Wed, 05 Nov 2025

Package phsopendata updated to version 1.0.1 with previous version 1.0.0 dated 2025-09-01

Title: Extract from the Scottish Health and Social Care Open Data Platform
Description: Extract and interact with data from the Scottish Health and Social Care Open Data platform <https://www.opendata.nhs.scot>.
Author: Public Health Scotland [cph], Csilla Scharle [aut, cre], James Hayes [aut] , David Aikman [aut], Ross Hull [aut]
Maintainer: Csilla Scharle <csilla.scharle2@phs.scot>

Diff between phsopendata versions 1.0.0 dated 2025-09-01 and 1.0.1 dated 2025-11-05

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Package randomForestSRC updated to version 3.4.4 with previous version 3.4.3 dated 2025-10-10

Title: Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)
Description: Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes. Extreme random forests and randomized splitting. Suite of imputation methods for missing data. Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout importance. Case-specific importance. Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.
Author: Hemant Ishwaran [aut], Udaya B. Kogalur [aut, cre]
Maintainer: Udaya B. Kogalur <ubk@kogalur.com>

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Package PubChemR updated to version 2.1.7 with previous version 2.1.6 dated 2025-10-30

Title: Interface to the 'PubChem' Database for Chemical Data Retrieval
Description: Provides an interface to the 'PubChem' database via the PUG REST <https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest> and PUG View <https://pubchem.ncbi.nlm.nih.gov/docs/pug-view> services. This package allows users to automatically access chemical and biological data from 'PubChem', including compounds, substances, assays, and various other data types. Functions are available to retrieve data in different formats, perform searches, and access detailed annotations.
Author: Selcuk Korkmaz [aut, cre] , Bilge Eren Yamasan [aut] , Dincer Goksuluk [aut]
Maintainer: Selcuk Korkmaz <selcukorkmaz@gmail.com>

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Package opalr updated to version 3.5.0 with previous version 3.4.2 dated 2024-09-18

Title: 'Opal' Data Repository Client and 'DataSHIELD' Utils
Description: Data integration Web application for biobanks by 'OBiBa'. 'Opal' is the core database application for biobanks. Participant data, once collected from any data source, must be integrated and stored in a central data repository under a uniform model. 'Opal' is such a central repository. It can import, process, validate, query, analyze, report, and export data. 'Opal' is typically used in a research center to analyze the data acquired at assessment centres. Its ultimate purpose is to achieve seamless data-sharing among biobanks. This 'Opal' client allows to interact with 'Opal' web services and to perform operations on the R server side. 'DataSHIELD' administration tools are also provided.
Author: Yannick Marcon [aut, cre] , Amadou Gaye [ctb] , OBiBa group [cph]
Maintainer: Yannick Marcon <yannick.marcon@obiba.org>

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Package mirai updated to version 2.5.2 with previous version 2.5.1 dated 2025-10-06

Title: Minimalist Async Evaluation Framework for R
Description: Designed for simplicity, a 'mirai' evaluates an R expression asynchronously, locally or distributed over the network. Built on 'nanonext' and 'NNG' for modern networking and concurrency, scales efficiently to millions of tasks over thousands of persistent parallel processes. Provides optimal scheduling over fast 'IPC', TCP, and TLS connections, integrating with SSH or cluster managers. Implements event-driven promises for reactive programming, and supports custom serialization for cross-language data types.
Author: Charlie Gao [aut, cre] , Joe Cheng [ctb], Posit Software, PBC [cph, fnd] , Hibiki AI Limited [cph]
Maintainer: Charlie Gao <charlie.gao@posit.co>

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Package dataverse updated to version 0.3.16 with previous version 0.3.15 dated 2025-05-20

Title: Client for Dataverse 4+ Repositories
Description: Provides access to Dataverse APIs <https://dataverse.org/> (versions 4-5), enabling data search, retrieval, and deposit. For Dataverse versions <= 3.0, use the archived 'dvn' package <https://cran.r-project.org/package=dvn>.
Author: Shiro Kuriwaki [aut, cre] , Will Beasley [aut] , Thomas J. Leeper [aut] , Philip Durbin [aut] , Sebastian Karcher [aut] , Jan Kanis [ctb], Edward Jee [ctb], Johannes Gruber [ctb] , Martin Morgan [ctb] , Konrad Oberwimmer [ctb]
Maintainer: Shiro Kuriwaki <shirokuriwaki@gmail.com>

Diff between dataverse versions 0.3.15 dated 2025-05-20 and 0.3.16 dated 2025-11-05

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Package candisc updated to version 1.0.0 with previous version 0.9.0 dated 2024-05-06

Title: Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis
Description: Functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA' design and is equivalent to canonical correlation analysis between a set of quantitative response variables and a set of dummy variables coded from the factor variable. The 'candisc' package generalizes this to higher-way 'MANOVA' designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are now provided for canonical correlation analysis when all predictors are quantitative. Methods for linear discriminant analysis are now included.
Author: Michael Friendly [aut, cre] , John Fox [aut]
Maintainer: Michael Friendly <friendly@yorku.ca>

Diff between candisc versions 0.9.0 dated 2024-05-06 and 1.0.0 dated 2025-11-05

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Package GHRexplore updated to version 0.2.1 with previous version 0.2.0 dated 2025-09-05

Title: Exploratory Analysis of Temporal and Spatio-Temporal Health Data
Description: A collection of commonly used visualizations of temporal and spatio-temporal health data including case counts, incidence rates, and covariates. The available plot types include time series, heatmaps, seasonality plots, maps and more. The package supports standard data transformations such as temporal and spatial aggregations, while offering extensive customization options for the resulting figures.
Author: Carles Mila [aut, cre] , Giovenale Moirano [aut] , Anna B. Kawiecki [aut] , Rachel Lowe [aut, cph]
Maintainer: Carles Mila <carles.milagarcia@bsc.es>

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Package bWGR updated to version 2.2.15 with previous version 2.2.14 dated 2025-10-29

Title: Bayesian Whole-Genome Regression
Description: Whole-genome regression methods on Bayesian framework fitted via EM or Gibbs sampling, single step (<doi:10.1534/g3.119.400728>), univariate and multivariate (<doi:10.1186/s12711-022-00730-w>, <doi:10.1093/genetics/iyae179>), with optional kernel term and sampling techniques (<doi:10.1186/s12859-017-1582-3>).
Author: Alencar Xavier [aut, cre] , William Muir [aut], David Habier [aut], Kyle Kocak [aut], Shizhong Xu [aut], Katy Rainey [aut]
Maintainer: Alencar Xavier <alenxav@gmail.com>

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Package terra updated to version 1.8-80 with previous version 1.8-70 dated 2025-09-26

Title: Spatial Data Analysis
Description: Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/> to get started. 'terra' replaces the 'raster' package ('terra' can do more, and it is faster and easier to use).
Author: Robert J. Hijmans [cre, aut] , Marcia Barbosa [ctb] , Roger Bivand [ctb] , Andrew Brown [ctb] , Michael Chirico [ctb] , Emanuele Cordano [ctb] , Krzysztof Dyba [ctb] , Edzer Pebesma [ctb] , Barry Rowlingson [ctb] , Michael D. Sumner [ctb]
Maintainer: Robert J. Hijmans <r.hijmans@gmail.com>

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Package odeGUTS updated to version 1.1.0 with previous version 1.0.3 dated 2024-10-04

Title: Solve ODE for GUTS-RED-SD and GUTS-RED-IT Using Compiled Code
Description: Allows performing forwards prediction for the General Unified Threshold model of Survival using compiled ode code. This package was created to avoid dependency with the 'morse' package that requires the installation of 'JAGS'. This package is based on functions from the 'morse' package v3.3.1: Virgile Baudrot, Sandrine Charles, Marie Laure Delignette-Muller, Wandrille Duchemin, Benoit Goussen, Nils Kehrein, Guillaume Kon-Kam-King, Christelle Lopes, Philippe Ruiz, Alexander Singer and Philippe Veber (2021) <https://CRAN.R-project.org/package=morse>.
Author: Benoit Goussen [aut] , Carlo Romoli [cre]
Maintainer: Carlo Romoli <carlo.romoli@ibacon.com>

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Package mwcsr updated to version 0.1.10 with previous version 0.1.9 dated 2024-09-09

Title: Solvers for Maximum Weight Connected Subgraph Problem and Its Variants
Description: Algorithms for solving various Maximum Weight Connected Subgraph Problems, including variants with budget constraints, cardinality constraints, weighted edges and signals. The package represents an R interface to high-efficient solvers based on relax-and-cut approach (Álvarez-Miranda E., Sinnl M. (2017) <doi:10.1016/j.cor.2017.05.015>) mixed-integer programming (Loboda A., Artyomov M., and Sergushichev A. (2016) <doi:10.1007/978-3-319-43681-4_17>) and simulated annealing.
Author: Alexander Loboda [aut, cre], Nikolay Poperechnyi [aut], Eduardo Alvarez-Miranda [aut], Markus Sinnl [aut], Alexey Sergushichev [aut], Paul Hosler Jr. [cph] , www.hamcrest.org [cph] , Barak Naveh and Contributors [cph] , The Apache Software Foundation [...truncated...]
Maintainer: Alexander Loboda <aleks.loboda@gmail.com>

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Package MultiATSM updated to version 1.5.1 with previous version 1.5.0 dated 2025-10-23

Title: Multicountry Term Structure of Interest Rates Models
Description: Package for estimating, analyzing, and forecasting multi-country macro-finance affine term structure models (ATSMs). All setups build on the single-country unspanned macroeconomic risk framework from Joslin, Priebsch, and Singleton (2014, JF) <doi:10.1111/jofi.12131>. Multicountry extensions by Jotikasthira, Le, and Lundblad (2015, JFE) <doi:10.1016/j.jfineco.2014.09.004>, Candelon and Moura (2023, EM) <doi:10.1016/j.econmod.2023.106453>, and Candelon and Moura (2024, JFEC) <doi:10.1093/jjfinec/nbae008> are also available. The package also provides tools for bias correction as in Bauer Rudebusch and Wu (2012, JBES) <doi:10.1080/07350015.2012.693855>, bootstrap analysis, and several graphical/numerical outputs.
Author: Rubens Moura [aut, cre]
Maintainer: Rubens Moura <rubens.gtmoura@gmail.com>

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Package mas updated to version 0.4 with previous version 0.3 dated 2024-04-08

Title: Multi-Population Association Studies
Description: Mixed model-based genome-wide association analysis that accommodate population membership information, variance adjustment, and correlated responses.
Author: Alencar Xavier [aut, cre] , Shizhong Xu [aut, ctb]
Maintainer: Alencar Xavier <alenxav@gmail.com>

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Package nuggets updated to version 2.1.0 with previous version 2.0.2 dated 2025-10-30

Title: Extensible Framework for Data Pattern Exploration
Description: A framework for systematic exploration of association rules (Agrawal et al., 1994, <https://www.vldb.org/conf/1994/P487.PDF>), contrast patterns (Chen, 2022, <doi:10.48550/arXiv.2209.13556>), emerging patterns (Dong et al., 1999, <doi:10.1145/312129.312191>), subgroup discovery (Atzmueller, 2015, <doi:10.1002/widm.1144>), and conditional correlations (Hájek, 1978, <doi:10.1007/978-3-642-66943-9>). User-defined functions may also be supplied to guide custom pattern searches. Supports both crisp (Boolean) and fuzzy data. Generates candidate conditions expressed as elementary conjunctions, evaluates them on a dataset, and inspects the induced sub-data for statistical, logical, or structural properties such as associations, correlations, or contrasts. Includes methods for visualization of logical structures and supports interactive exploration through integrated Shiny applications.
Author: Michal Burda [aut, cre]
Maintainer: Michal Burda <michal.burda@osu.cz>

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Package hmmTMB updated to version 1.1.2 with previous version 1.1.1 dated 2025-09-11

Title: Fit Hidden Markov Models using Template Model Builder
Description: Fitting hidden Markov models using automatic differentiation and Laplace approximation, allowing for fast inference and flexible covariate effects (including random effects and smoothing splines) on model parameters. The package is described by Michelot (2025) <doi:10.18637/jss.v114.i05>.
Author: Theo Michelot [aut, cre], Richard Glennie [aut, ctb]
Maintainer: Theo Michelot <theo.michelot@dal.ca>

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Package DER updated to version 1.2 with previous version 1.1 dated 2025-05-28

Title: Income Polarization Index
Description: The DER (or PaF) income polarization index as proposed by Duclos J. Y., Esteban, J. and Ray D. (2004). "Polarization: concepts, measurement, estimation". Econometrica, 72(6): 1737--1772. <doi:10.1111/j.1468-0262.2004.00552.x>. The index may be computed for a single or for a range of values of the alpha-parameter. Bootstrapping is also available.
Author: Michail Tsagris [aut, cre], Christos Adam [aut]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>

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Package MLwrap updated to version 0.2.2 with previous version 0.2.1 dated 2025-10-21

Title: Machine Learning Modelling for Everyone
Description: A minimal library specifically designed to make the estimation of Machine Learning (ML) techniques as easy and accessible as possible, particularly within the framework of the Knowledge Discovery in Databases (KDD) process in data mining. The package provides essential tools to structure and execute each stage of a predictive or classification modeling workflow, aligning closely with the fundamental steps of the KDD methodology, from data selection and preparation, through model building and tuning, to the interpretation and evaluation of results using Sensitivity Analysis. The 'MLwrap' workflow is organized into four core steps; preprocessing(), build_model(), fine_tuning(), and sensitivity_analysis(). These steps correspond, respectively, to data preparation and transformation, model construction, hyperparameter optimization, and sensitivity analysis. The user can access comprehensive model evaluation results including fit assessment metrics, plots, predictions, and performance diagn [...truncated...]
Author: Javier Martinez Garcia [aut] , Juan Jose Montano Moreno [ctb] , Albert Sese [cre, ctb]
Maintainer: Albert Sese <albert.sese@uib.es>

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Package comets updated to version 0.2-2 with previous version 0.2-1 dated 2025-09-10

Title: Covariance Measure Tests for Conditional Independence
Description: Covariance measure tests for conditional independence testing against conditional covariance and nonlinear conditional mean alternatives. The package implements versions of the generalised covariance measure test (Shah and Peters, 2020, <doi:10.1214/19-aos1857>) and projected covariance measure test (Lundborg et al., 2023, <doi:10.1214/24-AOS2447>). The tram-GCM test, for censored responses, is implemented including the Cox model and survival forests (Kook et al., 2024, <doi:10.1080/01621459.2024.2395588>). Application examples to variable significance testing and modality selection can be found in Kook and Lundborg (2024, <doi:10.1093/bib/bbae475>).
Author: Lucas Kook [aut, cre] , Anton Rask Lundborg [ctb]
Maintainer: Lucas Kook <lucasheinrich.kook@gmail.com>

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Package bgev updated to version 0.2 with previous version 0.1 dated 2024-03-14

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

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Package tempdisagg updated to version 1.2.0 with previous version 1.1.1 dated 2023-08-08

Title: Methods for Temporal Disaggregation and Interpolation of Time Series
Description: Temporal disaggregation methods are used to disaggregate and interpolate a low frequency time series to a higher frequency series, where either the sum, the mean, the first or the last value of the resulting high frequency series is consistent with the low frequency series. Temporal disaggregation can be performed with or without one or more high frequency indicator series. Contains the methods of Chow-Lin, Santos-Silva-Cardoso, Fernandez, Litterman, Denton and Denton-Cholette, summarized in Sax and Steiner (2013) <doi:10.32614/RJ-2013-028>. Supports most R time series classes.
Author: Christoph Sax [aut, cre] , Peter Steiner [aut], Tommaso Di Fonzo [ctb], Nelson Stevens [ctb], Tobias Schieferdecker [ctb]
Maintainer: Christoph Sax <christoph.sax@gmail.com>

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Package FARS updated to version 0.7.1 with previous version 0.7.0 dated 2025-10-26

Title: Factor-Augmented Regression Scenarios
Description: Provides a comprehensive framework in R for modeling and forecasting economic scenarios based on multi-level dynamic factor model. The package enables users to: (i) extract global and group-specific factors using a flexible multi-level factor structure; (ii) compute asymptotically valid confidence regions for the estimated factors, accounting for uncertainty in the factor loadings; (iii) obtain estimates of the parameters of the factor-augmented quantile regressions together with their standard deviations; (iv) recover full predictive conditional densities from estimated quantiles; (v) obtain risk measures based on extreme quantiles of the conditional densities; (vi) estimate the conditional density and the corresponding extreme quantiles when the factors are stressed.
Author: Gian Pietro Bellocca [aut, cre], Ignacio Garron [aut], Vladimir Rodriguez-Caballero [aut], Esther Ruiz [aut]
Maintainer: Gian Pietro Bellocca <gbellocc@est-econ.uc3m.es>

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Package BayesChange updated to version 2.1.2 with previous version 2.1.1 dated 2025-10-17

Title: Bayesian Methods for Change Points Analysis
Description: Perform change points detection on univariate and multivariate time series according to the methods presented by Asael Fabian Martínez and Ramsés H. Mena (2014) <doi:10.1214/14-BA878> and Corradin, Danese and Ongaro (2022) <doi:10.1016/j.ijar.2021.12.019>. It also clusters different types of time dependent data with common change points, see "Model-based clustering of time-dependent observations with common structural changes" (Corradin,Danese,KhudaBukhsh and Ongaro, 2024) <doi:10.48550/arXiv.2410.09552> for details.
Author: Luca Danese [aut, cre, cph] , Riccardo Corradin [aut], Andrea Ongaro [aut]
Maintainer: Luca Danese <l.danese1@campus.unimib.it>

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