1.0)
Imports: parallel, stats
Suggests: testthat (>= 3.2.0), knitr, rmarkdown
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-10-21 15:57:49 UTC; liukaixin
Author: Kaixin Liu [aut, cre]
Maintainer: Kaixin Liu <k.liu31@lse.ac.uk>
Repository: CRAN
Date/Publication: 2026-11-03 14:10:08 UTC

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New package xlum with initial version 0.2.0
Package: xlum
Title: Read, Write, and Convert XLUM Data
Version: 0.2.0
Date: 2026-10-19
Maintainer: Sebastian Kreutzer <maintainer_luminescence@r-luminescence.org>
Description: Support for the XLUM file format (Kreutzer et al. (2023) <doi:10.5194/gchron-5-271-2023>) for exchange and long-term preservation of luminescence data. The package facilitates the import and export of XLUM files and the conversion from other ASCII and binary formats commonly used to store luminescence (dating) data.
License: GPL-3
URL: https://r-lum.github.io/xlum/
BugReports: https://github.com/R-Lum/xlum/issues
Depends: R (>= 4.5)
Imports: base64enc (>= 0.1), Luminescence (>= 1.3.1), utils, xml2 (>= 1.6.0)
Suggests: spelling (>= 2.3.2), testthat (>= 3.3.2)
Encoding: UTF-8
Language: en-GB
NeedsCompilation: no
Packaged: 2026-10-19 21:20:01 UTC; kreutzer
Author: Sebastian Kreutzer [aut, cre] , Marco Colombo [aut] , Christoph Burow [ctb]
Repository: CRAN
Date/Publication: 2026-11-03 12:30:02 UTC

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New package inlaops with initial version 0.1.2
Package: inlaops
Title: Deployment, Versioning and Scoring Tools for 'inlabru' Models
Version: 0.1.2
Description: Serves Bayesian spatial and latent Gaussian models fit with 'inlabru' as versioned Representational State Transfer (REST) application programming interfaces (APIs). Includes a 'vetiver' bridge (versioning, REST API serving with posterior uncertainty, 'Docker' packaging) for 'bru' objects, a 'parsnip' model constructor scoped to fixed-effects models, and predictive scoring helpers (continuous ranked probability, log, squared-error and Dawid-Sebastiani scores) computed from inlabru::generate() posterior samples via 'scoringRules'.
License: GPL (>= 3)
URL: https://github.com/novica/inlaops, https://novica.github.io/inlaops/
BugReports: https://github.com/novica/inlaops/issues
Additional_repositories: https://inla.r-inla-download.org/R/stable
Encoding: UTF-8
Imports: dplyr, inlabru (>= 2.15.0), rlang, tibble
Suggests: broom, callr, fmesher, ggplot2, hardhat, httpuv, httr2, INLA, jsonlite, knitr, parsnip, pins, plumber, purrr, recipes, rsample, scoringRules, sf, sn, testthat (>= 3.0.0), vetiver, yardstick, quarto
Enhances: sp
VignetteBuilder: quarto
NeedsCompilation: no
Packaged: 2026-10-21 06:00:07 UTC; novica
Author: Novica Nakov [aut, cre, cph]
Maintainer: Novica Nakov <nnovica@gmail.com>
Repository: CRAN
Date/Publication: 2026-11-03 13:10:02 UTC

More information about inlaops at CRAN
Permanent link

Package fritools updated to version 4.7.0 with previous version 4.6.0 dated 2026-02-18

Title: Utilities for the Forest Research Institute of the State Baden-Wuerttemberg
Description: Miscellaneous utilities, tools and helper functions for finding and searching files on disk, searching for and removing R objects from the workspace. Does not import or depend on any third party package, but on core R only (i.e. it may depend on packages with priority 'base').
Author: Andreas Dominik Cullmann [aut, cre]
Maintainer: Andreas Dominik Cullmann <fvafrcu@mailbox.org>

Diff between fritools versions 4.6.0 dated 2026-02-18 and 4.7.0 dated 2026-11-03

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More information about fritools at CRAN
Permanent link

New package uncertR with initial version 0.1.3
Package: uncertR
Title: Bootstrap Prediction Uncertainty and Its Biological Drivers in Gene-Expression Models
Version: 0.1.3
Description: Fits bootstrap ensembles of penalized regression models on bulk gene-expression data, derives per-sample uncertainty scores from out-of-bag predictions, and tests whether technical covariates explain that uncertainty. The bootstrap method is described by Efron and Tibshirani (1993, ISBN:9780412042317), and elastic-net regression follows Friedman, Hastie, and Tibshirani (2010) <doi:10.18637/jss.v033.i01>. The optional immunedeconv package, used for immune-cell deconvolution, is available from GitHub <https://github.com/omnideconv/immunedeconv>.
Encoding: UTF-8
Depends: R (>= 4.2.0)
License: MIT + file LICENSE
Imports: glmnet, future.apply, splines, stats, graphics
Suggests: testthat (>= 3.0.0), SummarizedExperiment, knitr, rmarkdown, immunedeconv
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-20 04:48:29 UTC; tanay
Author: Tanay Belapurkar [aut, cre]
Maintainer: Tanay Belapurkar <btanay041@gmail.com>
Repository: CRAN
Date/Publication: 2026-11-03 12:10:02 UTC

More information about uncertR at CRAN
Permanent link

Package MLE updated to version 2.0 with previous version 1.9 dated 2026-08-25

Title: Maximum Likelihood Estimation of Various Univariate and Multivariate Distributions
Description: Several functions for maximum likelihood estimation of various univariate and multivariate distributions. The list includes more than 100 functions for univariate continuous and discrete distributions, distributions that lie on the real line, the positive line, interval restricted, circular distributions. Further, multivariate continuous and discrete distributions, distributions for compositional and directional data, etc. Some references include Johnson N. L., Kotz S. and Balakrishnan N. (1994). "Continuous Univariate Distributions, Volume 1" <ISBN:978-0-471-58495-7>, Johnson, Norman L. Kemp, Adrianne W. Kotz, Samuel (2005). "Univariate Discrete Distributions". <ISBN:978-0-471-71580-1> and Mardia, K. V. and Jupp, P. E. (2000). "Directional Statistics". <ISBN:978-0-471-95333-3>.
Author: Michail Tsagris [aut, cre], Sofia Piperaki [aut], Muhammad Imran [ctb], Rafail Vargiakakis [aut], Nikolaos Kontemeniotis [aut]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>

Diff between MLE versions 1.9 dated 2026-08-25 and 2.0 dated 2026-11-03

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More information about MLE at CRAN
Permanent link

New package lightson with initial version 0.2.0
Package: lightson
Title: Download and Analyse Nighttime Lights Satellite Data
Version: 0.2.0
Description: Downloads and processes nighttime lights satellite data from the 'NASA' 'VIIRS' Black Marble product suite (daily, monthly, and annual composites; Roman et al. (2018) <doi:10.1016/j.rse.2018.03.017>) served by the 'LAADS DAAC' archive <https://ladsweb.modaps.eosdis.nasa.gov>, and from the Indian Space Research Organisation's 'Bhuvan' nighttime lights portal. Provides zonal statistics over arbitrary polygons, returning tidy region-by-time panels, along with plotting helpers.
License: MIT + file LICENSE
Depends: R (>= 4.1.0)
Encoding: UTF-8
Imports: httr2 (>= 1.0.0), terra (>= 1.7.0), sf (>= 1.0.0), jsonlite, digest
Suggests: geodata, ggplot2, patchwork, scales, withr, testthat (>= 3.1.7), knitr, rmarkdown
VignetteBuilder: knitr
URL: https://lightson.saketlab.org, https://github.com/saketlab/lightson
BugReports: https://github.com/saketlab/lightson/issues
NeedsCompilation: no
Packaged: 2026-10-20 05:57:13 UTC; mcclintock
Author: Saket Choudhary [aut, cre]
Maintainer: Saket Choudhary <saketc@iitb.ac.in>
Repository: CRAN
Date/Publication: 2026-11-03 12:20:02 UTC

More information about lightson at CRAN
Permanent link

Package tectonicr updated to version 0.4.10 with previous version 0.4.9 dated 2026-08-29

Title: Analyzing the Orientation of Maximum Horizontal Stress
Description: Models the direction of the maximum horizontal stress using relative plate motion parameters. Statistical algorithms to evaluate the modeling results compared with the observed data. Provides plots to visualize the results. Methods described in Stephan et al. (2023) <doi:10.1038/s41598-023-42433-2> and Wdowinski (1998) <doi:10.1016/S0079-1946(98)00091-3>.
Author: Tobias Stephan [aut, cre]
Maintainer: Tobias Stephan <tobias.stephan1@yahoo.com>

Diff between tectonicr versions 0.4.9 dated 2026-08-29 and 0.4.10 dated 2026-11-03

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 vignettes/install_extras             |    2 
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 vignettes/tectonicr_references.bib   |  152 +--
 126 files changed, 12258 insertions(+), 11335 deletions(-)

More information about tectonicr at CRAN
Permanent link

New package stagelife with initial version 0.1.0
Package: stagelife
Title: Random Number Generation and Inferences for Two-Stage Life-Testing Data
Version: 0.1.0
Description: Provides random number generation, estimation, and hypothesis testing for two-stage life-testing experiments in which units begin on a baseline stress and, at a fixed or random change point, some or all surviving units move to an elevated stress ("stage life testing"), following the cumulative-exposure model of Laumen and Cramer (2019), "Stage life testing", <doi:10.1002/nav.21874>. Twelve baseline lifetime distributions are supplied (Weibull, Power Lindley, Exponentiated Lindley, Exponentiated Exponential, Marshall-Olkin Exponential, Lomax, Exponentiated Teissier, Log-Logistic, Chen, Log-normal, Gamma, and Power Muth/Power Teissier) together with three parameter-sharing cases (common, shape1, shape11). The package implements data simulation under both the fixed-proportion (typeP) and fixed-count (typeM) rules and the simple-step-stress special case, maximum likelihood estimation using a hybrid Nelder-Mead and BFGS optimisation routine, asymptotic (Wald) and non-parametric/parame [...truncated...]
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 4.1.0)
Imports: compiler, ggplot2, graphics, lamW, parallel, stats
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
URL: https://github.com/vksharma-bhu/stagelife
BugReports: https://github.com/vksharma-bhu/stagelife/issues
NeedsCompilation: no
Packaged: 2026-10-16 16:05:32 UTC; root
Author: Vikas Kumar Sharma [aut, cre]
Maintainer: Vikas Kumar Sharma <vksharma@bhu.ac.in>
Repository: CRAN
Date/Publication: 2026-11-03 12:00:02 UTC

More information about stagelife at CRAN
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Package NetSimR updated to version 0.3.3 with previous version 0.3.2 dated 2026-09-30

Title: Actuarial Functions for Non-Life Insurance Modelling
Description: Assists actuaries and other insurance modellers in pricing, reserving and capital modelling for non-life insurance and reinsurance modelling. Provides functions that help model excess levels, capping and pure Incurred but not reported claims (pure IBNR). Includes capped mean, exposure curves and increased limit factor curves (ILFs) for LogNormal, Gamma, Pareto, Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes mean, probability density function (pdf), cumulative probability function (cdf) and inverse cumulative probability function for Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes calculating pure IBNR exposure with LogNormal and Gamma distribution for reporting delay. Includes three 'shiny' tools: a claims simulator with reinsurance structures, a generalised linear model fitting tool, and a claims frequency and severity distribution fitting tool. Methods used in the package refer to Free for All by Yiannis Parizas (2023) <https://www. [...truncated...]
Author: Yiannis Parizas [aut, cre]
Maintainer: Yiannis Parizas <yiannis.parizas@gmail.com>

Diff between NetSimR versions 0.3.2 dated 2026-09-30 and 0.3.3 dated 2026-11-03

 DESCRIPTION                          |    8 
 MD5                                  |   27 -
 NEWS                                 |   13 
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 R/distribution_fitting_tool_UI.R     |  622 +++++++++++++++++------------------
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 man/distribution_fitting_tool_UI.Rd  |   14 
 man/shiny_simulator_ui.Rd            |   10 
 tests/testthat/test-ui-portability.R |only
 tests/testthat/test-validation.R     |    2 
 15 files changed, 671 insertions(+), 631 deletions(-)

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Package MDgof updated to version 1.3.0 with previous version 1.2.1 dated 2026-10-14

Title: Various Methods for the Goodness-of-Fit Problem in D>1 Dimensions
Description: Provides multivariate goodness-of-fit testing with a common interface for several test statistics. Null models may be simple or include parameter estimation, with p-values obtained by parametric bootstrap simulation. The function gof_test_adjusted_pvalue() combines several tests and computes a p-value adjusted for simultaneous inference. The function gof_power() estimates test power. The functions hybrid_test() and hybrid_power() use Monte Carlo samples under the null together with two-sample procedures. The function run.studies() supports systematic power comparisons of user-supplied and included methods across case studies. See the included vignettes for method details and references.
Author: Wolfgang Rolke [aut, cre]
Maintainer: Wolfgang Rolke <wolfgang.rolke@upr.edu>

Diff between MDgof versions 1.2.1 dated 2026-10-14 and 1.3.0 dated 2026-11-03

 MDgof-1.2.1/MDgof/inst/legacy-tests                                  |only
 MDgof-1.3.0/MDgof/DESCRIPTION                                        |    8 
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 MDgof-1.3.0/MDgof/NEWS.md                                            |    5 
 MDgof-1.3.0/MDgof/R/RcppExports.R                                    |   13 
 MDgof-1.3.0/MDgof/R/case.studies.cont.D5.R                           |   23 
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Package jmBIG updated to version 0.1.4 with previous version 0.1.3 dated 2025-01-19

Title: Joint Longitudinal and Survival Model for Big Data
Description: Provides analysis tools for big data where the sample size is very large. It offers a suite of functions for fitting and predicting joint models, which allow for the simultaneous analysis of longitudinal and time-to-event data. This statistical methodology is particularly useful in medical research where there is often interest in understanding the relationship between a longitudinal biomarker and a clinical outcome, such as survival or disease progression. This can be particularly useful in a clinical setting where it is important to be able to predict how a patient's health status may change over time. Overall, this package provides a comprehensive set of tools for joint modeling of BIG data obtained as survival and longitudinal outcomes with both Bayesian and non-Bayesian approaches. Its versatility and flexibility make it a valuable resource for researchers in many different fields, particularly in the medical and health sciences.
Author: Atanu Bhattacharjee [aut, cre, ctb], Bhrigu Kumar Rajbongshi [aut, ctb], Gajendra K Vishwakarma [aut, ctb]
Maintainer: Atanu Bhattacharjee <atanustat@gmail.com>

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Package srr updated to version 1.1.0 with previous version 1.0.0 dated 2026-06-23

Title: 'rOpenSci' Software Review 'Roclets'
Description: Companion package to 'rOpenSci' statistical software review project ('rOpenSci' authors, 2021; <doi:10.5281/zenodo.5556756>), to facilitate documenting software compliance with statistical standards using 'Roxygen2' 'roclets'.
Author: Maelle Salmon [cre, aut]
Maintainer: Maelle Salmon <reviewtools@ropensci.org>

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Package rmumps updated to version 5.9.1-1 with previous version 5.2.1-43 dated 2026-05-28

Title: Wrapper for 'MUMPS' Library
Description: Some basic features of 'MUMPS' (Multifrontal Massively Parallel sparse direct Solver) are wrapped in a class whose methods can be used for sequentially solving a sparse linear system (symmetric or not) with one or many right hand sides (dense or sparse). There is a possibility to do separately symbolic analysis, LU (or LDL^t) factorization and system solving. The ordering libraries 'PORD' and 'METIS' are included. 'MUMPS' method was first described in Amestoy et al. (2001) <doi:10.1137/S0895479899358194> and Amestoy et al. (2006) <doi:10.1016/j.parco.2005.07.004>.
Author: Serguei Sokol [aut], Balasubramanian Narasimhan [ctb, cph, cre] , Emmanuel Agullo [ctb] , Patrick Amestoy [ctb, cph] , Maurice Bremond [ctb] , Alfredo Buttari [ctb] , Philippe Combes [ctb] , Marie Durand [ctb] , Aurelia Fevre [ctb] , Matthieu Gerest [...truncated...]
Maintainer: Balasubramanian Narasimhan <naras@stanford.edu>

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Package regsensitivity updated to version 0.3.0 with previous version 0.2.0 dated 2026-10-20

Title: Regression Sensitivity Analysis for Omitted Variable Bias
Description: Sensitivity analysis for linear regression coefficients in the presence of omitted variables. Implements the identified-set and breakdown-point analyses of Diegert, Masten and Poirier (2026) <doi:10.48550/arXiv.2206.02303>, Oster (2019) <doi:10.1080/07350015.2016.1227711> and Masten and Poirier (2026) <doi:10.1257/aer.20230242>. Provides bounds on regression coefficients under relaxations of the no-omitted-variables assumption indexed by interpretable sensitivity parameters, the breakdown point of a hypothesis about the long-regression coefficient, and plotting helpers.
Author: Mike Nguyen [aut, cre, cph]
Maintainer: Mike Nguyen <nguyennghia1301@gmail.com>

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Package LifemapR updated to version 1.1.7 with previous version 1.1.6 dated 2025-08-27

Title: Data Visualisation on 'Lifemap' Tree
Description: Allow to visualise data on the NCBI phylogenetic tree as presented in Lifemap <https://lifemap.cnrs.fr/>. It takes as input a dataframe with at least a "taxid" column containing NCBI format TaxIds and allows to draw multiple layers with different visualisation tools.
Author: Cassandra Bompard [aut], Damien M. de Vienne [aut] , Julien Barnier [aut] , Aurelie Siberchicot [ctb, cre]
Maintainer: Aurelie Siberchicot <aurelie.siberchicot@univ-lyon1.fr>

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Package functionals updated to version 0.6.0 with previous version 0.5.1 dated 2026-08-21

Title: Functional Programming with Parallelism and Progress Tracking
Description: Provides functional tools such as fmap(), fwalk(), and fapply() to iterate over vectors, data frames, or grouped data with optional parallelism and real-time progress tracking. Progress updates reflect completed tasks across sequential, multicore, and cluster-backed execution. A uniform error policy keeps a run going when individual tasks fail, and optional per-task L'Ecuyer-CMRG random-number streams make parallel Monte Carlo work reproducible regardless of the number of cores. Designed for readable and reproducible workflows, including simulation and benchmarking.
Author: Imad El Badisy [aut, cre]
Maintainer: Imad El Badisy <elbadisyimad@gmail.com>

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Package fastgbm updated to version 0.7.0 with previous version 0.6.1 dated 2026-09-01

Title: Fast Histogram Gradient Boosting for Regression, Classification, and Survival Analysis
Description: A fast gradient boosting machine covering four task types with one interface: regression (squared error), binary and multiclass classification (logistic and one-vs-rest), and right-censored survival analysis via Cox (Breslow ties), accelerated failure time (normal location-scale), or piecewise-exponential objectives, and competing risks via Fine-Gray subdistribution hazards, cause-specific hazards, or coupled cause-specific hazards, following 'CoxBoost' (Binder et al. 2009) <doi:10.1093/bioinformatics/btp088>. Provides native missing-value routing, baseline-hazard estimation and survival-probability prediction for the survival objectives, and deterministic multi-threaded training via 'RcppParallel'. Methods are described in Friedman (2001) <doi:10.1214/aos/1013203451>.
Author: Imad El Badisy [aut, cre]
Maintainer: Imad El Badisy <elbadisyimad@gmail.com>

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Package explainer updated to version 1.0.3 with previous version 1.0.2 dated 2024-09-30

Title: Machine Learning Model Explainer
Description: It enables detailed interpretation of complex classification and regression models through Shapley analysis including data-driven characterization of subgroups of individuals. Furthermore, it facilitates multi-measure model evaluation, model fairness, and decision curve analysis. Additionally, it offers enhanced visualizations with interactive elements.
Author: Ramtin Zargari Marandi [aut, cre]
Maintainer: Ramtin Zargari Marandi <ramtin.zargari@gmail.com>

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Package ukhousing updated to version 0.2.0 with previous version 0.1.0 dated 2026-04-21

Title: Access UK Housing Data from Land Registry, Planning, and 'ONS'
Description: Fetch UK housing data from official sources. Access the UK House Price Index and Price Paid Data from 'HM Land Registry' <https://landregistry.data.gov.uk/>, planning application, brownfield land, and local plan data from 'planning.data.gov.uk' <https://www.planning.data.gov.uk/>, and the Price Index of Private Rents from the 'Office for National Statistics'. Data covers all 441 UK local authorities from 1995 to the present. Functions accept flexible filters (postcode, local authority, property type, date range) and return tidy data frames. Downloaded data is cached locally for subsequent calls.
Author: Charles Coverdale [aut, cre]
Maintainer: Charles Coverdale <charlesfcoverdale@gmail.com>

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Package SurveyNCD updated to version 0.2.0 with previous version 0.1.0 dated 2026-09-28

Title: Survey-Weighted Analysis of Self-Reported Health Indicators
Description: Design-based analysis of population health survey data from the World Health Organization (WHO) Stepwise Approach to Non-Communicable Disease (NCD) Risk Factor Surveillance (STEPS), Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and similar complex sample surveys, where chronic conditions are self-reported rather than coded with the International Classification of Diseases (ICD). Provides survey design constructors for DHS, MICS and STEPS microdata; design-based prevalence for any binary indicators and groupings; a self-reported multimorbidity count based on the Functional Comorbidity Index (FCI) of Groll et al. (2005) <doi:10.1016/j.jclinepi.2004.10.018>, with condition-pattern analysis, sensitivity bounds for missing answers, and direct age standardisation to the WHO World Standard Population; standard, generalized, Erreygers and Wagstaff concentration indices with design-based standard errors following Kakwani, Wagstaff and van Doorslaer (1997) [...truncated...]
Author: Sujon Mia [aut, cre] , Md. Atiqul Islam [ctb]
Maintainer: Sujon Mia <sujonsgc@gmail.com>

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Package surveycore readmission to version 1.2.0 with previous version 1.0.0 dated 2026-06-08

Title: Core Survey Analysis Infrastructure
Description: A modern, 'S7'-based foundation for survey analysis spanning both probability and non-probability samples. Probability sample designs include Taylor series linearization, replicate weights (BRR, Fay, jackknife, bootstrap), and two-phase estimation, following 'Lumley' (2004) <doi:10.18637/jss.v009.i08>. Non-probability sample designs support bootstrap and jackknife variance estimation for opt-in panels and convenience samples. Provides a unified estimator interface for means, frequencies, totals, quantiles, ratios, correlations, regression, and t-tests, with weighted 'polychoric' and 'polyserial' correlation following 'Mannan' (2025) <doi:10.2139/ssrn.6580480>. A metadata system preserves 'haven'-style variable labels, value labels, and question-preface attributes through all operations, alongside dataset-level metadata that records the survey name, the fielding vendor, and the field period. Uses a 'tidyselect' interface throughout.
Author: Jacob Dennen [aut, cre, cph] , Thomas Lumley [ctb, cph]
Maintainer: Jacob Dennen <jdenn0514@gmail.com>

This is a re-admission after prior archival of version 1.0.0 dated 2026-06-08

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Package pollimetry readmission to version 1.1.1 with previous version 1.0.1 dated 2018-11-19

Title: Estimate Pollinator Body Size and Co-Varying Ecological Traits
Description: Tools to estimate pollinator body size and co-varying traits. This package contains Bayesian predictive models of pollinator body size (for bees and hoverflies) and bee foraging range, as well as preexisting predictive models for pollinator body size (currently implemented for ants, bees, butterflies, flies, moths and wasps), bee tongue length, foraging range, total field nectar loads and wing loading. An additional GitHub repository <https://github.com/liamkendall/pollimetrydata> provides model objects to use the bodysize and foraging.range functions internally. The Bayesian models are described in Kendall et al. (2019) <doi:10.1002/ece3.4835> & Kendall et al. (2022) <doi:10.1002/ecy.3809>.
Author: Liam Kendall [aut, cre], Ignasi Bartomeus [aut]
Maintainer: Liam Kendall <liam.k.kendall@gmail.com>

This is a re-admission after prior archival of version 1.0.1 dated 2018-11-19

Diff between pollimetry versions 1.0.1 dated 2018-11-19 and 1.1.1 dated 2026-11-03

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Package StanHeaders updated to version 2.39.1.1 with previous version 2.39.1 dated 2026-09-02

Title: C++ Header Files for Stan
Description: The C++ header files of the Stan project are provided by this package, but it contains little R code or documentation. The main reference is the vignette. There is a shared object containing part of the 'CVODES' library, but its functionality is not accessible from R. 'StanHeaders' is primarily useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The [...truncated...]
Author: Ben Goodrich [cre, aut], Joshua Pritikin [ctb], Andrew Gelman [aut], Bob Carpenter [aut], Matt Hoffman [aut], Daniel Lee [aut], Michael Betancourt [aut], Marcus Brubaker [aut], Jiqiang Guo [aut], Peter Li [aut], Allen Riddell [aut], Marco Inacio [aut [...truncated...]
Maintainer: Ben Goodrich <benjamin.goodrich@columbia.edu>

Diff between StanHeaders versions 2.39.1 dated 2026-09-02 and 2.39.1.1 dated 2026-11-03

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Package qrfactor (with last version 1.6) was removed from CRAN

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

2026-09-15 1.6
2014-01-02 1.4
2013-04-18 1.3
2012-02-21 1.2
2011-11-14 1.1
2011-11-03 1.0

Permanent link
Package iSTATS (with last version 1.8) was removed from CRAN

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

2026-10-10 1.8
2023-12-05 1.7
2023-08-11 1.6
2020-07-01 1.5
2020-06-04 1.4
2020-05-29 1.3
2020-02-11 1.2
2019-11-27 1.1.0
2019-08-23 0.1.0

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Package swfscDAS updated to version 0.7.0 with previous version 0.6.4 dated 2025-06-10

Title: Processing DAS Data Files
Description: Process and summarize DAS data files. These files are typically, but do not have to be DAS <https://repository.library.noaa.gov/view/noaa/3174> data produced by the Southwest Fisheries Science Center (SWFSC) program 'WinCruz'. This package standardizes and streamlines basic DAS data processing, and includes a PDF with the DAS data format requirements expected by the package.
Author: Sam Woodman [aut, cre]
Maintainer: Sam Woodman <sam.woodman@noaa.gov>

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Package stddiff.spark updated to version 1.1.0 with previous version 1.0 dated 2026-01-15

Title: Calculate the Standardized Difference for Numeric, Binary and Category Variables in Apache Spark
Description: Provides functions to compute standardized differences for numeric, binary, and categorical variables on Apache Spark DataFrames using 'sparklyr'. The implementation mirrors the methods used in the 'stddiff' package but operates on distributed data. See Zhicheng Du, Yuantao Hao (2022) <doi:10.32614/CRAN.package.stddiff> for reference.
Author: Alicja Januszkiewicz [aut, cre, cph]
Maintainer: Alicja Januszkiewicz <cran.alicja.januszkiewicz@gmail.com>

Diff between stddiff.spark versions 1.0 dated 2026-01-15 and 1.1.0 dated 2026-11-03

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 stddiff.spark-1.1.0/stddiff.spark/README.md                           |   42 
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 stddiff.spark-1.1.0/stddiff.spark/man/stddiff.numeric.Rd              |   19 
 stddiff.spark-1.1.0/stddiff.spark/man/validate_stddiff_inputs.Rd      |    2 
 stddiff.spark-1.1.0/stddiff.spark/tests/testthat/test_stddiff_spark.R | 1007 ++++++--
 12 files changed, 1593 insertions(+), 853 deletions(-)

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Package stan4bart updated to version 0.0-13.1 with previous version 0.0-13 dated 2026-08-21

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

Diff between stan4bart versions 0.0-13 dated 2026-08-21 and 0.0-13.1 dated 2026-11-03

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Package BBNI updated to version 0.2.3 with previous version 0.2.2 dated 2026-08-19

Title: Bayesian Inference of Boolean Genetic Networks
Description: Implements a fully Bayesian Markov chain Monte Carlo (MCMC) approach for inferring the topology and Boolean logic transition functions of gene regulatory networks from noisy, binary time-series expression data. Network structure and Boolean rules are sampled jointly from their posterior distribution, providing principled uncertainty quantification rather than a single point estimate. Method described in Han et al. (2014) <doi:10.1371/journal.pone.0115806>.
Author: Anson Li [aut, cre], Shengtong Han [aut]
Maintainer: Anson Li <liyuanrui618@gmail.com>

Diff between BBNI versions 0.2.2 dated 2026-08-19 and 0.2.3 dated 2026-11-03

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