CRAN Updates

xtpqardl: Panel Quantile Autoregressive Distributed Lag Model Estimation of Panel Quantile Autoregressive Distributed Lag (PQARDL) models that combine panel ARDL methodology with quantile regression. Supports Pooled Mean Group (PMG), Mean Group (MG), and Dynamic Fixed Effects (DFE) estimators across multiple quantiles. Computes long-run cointegrating parameters, error correction term speed of adjustment, half-life of adjustment, and performs Wald tests for parameter equality across quantiles. Based on the econometric frameworks of Pesaran, Shin, and Smith (1999) &lt;<a href="https://doi.org/10.1080%2F01621459.1999.10474156" target="_top">doi:10.1080/01621459.1999.10474156</a>&gt;, Cho, Kim, and Shin (2015) &lt;<a href="https://doi.org/10.1016%2Fj.jeconom.2015.02.030" target="_top">doi:10.1016/j.jeconom.2015.02.030</a>&gt;, and Bildirici and Kayikci (2022) &lt;<a href="https://doi.org/10.1016%2Fj.energy.2022.124303" target="_top">doi:10.1016/j.energy.2022.124303</a>&gt;.

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

xtdhcoint: Durbin-Hausman Panel Cointegration Tests Implements the Durbin-Hausman panel cointegration tests of Westerlund (2008) &lt;<a href="https://doi.org/10.1002%2Fjae.963" target="_top">doi:10.1002/jae.963</a>&gt;. The tests are robust to cross-sectional dependence through common factor extraction using principal components. Provides both group-mean (DHg) and panel (DHp) test statistics with automatic factor number selection via information criteria.

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

wstats: Weighted Descriptive Statistics Weighted versions of common descriptive statistics (variance, standard deviation, covariance, correlation, quantiles).

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

tempodisco: Temporal Discounting Models Tools for working with temporal discounting data, designed for behavioural researchers to simplify data cleaning/scoring and model fitting. The package implements widely used methods such as computing indifference points from adjusting amount task (Frye et al., 2016, &lt;<a href="https://doi.org/10.3791%2F53584" target="_top">doi:10.3791/53584</a>&gt;), testing for non-systematic discounting per the criteria of Johnson &amp; Bickel (2008, &lt;<a href="https://doi.org/10.1037%2F1064-1297.16.3.264" target="_top">doi:10.1037/1064-1297.16.3.264</a>&gt;), scoring questionnaires according to the methods of Kirby et al. (1999, &lt;<a href="https://doi.org/10.1037%2F%2F0096-3445.128.1.78" target="_top">doi:10.1037//0096-3445.128.1.78</a>&gt;) and Wileyto et al (2004, &lt;<a href="https://doi.org/10.3758%2FBF03195548" target="_top">doi:10.3758/BF03195548</a>&gt;), Bayesian model selection using a range of discount functions (Franck et al., 2015, &lt;<a href="https://doi.org/10.1002%2Fjeab.128" target="_top">doi:10.1002/jeab.128</a>&gt;), drift diffusion models of discounting (Peters &amp; D'Esposito, 2020, &lt;<a href="https://doi.org/10.1371%2Fjournal.pcbi.1007615" target="_top">doi:10.1371/journal.pcbi.1007615</a>&gt;), and model-agnostic measures of discounting such as area under the curve (Myerson et al., 2001, &lt;<a href="https://doi.org/10.1901%2Fjeab.2001.76-235" target="_top">doi:10.1901/jeab.2001.76-235</a>&gt;) and ED50 (Yoon &amp; Higgins, 2008, &lt;<a href="https://doi.org/10.1016%2Fj.drugalcdep.2007.12.011" target="_top">doi:10.1016/j.drugalcdep.2007.12.011</a>&gt;).

New on CRAN: tempodisco (2.1.0). View at https://CRAN.R-project.org/package=tempodisco

rMIDAS2: Multiple Imputation with 'MIDAS2' Denoising Autoencoders Fits 'MIDAS' denoising autoencoder models for multiple imputation of missing data, generates multiply-imputed datasets, computes imputation means, and runs Rubin's rules regression analysis. Wraps the 'MIDAS2' 'Python' engine via a local 'FastAPI' server over 'HTTP', so no 'reticulate' dependency is needed at runtime. Methods are described in Lall and Robinson (2022) &lt;<a href="https://doi.org/10.1017%2Fpan.2020.49" target="_top">doi:10.1017/pan.2020.49</a>&gt; and Lall and Robinson (2023) &lt;<a href="https://doi.org/10.18637%2Fjss.v107.i09" target="_top">doi:10.18637/jss.v107.i09</a>&gt;.

New on CRAN: rMIDAS2 (0.1.1). View at https://CRAN.R-project.org/package=rMIDAS2

readoecd: Download and Tidy Data from the OECD Provides clean, tidy access to key economic indicators published by the Organisation for Economic Co-operation and Development (OECD), covering GDP, CPI inflation, unemployment, tax revenue, government deficit, health expenditure, education expenditure, income inequality, labour productivity, and current account balance across all 38 OECD member countries. Data is downloaded from the OECD Data Explorer API &lt;<a href="https://data-explorer.oecd.org" target="_top">https://data-explorer.oecd.org</a>&gt; on first use and cached locally for subsequent calls. Returns tidy long-format data frames ready for analysis and visualisation.

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

PubMatrixR: PubMed Pairwise Co-Occurrence Matrix Construction and Visualization Queries the 'NCBI' (National Center for Biotechnology Information) Entrez 'E-utilities' API to count pairwise co-occurrences between two sets of terms in 'PubMed' or 'PubMed Central'. It returns a matrix-like data frame of publication counts and can export hyperlink-enabled results in CSV or ODS format. The package also provides heatmap helpers for exploratory visualization of overlap patterns. Based on the method described in Becker et al. (2003) "PubMatrix: a tool for multiplex literature mining" &lt;<a href="https://doi.org/10.1186%2F1471-2105-4-61" target="_top">doi:10.1186/1471-2105-4-61</a>&gt;.

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

ons: Download Data from the Office for National Statistics Provides functions to download and tidy statistical data published by the Office for National Statistics &lt;<a href="https://www.ons.gov.uk" target="_top">https://www.ons.gov.uk</a>&gt;. Covers GDP, inflation (CPI, CPIH, RPI), unemployment, employment, wages, trade, retail sales, house prices, productivity, population, and public sector finances. Most series are fetched from the ONS website using its CSV time series endpoint. House price data is sourced from HM Land Registry &lt;<a href="https://www.gov.uk/government/organisations/land-registry" target="_top">https://www.gov.uk/government/organisations/land-registry</a>&gt;. Data is cached locally between sessions.

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

lmmprobe: Sparse High-Dimensional Linear Mixed Modeling with a Partitioned Empirical Bayes ECM Algorithm Implements a partitioned Empirical Bayes Expectation Conditional Maximization (ECM) algorithm for sparse high-dimensional linear mixed modeling as described in Zgodic, Bai, Zhang, and McLain (2025) &lt;<a href="https://doi.org/10.1007%2Fs11222-025-10649-z" target="_top">doi:10.1007/s11222-025-10649-z</a>&gt;. The package provides efficient estimation and inference for mixed models with high-dimensional fixed effects.

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

INCVCommunityDetection: Inductive Node-Splitting Cross-Validation for Community Detection Implements Inductive Node-Splitting Cross-Validation (INCV) for selecting the number of communities in stochastic block models. Provides f-fold and random-split node-level cross-validation, along with competing methods including CROISSANT, Edge Cross-Validation (ECV), and Node Cross-Validation (NCV). Supports both SBM and Degree-Corrected Block Models (DCBM), with multiple loss functions (L2, binomial deviance, AUC). Includes network simulation utilities for SBM, RDPG, and latent space models.

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

hmrc: Download and Tidy HMRC Statistical Data Provides functions to download, parse, and tidy statistical data published by HM Revenue and Customs (HMRC) on GOV.UK. Covers monthly tax receipts (41 tax heads from 2016), VAT (from 1973), fuel duties (from 1990), tobacco duties (from 1991), annual Corporation Tax receipts, stamp duty, research and development tax credit statistics (from 2000), tax gap estimates, Income Tax liabilities by income range, and monthly property transaction counts. File URLs are resolved at runtime via the GOV.UK Content API &lt;<a href="https://www.gov.uk/api/content" target="_top">https://www.gov.uk/api/content</a>&gt;, so data is always current without hardcoded URLs. Files are cached locally between sessions.

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

fbardl: Fourier Bootstrap ARDL Cointegration Test Implements the Fourier Bootstrap Autoregressive Distributed Lag (FBARDL) bounds testing approach for cointegration analysis. Combines the Pesaran, Shin &amp; Smith (2001) &lt;<a href="https://doi.org/10.1002%2Fjae.616" target="_top">doi:10.1002/jae.616</a>&gt; ARDL bounds testing framework with Fourier terms to capture structural breaks following Yilanci, Bozoklu &amp; Gorus (2020) &lt;<a href="https://doi.org/10.1080%2F00036846.2019.1686454" target="_top">doi:10.1080/00036846.2019.1686454</a>&gt;, and bootstrap critical values based on McNown, Sam &amp; Goh (2018) &lt;<a href="https://doi.org/10.1080%2F00036846.2017.1366643" target="_top">doi:10.1080/00036846.2017.1366643</a>&gt; and Bertelli, Vacca &amp; Zoia (2022) &lt;<a href="https://doi.org/10.1016%2Fj.econmod.2022.105987" target="_top">doi:10.1016/j.econmod.2022.105987</a>&gt;. Features include automatic lag selection via AIC/BIC, optimal Fourier frequency selection by minimum SSR, long-run and short-run coefficient estimation, diagnostic tests, and dynamic multiplier analysis.

New on CRAN: fbardl (1.0.2). View at https://CRAN.R-project.org/package=fbardl

boe: Download Data from the Bank of England Statistical Database Provides functions to download and tidy statistical data published by the Bank of England &lt;<a href="https://www.bankofengland.co.uk" target="_top">https://www.bankofengland.co.uk</a>&gt;. Covers Bank Rate, SONIA, gilt yields, exchange rates, mortgage rates, mortgage approvals, consumer credit, and money supply. Series are fetched from the Bank of England Interactive Statistical Database using its CSV endpoint. Data is cached locally between sessions.

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

Updates on CRAN: daltoolbox (1.3.727), dartR.base (1.2.2), healthiar (0.2.4), mixtur (1.2.3), MPGE (1.0.1), msigdbr (26.1.0), rpanel (1.1-6), rswipl (10.1.5), SHARK4R (1.1.1), SQUAREM (2026.1), stratallo (3.0.0), TriLLIEM (0.1.1)

Updates on CRAN: baseq (0.2.0), IxPopDyMod (0.4.0), orthanc (0.2.0)

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)