CRAN Updates

WBI: Wasserstein Bipolarization Index Computation of the Wasserstein Bipolarization Index as described in Lee and Sobel (Forthcoming) &lt;<a href="https://doi.org/10.48550%2FarXiv.2408.03331" target="_top">doi:10.48550/arXiv.2408.03331</a>&gt;. Provides both asymptotic (Sommerfeld, 2017 &lt;<a href="https://ediss.uni-goettingen.de/bitstream/handle/11858/00-1735-0000-0023-3FA1-C/DissertationSommerfeldRev.pdf?sequence=1" target="_top">https://ediss.uni-goettingen.de/bitstream/handle/11858/00-1735-0000-0023-3FA1-C/DissertationSommerfeldRev.pdf?sequence=1</a>&gt;) and bootstrap methods (Efron and Narasimhan, 2020 &lt;<a href="https://doi.org/10.1080%2F10618600.2020.1714633" target="_top">doi:10.1080/10618600.2020.1714633</a>&gt;) for calculating confidence intervals.

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

SHRED: Setwise Hierarchical Rate of Erroneous Discovery Setwise Hierarchical Rate of Erroneous Discovery (SHRED) methods for setwise variable selection with false discovery rate (FDR) control. Setwise variable selection means that sets of variables may be selected when the true variable cannot be identified. This allows us to maintain FDR control but increase power. Details of the SHRED methods are in Organ, Kenney &amp; Gu (2026) &lt;<a href="https://doi.org/10.48550%2FarXiv.2603.02160" target="_top">doi:10.48550/arXiv.2603.02160</a>&gt;.

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

rmsBMA: Reduced Model Space Bayesian Model Averaging Implements Bayesian model averaging for settings with many candidate regressors relative to the available sample size, including cases where the number of regressors exceeds the number of observations. By restricting attention to models with at most M regressors, the package supports reduced model space inference, thereby preserving degrees of freedom for estimation. It provides posterior summaries, Extreme Bounds Analysis, model selection procedures, joint inclusion measures, and graphical tools for exploring model probabilities, model size distributions, and coefficient distributions. The methodological approach follows Doppelhofer and Weeks (2009) &lt;<a href="https://doi.org/10.1002%2Fjae.1046" target="_top">doi:10.1002/jae.1046</a>&gt;.

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

RFmstate: Random Forest-Based Multistate Survival Analysis Fits cause-specific random survival forests for flexible multistate survival analysis with covariate-adjusted transition probabilities computed via product-integral. State transitions are modeled by random forests. Subject-specific transition probability matrices are assembled from predicted cumulative hazards using the product-integral formula. Also provides a standalone Aalen-Johansen nonparametric estimator as a covariate-free baseline. Supports arbitrary state spaces with any number of states (three or more) and any set of allowed transitions, applicable to clinical trials, disease progression, reliability engineering, and other domains where subjects move among discrete states over time. Provides per-transition feature importance, bias-variance diagnostics, and comprehensive visualizations. Handles right censoring and competing transitions. Methods are described in Ishwaran et al. (2008) &lt;<a href="https://doi.org/10.1214%2F08-AOAS169" target="_top">doi:10.1214/08-AOAS169</a>&gt; for random survival forests, Putter et al. (2007) &lt;<a href="https://doi.org/10.1002%2Fsim.2712" target="_top">doi:10.1002/sim.2712</a>&gt; for multistate competing risks decomposition, and Aalen and Johansen (1978) &lt;<a href="https://www.jstor.org/stable/4615704" target="_top">https://www.jstor.org/stable/4615704</a>&gt; for the nonparametric estimator.

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

ModalCens: Parametric Modal Regression with Right Censoring Implements parametric modal regression for continuous positive distributions of the exponential family under right censoring. Provides functions to link the conditional mode to a linear predictor using reparameterizations for Gamma, Beta, Weibull, and Inverse Gaussian families. Includes maximum likelihood estimation via numerical optimization, asymptotic inference based on the observed Fisher information matrix, and model diagnostics using randomized quantile residuals.

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

ICEHmeasures: The Equiplot Graph and Complex Inequality Measures Generates the equiplot, an iconic dot-plot graph for visualizing inequalities, as well as three complex inequality measures: the slope index of inequality, the concentration index and the mean absolute difference to the mean. For more details see World Health Organization (2013) &lt;<a href="https://www.who.int/docs/default-source/gho-documents/health-equity/handbook-on-health-inequality-monitoring/handbook-on-health-inequality-monitoring.pdf" target="_top">https://www.who.int/docs/default-source/gho-documents/health-equity/handbook-on-health-inequality-monitoring/handbook-on-health-inequality-monitoring.pdf</a>&gt;.

New on CRAN: ICEHmeasures (1.0.1). View at https://CRAN.R-project.org/package=ICEHmeasures

glasstabs: Animated Glass-Style Tabs and Multi-Select Filter for 'Shiny' Tools for creating animated glassmorphism-style tab navigation and multi-select dropdown filters in 'shiny' applications. The package provides a tab navigation component and a searchable multi-select widget with multiple checkbox indicator styles, select-all controls, and customizable colour themes. The widgets are compatible with standard 'shiny' layouts and 'bs4Dash' dashboards.

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

earthUI: Interactive 'shiny' GUI for the 'earth' Package Provides a 'shiny'-based graphical user interface for the 'earth' package, enabling interactive building and exploration of Multivariate Adaptive Regression Splines (MARS) models. Features include data import from CSV and 'Excel' files, automatic detection of categorical variables, interactive control of interaction terms via an allowed matrix, comprehensive model diagnostics with variable importance and partial dependence plots, and publication-quality report generation via 'Quarto'.

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

badp: Bayesian Averaging for Dynamic Panels Implements Bayesian model averaging for dynamic panels with weakly exogenous regressors as described in the paper by Moral-Benito (2013, &lt;<a href="https://doi.org/10.1080%2F07350015.2013.818003" target="_top">doi:10.1080/07350015.2013.818003</a>&gt;). The package provides functions to estimate dynamic panel data models and analyze the results of the estimation.

New on CRAN: badp (0.4.0). View at https://CRAN.R-project.org/package=badp

ardlverse: Comprehensive ARDL Modeling Framework: Panel, Bootstrap, Quantile-Nonlinear, and Fourier Extensions A unified framework for Autoregressive Distributed Lag (ARDL) modeling and cointegration analysis. Implements Panel ARDL with Pooled Mean Group (PMG), Mean Group (MG), and Dynamic Fixed Effects (DFE) estimators following Pesaran, Shin &amp; Smith (1999) &lt;<a href="https://doi.org/10.1002%2Fjae.616" target="_top">doi:10.1002/jae.616</a>&gt;. Provides bootstrap-based bounds testing per Pesaran, Shin &amp; Smith (2001) &lt;<a href="https://doi.org/10.1002%2Fjae.616" target="_top">doi:10.1002/jae.616</a>&gt;. Includes Quantile Nonlinear ARDL (QNARDL) combining distributional and asymmetric effects based on Shin, Yu &amp; Greenwood-Nimmo (2014) &lt;<a href="https://doi.org/10.1007%2F978-1-4899-8008-3_9" target="_top">doi:10.1007/978-1-4899-8008-3_9</a>&gt;, and Fourier ARDL for modeling smooth structural breaks following Enders &amp; Lee (2012) &lt;<a href="https://doi.org/10.1016%2Fj.econlet.2012.05.019" target="_top">doi:10.1016/j.econlet.2012.05.019</a>&gt;. Features include Augmented ARDL (AARDL) with deferred t and F tests, Multiple-Threshold NARDL for complex asymmetries, Rolling/Recursive ARDL for time-varying relationships, and Panel NARDL for nonlinear panel cointegration. All methods include comprehensive diagnostics, publication-ready outputs, and visualization tools.

New on CRAN: ardlverse (1.1.2). View at https://CRAN.R-project.org/package=ardlverse

Updates on CRAN: mvtnorm (1.3-5), nuggets (2.2.0), prodlim (2026.03.11), rdcmchecks (0.1.1), rjd3tramoseats (3.7.1), shinyBS (0.64.0)

Updates on CRAN: bayesics (2.1.1), ipd (0.4.1), rtestim (1.0.2)

piecemeal: Wrangle Large Simulation Studies An 'R6' class to set up, run, monitor, collate, and debug large simulation studies comprising many small independent replications and treatment configurations. Parallel processing, reproducibility, fault- and error-tolerance, and ability to resume an interrupted or timed-out simulation study are built in.

New on CRAN: piecemeal (0.2.0). View at https://CRAN.R-project.org/package=piecemeal

faux: Simulation for Factorial Designs Create datasets with factorial structure through simulation by specifying variable parameters. Extended documentation at &lt;<a href="https://www.scienceverse.org/faux/" target="_top">https://www.scienceverse.org/faux/</a>&gt;. Described in DeBruine (2020) &lt;<a href="https://doi.org/10.5281%2Fzenodo.2669586" target="_top">doi:10.5281/zenodo.2669586</a>&gt;.

New on CRAN: faux (1.2.4). View at https://CRAN.R-project.org/package=faux

Updates on CRAN: ILSAmerge (1.4.0), lotri (1.0.3), quickpsy (0.1.5.2), rjd3providers (3.7.1), rjd3x13 (3.7.1), sumer (1.3.0), units (1.0-1), WaterBalanceR (0.1.20), wrProteo (2.0.0.2)

Updates on CRAN: accrualPlot (1.0.10), astsa (2.5), blocking (1.0.2), cpmr (0.1.1), CSIndicators (1.2.0), cyclocomp (1.1.2), DDESONN (7.1.11), GGIRread (1.0.8), huge (1.5)

pgenlibr: 'PLINK' 2 Binary (.pgen) Reader A thin wrapper over 'PLINK' 2's core libraries which provides an R interface for reading .pgen files. A minimal .pvar loader is also included. Chang et al. (2015) &lt;<a href="https://doi.org/10.1186%2Fs13742-015-0047-8" target="_top">doi:10.1186/s13742-015-0047-8</a>&gt;.

New on CRAN: pgenlibr (0.5.5). View at https://CRAN.R-project.org/package=pgenlibr

llmjson: Repair Malformed JSON Strings Repairs malformed JSON strings, particularly those generated by Large Language Models. Handles missing quotes, trailing commas, unquoted keys, and other common JSON syntax errors.

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

easyRaschBayes: Bayesian Rasch Analysis Using 'brms' Reproduces classic Rasch psychometric analysis features using Bayesian item response theory models fitted with 'brms' following Bürkner (2021) &lt;<a href="https://doi.org/10.18637%2Fjss.v100.i05" target="_top">doi:10.18637/jss.v100.i05</a>&gt; and Bürkner (2020) &lt;<a href="https://doi.org/10.3390%2Fjintelligence8010005" target="_top">doi:10.3390/jintelligence8010005</a>&gt;. Supports both dichotomous and polytomous Rasch models. Features include posterior predictive item fit, conditional infit, item-restscore associations, person fit, differential item functioning, local dependence assessment via Q3 residual correlations, dimensionality assessment with residual principal components analysis, person-item targeting plots, item category probability curves, and reliability using relative measurement uncertainty following Bignardi et al. (2025) &lt;<a href="https://doi.org/10.31234%2Fosf.io%2Fh54k8_v1" target="_top">doi:10.31234/osf.io/h54k8_v1</a>&gt;.

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

Updates on CRAN: panelView (1.2.0), presize (0.3.11), randotools (0.2.6), RcppDE (0.1.9), rJava (1.0-15), rtmpinvi (0.2.0), tidyILD (0.3.0), tidyterra (1.1.0), yamlet (1.3.4)

Updates on CRAN: BIOMASS (2.2.7), countyhealthR (0.1.5), Directional (7.4), effectsize (1.0.2), fastkmedoids (1.5), LUCIDus (3.1.0), mlmRev (1.0-9), NNS (11.6.5)

Updates on CRAN: daltoolboxdp (1.2.747), eventreport (0.1.2), LLSR (0.0.4), mbg (1.1.2), ORscraper (0.1.1), qol (1.2.2), RivRetrieve (0.1.9), tramvs (0.0-9)

wefnexus: Water-Energy-Food-Nutrient-Carbon Nexus Analysis for Agronomic Systems Provides functions for analysing Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus interactions in agricultural production systems. Includes functions for computing water use efficiency (WUE), water productivity (WP), and water footprint (WF) including green, blue, and grey components following the methodology of Hoekstra et al. (2011, ISBN:9781849712798). Includes energy budgeting tools for energy use efficiency (EUE), energy return on investment (EROI), net energy (NE), and energy productivity (EP). Computes nutrient use efficiency (NUE) metrics including agronomic efficiency (AE), physiological efficiency (PE), recovery efficiency (RE), and partial factor productivity (PFP) as defined by Dobermann (2007) &lt;<a href="https://digitalcommons.unl.edu/agronomyfacpub/316/" target="_top">https://digitalcommons.unl.edu/agronomyfacpub/316/</a>&gt; and Congreves et al. (2021) &lt;<a href="https://doi.org/10.3389%2Ffpls.2021.637108" target="_top">doi:10.3389/fpls.2021.637108</a>&gt;. Estimates carbon footprint (CF), greenhouse gas (GHG) emissions, soil organic carbon (SOC) stocks, and global warming potential (GWP) using Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) default values (CH4 = 27, N2O = 273) as reported in Forster et al. (2021) &lt;<a href="https://doi.org/10.1017%2F9781009157896.009" target="_top">doi:10.1017/9781009157896.009</a>&gt;. Computes composite Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus indices, trade-off correlation matrices, and generates radar and heatmap visualizations for comparing agricultural treatments. Supports conservation agriculture (CA), irrigated and rain-fed systems, and arid and semi-arid production environments. Methods follow Lal (2004) &lt;<a href="https://doi.org/10.1016%2Fj.envint.2004.03.005" target="_top">doi:10.1016/j.envint.2004.03.005</a>&gt; for carbon emissions from farm operations, and Hoover et al. (2023) &lt;<a href="https://doi.org/10.1016%2Fj.scitotenv.2022.160992" target="_top">doi:10.1016/j.scitotenv.2022.160992</a>&gt; for water use efficiency indicators.

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

sparselu: Sparse LU Decomposition via SuiteSparse Provides an interface to the SuiteSparse UMFPACK LU factorisation routines for sparse matrices stored in compressed column format. Implements the algorithm described in Davis (2004) &lt;<a href="https://doi.org/10.1145%2F992200.992206" target="_top">doi:10.1145/992200.992206</a>&gt;.

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

rmdcev: Kuhn-Tucker and Multiple Discrete-Continuous Extreme Value Models Estimates and simulates Kuhn-Tucker demand models with individual heterogeneity. The package implements the multiple-discrete continuous extreme value (MDCEV) model and the Kuhn-Tucker specification common in the environmental economics literature on recreation demand. Latent class and random parameters specifications can be implemented and the models are fit using maximum likelihood estimation or Bayesian estimation. All models are implemented in 'Stan' (see Stan Development Team, 2019) &lt;<a href="https://mc-stan.org/" target="_top">https://mc-stan.org/</a>&gt;. The package also implements demand forecasting (Pinjari and Bhat (2011) &lt;<a href="https://repositories.lib.utexas.edu/handle/2152/23880" target="_top">https://repositories.lib.utexas.edu/handle/2152/23880</a>&gt;) and welfare calculation (Lloyd-Smith (2018) &lt;<a href="https://doi.org/10.1016%2Fj.jocm.2017.12.002" target="_top">doi:10.1016/j.jocm.2017.12.002</a>&gt;) for policy simulation. 'Stan' models can be estimated using either the 'cmdstanr' (default) or 'rstan' backend. If using 'cmdstanr', then user will need to install 'cmdstanr' manually &lt;<a href="https://mc-stan.org/cmdstanr/" target="_top">https://mc-stan.org/cmdstanr/</a>&gt;.

New on CRAN: rmdcev (1.3.0). View at https://CRAN.R-project.org/package=rmdcev

rapidcodeR: Optimized Data Analysis System for AI-Based Text Processing Extracts machine-readable variables from natural language text using AI APIs. Optimized for speed and cost efficiency through parallel processing and direct CSV-formatted responses from language models. Supports multiple AI providers with robust error handling and automatic retry mechanisms for failed extractions.

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

GMX: Extended Graphical Model Checks for the Rasch Family of Models The function plotLRT() draws pairwise graphical model checks for the Rasch Model (RM; Rasch, 1960), the Partial Credit Model(PCM; Masters, 1982), and the Rating Scale Model (RSM; Andrich, 1978) using the output object of eRm::LRtest(). The function cLRT() provides a conditional Likelihood Ratio Test (Andersen, 1973), using the routines of 'psychotools'. Users may choose to plot the threshold parameters, the cumulative thresholds, the average thresholds per item, or the person parameters. Extended coloring options allow for automated item-wise or threshold-wise coloring. For multi-group splits, all pairwise group comparisons are drawn automatically. For more details see Andersen (1973) &lt;<a href="https://doi.org/10.1007%2FBF02291180" target="_top">doi:10.1007/BF02291180</a>&gt;, Andrich (1978) &lt;<a href="https://doi.org/10.1007%2FBF02293814" target="_top">doi:10.1007/BF02293814</a>&gt;, Masters (1982) &lt;<a href="https://doi.org/10.1007%2FBF02296272" target="_top">doi:10.1007/BF02296272</a>&gt; and Rasch (1960, ISBN:9780598554512).

New on CRAN: GMX (0.9-2). View at https://CRAN.R-project.org/package=GMX

ggsky: Galactic and Equatorial Coordinate Implementation for 'ggplot2' Simple tools to draw sky maps in 'ggplot2' using galactic or equatorial coordinates. Includes custom coordinate systems, grid labels, and helpers for sky map breaks.

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

DTFM: Distributed Online Covariance Matrix Tests for Truncated Factor Model The truncated factor model is a statistical model designed to handle specific data structures in data analysis. 'DTFM' is a powerful tool designed to efficiently process and analyze distributed datasets. The philosophy of the package is described in Guo et al. (2023) &lt;<a href="https://doi.org/10.1007%2Fs00180-022-01270-z" target="_top">doi:10.1007/s00180-022-01270-z</a>&gt;.

New on CRAN: DTFM (0.1.5). View at https://CRAN.R-project.org/package=DTFM

bs4Dashkit: Branding, Theme Application and Navigation Utilities for 'bs4Dash' Dashboards Provides branding, theme application, and navigation utilities for applications built with 'bs4Dash' and 'shiny'. Supports configurable sidebar brand display modes, hover-expand behavior, and theme customization using CSS variables. Includes standardized navigation components such as refresh and help controls, along with helpers for common navigation bar and footer layouts.

New on CRAN: bs4Dashkit (0.1.0). View at https://CRAN.R-project.org/package=bs4Dashkit