spFFBS: Spatiotemporal Propagation for Multivariate Bayesian Dynamic Learning Implementation of the Forward Filtering Backward Sampling (FFBS) algorithm with Dynamic Bayesian Predictive Stacking (DYNBPS) integration for multivariate spatiotemporal models, as introduced in "Adaptive Markovian Spatiotemporal Transfer Learning in Multivariate Bayesian Modeling" (Presicce and Banerjee, 2026+) &lt;<a href="https://doi.org/10.48550%2FarXiv.2602.08544" target="_top">doi:10.48550/arXiv.2602.08544</a>&gt;. This methodology enables efficient Bayesian multivariate spatiotemporal modeling, utilizing dynamic predictive stacking to improve inference across multivariate time series of spatial datasets. The core functions leverage 'C++' for high-performance computation, making the framework well-suited for large-scale spatiotemporal data analysis in parallel computing environments.

New on CRAN: spFFBS (0.0-2). View at https://CRAN.R-project.org/package=spFFBS

scrutr: Scrutinizing Collections of Structured Datasets Provides a coherent interface for exploring and transforming multiple related data frames that share a common structure. Complements single-dataset inspection tools by operating across an entire collection at once. Also includes lightweight utilities for related file and folder management tasks.

New on CRAN: scrutr (0.3.0). View at https://CRAN.R-project.org/package=scrutr

RobustFlow: Robustness and Drift Auditing for Longitudinal Decision Systems Provides tools for constructing longitudinal decision paths, quantifying temporal drift, tracking subgroup disparity trajectories, and stress-testing longitudinal conclusions under hidden bias. Implements three signature metrics: the Drift Intensity Index (DII), which measures structural instability in transition dynamics using the Frobenius norm of consecutive transition matrix differences; the Bias Amplification Index (BAI), which quantifies whether group disparities widen or converge over time; and the Temporal Fragility Index (TFI), which estimates the minimum hidden-bias perturbation required to nullify a longitudinal trend conclusion. An interactive 'shiny' application supports exploratory analysis, visualization, and reproducible reporting. Methods are motivated by applications in educational and social science research, including the Early Childhood Longitudinal Study (ECLS). The DII is based on the Frobenius norm as described in Golub and Van Loan (2013, ISBN:9781421407944). The TFI extends the hidden-bias sensitivity framework of Rosenbaum (2002, ISBN:9781441912633). The BAI draws on disparity-trajectory methods discussed in Duncan and Murnane (2011, ISBN:9780871542731).

New on CRAN: RobustFlow (0.1.1). View at https://CRAN.R-project.org/package=RobustFlow

randompack: Fast Random Number Generation with Multiple Engines and Distributions Random number generation library implemented in C with multiple engines and distribution functions, providing an R interface focused on correctness, speed, and reproducibility. Supports various PRNGs including xoshiro256++/**, PCG64, Philox, and ChaCha20, with methods for continuous, discrete, and multivariate distributions.

New on CRAN: randompack (0.1.3). View at https://CRAN.R-project.org/package=randompack

melidosData: Load Data from the MeLiDos Field Study In the MeLiDos field study, personal light exposure data were collected in 9 sites, 7 countries, and 196 participants following the Guidolin et al. (2024) &lt;<a href="https://doi.org/10.1186%2Fs12889-024-20206-4" target="_top">doi:10.1186/s12889-024-20206-4</a>&gt; protocol. Data originate from wearable devices collecting personal light exposure at the eye level, chest, and the wrist. Questionnaires were collected via 'REDCap' and contain demographic information as well as chronotype, current conditions, sleep diaries, wear logs, and many more. This package makes loading the data from the respective repositories (&lt;<a href="https://github.com/MeLiDosProject" target="_top">https://github.com/MeLiDosProject</a>&gt;) into R a breeze. It further contains some quality of life functions for label handling and data from 'REDCap'.

New on CRAN: melidosData (1.0.6). View at https://CRAN.R-project.org/package=melidosData

IOBR: Immune Oncology Biological Research Provides six modules for tumor microenvironment (TME) analysis based on multi-omics data. These modules cover data preprocessing, TME estimation, TME infiltrating patterns, cellular interactions, genome and TME interaction, and visualization for TME relevant features, as well as modelling based on key features. It integrates multiple microenvironmental analysis algorithms and signature estimation methods, simplifying the analysis and downstream visualization of the TME. In addition to providing a quick and easy way to construct gene signatures from single-cell RNA-seq data, it also provides a way to construct a reference matrix for TME deconvolution from single-cell RNA-seq data. The analysis pipeline and feature visualization are user-friendly and provide a comprehensive description of the complex TME, offering insights into tumour-immune interactions (Zeng D, et al. (2024) &lt;<a href="https://doi.org/10.1016%2Fj.crmeth.2024.100910" target="_top">doi:10.1016/j.crmeth.2024.100910</a>&gt;. Fang Y, et al. (2025) &lt;<a href="https://doi.org/10.1002%2Fmdr2.70001" target="_top">doi:10.1002/mdr2.70001</a>&gt;).

New on CRAN: IOBR (2.2.0). View at https://CRAN.R-project.org/package=IOBR

factorana: Factor Model Estimation with Latent Variables A flexible framework for estimating factor models with multiple latent variables. Supports linear, probit, ordered probit, and multinomial logit model components. Features include multi-stage estimation, automatic parameter initialization, analytical gradients and Hessians, and parallel estimation. Methods are described in Heckman, Humphries, and Veramendi (2016) &lt;<a href="https://doi.org/10.1016%2Fj.jeconom.2015.12.001" target="_top">doi:10.1016/j.jeconom.2015.12.001</a>&gt;, Heckman, Humphries, and Veramendi (2018) &lt;<a href="https://doi.org/10.1086%2F698760" target="_top">doi:10.1086/698760</a>&gt;, and Humphries, Joensen, and Veramendi (2024) &lt;<a href="https://doi.org/10.1257%2Fpandp.20241026" target="_top">doi:10.1257/pandp.20241026</a>&gt;.

New on CRAN: factorana (1.2.0). View at https://CRAN.R-project.org/package=factorana

dcvar: Dynamic Copula VAR Models for Time-Varying Dependence Fits Bayesian copula vector autoregressive models for bivariate time series with dynamic, regime-switching, and constant dependence structures. The package includes simulation, data preparation, estimation with 'Stan' through 'rstan' or 'cmdstanr', posterior summaries, diagnostics, trajectory extraction, fitted and predictive summaries, and approximate leave-one-out cross-validation model comparison for supported fits. For Bayesian computation and model comparison, see Carpenter et al. (2017) &lt;<a href="https://doi.org/10.18637%2Fjss.v076.i01" target="_top">doi:10.18637/jss.v076.i01</a>&gt; and Vehtari, Gelman and Gabry (2017) &lt;<a href="https://doi.org/10.1007%2Fs11222-016-9696-4" target="_top">doi:10.1007/s11222-016-9696-4</a>&gt;.

New on CRAN: dcvar (0.1.0). View at https://CRAN.R-project.org/package=dcvar

Updates on CRAN: audubon (0.6.3), CDSim (0.1.2), fastDummies (1.7.6), ggforestplotR (0.1.1), ggmlR (0.7.6), ggtangle (0.1.2), INLAtools (0.1.3), nanotime (0.3.14), praatpicture (1.8.0), Rmpi (0.7-3.4), tok (0.2.2), torchvision (0.9.0)

Removed from CRAN: ART (1.0), FuzzyLP (0.1-7), gamlss.demo (4.3-3), gamlss.foreach (1.1-6), laketemps (0.5.1), localsolver (2.3), rethinker (1.1.0), rFerns (5.0.0), rtape (2.2), rtematres (0.2), SASmixed (1.0-4), SRCS (1.1), tuple (0.4-02), welchADF (0.3.2)

slxr: Spatial-X (SLX) Models for Applied Researchers Tools for estimating, interpreting, and visualizing Spatial-X (SLX) regression models. Provides a formula-based interface with first-class support for variable-specific weights matrices, higher-order spatial lags, temporally-lagged spatial variables (TSLS), and tidy effects decomposition (direct, indirect, total). Designed to lower the barrier to SLX modeling for applied researchers who already work with 'sf' and 'lm'-style formulas. Methods follow Wimpy, Whitten, and Williams (2021) &lt;<a href="https://doi.org/10.1086%2F710089" target="_top">doi:10.1086/710089</a>&gt;.

New on CRAN: slxr (0.1.1). View at https://CRAN.R-project.org/package=slxr

rCoreGage: Data Quality Check Framework for Clinical and Analytical Data A configuration-driven framework for running domain-level data quality checks and consolidating findings into structured Excel reports with role-based feedback routing. It supports trial-level and study-level checks across multiple data domains. Reports are routed to separate feedback channels for Data Management (DM), Medical Writing (MW), Study Data Tabulation Model (SDTM) programmers, and Analysis Data Model (ADaM) programmers, as well as other relevant data roles. Reviewer responses are incorporated automatically on re-run.

New on CRAN: rCoreGage (1.0.0). View at https://CRAN.R-project.org/package=rCoreGage

OdysseusCharacterizationModule: Handy and Minimalistic Common Data Model Characterization Extracts covariates from Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) domains using an R-only pipeline. Supports configurable temporal windows, domain-specific covariates for drug exposure, drug era (including Anatomical Therapeutic Chemical (ATC) groupings), condition occurrence, condition era, concept sets and cohorts. Methods are based on the Observational Health Data Sciences and Informatics (OHDSI) framework described in Hripcsak et al. (2015) &lt;<a href="https://doi.org/10.1038%2Fsdata.2015.35" target="_top">doi:10.1038/sdata.2015.35</a>&gt; and "The Book of OHDSI" OHDSI (2019, ISBN:978-1-7923-0589-8).

New on CRAN: OdysseusCharacterizationModule (0.0.1). View at CRAN.R-project.org/package=OdysseusCharacte...

NRMSampling: Sampling Design and Estimation Methods for Natural Resource Management Provides functions for probability and non-probability sampling design, sample selection, and population estimation tailored to natural resource management. Probability methods include simple random sampling, stratified sampling, systematic sampling, cluster sampling, and probability-proportional-to-size sampling. Non-probability methods include convenience, judgement-based, and quota sampling. Estimation functions cover means, totals, ratio estimators, regression estimators, and the unequal-probability estimator of Horvitz and Thompson (1952, &lt;<a href="https://doi.org/10.2307%2F2280784" target="_top">doi:10.2307/2280784</a>&gt;) for unequal-probability designs. Utilities support biomass, soil-loss, and carbon-stock estimation from field plots. Spatial extensions provide random, systematic, stratified, and raster-weighted sampling within geographic polygons using the 'sf' and 'terra' packages, with extraction of remote-sensing covariates at sample locations. Applications include forest inventory, soil erosion monitoring, watershed studies, and ecological field surveys.

New on CRAN: NRMSampling (0.2.2). View at https://CRAN.R-project.org/package=NRMSampling

mdbplyr: A Native Lazy Analytical Backend for MongoDB Provides a disciplined, lazy subset of 'dplyr' semantics for MongoDB aggregation pipelines. Queries remain lazy until collect() and compile into MongoDB-native aggregation stages.

New on CRAN: mdbplyr (0.3.0). View at https://CRAN.R-project.org/package=mdbplyr

llmclean: 'LLM'-Assisted Data Cleaning with Multi-Provider Support Detects and suggests fixes for semantic inconsistencies in data frames by calling large language models (LLMs) through a unified, provider-agnostic interface. Supported providers include 'OpenAI' ('GPT-4o', 'GPT-4o-mini'), 'Anthropic' ('Claude'), 'Google' ('Gemini'), 'Groq' (free-tier 'LLaMA' and 'Mixtral'), and local 'Ollama' models. The package identifies issues that rule-based tools cannot detect: abbreviation variants, typographic errors, case inconsistencies, and malformed values. Results are returned as tidy data frames with column, row index, detected value, issue type, suggested fix, and confidence score. An offline fallback using statistical and fuzzy-matching methods is provided for use without any API key. Interactive fix application with human review is supported via 'apply_fixes()'. Methods follow de Jonge and van der Loo (2013) &lt;<a href="https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf" target="_top">https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf</a>&gt; and Chaudhuri et al. (2003) &lt;<a href="https://doi.org/10.1145%2F872757.872796" target="_top">doi:10.1145/872757.872796</a>&gt;.

New on CRAN: llmclean (0.1.0). View at https://CRAN.R-project.org/package=llmclean

ivcheck: Tests for Instrumental Variable Validity Implements tests for the identifying assumptions of instrumental variable models, the local exclusion restriction and monotonicity conditions required for local average treatment effect identification. Covers Kitagawa (2015) &lt;<a href="https://doi.org/10.3982%2FECTA11974" target="_top">doi:10.3982/ECTA11974</a>&gt;, Mourifie and Wan (2017) &lt;<a href="https://doi.org/10.1162%2FREST_a_00622" target="_top">doi:10.1162/REST_a_00622</a>&gt;, and Frandsen, Lefgren, and Leslie (2023) &lt;<a href="https://doi.org/10.1257%2Faer.20201860" target="_top">doi:10.1257/aer.20201860</a>&gt;. Includes a one-shot wrapper that runs all applicable tests on a fitted instrumental variable model. Dispatches on 'fixest' and 'ivreg' model objects.

New on CRAN: ivcheck (0.1.1). View at https://CRAN.R-project.org/package=ivcheck

finlabR: Portfolio Analytics and Simulation Toolkit Tools for portfolio construction and risk analytics, including mean-variance optimization, conditional value at risk (expected shortfall) minimization, risk parity, regime clustering, correlation analysis, Monte Carlo simulation, and option pricing. Includes utilities for portfolio evaluation, clustering, and risk reporting. Methods are based in part on Markowitz (1952) &lt;<a href="https://doi.org/10.1111%2Fj.1540-6261.1952.tb01525.x" target="_top">doi:10.1111/j.1540-6261.1952.tb01525.x</a>&gt;, Rockafellar and Uryasev (2000) &lt;<a href="https://doi.org/10.21314%2FJOR.2000.038" target="_top">doi:10.21314/JOR.2000.038</a>&gt;, Maillard et al. (2010) &lt;<a href="https://doi.org/10.3905%2Fjpm.2010.36.4.060" target="_top">doi:10.3905/jpm.2010.36.4.060</a>&gt;, Black and Scholes (1973) &lt;<a href="https://doi.org/10.1086%2F260062" target="_top">doi:10.1086/260062</a>&gt;, and Cox et al. (1979) &lt;<a href="https://doi.org/10.1016%2F0304-405X%2879%2990015-1" target="_top">doi:10.1016/0304-405X(79)90015-1</a>&gt;.

New on CRAN: finlabR (1.0.0). View at https://CRAN.R-project.org/package=finlabR

Updates on CRAN: aftables (2.0.1), dvir (3.4.0), extraDistr (1.10.0.3), ggguides (1.1.5), ggseg3d (2.1.1), GREENeR (1.0.2), pkggraph (0.3.1), processx (3.9.0), RDesk (1.0.5), S7 (0.2.2), SCIproj (1.0.1), soilassessment (1.3.0), TesiproV (0.9.6)

weightederm: Weighted Empirical Risk Minimization for Changepoint Regression R interface to the 'weightederm' package for 'Python', which provides 'scikit-learn'-style estimators for offline change point regression (data segmentation) via weighted empirical risk minimization. Supports least-squares, Huber, and logistic losses with fixed or cross-validated numbers of change points. Wraps 'Python' via 'reticulate'. Arpino and Venkataramanan (2026) &lt;<a href="https://doi.org/10.48550%2FarXiv.2604.11746" target="_top">doi:10.48550/arXiv.2604.11746</a>&gt;.

New on CRAN: weightederm (0.1.0). View at https://CRAN.R-project.org/package=weightederm

wARMASVp: Winsorized ARMA Estimation for Higher-Order Stochastic Volatility Models Estimation, simulation, hypothesis testing, and forecasting for univariate higher-order stochastic volatility SV(p) models. Supports Gaussian, Student-t, and Generalized Error Distribution (GED) innovations, with optional leverage effects. Estimation uses closed-form Winsorized ARMA-SV (W-ARMA-SV) moment-based methods that avoid numerical optimization. Hypothesis testing includes Local Monte Carlo (LMC) and Maximized Monte Carlo (MMC) procedures for leverage effects, heavy tails, and autoregressive order selection. Forecasting is based on Kalman filtering and smoothing. See Ahsan and Dufour (2021) &lt;<a href="https://doi.org/10.1016%2Fj.jeconom.2020.01.018" target="_top">doi:10.1016/j.jeconom.2020.01.018</a>&gt;, Ahsan, Dufour, and Rodriguez Rondon (2025) for details.

New on CRAN: wARMASVp (0.1.0). View at https://CRAN.R-project.org/package=wARMASVp

ucminfcpp: 'C++' Reimplementation of the 'ucminf' Unconstrained Nonlinear Optimizer A modern 'C++17/ reimplementation of the 'UCMINF/ algorithm for unconstrained nonlinear optimization (Nielsen and Mortensen, 2011, &lt;<a href="https://doi.org/10.32614%2FCRAN.package.ucminf" target="_top">doi:10.32614/CRAN.package.ucminf</a>&gt;), offering full API compatibility with the original 'ucminf' R package but developed independently. The optimizer core has been rewritten in 'C' with a modern header-only 'C++17' interface, zero-allocation line search, and an 'Rcpp' interface. The goal is numerical equivalence with improved performance, reproducibility, and extensibility. Includes extensive test coverage, performance regression tests, and compatibility checks against 'ucminf'. This package is not affiliated with the original maintainers but acknowledges their authorship of the algorithm and the original R interface.

New on CRAN: ucminfcpp (1.0.0). View at https://CRAN.R-project.org/package=ucminfcpp

tinypkgr: Minimal R Package Development Utilities Lightweight wrappers around 'R CMD INSTALL', 'R CMD check', 'R CMD build', 'win-builder' uploads, and 'CRAN' submission. Provides functions for installing, loading, checking, building, and submitting R packages with minimal dependencies (only 'curl' for uploads). Background on R package development is in Wickham and Bryan (2023, ISBN:9781098134945), "Writing R Extensions" &lt;<a href="https://cran.r-project.org/doc/manuals/R-exts.html" target="_top">https://cran.r-project.org/doc/manuals/R-exts.html</a>&gt;, and the 'CRAN' Repository Policy &lt;<a href="https://cran.r-project.org/web/packages/policies.html" target="_top">https://cran.r-project.org/web/packages/policies.html</a>&gt;.

New on CRAN: tinypkgr (0.2.1). View at https://CRAN.R-project.org/package=tinypkgr

spopt: Spatial Optimization for Regionalization, Facility Location, and Market Analysis Implements spatial optimization algorithms across several problem families including contiguity-constrained regionalization, discrete facility location, market share analysis, and least-cost corridor and route optimization over raster cost surfaces. Facility location problems also accept user-supplied network travel-time matrices. Uses a 'Rust' backend via 'extendr' for graph and routing algorithms, and the 'HiGHS' solver via the 'highs' package for facility location mixed-integer programs. Method-level references are provided in the documentation of the individual functions.

New on CRAN: spopt (0.1.2). View at https://CRAN.R-project.org/package=spopt

jackstraw: Statistical Inference for Unsupervised Learning Test for association between the observed data and their estimated latent variables. The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related unsupervised learning algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq (scRNA-seq).

New on CRAN: jackstraw (1.3.21). View at https://CRAN.R-project.org/package=jackstraw

hanyupinyin: Convert Chinese Characters into Hanyu Pinyin Convert Chinese characters into Hanyu Pinyin (the official romanization system for Standard Chinese) with support for tones, toneless output, initials, URL slugs, and valid R variable names. The package was inspired by the now-orphaned CRAN package 'pinyin' (archived in April 2026 after the maintainer became unreachable). 'hanyupinyin' is a ground-up rewrite using the authoritative Unicode Unihan database, a vectorized engine, and modern R practices. Dictionary data are derived from the Unicode Unihan Database (Unicode Consortium, 2025) &lt;<a href="https://www.unicode.org/reports/tr38/" target="_top">https://www.unicode.org/reports/tr38/</a>&gt;.

New on CRAN: hanyupinyin (0.1.1). View at https://CRAN.R-project.org/package=hanyupinyin

GLMBasedRaschEstimation: GLM-Based Estimation for Rasch Model Parameters Provides functions for estimating Rasch model parameters using the Generalized Linear Model (GLM) framework. The methods implemented are based on Brown (2018, ISBN:978-3-319-93547-8) &lt;<a href="https://doi.org/10.1007%2F978-3-319-93549-2" target="_top">doi:10.1007/978-3-319-93549-2</a>&gt; and Debelak et al. (2022, ISBN:978-1-138-71046-7) &lt;<a href="https://doi.org/10.1201%2F9781315200620" target="_top">doi:10.1201/9781315200620</a>&gt;.

New on CRAN: GLMBasedRaschEstimation (0.1.2). View at CRAN.R-project.org/package=GLMBasedRaschEst...

EpiNova: Flexible Extended State-Space Epidemiological Models with Modern Inference An extended epidemiological modelling framework that goes beyond the classical SIR (Susceptible-Infectious-Recovered) model. Supports SEIR (Susceptible-Exposed-Infectious-Recovered), SEIRD (Susceptible-Exposed-Infectious-Recovered-Deceased), SVEIRD (Susceptible-Vaccinated-Exposed-Infectious-Recovered-Deceased), and age-stratified compartmental models with flexible intervention functions (spline-based, Gaussian process, or user-defined). Inference is available via maximum likelihood or sequential Monte Carlo (SMC, also known as particle filtering) with no external binary dependencies. Includes a dependency-free real-time effective reproduction number (Rt) estimator, spatial multi-patch models with gravity-model mobility, ensemble forecasting via Bayesian model averaging (BMA), and proper scoring rules including CRPS (Continuous Ranked Probability Score), coverage, and MAE (Mean Absolute Error) for forecast evaluation. Methods follow Anderson and May (1991, ISBN:9780198545996), Doucet, de Freitas, and Gordon (2001) &lt;<a href="https://doi.org/10.1007%2F978-1-4757-3437-9" target="_top">doi:10.1007/978-1-4757-3437-9</a>&gt;, Cori et al. (2013) &lt;<a href="https://doi.org/10.1093%2Faje%2Fkwt133" target="_top">doi:10.1093/aje/kwt133</a>&gt;, and Gneiting and Raftery (2007) &lt;<a href="https://doi.org/10.1198%2F016214506000001437" target="_top">doi:10.1198/016214506000001437</a>&gt;.

New on CRAN: EpiNova (0.1.0). View at https://CRAN.R-project.org/package=EpiNova

bunddev: Discover and Call 'Bund.dev' APIs Provides a registry of APIs listed on &lt;<a href="https://bund.dev" target="_top">https://bund.dev</a>&gt; and a core 'OpenAPI' client layer to explore specs and perform requests. Adapter helpers return tidy data frames for supported APIs, with optional response caching and rate limiting guidance.

New on CRAN: bunddev (0.2.3). View at https://CRAN.R-project.org/package=bunddev

Updates on CRAN: metacore (0.3.0), olsrr (0.7.0), qs2 (0.2.0), RChASM (1.0.1), rtables (0.6.16), Seurat (5.5.0), tweedie (3.0.19), utsf (1.3.3), valdr (3.0.0), vartest (1.5)