Tue, 06 Oct 2026

Package SporeLag updated to version 0.1.2 with previous version 0.1.1 dated 2026-08-04

Title: Lagged and Moving-Average Exposure Features for Aeroallergen Epidemiology
Description: Deterministic, group-safe utilities that transform daily environmental exposure series (pollen and spore counts, with support for other time-varying exposures such as ozone and particulate matter) into analysis-ready lagged and moving-average exposure features. Functions validate temporal regularity, assign ISO 8601 weeks and configurable seasons, impute missing daily values transparently, and construct lagged and windowed exposures suitable for environmental epidemiology and public health analyses.
Author: Felix E. Rivera-Mariani [aut, cre]
Maintainer: Felix E. Rivera-Mariani <felixrm@friveram.com>

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Package SpecsVerification updated to version 0.5-4 with previous version 0.5-3 dated 2020-02-26

Title: Forecast Verification Routines for Ensemble Forecasts of Weather and Climate
Description: A collection of forecast verification routines developed for the SPECS FP7 project. The emphasis is on comparative verification of ensemble forecasts of weather and climate.
Author: Stefan Siegert [aut, cre], Jonas Bhend [ctb], Igor Kroener [ctb], Matteo De Felice [ctb]
Maintainer: Stefan Siegert <s.siegert@exeter.ac.uk>

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Package sonify updated to version 0.1-0 with previous version 0.0-1 dated 2017-02-01

Title: Data Sonification - Turning Data into Sound
Description: Sonification (or audification) is the process of representing data by sounds in the audible range. This package provides the R function sonify() that transforms univariate data, sampled at regular or irregular intervals, into a continuous sound with time-varying frequency. The ups and downs in frequency represent the ups and downs in the data. Sonify provides a substitute for R's plot function to simplify data analysis for the visually impaired.
Author: Stefan Siegert [aut, cre], Robin Williams [aut]
Maintainer: Stefan Siegert <s.siegert@exeter.ac.uk>

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Package deepspat updated to version 0.3.5 with previous version 0.3.4 dated 2026-09-30

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

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Package conquestr updated to version 1.8.2 with previous version 1.5.5 dated 2025-08-23

Title: An R Package to Extend 'ACER ConQuest'
Description: Extends 'ACER ConQuest' through a family of functions designed to improve graphical outputs and help with advanced analysis (e.g., differential item functioning). Allows R users to call 'ACER ConQuest' from within R and read 'ACER ConQuest' System Files (generated by the command `put` <https://conquestmanual.acer.org/s4-00.html#put>). Requires 'ACER ConQuest' version 5.40 or later. A demonstration version can be downloaded from <https://shop.acer.org/collections/acer-conquest-5>.
Author: Dan Cloney [aut, cre] , Ray Adams [aut]
Maintainer: Dan Cloney <dan.cloney@acer.org>

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Package arulesNBMiner updated to version 0.1.10 with previous version 0.1.9 dated 2025-12-09

Title: Mining NB-Frequent Itemsets and NB-Precise Rules
Description: NBMiner is an implementation of the model-based mining algorithm for mining NB-frequent itemsets and NB-precise rules. Michael Hahsler (2006) <doi:10.1007/s10618-005-0026-2>.
Author: Michael Hahsler [aut, cre, cph]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>

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Package vrpr updated to version 0.2.2 with previous version 0.2.1 dated 2026-09-30

Title: Vehicle Routing Problem Solver Built on 'PyVRP'
Description: A 'tidyverse'-style interface to high-performance vehicle routing problem (VRP) solving. Vendors the C++ core of the 'PyVRP' solver (<https://github.com/PyVRP/PyVRP>) and rewires it through 'cpp11', with no 'Python' runtime dependency. Supports the capacitated VRP, time windows, multiple depots, heterogeneous fleets, prize-collecting and multi-trip variants, driven by an iterated local search metaheuristic.
Author: Andre Leite [aut, cre] , Marcos Wasiliew [aut] , Hugo Vasconcelos [aut] , Carlos Amorim [aut] , Diogo Bezerra [aut] , Julia Nascimento Barreto [aut] , Niels Wouda [ctb, cph] , Thibaut Vidal [cph] , ORTEC [cph]
Maintainer: Andre Leite <leite@castlab.org>

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Package SouthParkRshiny updated to version 1.1.1 with previous version 1.0.0 dated 2024-03-09

Title: Data and 'Shiny' Application for the Tv Show 'SouthPark'
Description: Ratings, votes, swear words and sentiments are analysed for the show 'SouthPark' through a 'Shiny' application after web scraping from 'IMDB' and the website <https://southpark.fandom.com/wiki/South_Park_Archives>.
Author: Amalan Mahendran [aut, cre]
Maintainer: Amalan Mahendran <amalan0595@gmail.com>

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Package smatr updated to version 3.5-2 with previous version 3.5-1 dated 2026-07-28

Title: (Standardised) Major Axis Estimation and Testing Routines
Description: Methods for fitting bivariate lines in allometry using the major axis (MA) or standardised major axis (SMA), and for making inferences about such lines. The available methods of inference include confidence intervals and one-sample tests for slope and elevation, testing for a common slope or elevation amongst several allometric lines, constructing a confidence interval for a common slope or elevation, and testing for no shift along a common axis, amongst several samples. See Warton et al. 2012 <doi:10.1111/j.2041-210X.2011.00153.x> for methods description.
Author: David Warton [aut], Remko Duursma [aut], Daniel Falster [aut, cre] , Sara Taskinen [aut], Fonti Kar [aut]
Maintainer: Daniel Falster <daniel.falster@unsw.edu.au>

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Package pams updated to version 0.2.0 with previous version 0.1.0 dated 2026-03-31

Title: Profile Analysis via Multidimensional Scaling
Description: Implements Profile Analysis via Multidimensional Scaling (PAMS) for the identification of population-level core response profiles from cross-sectional and longitudinal person-score data. Each person profile is decomposed into a level component (the person mean) and a pattern component (ipsatized subscores). PAMS uses nonmetric multidimensional scaling via the SMACOF algorithm to identify a small number of core profiles that represent the central response patterns in a sample of any size. Bootstrap standard errors and bias-corrected and accelerated (BCa) confidence intervals for individual core profile coordinates are estimated, enabling significance testing of coordinates that is not available in other profile analysis methods such as cluster profile analysis or latent profile analysis. Person-level weights, R-squared values, and partial correlations with core profiles are also estimated, allowing individual profiles to be interpreted in terms of the core profile structure. PAMS can be [...truncated...]
Author: Se-Kang Kim [aut, cre] , Donghoh Kim [aut]
Maintainer: Se-Kang Kim <sekangandroid@gmail.com>

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Package isopam updated to version 3.7 with previous version 3.6 dated 2026-03-27

Title: Clustering of Sites with Species Data
Description: Clustering algorithm developed for use with plot inventories of species. It groups plots by subsets of diagnostic species rather than overall species composition. There is an unsupervised and a supervised mode, the latter accepting suggestions for species with greater weight and cluster medoids.
Author: Sebastian Schmidtlein [aut, cre] , Jason Collison [aut], Robin Pfannendoerfer [aut], Lubomir Tichy [ctb]
Maintainer: Sebastian Schmidtlein <schmidtlein@kit.edu>

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Package EMGCR updated to version 0.3.0 with previous version 0.2.0 dated 2025-11-18

Title: Mixture Cure Rate Models with Flexible Link Functions via the EM Algorithm
Description: Fits mixture cure rate models by the Expectation-Maximization (EM) algorithm. The incidence component (the probability of being uncured) accepts the logit, probit, cauchit, power logit and reversed power logit link functions, and the latency component accepts the exponential, Rayleigh, Weibull, log-normal, log-logistic and inverse Gaussian distributions. The package provides parameter estimates with standard errors, simulation of data from the model, and diagnostic tools based on residuals and simulated envelopes. The methods build on Berkson and Gage (1952) <doi:10.2307/2281318>, Dempster, Laird and Rubin (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x> and Bazán, Torres-Avilés, Suzuki and Louzada (2017) <doi:10.1002/asmb.2215>.
Author: Chaeyeon Yoo [aut], Dipak K. Dey [aut], Victor H. Lachos [aut], Jalmar M. F. Carrasco [aut, cre]
Maintainer: Jalmar M. F. Carrasco <carrasco.jalmar@ufba.br>

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Package Compositionalcln updated to version 1.1 with previous version 1.0 dated 2026-09-22

Title: Modelling Compositional Data with Zero Values
Description: Modelling structural zeros in compositional data using a conditional logistic normal model as described by Aitchison (1986), where MLE (Maximum Likelihood Estimation) is performed via the EM (Expectation-Maximization) algorithm. The relevant paper is Alzeley and Tsagris (2026) <doi:10.48550/arXiv.2608.29954>.
Author: Michail Tsagris [aut, cre]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>

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Package Colossus updated to version 1.6.3 with previous version 1.6.2 dated 2026-09-22

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

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Package WpProj updated to version 0.3 with previous version 0.2.3 dated 2025-02-05

Title: Linear p-Wasserstein Projections
Description: Performs Wasserstein projections from the predictive distributions of any model into the space of predictive distributions of linear models. We utilize L1 penalties to also reduce the complexity of the model space. This package employs the methods as described in Dunipace, Eric and Lorenzo Trippa (2020) <doi:10.48550/arXiv.2012.09999>.
Author: Eric Dunipace [aut, cre] , Clemens Schmid [ctb] , Espen Bernton [ctb] , Mathieu Gerber [ctb] , Pierre Jacob [ctb] , Bin Dai [ctb] , Jared Huling [ctb] , Yixuan Qiu [ctb] , Dominic Schuhmacher [ctb] , Nicolas Bonneel [ctb]
Maintainer: Eric Dunipace <edunipace@mail.harvard.edu>

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Package tigger updated to version 1.2.0 with previous version 1.1.3 dated 2026-04-17

Title: Infers Novel Immunoglobulin Alleles from Sequencing Data
Description: Infers the V genotype of an individual from immunoglobulin (Ig) repertoire sequencing data (AIRR-Seq, Rep-Seq). Includes detection of any novel alleles. This information is then used to correct existing V allele calls from among the sample sequences. Citations: Gadala-Maria, et al (2015) <doi:10.1073/pnas.1417683112>, Gadala-Maria, et al (2019) <doi:10.3389/fimmu.2019.00129>.
Author: Daniel Gadala-Maria [aut], Susanna Marquez [aut, cre], Moriah Cohen [aut], Ayelet Peres [aut], Jason Vander Heiden [aut], Gur Yaari [aut], Steven Kleinstein [aut, cph]
Maintainer: Susanna Marquez <susanna.marquez@yale.edu>

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Package starsExtra updated to version 0.2.9 with previous version 0.2.8 dated 2024-01-13

Title: Miscellaneous Functions for Working with 'stars' Rasters
Description: Miscellaneous functions for working with 'stars' objects, mainly single-band rasters. Currently includes functions for: (1) focal filtering, (2) detrending of Digital Elevation Models, (3) calculating flow length, (4) calculating the Convergence Index, (5) calculating topographic aspect and topographic slope.
Author: Michael Dorman [aut, cre]
Maintainer: Michael Dorman <dorman@post.bgu.ac.il>

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Package phutil updated to version 0.0.3 with previous version 0.0.2 dated 2026-04-17

Title: Persistence Homology Utilities
Description: A low-level package for hosting persistence data. It is part of the 'TDAverse' suite of packages, which is designed to provide a collection of packages for enabling machine learning and data science tasks using persistent homology. Implements a class for hosting persistence data, a number of coercers from and to already existing and used data structures from other packages and functions to compute distances between persistence diagrams. A formal definition and study of bottleneck and Wasserstein distances can be found in Bubenik, Scott and Stanley (2023) <doi:10.1007/s41468-022-00103-8>. Their implementation in 'phutil' relies on the 'C++' Hera library developed by Kerber, Morozov and Nigmetov (2017) <doi:10.1145/3064175>.
Author: Aymeric Stamm [aut, cre] , Jason Cory Brunson [aut] , Michael Kerber [ctb] , Dmitriy Morozov [ctb] , Arnur Nigmetov [ctb]
Maintainer: Aymeric Stamm <aymeric.stamm@cnrs.fr>

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Package lbfgsb3c updated to version 2026-3.5 with previous version 2024-3.5 dated 2024-09-17

Title: Limited Memory BFGS Minimizer with Bounds on Parameters with optim() 'C' Interface
Description: Interfacing to Nocedal et al. L-BFGS-B.3.0 (See <http://users.iems.northwestern.edu/~nocedal/lbfgsb.html>) limited memory BFGS minimizer with bounds on parameters. This is a fork of 'lbfgsb3'. This registers a 'R' compatible 'C' interface to L-BFGS-B.3.0 that uses the same function types and optimization as the optim() function (see writing 'R' extensions and source for details). This package also adds more stopping criteria as well as allowing the adjustment of more tolerances.
Author: Matthew L Fidler [aut, cre] , John C Nash [aut], Ciyou Zhu [aut], Richard Byrd [aut], Jorge Nocedal [aut], Jose Luis Morales [aut]
Maintainer: Matthew L Fidler <matthew.fidler@gmail.com>

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Package gamlss updated to version 5.5-5 with previous version 5.5-0 dated 2025-08-19

Title: Generalized Additive Models for Location Scale and Shape
Description: Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.
Author: Mikis Stasinopoulos [aut, cre, cph] , Robert Rigby [aut] , Vlasios Voudouris [ctb], Calliope Akantziliotou [ctb], Marco Enea [ctb], Daniil Kiose [ctb] , Achim Zeileis [ctb]
Maintainer: Mikis Stasinopoulos <d.stasinopoulos@gre.ac.uk>

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Package datasus updated to version 0.16.2 with previous version 0.16.1 dated 2026-09-05

Title: An Interface to Brazilian Public Health Data
Description: Retrieves public health data from the Department of Informatics ('DATASUS') of the Brazilian Unified Health System ('Sistema Unico de Saude', 'SUS') through its online tabulation service and open-data catalog. It covers the Mortality Information System ('SIM'), Live Birth Information System ('SINASC'), Hospital Information System of the Unified Health System ('SIH/SUS'), Outpatient Information System of the Unified Health System ('SIA/SUS'), National Register of Health Establishments ('CNES'), Notifiable Diseases Information System ('SINAN'), National Immunization Program ('PNI'), Cancer Information System ('SISCAN'), and Food and Nutrition Surveillance System ('SISVAN'). Contemporary sources from the 'OpenDataSUS' portal include Events Supposedly Attributable to Vaccination or Immunization ('ESAVI'), influenza-like illness notifications from 'e-SUS Notifica', individual vaccination doses, coronavirus disease 2019 (COVID-19) hospital occupancy, and record-level mortality, live-birth an [...truncated...]
Author: Renato Prado Siqueira [aut, cre]
Maintainer: Renato Prado Siqueira <rpradosiqueira@gmail.com>

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More information about datasus at CRAN
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Package beautils updated to version 0.3.0 with previous version 0.2.0 dated 2026-06-24

Title: Field Planning and Biostatistics Utilities
Description: A toolkit for agricultural trial planning, experimental design, and applied biostatistics. Supports generating field designs such as Latin Square, Alpha-Lattice (Patterson and Williams, 1976 <doi:10.2307/2335087>), and Factorial layouts, with automatic fieldbook creation and layout sketching. Includes a built-in QR Code engine (powered by the 'Nayuki' C library <https://github.com/nayuki/QR-Code-generator>) for generating printable plot labels in PDF. Also provides descriptive statistics utilities for quantitative variables following Field, Miles, and Field (2012, ISBN:978-1-4462-0045-2).
Author: Tiago Olivoto [aut, cre]
Maintainer: Tiago Olivoto <tiagoolivoto@gmail.com>

Diff between beautils versions 0.2.0 dated 2026-06-24 and 0.3.0 dated 2026-10-06

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 47 files changed, 693 insertions(+), 303 deletions(-)

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Package mmrm updated to version 0.3.19 with previous version 0.3.18 dated 2026-06-19

Title: Mixed Models for Repeated Measures
Description: Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) <doi:10.1177/009286150804200402> for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'.
Author: Daniel Sabanes Bove [aut, cre] , Liming Li [aut] , Julia Dedic [aut], Doug Kelkhoff [aut], Kevin Kunzmann [aut], Brian Matthew Lang [aut], Christian Stock [aut], Ya Wang [aut], Craig Gower-Page [ctb], Dan James [aut], Jonathan Sidi [aut], Daniel Leib [...truncated...]
Maintainer: Daniel Sabanes Bove <daniel.sabanes_bove@rconis.com>

Diff between mmrm versions 0.3.18 dated 2026-06-19 and 0.3.19 dated 2026-10-06

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Package mlt updated to version 1.8-3 with previous version 1.8-2 dated 2026-08-21

Title: Most Likely Transformations
Description: Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020) <DOI:10.18637/jss.v092.i01>. Shift-scale (Siegfried et al, 2023, <DOI:10.1080/00031305.2023.2203177>) and multivariate (Klein et al, 2022, <DOI:10.1111/sjos.12501>) transformation models are part of this package. A package vignette is available from <DOI:10.32614/CRAN.package.mlt.docreg> and more convenient user interfaces to many models from <DOI:10.32614/CRAN.package.tram>.
Author: Torsten Hothorn [aut, cre]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between mlt versions 1.8-2 dated 2026-08-21 and 1.8-3 dated 2026-10-06

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Package ggpicrust2 updated to version 2.5.19 with previous version 2.5.17 dated 2026-06-05

Title: Make 'PICRUSt2' Output Analysis and Visualization Easier
Description: Provides a convenient way to analyze and visualize 'PICRUSt2' output with pre-defined plots and functions. Allows for generating statistical plots about microbiome functional predictions and offers customization options. Features a one-click option for creating publication-level plots, saving time and effort in producing professional-grade figures. Streamlines the 'PICRUSt2' analysis and visualization process. For more details, see Yang et al. (2023) <doi:10.1093/bioinformatics/btad470>.
Author: Chen Yang [aut, cre], Liangliang Zhang [aut]
Maintainer: Chen Yang <cafferychen7850@gmail.com>

Diff between ggpicrust2 versions 2.5.17 dated 2026-06-05 and 2.5.19 dated 2026-10-06

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 98 files changed, 16920 insertions(+), 4615 deletions(-)

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

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

2018-06-27 0.1.0

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New package scorecraft with initial version 0.3.0
Package: scorecraft
Title: Scorecard Development and Internal Ratings-Based Risk Parameters
Version: 0.3.0
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...]
License: MIT + file LICENSE
URL: https://github.com/evandeilton/scorecraft
BugReports: https://github.com/evandeilton/scorecraft/issues
Encoding: UTF-8
Language: en-GB
Depends: R (>= 4.1.0)
Imports: data.table (>= 1.14.0), OptimalBinningWoE (>= 1.13.4), xgboost, stats, utils, graphics, parallel, Rcpp (>= 1.0.10)
LinkingTo: Rcpp, RcppArmadillo
Suggests: glmnet, lightgbm, ranger, DBI, odbc, RSQLite, duckdb, openxlsx, betareg, bit64, testthat (>= 3.0.0), knitr, rmarkdown, withr
LazyData: true
LazyDataCompression: xz
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-09-25 22:47:44 UTC; evandeilton
Author: Jose Evandeilton Lopes [aut, cre, cph]
Maintainer: Jose Evandeilton Lopes <evandeilton@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-06 16:10:02 UTC

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New package hilldiv3 with initial version 3.0.0
Package: hilldiv3
Title: Integral Analysis of Diversity Based on Hill Numbers
Version: 3.0.0
Description: Measures and compares the diversity of biological communities (e.g. tables of operational taxonomic units (OTUs), amplicon sequence variants (ASVs) or metagenome-assembled genomes (MAGs)) based on Hill numbers, in a unified framework for neutral, phylogenetic and functional diversity measurement, diversity partitioning, (dis)similarity measurement, diversity profiles, evenness and redundancy. The statistical framework encompasses richness, Shannon and Simpson diversity, Faith's phylogenetic diversity (PD), Rao's quadratic entropy and Sorensen- and UniFrac-type dissimilarities, all grounded in a single Hill-number framework. Methods are described in Jost (2007) <doi:10.1890/06-1736.1>, Chao et al. (2010) <doi:10.1098/rstb.2010.0272>, Chiu et al. (2014) <doi:10.1890/12-0960.1> and reviewed in Alberdi & Gilbert (2019) <doi:10.1111/1755-0998.13014>. Optional import adapters interoperate with the Bioconductor packages 'phyloseq', 'SummarizedExperiment' and 'TreeS [...truncated...]
License: GPL-3
Language: en-GB
URL: https://github.com/alberdilab/hilldiv3, https://alberdilab.github.io/hilldiv3/
BugReports: https://github.com/alberdilab/hilldiv3/issues
Depends: R (>= 4.1.0)
Imports: ape, cli, grDevices, graphics, methods, rlang, stats, utils
Suggests: cluster, furrr, future, ggplot2, knitr, patchwork, phyloseq, progressr, rmarkdown, SummarizedExperiment, testthat (>= 3.0.0), tibble, TreeSummarizedExperiment, vegan
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2026-09-26 03:27:49 UTC; anttonalberdi
Author: Antton Alberdi [aut, cre]
Maintainer: Antton Alberdi <antton.alberdi@sund.ku.dk>
Repository: CRAN
Date/Publication: 2026-10-06 16:40:02 UTC

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New package hal with initial version 0.1.4
Package: hal
Title: Agentic Coding Assistants
Version: 0.1.4
Description: A native R interface to agentic coding assistants. Talks directly to local command line interfaces ('GitHub Copilot', 'Anthropic' 'Claude Code') over standard input/output and to a local 'vscode.lm' bridge inside 'Positron', providing multi-model chat, tool calling, and in-session R evaluation. Supports a single stateful hal() session as well as disposable hal_ask() / hal_do() pipeline verbs, plot capture so models can see graphics, verified data transforms, and user-defined tools.
License: MIT + file LICENSE
URL: https://arclite-red.github.io/hal/, https://github.com/ArcLite-Red/hal
BugReports: https://github.com/ArcLite-Red/hal/issues
Depends: R (>= 4.1)
Imports: cli, curl, jsonlite, processx, R6, rlang (>= 1.1.0)
Suggests: covr, dplyr, ellmer, ggplot2, knitr, openxlsx2, rmarkdown, rstudioapi, styler, testthat (>= 3.0.0), withr
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-09-25 23:04:13 UTC; sogre
Author: Mark Dippold [aut, cre]
Maintainer: Mark Dippold <arclitered@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-06 16:20:02 UTC

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New package GarrettRank with initial version 0.1.5
Package: GarrettRank
Title: Garrett Ranking Analysis and Visualization
Version: 0.1.5
Description: Performs Garrett ranking analysis of respondent-ranked items such as constraints, problems, factors, or priorities. The package converts respondent rankings into Garrett scores, calculates mean Garrett scores and final ranks and provides methods for summarizing,tabulating and visualizing ranking results. It also provides Kendall's coefficient of concordance for assessing the degree of agreement among respondents. Garrett ranking does not accommodate tied ranks and Kendall's coefficient of concordance is likewise computed for untied ranking data.For more details see Garrett and Woodworth (1969) <https://books.google.com/books?id=aoqSmQEACAAJ> and Buragohain and Dubey (2021) <doi:10.5958/2454-552X.2021.00055.4>.
License: GPL-3
Encoding: UTF-8
Imports: ggplot2, pheatmap, rlang
Suggests: readxl, testthat (>= 3.0.0)
Depends: R (>= 3.5)
LazyData: true
NeedsCompilation: no
Packaged: 2026-09-26 03:11:27 UTC; bejoy
Author: Blesson B. Varghese [aut, cre], Adarsh V S [aut], Bhavana Sajeev [aut], Azhanuo Rutsa [aut], Joe Shiney M A [aut]
Maintainer: Blesson B. Varghese <blessonvarghese1234@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-06 16:40:08 UTC

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New package electedBR with initial version 0.1.0
Package: electedBR
Title: Brazilian Election Winners and Sitting Members of Congress
Version: 0.1.0
Description: Retrieves the candidates elected in Brazilian municipal and general elections since 2018, consolidated from the open data of the Superior Electoral Court (TSE, <https://dadosabertos.tse.jus.br/>) and distributed as yearly Parquet files, together with the federal deputies and senators currently serving according to the open data APIs of the Chamber of Deputies (<https://dadosabertos.camara.leg.br/>) and the Federal Senate (<https://www12.senado.leg.br/dados-abertos>). Election results and current office holding are kept as distinct queries; electoral roles, service history and provenance are preserved. Every function returns a tibble with English column names and has a Portuguese alias.
License: GPL-3
URL: https://github.com/StrategicProjects/electedBR, https://strategicprojects.github.io/electedBR/
BugReports: https://github.com/StrategicProjects/electedBR/issues
Encoding: UTF-8
Language: en-US
LazyData: true
Depends: R (>= 4.1.0)
Imports: dplyr (>= 1.1.0), httr2 (>= 1.0.0), nanoparquet, rlang, tibble, tools, utils
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), withr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-09-25 21:57:03 UTC; leite
Author: Andre Leite [aut, cre] , Marcos Wasiliew [aut] , Hugo Vasconcelos [aut] , Carlos Amorim [aut] , Diogo Bezerra [aut]
Maintainer: Andre Leite <leite@castlab.org>
Repository: CRAN
Date/Publication: 2026-10-06 15:20:02 UTC

More information about electedBR at CRAN
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New package betbetter with initial version 0.1.0
Package: betbetter
Title: Access the Bet Better Open Sports Model API
Version: 0.1.0
Description: Retrieves published statistical model estimates for upcoming sporting fixtures from the Bet Better open API, covering Australian rules football, baseball, basketball, American football, ice hockey, association football, tennis and mixed martial arts. For each rated selection the interface returns the model's estimated probability that the selection occurs, together with the decimal odds implied by that probability. No registration or authentication is required. Bookmaker prices are not published by the API and are therefore not available through this package.
License: MIT + file LICENSE
URL: https://betbetter.world/api/, https://betbetter.world/studies/
BugReports: https://betbetter.world/About
Encoding: UTF-8
Imports: httr2, jsonlite
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-09-18 04:04:54 UTC; Administrator
Author: Edward Glush [aut, cre]
Maintainer: Edward Glush <edward.glush@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-06 15:50:02 UTC

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New package zuhtml with initial version 0.1.0
Package: zuhtml
Title: Parse 'HTML' with a Bundled 'Gumbo' Parser
Version: 0.1.0
Description: Parses real-world 'HTML' with a bundled copy of the 'Gumbo' parser (<https://codeberg.org/gumbo-parser/gumbo-parser>), which follows the 'WHATWG' parsing algorithm, so that no system library is required. Documents become immutable trees navigated with a documented subset of 'CSS' selectors. Attributes, text, lists, tables, links, forms and page metadata ('JSON-LD', microdata) are extracted into ordinary character vectors, lists and data frames, and nodes convert to 'Markdown'. Input is a string, raw bytes, a file, a URL or a connection, and raw input is decoded as browsers decode it, from a byte-order mark or a '<meta>' declaration. Parsing is bounded by limits on input size, native memory and nesting depth.
License: MIT + file LICENSE
Copyright: file inst/COPYRIGHTS
URL: https://github.com/pedrobtz/zuhtml, https://pedrobtz.github.io/zuhtml/
BugReports: https://github.com/pedrobtz/zuhtml/issues
Depends: R (>= 4.1)
Suggests: jsonlite, knitr, rmarkdown, testthat (>= 3.0.0), withr
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2026-09-25 15:40:02 UTC; pbtz
Author: Pedro Baltazar [aut, cre, cph], Google Inc. [cph] , Bjoern Hoehrmann [cph]
Maintainer: Pedro Baltazar <pedrobtz@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-06 14:10:02 UTC

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New package ggextreme with initial version 0.1.0
Package: ggextreme
Title: Bar Chart Races and Interactive Plots for Clinical Research
Version: 0.1.0
Description: Presentation quality charts built on 'ggplot2' that the package itself does not provide. The bar chart race interpolates values on a uniform time grid, ranks every frame on its own values and eases bars into new positions, so a reordering field stays readable. Frames are ordinary 'ggplot2' objects laid out on a fixed pixel grid, which keeps the axis and label column from drifting between them, and are encoded to GIF or MP4. Circular images, such as the bundled country flags, can be placed at the end of each bar. Causal diagrams are drawn as directed acyclic graphs whose nodes and arrows each carry a rationale and references, shown on hover and opened with clickable links on click, through 'ggiraph'; the same diagram is also available as a static 'ggplot2' object. Network plots for network meta-analysis are drawn from arm level data, with the baseline characteristics and outcomes of every arm shown side by side on click. Forest plots for 'metafor' and 'meta' fits carry each study's reco [...truncated...]
License: MIT + file LICENSE
Copyright: See file inst/COPYRIGHTS for the bundled Lato font, country flag artwork and world map.
Encoding: UTF-8
LazyData: true
Depends: R (>= 4.1)
Imports: gdtools (>= 0.5.0), ggiraph (>= 0.9.6), ggplot2 (>= 3.5.0), grDevices, grid, htmltools, htmlwidgets, igraph, jsonlite, parallel, ragg, rlang, scales, splines, stats, systemfonts (>= 1.2.4), tools, utils
Suggests: av, geepack, gifski, glmmTMB, gt, knitr, lme4, logistf, magick, MASS, meta, metadat, metafor, mgcv, netmeta, nlme, nnet, ordinal, quantreg, rmarkdown, rms, rsvg, sf, survival, testthat (>= 3.0.0)
VignetteBuilder: knitr
URL: https://choxos.github.io/ggextreme/, https://github.com/choxos/ggextreme
BugReports: https://github.com/choxos/ggextreme/issues
NeedsCompilation: no
Packaged: 2026-09-25 16:22:51 UTC; choxos
Author: Ahmad Sofi-Mahmudi [aut, cre, cph]
Maintainer: Ahmad Sofi-Mahmudi <a.sofimahmudi@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-06 14:50:02 UTC

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New package datazoom.saude with initial version 0.1.1
Package: datazoom.saude
Title: Download Brazilian Health Data from DATASUS
Version: 0.1.1
Description: Provides a comprehensive set of functions to easily download, clean, and standardize various public health datasets from DATASUS <https://datasus.saude.gov.br/>, the Department of Informatics of the Brazilian Unified Health System (SUS). This package streamlines access to crucial health information, including mortality (SIM), hospital admissions (SIH), live births (SINASC), hospital beds (CNES-LT), and outpatient procedures (SIASUS), making the data ready for epidemiological and public health analyses.
License: MIT + file LICENSE
URL: https://datazoom.com.br/en/dz_saude/
Depends: R (>= 4.0)
Imports: data.table, dplyr, Hmisc, janitor, magrittr, purrr, readr, readxl, sf, stringr, tibble, utils, XML, lubridate, tidyselect, jsonlite, tidyr, stats
Suggests: foreign, googledrive, knitr, RCurl, rmarkdown, terra, testthat (>= 3.0.0), units
LinkingTo: Rcpp
VignetteBuilder: knitr
Encoding: UTF-8
BugReports: https://github.com/datazoompuc/datazoom.saude/issues
NeedsCompilation: yes
Packaged: 2026-09-23 20:59:15 UTC; luanalaport
Author: Laura Tavares Regadas [aut, cre], Luana Laport Santos [aut], Joao Paulo dos Santos P. Barbosa [aut], DataZoom [fnd], Vanndher Amos B. A. de Souza [aut], Daniela Petruzalek [ctb], Mark Adler [ctb]
Maintainer: Laura Tavares Regadas <lauratregadas@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-06 13:40:02 UTC

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New package ambs with initial version 0.1.0
Package: ambs
Title: Bayesian Alpha-Mixture Survival Models
Version: 0.1.0
Description: Implements Bayesian estimation for alpha-mixture survival models with right-censored data. Weibull-Weibull, Gamma-Weibull, and Lognormal-Lognormal component specifications are supported, with all component parameters treated as unknown. The package provides identifiability handling, adaptive Markov chain Monte Carlo sampling, convergence diagnostics, model comparison criteria, and posterior survival, hazard, and density estimation. The methodology extends the framework described by Luan et al. (2026) <doi:10.3390/math14101772>. Danish Ezwan, David Goldberg, and Ting Huang contributed equally to the package.
License: GPL (>= 3)
Depends: R (>= 4.0.0)
Imports: graphics, stats, utils
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
Suggests: survival, testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2026-09-25 14:19:20 UTC; zhexuanyang
Author: Zhexuan Yang [aut, cre], Danish Ezwan [ctb], David Goldberg [ctb], Ting Huang [ctb]
Maintainer: Zhexuan Yang <zky5198@psu.edu>
Repository: CRAN
Date/Publication: 2026-10-06 14:00:02 UTC

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Package ICESat2VegR updated to version 0.0.4 with previous version 0.0.3 dated 2026-09-28

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 0.0.3 dated 2026-09-28 and 0.0.4 dated 2026-10-06

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Package forrel updated to version 1.10.0 with previous version 1.9.0 dated 2026-06-28

Title: Forensic Pedigree Analysis and Relatedness Inference
Description: Forensic applications of pedigree analysis, including likelihood ratios for relationship testing, general relatedness inference, marker simulation, and power analysis. 'forrel' is part of the 'pedsuite', a collection of packages for pedigree analysis, further described in the book 'Pedigree Analysis in R' (Vigeland, 2021, ISBN:9780128244302). Several functions deal specifically with power analysis in missing person cases, implementing methods described in Vigeland et al. (2020) <doi:10.1016/j.fsigen.2020.102376>. Methods for checking pairwise relationships are described in Egeland and Vigeland (2025) <doi:10.1016/j.fsigen.2025.103270>. Data exchange with the 'Familias' software (Egeland et al. (2000) <doi:10.1016/S0379-0738(00)00147-X>) is supported through the 'pedFamilias' package.
Author: Magnus Dehli Vigeland [aut, cre] , Thore Egeland [ctb]
Maintainer: Magnus Dehli Vigeland <m.d.vigeland@medisin.uio.no>

Diff between forrel versions 1.9.0 dated 2026-06-28 and 1.10.0 dated 2026-10-06

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Package pkgcheck updated to version 0.3.3 with previous version 0.3.2 dated 2026-10-02

Title: Package Checks for 'rOpenSci'
Description: Check whether a package is ready for submission to the 'rOpenSci' peer review system ('rOpenSci' authors (2026) <doi:10.5281/zenodo.2553043> "'rOpenSci' Packages: Development, Maintenance, and Peer Review"). Incorporates the 'goodpractice' package and many additional checks, including aspects related to maintenance of online public code repositories.
Author: Mark Padgham [aut, cre] , Maelle Salmon [aut], Jacob Wujciak-Jens [aut] , Kelli F. Johnson [ctb] , Eunseop Kim [aut] , Katrina Brock [ctb] , Andy Teucher [aut] , Eric R. Scott [aut]
Maintainer: Mark Padgham <mark.padgham@email.com>

Diff between pkgcheck versions 0.3.2 dated 2026-10-02 and 0.3.3 dated 2026-10-06

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Package metrosp updated to version 2.0.0 with previous version 1.2.1 dated 2026-09-03

Title: São Paulo Metro Passenger Demand Data
Description: Provides passenger demand data for the São Paulo metro system, covering 2012 to 2026. Datasets include monthly passenger entries and transported counts by line, average weekday passengers transported by station, daily station entries, and spatial geometries for metro and commuter train lines and stations. The bundled datasets are a fixed snapshot, so analyses stay reproducible and examples run offline. More recent data is published to 'GitHub' releases as the upstream sources are updated, and read_metro_demand() downloads, caches, and reads it, optionally pinned to a dated monthly batch.
Author: Vinicius Oike [aut, cre, cph]
Maintainer: Vinicius Oike <viniciusoike@gmail.com>

Diff between metrosp versions 1.2.1 dated 2026-09-03 and 2.0.0 dated 2026-10-06

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Package bigDM updated to version 0.5.9 with previous version 0.5.8 dated 2026-08-19

Title: Scalable Bayesian Disease Mapping Models for High-Dimensional Data
Description: Implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 <doi:10.1016/j.spasta.2021.100496>; Orozco-Acosta et al., 2023 <doi:10.1016/j.cmpb.2023.107403> and Vicente et al., 2023 <doi:10.1007/s11222-023-10263-x>). The creation and develpment of this package has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001).
Author: Aritz Adin [aut, cre] , Erick Orozco-Acosta [aut] , Maria Dolores Ugarte [aut]
Maintainer: Aritz Adin <aritz.adin@unavarra.es>

Diff between bigDM versions 0.5.8 dated 2026-08-19 and 0.5.9 dated 2026-10-06

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More information about bigDM at CRAN
Permanent link

Package renv updated to version 1.3.1 with previous version 1.3.0 dated 2026-09-29

Title: Project Environments
Description: A dependency management toolkit for R. Using 'renv', you can create and manage project-local R libraries, save the state of these libraries to a 'lockfile', and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible.
Author: Kevin Ushey [aut, cre] , Hadley Wickham [aut] , Posit Software, PBC [cph, fnd]
Maintainer: Kevin Ushey <kevin@rstudio.com>

Diff between renv versions 1.3.0 dated 2026-09-29 and 1.3.1 dated 2026-10-06

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Package easyRasch2 updated to version 1.4.0 with previous version 1.3.1 dated 2026-09-13

Title: Psychometric Analysis with Rasch Measurement Theory
Description: Streamlines reproducible Rasch measurement theory analyses for ordinal item-response data, combining estimation routines from 'eRm', 'psychotools', 'mirt', 'iarm', and 'lavaan' with consistent diagnostic, plotting, and reporting layers. Covers the four basic psychometric criteria summarised by Christensen et al. (2021) <doi:10.1111/sms.13908> -- unidimensionality, local independence, ordered response category thresholds, and invariance across subgroups -- together with item fit, targeting, reliability, category functioning, and descriptive item-response plots. A distinguishing feature is the use of simulation-based critical values to replace rule-of-thumb cutoffs for conditional infit mean-square, item-rest-score gamma, Yen's Q3 local-dependence statistic, the largest residual-PCA eigenvalue, ordinal CFA fit indices, and partial-gamma DIF and local-dependence coefficients, optionally augmented with multiplicity-corrected bootstrap p-values. Outputs are knitr::kable() tables and ' [...truncated...]
Author: Magnus Johansson [aut, cre] , Nicklas Korsell [ctb] , Mirka Henninger [ctb] , Jan Radek [ctb]
Maintainer: Magnus Johansson <pgmj@pm.me>

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Package SuperCell updated to version 1.2 with previous version 1.1 dated 2025-12-16

Title: Simplification of scRNA-Seq Data by Merging Together Similar Cells
Description: Aggregates large single-cell data into metacell dataset by merging together gene expression of very similar cells. 'SuperCell' uses 'velocyto.R' <doi:10.1038/s41586-018-0414-6> <https://github.com/velocyto-team/velocyto.R> for RNA velocity and 'WeightedCluster' <doi:10.12682/lives.2296-1658.2013.24> <https://mephisto.unige.ch/weightedcluster/> for weighted clustering on metacells. We also recommend installing 'scater' Bioconductor package <doi:10.18129/B9.bioc.scater> <https://bioconductor.org/packages/release/bioc/html/scater.html>.
Author: Mariia Bilous [aut], Matei Teleman [cre]
Maintainer: Matei Teleman <matei.teleman@unil.ch>

Diff between SuperCell versions 1.1 dated 2025-12-16 and 1.2 dated 2026-10-06

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Package midasINLA updated to version 0.1.2 with previous version 0.1.1 dated 2026-10-01

Title: Spatial MIDAS Models Using INLA
Description: Provides tools for fitting spatial Mixed-Data Sampling (MIDAS) regression models using Integrated Nested Laplace Approximation (INLA) (Rue et al., 2009) <doi:10.1111/j.1467-9868.2008.00700.x>. The package is designed for settings where responses and explanatory variables are observed at different temporal frequencies and supports both constant and spatially varying regression coefficients.
Author: Stephen Jun Villejo [aut, cre], Havard Rue [aut], Marta Blangiardo [aut]
Maintainer: Stephen Jun Villejo <s.villejo@imperial.ac.uk>

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Package Compind updated to version 3.7 with previous version 3.6 dated 2026-07-10

Title: Composite Indicators Functions
Description: A collection of functions to calculate Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps, as described in OECD Handbook on constructing composite indicators: methodology and user guide, 2008, 'Vidoli' and 'Fusco' and 'Mazziotta' <doi:10.1007/s11205-014-0710-y>, 'Mazziotta' and 'Pareto' (2016) <doi:10.1007/s11205-015-0998-2>, 'Van Puyenbroeck and 'Rogge' <doi:10.1016/j.ejor.2016.07.038> and other authors.
Author: Francesco Vidoli [aut, cre], Elisa Fusco [aut]
Maintainer: Francesco Vidoli <fvidoli@gmail.com>

Diff between Compind versions 3.6 dated 2026-07-10 and 3.7 dated 2026-10-06

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Package vennDiagramLab updated to version 2.9.0 with previous version 2.4.2 dated 2026-06-10

Title: Headless Venn Diagram Analysis and Rendering
Description: Headless companion to the 'Venn Diagram Lab' web tool (<https://www.venndiagramlab.org/>). Build, render, and statistically analyze Venn / 'UpSet' diagrams from 'CSV' / 'TSV' / 'GMT' / 'GMX' inputs. Provides the same 44 SVG models, intersection / 'Jaccard' / hypergeometric statistics, and PDF report layout as the web tool, with byte-equivalent 'TSV' exports (parity-tested against the published Python package). Integrates with 'ggplot2', 'tidygraph', and 'broom'.
Author: Zoltan Dul [aut, cre] , Marton Oelbei [aut] , N. Shaun B. Thomas [aut], Azeddine Si Ammour [aut] , Attila Csikasz-Nagy [aut]
Maintainer: Zoltan Dul <zoltan.dul@gmail.com>

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Package piqp updated to version 0.6.4.1 with previous version 0.6.4 dated 2026-10-02

Title: R Interface to Proximal Interior Point Quadratic Programming Solver
Description: An embedded proximal interior point quadratic programming solver, which can solve dense and sparse quadratic programs, described in Schwan, Jiang, Kuhn, and Jones (2023) <doi:10.48550/arXiv.2304.00290>. Combining an infeasible interior point method with the proximal method of multipliers, the algorithm can handle ill-conditioned convex quadratic programming problems without the need for linear independence of the constraints. The solver is written in header only 'C++ 14' leveraging the 'Eigen' library for vectorized linear algebra. For small dense problems, vectorized instructions and cache locality can be exploited more efficiently. Allocation free problem updates and re-solves are also provided.
Author: Balasubramanian Narasimhan [aut, cre], Roland Schwan [aut, cph], Yuning Jiang [aut], Daniel Kuhn [aut], Colin N. Jones [aut]
Maintainer: Balasubramanian Narasimhan <naras@stanford.edu>

Diff between piqp versions 0.6.4 dated 2026-10-02 and 0.6.4.1 dated 2026-10-06

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Package enrichit updated to version 0.2.6 with previous version 0.2.5 dated 2026-09-24

Title: 'C++' Implementations of Functional Enrichment Analysis
Description: Fast implementations of functional enrichment analysis methods using 'C++' via 'Rcpp'. Currently provides Over-Representation Analysis (ORA), Gene Set Enrichment Analysis (GSEA), Weighted Enrichment Analysis for ORA and GSEA, Network-based Set Enrichment Analysis (NSEA), multi-layer network-based enrichment, and multi-omics integration workflows. Additional features include early fusion at the feature level, late fusion at the pathway level, multi-omics contribution tracing, topology-aware explanation helpers, Bayesian term selection, and extremely fast Random Walk with Restart (RWR) using 'RcppEigen'. The enrichment methods build on GSEA by Subramanian et al. (2005) <doi:10.1073/pnas.0506580102>, the multilevel strategy derived from 'fgsea' by Korotkevich et al. (2021) <doi:10.1101/060012>, and network-based enrichment ideas described by Glaab et al. (2012) <doi:10.1093/bioinformatics/bts389>.
Author: Guangchuang Yu [aut, cre]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>

Diff between enrichit versions 0.2.5 dated 2026-09-24 and 0.2.6 dated 2026-10-06

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New package rsDCM with initial version 0.1.0
Package: rsDCM
Title: Robust and Sparse Dynamic Causal Modelling for Functional MRI
Version: 0.1.0
Date: 2026-09-24
Maintainer: Godfred Arhin <arhin0122@gmail.com>
Description: Provides a robust and sparse method for group-level Dynamic Causal Modelling (DCM) of functional magnetic resonance imaging (fMRI) data: Student-t weighting of subjects for robustness, combined with a nonlocal product-moment (pMOM) spike-and-slab prior for sparse selection of group-level effects (<doi:10.48550/arXiv.2609.06379>). The package also provides an R implementation of single-subject DCM for fMRI using variational Laplace inversion (Friston et al., 2003 <doi:10.1016/S1053-8119(03)00202-7>), including the bilinear neural state equation and the Buxton-Friston hemodynamic response model, ported from the 'SPM25' (version 25.01.02) toolbox for 'MATLAB'.
License: GPL-2
Encoding: UTF-8
LazyData: true
Depends: R (>= 4.0.0)
Imports: Matrix, expm, MASS, methods, stats, utils
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, R.matlab, withr
URL: https://github.com/Kay202/rsDCM
BugReports: https://github.com/Kay202/rsDCM/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-09-25 04:09:30 UTC; nsanyal
Author: Godfred Arhin [aut, cre, trl], Nilotpal Sanyal [aut], SPM25 Authors [ctb, cph] implementation; see LICENSE.note)
Repository: CRAN
Date/Publication: 2026-10-06 07:40:02 UTC

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New package rgrind with initial version 0.1.0
Package: rgrind
Title: Gamified Algorithmic and Data Wrangling Challenges for R
Version: 0.1.0
Description: An in-console, gamified learning and practice engine for R. Solve algorithmic and data-wrangling challenges directly in the R console, get instant styled feedback with worked explanations, and track your progress locally with a solving streak, attempt history, and an activity heatmap.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: cli
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), tibble
URL: https://devwebwacky.github.io/rgrind/
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-09-24 23:36:27 UTC; Wacky
Author: Uwakmfon Paul [aut, cre]
Maintainer: Uwakmfon Paul <uwakmfon31@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-06 07:40:09 UTC

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New package quadriceps with initial version 0.1.0
Package: quadriceps
Title: Positive-Weight Cubature Rules for the Gaussian Weight and the Cube
Version: 0.1.0
Description: Positive-weight cubature rules in several dimensions for the Gaussian weight (ghpos) and the uniform weight on the cube (lepos): the smallest rules known to the author for dimensions 2 to 5, with a fallback to the cheapest tensor product of lower-dimensional rules elsewhere. The rules are described in Pinkse (2026) <doi:10.48550/arXiv.2609.26840>, <doi:10.5281/zenodo.22904159>. The R twin of the 'Julia' package 'Quadriceps.jl'.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 3.5.0)
URL: https://github.com/NittanyLion/quadriceps-r
BugReports: https://github.com/NittanyLion/quadriceps-r/issues
NeedsCompilation: no
Packaged: 2026-09-24 14:54:46 UTC; cap19@AD.PSU.EDU
Author: Joris Pinkse [aut, cre, cph], Moustapha Diallo [cph] )
Maintainer: Joris Pinkse <pinkse@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-06 07:20:02 UTC

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Package peruocc updated to version 0.1.1 with previous version 0.1.0 dated 2026-09-21

Title: Query and Standardize Biodiversity Occurrences in Peru
Description: Facilitates the retrieval, spatial validation, and integration of flora and fauna occurrence records across administrative units (districts and provinces) in Peru. Retrieves official boundary geometries via 'geoperu', queries and consolidates observations from the Global Biodiversity Information Facility (GBIF, <https://www.gbif.org/>) and 'iNaturalist' (<https://www.inaturalist.org/>), and standardizes attributes into a unified Darwin Core aligned structure. Designed for biodiversity assessments and spatial workflows within user-defined areas of interest.
Author: Paul E. Santos Andrade [aut, cre, cph]
Maintainer: Paul E. Santos Andrade <paulefrens@gmail.com>

Diff between peruocc versions 0.1.0 dated 2026-09-21 and 0.1.1 dated 2026-10-06

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Package mirt updated to version 1.48 with previous version 1.47 dated 2026-08-20

Title: Multidimensional Item Response Theory
Description: Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported, as well as a wide family of probabilistic unfolding models.
Author: Phil Chalmers [aut, cre] , Joshua Pritikin [ctb], Alexander Robitzsch [ctb], Mateusz Zoltak [ctb], KwonHyun Kim [ctb], Carl F. Falk [ctb], Adam Meade [ctb], Lennart Schneider [ctb], David King [ctb], Chen-Wei Liu [ctb], Ogreden Oguzhan [ctb], Samuel [...truncated...]
Maintainer: Phil Chalmers <rphilip.chalmers@gmail.com>

Diff between mirt versions 1.47 dated 2026-08-20 and 1.48 dated 2026-10-06

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Package lmeInfo updated to version 0.3.3 with previous version 0.3.2 dated 2023-04-17

Title: Information Matrices for 'lmeStruct' and 'glsStruct' Objects
Description: Provides analytic derivatives and information matrices for fitted linear mixed effects (lme) models and generalized least squares (gls) models estimated using lme() (from package 'nlme') and gls() (from package 'nlme'), respectively. The package includes functions for estimating the sampling variance-covariance of variance component parameters using the inverse Fisher information. The variance components include the parameters of the random effects structure (for lme models), the variance structure, and the correlation structure. The expected and average forms of the Fisher information matrix are used in the calculations, and models estimated by full maximum likelihood or restricted maximum likelihood are supported. The package also includes a function for estimating standardized mean difference effect sizes (Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>) based on fitted lme or gls models.
Author: James Pustejovsky [aut] , Man Chen [aut, cre]
Maintainer: Man Chen <manchen9005@gmail.com>

Diff between lmeInfo versions 0.3.2 dated 2023-04-17 and 0.3.3 dated 2026-10-06

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New package IncAAM with initial version 1.8
Package: IncAAM
Title: Bayesian Age-at-Maturity Analysis from Increment-Width Growth Series
Version: 1.8
Description: Bayesian hierarchical nonlinear mixed-effects analysis for estimating age at sexual maturity from annual increment-width growth series. The method estimates age at maturity using tangent-ratio and jerk approaches and provides a posterior threshold-crossing ogive with an estimated A50. Methodological details and applications are described in Campana, Smoliński, Morrongiello and Black (2026, in press).
License: GPL-3
Encoding: UTF-8
Imports: dplyr, tidyr, purrr, tibble, ggplot2, posterior, minpack.lm, brms
Suggests: testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2026-09-14 09:36:01 UTC; ssmolinski
Author: Steven Campana [aut, cre], Szymon Smolinski [aut], Bryan Black [aut], John Morrongiello [aut]
Maintainer: Steven Campana <steven.e.campana@gmail.com>
Depends: R (>= 4.1.0)
LazyData: true
Repository: CRAN
Date/Publication: 2026-10-06 07:30:22 UTC

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Package depguard updated to version 0.2.0 with previous version 0.1.0 dated 2026-09-24

Title: Manifest-Based Dependency Conflict Detection for Sandboxed and Desktop R Sessions
Description: Lightweight, offline-first checking of R package dependencies against the currently installed environment, without requiring a full project lockfile. Verifies a declared manifest of package versions, including version constraints declared by transitive dependencies, reports session-level snapshot differences (including stale versions still loaded in a running session), detects packages shadowed by another library, and offers single-package version rollback. Designed for hosted notebooks (e.g. Kaggle, Colab, Binder) where 'renv'-style lockfile ownership is impractical, and equally usable on a normal desktop.
Author: Samruddhi Amol Shah [aut, cre], Kartik Patel [aut], Amrit Pal [ctb]
Maintainer: Samruddhi Amol Shah <samruddhi.bitnbyte@gmail.com>

Diff between depguard versions 0.1.0 dated 2026-09-24 and 0.2.0 dated 2026-10-06

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New package delta.sharing with initial version 0.2.1
Package: delta.sharing
Title: Client for the Delta Sharing Protocol
Version: 0.2.1
Description: Connects R to 'Delta Sharing' servers for share discovery, table metadata, snapshot reads, and change data feed reads.
License: Apache License (>= 2)
URL: https://zacdav-db.github.io/delta-sharing-r/, https://github.com/zacdav-db/delta-sharing-r
BugReports: https://github.com/zacdav-db/delta-sharing-r/issues
Depends: R (>= 4.3.0)
Encoding: UTF-8
Imports: arrow, cli (>= 3.0.0), fs, httr2 (>= 1.2.0), jose, jsonlite, nanoarrow (>= 0.8.0), openssl, purrr (>= 1.0.0), R6, rlang (>= 1.2.0), tibble
Suggests: bit64, DBI, dplyr, duckdb, knitr, rmarkdown, testthat (>= 3.1.7), withr
VignetteBuilder: knitr
SystemRequirements: Cargo (Rust's package manager), rustc (>= 1.88)
NeedsCompilation: yes
Packaged: 2026-09-25 05:09:28 UTC; runner
Author: Zac Davies [aut, cre], Databricks [cph]
Maintainer: Zac Davies <zac@databricks.com>
Repository: CRAN
Date/Publication: 2026-10-06 07:40:21 UTC

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New package datascan with initial version 0.1.1
Package: datascan
Title: Scan Data for Quick Structural Summaries and Checks
Version: 0.1.1
Description: Scans data for checking columns that are constant, one-to-one, missing or all unique. Users can also try to identify the columns that uniquely index the observational unit and whether some columns are nested or complete.
License: MIT + file LICENSE
Encoding: UTF-8
Suggests: testthat (>= 3.0.0)
Imports: cli, dplyr, glue, magrittr, pillar, rlang, tibble, tidyr, tidyselect
Depends: R (>= 4.1.0)
LazyData: true
URL: https://github.com/emitanaka/datascan, https://emitanaka.org/datascan/
BugReports: https://github.com/emitanaka/datascan/issues
NeedsCompilation: no
Packaged: 2026-09-25 05:46:27 UTC; emitanaka
Author: Emi Tanaka [aut, cre, cph]
Maintainer: Emi Tanaka <dr.emi.tanaka@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-06 07:40:28 UTC

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New package causaljudgment with initial version 0.1.0
Package: causaljudgment
Title: Computational Models of Causal Judgment
Version: 0.1.0
Date: 2026-09-08
Description: Provides computational implementations of models of causal judgment, including the Counterfactual Effect Size (CES) model of Quillien and Lucas (2023) <doi:10.1037/rev0000428> and the Necessity-Sufficiency (NS) model of Icard, Kominsky and Knobe (2017) <doi:10.1016/j.cognition.2017.01.010>. The package represents causal structures as binary Structural Causal Models and analytically computes causal judgments from counterfactual probability distributions.
License: MIT + file LICENSE
Encoding: UTF-8
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
Imports: dplyr
VignetteBuilder: knitr
BugReports: https://github.com/tadegquillien/causaljudgment/issues
URL: https://tadegquillien.github.io/causaljudgment/
NeedsCompilation: no
Packaged: 2026-09-25 11:27:49 UTC; tadeg
Author: Tadeg Quillien [aut, cre]
Maintainer: Tadeg Quillien <tadeg.quillien@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-06 07:50:02 UTC

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New package catchmentACS with initial version 0.6.0
Package: catchmentACS
Title: Isochrone-Based Area-Weighted ACS Aggregation
Version: 0.6.0
Description: Computes American Community Survey (ACS) estimates for the area within a given drive time of each of a set of points, such as the locations of pre-kindergarten classrooms. Such an area is called an isochrone. The drive-time areas come from a routing service, either the 'Open Source Routing Machine' ('OSRM', <https://project-osrm.org/>) or 'openrouteservice' (<https://openrouteservice.org/>), and the ACS 5-year estimates of census tracts come from the Census Bureau (<https://www.census.gov/data/developers.html>) through the 'tidycensus' package, one state at a time. The tracts that overlap an area are combined by area weighting, which uses two weights: the share of each tract's area that lies inside, for counts and rates, and each tract's share of the overlapping area, for the medians and per-person values of three ACS tables (median household income, median home value, and per capita income). A median or per-person value from any other table is added up like a count. [...truncated...]
License: MIT + file LICENSE
Copyright: see file COPYRIGHTS
Encoding: UTF-8
Language: en-US
LazyData: true
Depends: R (>= 4.1.0)
Imports: cli, digest, dplyr, httr2, jsonlite, rlang, sf (>= 1.0-12), tibble, tidycensus (>= 1.6), tidyr, tigris
Suggests: classInt, htmltools, knitr (>= 1.35), leaflet, openrouteservice, osrm (>= 4.0), rmarkdown, testthat (>= 3.0.0), withr
VignetteBuilder: knitr, rmarkdown
URL: https://github.com/joonho112/catchmentACS, https://joonho112.github.io/catchmentACS/
BugReports: https://github.com/joonho112/catchmentACS/issues
NeedsCompilation: no
Packaged: 2026-09-24 13:07:52 UTC; joonholee
Author: JoonHo Lee [aut, cre, cph]
Maintainer: JoonHo Lee <jlee296@ua.edu>
Repository: CRAN
Date/Publication: 2026-10-06 07:20:08 UTC

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Package bigbang updated to version 0.5.1 with previous version 0.5.0 dated 2026-10-02

Title: Build 'Tidyverse'-Style Meta-Packages from Local Package Files
Description: Turns a curated set of package archives (.tar.gz, .zip) into one meta-package in the style of the 'tidyverse', so that a group of interdependent packages can be distributed and installed as a single unit. The generated meta-package records the exact archive versions it was built from and installs its components in dependency order, so that whoever receives it does not have to work out which package to install first. The component archives are copied into the generated meta-package, so it is the only artifact that has to be distributed and no directory has to be agreed on between machines. Resolves dependencies by building a graph with topological ordering and cycle detection, classifies them as local or external, and detects implicit dependencies by scanning source code. Installation needs no repository access unless a component depends on a package that only exists in one, which suits teams working behind institutional firewalls. Generates the complete meta-package scaffold, including [...truncated...]
Author: Sebastian Lucas [aut, cre] , Richard Detomasi [ctb]
Maintainer: Sebastian Lucas <sebalucas@gmail.com>

Diff between bigbang versions 0.5.0 dated 2026-10-02 and 0.5.1 dated 2026-10-06

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New package BayesRE with initial version 0.1.0
Package: BayesRE
Title: Bayesian Multiple Linear Regression Estimation for Crop Yield
Version: 0.1.0
Description: Implements Bayesian multiple linear regression models for estimating crop yield response to climatic variables. Posterior predictions and regional sensitivity coefficients are returned and can be visualised with built-in plotting utilities. Methods are based on Stan (Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>) and Bayesian workflow described in Gelman et al. (2013, ISBN:9781439840955).
Depends: R (>= 4.0)
License: MIT + file LICENSE
Imports: rstan, ggplot2, bayesplot, stats
Encoding: UTF-8
LazyData: true
Suggests: testthat (>= 3.0.0), rmarkdown
NeedsCompilation: no
Packaged: 2026-09-25 11:21:46 UTC; iasri
Author: Prakash Kumar [aut, cre], Himadri Sekhar Roy [aut], Ranjit Kumar Paul [aut], Md. Yeasin [aut], Neeraj Budhlakoti [aut], Sunil Kumar Yadav [aut], Amrit Kumar Paul [aut]
Maintainer: Prakash Kumar <prakash289111@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-06 07:50:09 UTC

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Package spdesign updated to version 0.0.7 with previous version 0.0.6 dated 2026-07-02

Title: Designing Stated Preference Experiments
Description: Contemporary software commonly used to design stated preference experiments are expensive and the code is closed source. This is a free software package with an easy to use interface to make flexible stated preference experimental designs using state-of-the-art methods. For an overview of stated choice experimental design theory, see e.g., Rose, J. M. & Bliemer, M. C. J. (2014) in Hess S. & Daly. A. <doi:10.4337/9781781003152>. The package website can be accessed at <https://spdesign.edsandorf.me>. We acknowledge funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant INSPiRE (Grant agreement ID: 793163). The package features in Mariel et al. (2025) Environmental Valuation with Discrete Choice Experiments in R. (<doi:10.1007/978-3-031-89338-4>).
Author: Erlend Dancke Sandorf [aut, cre], Danny Campbell [aut]
Maintainer: Erlend Dancke Sandorf <erlend.dancke.sandorf@nmbu.no>

Diff between spdesign versions 0.0.6 dated 2026-07-02 and 0.0.7 dated 2026-10-06

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 spdesign-0.0.7/spdesign/vignettes/examples/mnl-design-with-supplied-candidate-set.R |    5 
 spdesign-0.0.7/spdesign/vignettes/syntax.Rmd                                        |   33 
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More information about spdesign at CRAN
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Package sparsediff updated to version 0.6.1 with previous version 0.4.0 dated 2026-06-08

Title: R Interface to the 'SparseDiffEngine' Sparse Differentiation Backend
Description: Bindings for the 'SparseDiffEngine' C library, the sparse Jacobian and Hessian differentiation backend used by 'CVXPY' for its Disciplined Nonlinear Programming (DNLP) extension. Provides low-level routines for building nonlinear expression graphs and evaluating sparse derivatives, intended as a backend for higher-level modeling layers such as 'CVXR'. This is the R analog of the 'sparsediffpy' Python package and wraps the same C library.
Author: Balasubramanian Narasimhan [aut, cre], Daniel Cederberg [aut, cph] , William Zijie Zhang [aut, cph]
Maintainer: Balasubramanian Narasimhan <naras@stanford.edu>

Diff between sparsediff versions 0.4.0 dated 2026-06-08 and 0.6.1 dated 2026-10-06

 sparsediff-0.4.0/sparsediff/src/sparsediffengine/include/utils/matrix_BTA.h                                  |only
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 sparsediff-0.6.1/sparsediff/DESCRIPTION                                                                      |   11 
 sparsediff-0.6.1/sparsediff/MD5                                                                              |  199 +
 sparsediff-0.6.1/sparsediff/NAMESPACE                                                                        |   14 
 sparsediff-0.6.1/sparsediff/NEWS.md                                                                          |only
 sparsediff-0.6.1/sparsediff/R/cpp11.R                                                                        |   40 
 sparsediff-0.6.1/sparsediff/R/sparsediff-atoms.R                                                             |  137 +
 sparsediff-0.6.1/sparsediff/R/sparsediff-package.R                                                           |   42 
 sparsediff-0.6.1/sparsediff/R/sparsediff-problem.R                                                           |   49 
 sparsediff-0.6.1/sparsediff/R/version.R                                                                      |    2 
 sparsediff-0.6.1/sparsediff/build/vignette.rds                                                               |binary
 sparsediff-0.6.1/sparsediff/cleanup                                                                          |   10 
 sparsediff-0.6.1/sparsediff/inst/doc/sparsediff.html                                                         |    2 
 sparsediff-0.6.1/sparsediff/man/engine_version.Rd                                                            |    2 
 sparsediff-0.6.1/sparsediff/man/sparsediff-affine.Rd                                                         |   15 
 sparsediff-0.6.1/sparsediff/man/sparsediff-bivariate.Rd                                                      |   14 
 sparsediff-0.6.1/sparsediff/man/sparsediff-elementwise.Rd                                                    |   17 
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 sparsediff-0.6.1/sparsediff/man/sparsediff-leaves.Rd                                                         |    4 
 sparsediff-0.6.1/sparsediff/man/sparsediff-matrix.Rd                                                         |   66 
 sparsediff-0.6.1/sparsediff/man/sparsediff-oracle.Rd                                                         |   47 
 sparsediff-0.6.1/sparsediff/man/sparsediff-package.Rd                                                        |   45 
 sparsediff-0.6.1/sparsediff/man/sparsediff-problem.Rd                                                        |    5 
 sparsediff-0.6.1/sparsediff/man/sparsediff-reduction.Rd                                                      |    5 
 sparsediff-0.6.1/sparsediff/src/Makevars.in                                                                  |    9 
 sparsediff-0.6.1/sparsediff/src/cblas_compat/sparsediff_cblas.h                                              |    5 
 sparsediff-0.6.1/sparsediff/src/cblas_shim.c                                                                 |   11 
 sparsediff-0.6.1/sparsediff/src/cpp11.cpp                                                                    |   81 
 sparsediff-0.6.1/sparsediff/src/sparsediff.cpp                                                               |  205 ++
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/include/atoms/affine.h                                      |   12 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/include/atoms/non_elementwise_full_dom.h                    |   10 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/include/subexpr.h                                           |   29 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/include/utils/Vec_macros.h                                  |   19 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/include/utils/cblas_wrapper.h                               |    8 
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 sparsediff-0.6.1/sparsediff/src/sparsediffengine/include/utils/matrix.h                                      |   15 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/include/utils/mini_numpy.h                                  |    3 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/include/utils/permuted_dense.h                              |  183 -
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 sparsediff-0.6.1/sparsediff/src/sparsediffengine/include/utils/r_io.h                                        |   12 
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 sparsediff-0.6.1/sparsediff/src/sparsediffengine/include/utils/utils.h                                       |   30 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/add.c                                      |    2 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/broadcast.c                                |    4 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/convolve.c                                 |   16 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/diag_mat.c                                 |    5 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/diag_vec.c                                 |    4 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/hstack.c                                   |   23 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/index.c                                    |   12 
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 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/neg.c                                      |    2 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/parameter.c                                |    2 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/promote.c                                  |    2 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/reshape.c                                  |    2 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/right_matmul.c                             |    8 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/scalar_mult.c                              |    5 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/sum.c                                      |   42 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/trace.c                                    |   12 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/transpose.c                                |    6 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/upper_tri.c                                |    5 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/variable.c                                 |    2 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/vector_mult.c                              |    7 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/affine/vstack.c                                   |    4 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/bivariate_full_dom/matmul.c                       |   49 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/bivariate_full_dom/multiply.c                     |   45 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/bivariate_restricted_dom/quad_over_lin.c          |   10 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/bivariate_restricted_dom/rel_entr.c               |    2 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/bivariate_restricted_dom/rel_entr_scalar_vector.c |    5 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/bivariate_restricted_dom/rel_entr_vector_scalar.c |    5 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/elementwise_full_dom/common.c                     |   13 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/elementwise_full_dom/power.c                      |    2 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/elementwise_restricted_dom/common.c               |    2 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/other/prod.c                                      |    2 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/other/prod_axis_one.c                             |   14 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/other/prod_axis_zero.c                            |   14 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/atoms/other/quad_form.c                                 |  262 ++
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/expr.c                                                  |   24 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/old-code/linear_op.c                                    |    6 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/old-code/old_permuted_dense.c                           |   44 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/problem.c                                               |   80 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/utils/COO_matrix.c                                      |   28 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/utils/CSC_matrix.c                                      |   40 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/utils/CSR_matrix.c                                      |   20 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/utils/CSR_sum.c                                         |    4 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/utils/int_double_pair.c                                 |    4 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/utils/linalg_dense_sparse_matmuls.c                     |   21 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/utils/linalg_sparse_matmuls.c                           |  111 -
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 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/utils/matrix_sum.c                                      |    4 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/utils/mini_numpy.c                                      |   15 
 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/utils/permuted_dense.c                                  |  999 +++-------
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 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/utils/sparse_matrix.c                                   |   78 
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 sparsediff-0.6.1/sparsediff/src/sparsediffengine/src/utils/utils.c                                           |   75 
 sparsediff-0.6.1/sparsediff/tests                                                                            |only
 105 files changed, 2305 insertions(+), 1410 deletions(-)

More information about sparsediff at CRAN
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Package npwbs updated to version 1.0 with previous version 0.5.0 dated 2026-08-04

Title: Nonparametric Multiple Change Point Detection Using Wild Binary Segmentation
Description: Implements nonparametric multiple change-point detection for univariate sequences using Wild Binary Segmentation, as described in Ross (2026) "Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation" <doi:10.48550/arXiv.2107.01742>. The package provides Mann--Whitney, Mood, Lepage, Cramér--von Mises, modified Baumgartner, standardised Zhang Z_C, and standardised Anderson--Darling rank-based statistics, together with method-specific thresholds for controlling the probability of incorrectly detecting a change point in a homogeneous sequence.
Author: Gordon J. Ross [aut, cre]
Maintainer: Gordon J. Ross <gordon.ross@ed.ac.uk>

Diff between npwbs versions 0.5.0 dated 2026-08-04 and 1.0 dated 2026-10-06

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 npwbs-1.0/npwbs/R/sysdata.rda                    |binary
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 15 files changed, 233 insertions(+), 88 deletions(-)

More information about npwbs at CRAN
Permanent link


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