CRAN Updates

Removed from CRAN: lwqs (0.5.0)

Updates on CRAN: derivmkts (0.2.5.1), ggOceanMaps (2.3.0), ggvis (0.4.10), ipft (0.7.3), nomisdata (0.1.2), rollupTree (0.4.1), semhelpinghands (0.1.14), taylor (4.0.0), TreatmentPatterns (3.1.2), xgboost (3.2.0.1)

Updates on CRAN: baggr (0.8), BioTIMEr (0.3.1), EEAaq (1.0.3), fEGarch (1.0.6), robcat (0.2), SaturnCoefficient (1.6), XML (3.99-0.22)

Updates on CRAN: CTT (2.3.4), daltoolbox (1.3.717), echarts4r (0.5.0), fastText (1.0.6), fastTextR (2.1.1), ggFishPlots (0.4.0), memoria (1.1.0), nanoarrow (0.8.0), nfl4th (1.0.5), NMAforest (0.1.3), SimDesign (2.23), timeplyr (1.1.2)

Removed from CRAN: nycOpenData (0.1.5), PUlasso (3.2.5)

Updates on CRAN: calcal (1.0.2), commecometrics (1.1.1), eoffice (0.2.3)

tidywikidatar: Explore 'Wikidata' Through Tidy Data Frames Query 'Wikidata' API &lt;<a href="https://www.wikidata.org/wiki/Wikidata:Main_Page" target="_top">https://www.wikidata.org/wiki/Wikidata:Main_Page</a>&gt; with ease, get tidy data frames in response, and cache data in a local database.

New on CRAN: tidywikidatar (0.6.1). View at https://CRAN.R-project.org/package=tidywikidatar

Updates on CRAN: arkdb (0.0.19), froggeR (1.0.0), GALLO (2.0), OmopConstructor (0.2.0), plinkQC (1.0.1), pprof (1.0.3), rapsimng (0.4.6), valytics (0.4.0)

tost.suite: Two One-Sided Tests for Equivalence Ports the 'Stata' ado package 'tost' which provides a suite of commands to perform two one-sided tests for equivalence following the approach by Schuirman (1987) &lt;<a href="https://doi.org/10.1007%2FBF01068419" target="_top">doi:10.1007/BF01068419</a>&gt;. Commands are provided for t tests on means, z tests on proportions, McNemar's test (1947) &lt;<a href="https://doi.org/10.1007%2FBF02295996" target="_top">doi:10.1007/BF02295996</a>&gt; on proportions and related tests, tests on the regression coefficients from OLS linear regression (not yet implementing all of the current regression options from the 'Stata' 'tostregress' command, e.g., survey regression options, estimation options, etc.), Wilcoxon's (1945) &lt;<a href="https://doi.org/10.2307%2F3001968" target="_top">doi:10.2307/3001968</a>&gt; signed rank tests, Wilcoxon-Mann-Whitney (1947) &lt;<a href="https://doi.org/10.1214%2Faoms%2F1177730491" target="_top">doi:10.1214/aoms/1177730491</a>&gt; rank sum tests, supporting inference about equivalence for a number of paired and unpaired, parametric and nonparametric study designs and data types. Each command tests a null hypothesis that samples were drawn from populations different by at least plus or minus some researcher-defined level of tolerance, which can be defined in terms of units of the data or rank units (Delta), or in units of the test statistic's distribution (epsilon) except for tost.rrp() and tost.rrpi(). Enough evidence rejects this null hypothesis in favor of equivalence within the tolerance. Equivalence intervals for all tests may be defined symmetrically or asymmetrically.

New on CRAN: tost.suite (3.1.9). View at https://CRAN.R-project.org/package=tost.suite

multigroup.vaccine: Analyze Outbreak Models of Multi-Group Populations with Vaccination Model infectious disease dynamics in populations with multiple subgroups having different vaccination rates, transmission characteristics, and contact patterns. Calculate final and intermediate outbreak sizes, form age-structured contact models with automatic fetching of U.S. census data, and explore vaccination scenarios with an interactive 'shiny' dashboard for a model with two subgroups, as described in Nguyen et al. (2024) &lt;<a href="https://doi.org/10.1016%2Fj.jval.2024.03.039" target="_top">doi:10.1016/j.jval.2024.03.039</a>&gt; and Duong et al. (2026) &lt;<a href="https://doi.org/10.1093%2Fofid%2Fofaf695.217" target="_top">doi:10.1093/ofid/ofaf695.217</a>&gt;.

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

hemat: Hematimetric Indices Calculator Tools to calculate Mean Corpuscular Volume, Mean Corpuscular Hemoglobin, and Mean Corpuscular Hemoglobin Concentration, which are essential for assessing red blood cell health and diagnosing blood disorders.

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

heaping: Correction of Heaping on Individual Level Provides methods for correcting heaping (digit preference) in survey data at the individual record level. Age heaping, where respondents disproportionately report ages ending in 0 or 5, is a common phenomenon that can distort demographic analyses. Unlike traditional smoothing methods that only correct aggregated statistics, this package corrects individual values by replacing a calculated proportion of heaped observations with draws from fitted truncated distributions (log-normal, normal, or uniform). Supports 5-year and 10-year heaping patterns, single heap correction, and optional model-based adjustment to preserve covariate relationships.

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

ggmlR: 'GGML' Tensor Operations for Machine Learning Provides 'R' bindings to the 'GGML' tensor library for efficient machine learning computation. Implements core tensor operations including element-wise arithmetic, reshaping, and matrix multiplication. Supports neural network layers (attention, convolutions, normalization), activation functions, and quantization. Features optimization/training API with 'AdamW' (Adam with Weight decay) and 'SGD' (Stochastic Gradient Descent) optimizers, 'MSE' (Mean Squared Error) and cross-entropy losses. Multi-backend support with CPU and optional 'Vulkan' GPU (Graphics Processing Unit) acceleration. See &lt;<a href="https://github.com/ggml-org/ggml" target="_top">https://github.com/ggml-org/ggml</a>&gt; for more information about the underlying library.

New on CRAN: ggmlR (0.5.1). View at https://CRAN.R-project.org/package=ggmlR

GAReg: Genetic Algorithms in Regression Provides a genetic algorithm framework for regression problems requiring discrete optimization over model spaces with unknown or varying dimension, where gradient-based methods and exhaustive enumeration are impractical. Uses a compact chromosome representation for tasks including spline knot placement and best-subset variable selection, with constraint-preserving crossover and mutation, exact uniform initialization under spacing constraints, steady-state replacement, and optional island-model parallelization from Lu, Lund, and Lee (2010, &lt;<a href="https://doi.org/10.1214%2F09-AOAS289" target="_top">doi:10.1214/09-AOAS289</a>&gt;). The computation is built on the 'GA' engine of Scrucca (2017, &lt;<a href="https://doi.org/10.32614%2FRJ-2017-008" target="_top">doi:10.32614/RJ-2017-008</a>&gt;) and 'changepointGA' engine from Li and Lu (2024, &lt;<a href="https://doi.org/10.48550%2FarXiv.2410.15571" target="_top">doi:10.48550/arXiv.2410.15571</a>&gt;). In challenging high-dimensional settings, 'GAReg' enables efficient search and delivers near-optimal solutions when alternative algorithms are not well-justified.

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

egnyte: Read and Write Files from 'Egnyte' Provides functions to read and write files from 'Egnyte' cloud storage using the 'Egnyte' API &lt;<a href="https://developers.egnyte.com/docs" target="_top">https://developers.egnyte.com/docs</a>&gt;. Supports both API key and 'OAuth' 2.0 authentication for file transfer operations.

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

appraise: Bias-Aware Evidence Synthesis in Systematic Reviews Implements a bias-aware framework for evidence synthesis in systematic reviews and health technology assessments, as described in Kabali (2025) &lt;<a href="https://doi.org/10.1111%2Fjep.70272" target="_top">doi:10.1111/jep.70272</a>&gt;. The package models study-level effect estimates by explicitly accounting for multiple sources of bias through prior distributions and propagates uncertainty using posterior simulation. Evidence across studies is combined using posterior mixture distributions rather than a single pooled likelihood, enabling probabilistic inference on clinically or policy-relevant thresholds. The methods are designed to support transparent decision-making when study relevance and bias vary across the evidence base.

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

Updates on CRAN: adapt3 (2.0.0), frontmatter (0.2.0), OmopViewer (0.7.0), qrlabelr (0.2.1), tarchetypes (0.14.0), targets (1.12.0), tidycensus (1.7.5)

Updates on CRAN: BCSreg (1.1.1), deepgp (1.2.1), historicalborrow (1.1.1), sentencepiece (0.2.5), SIBER (2.1.10), ThreeWay (1.1.4)

toonlite: Read, Write, Validate, Stream, and Convert TOON Data A minimal-dependency, performance-first R package for reading, writing, validating, streaming, and converting TOON (Token-Oriented Object Notation) data. Optimized for very large tabular files with robust diagnostics. Supports lossless JSON conversion and tabular CSV/Parquet/Feather conversion.

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

tabbitR: Weighted Cross-Tabulations Exported to 'Excel' Produces weighted cross-tabulation tables for one or more outcome variables across one or more breakdown variables, and exports them directly to 'Excel'. For each outcome-by-breakdown combination, the package creates a weighted percentage table and a corresponding unweighted count table, with transparent handling of missing values and light, readable formatting. Designed to support social survey analysis workflows that require large sets of consistent, publication-ready tables.

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

statuser: Statistical Tools Designed for End Users The statistical tools in this package do one of four things: 1) Enhance basic statistical functions with more flexible inputs, smarter defaults, and richer, clearer, and ready-to-use output (e.g., t.test2()) 2) Produce publication-ready commonly needed figures with one line of code (e.g., plot_cdf()) 3) Implement novel analytical tools developed by the authors (e.g., twolines()) 4) Deliver niche functions of high value to the authors that are not easily available elsewhere (e.g., clear(), convert_to_sql(), resize_images()).

New on CRAN: statuser (0.1.8). View at https://CRAN.R-project.org/package=statuser

ohvbd: One Health VBD Hub Interface with the One Health VBD (vector-borne disease) Hub &lt;<a href="https://vbdhub.org/" target="_top">https://vbdhub.org/</a>&gt; and related repositories (VectorByte &lt;<a href="https://www.vectorbyte.org" target="_top">https://www.vectorbyte.org</a>&gt;, GBIF &lt;<a href="https://www.gbif.org" target="_top">https://www.gbif.org</a>&gt; and AREAdata &lt;<a href="https://pearselab.github.io/areadata/" target="_top">https://pearselab.github.io/areadata/</a>&gt;) directly to find, download, and subset vector-borne disease data.

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

ocrRBBR: Explain Gene Expression with Boolean Rules of Chromatin States Infers Boolean rules among cis-regulatory regions using paired chromatin accessibility and gene expression data at bulk and single-cell levels. Links regulatory regions to target genes, providing insights into gene regulation mechanisms.

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

mvfmr: Functional Multivariable Mendelian Randomization Implements Multivariable Functional Mendelian Randomization (MV-FMR) to estimate time-varying causal effects of multiple longitudinal exposures on health outcomes. Extends univariable functional Mendelian Randomisation (MR) (Tian et al., 2024 &lt;<a href="https://doi.org/10.1002%2Fsim.10222" target="_top">doi:10.1002/sim.10222</a>&gt;) to the multivariable setting, enabling joint estimation of multiple time-varying exposures with pleiotropy and mediation scenarios. Key features include: (1) data-driven cross-validation for basis component selection, (2) handling of mediation pathways between exposures, (3) support for both continuous and binary outcomes using Generalized Method of Moments (GMM) and control function approaches, (4) one-sample and two-sample MR designs, (5) bootstrap inference and instrument diagnostics including Q-statistics for overidentification testing. Methods are described in Fontana et al. (2025) &lt;<a href="https://doi.org/10.48550%2FarXiv.2512.19064" target="_top">doi:10.48550/arXiv.2512.19064</a>&gt;.

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

MaxIntTools: Testing Maximal Interaction in Two-Mode Clustering via a Permutation Based Procedure Performs maximal interaction two-mode clustering, permutation tests, scree plots, and interaction visualizations for bicluster analysis. See Ahmed et al. (2025) &lt;<a href="https://doi.org/10.17605%2FOSF.IO%2FAWGXB" target="_top">doi:10.17605/OSF.IO/AWGXB</a>&gt;, Ahmed et al. (2023) &lt;<a href="https://doi.org/10.1007%2Fs00357-023-09434-2" target="_top">doi:10.1007/s00357-023-09434-2</a>&gt;, Ahmed et al. (2021) &lt;<a href="https://doi.org/10.1007%2Fs11634-021-00441-y" target="_top">doi:10.1007/s11634-021-00441-y</a>&gt;.

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

lpmec: Measurement Error Analysis and Correction Under Identification Restrictions Implements methods for analyzing latent variable models with measurement error correction, including Item Response Theory (IRT) models. Provides tools for various correction methods such as Bayesian Markov Chain Monte Carlo (MCMC), over-imputation, bootstrapping for robust standard errors, Ordinary Least Squares (OLS), and Instrumental Variables (IV) based approaches. Supports flexible specification of observable indicators and groupings for latent variable analyses in social sciences and other fields. Methods are described in a working paper (2025) &lt;<a href="https://doi.org/10.48550%2FarXiv.2507.22218" target="_top">doi:10.48550/arXiv.2507.22218</a>&gt;.

New on CRAN: lpmec (1.1.4). View at https://CRAN.R-project.org/package=lpmec

HaDeX2: Analysis and Visualisation of Hydrogen/Deuterium Exchange Mass Spectrometry Data Processing, analysis and visualization of Hydrogen Deuterium eXchange monitored by Mass Spectrometry experiments (HDX-MS). 'HaDeX2' introduces a new standardized and reproducible workflow for the analysis of the HDX-MS data, including uncertainty propagation, data aggregation and visualization on 3D structure. Additionally, it covers data exploration, quality control and generation of publication-quality figures. All functionalities are also available in the accompanying 'shiny' app.

New on CRAN: HaDeX2 (1.0.0). View at https://CRAN.R-project.org/package=HaDeX2

coreheat: Correlation Heatmaps Create correlation heatmaps from a numeric matrix. Ensembl Gene ID row names can be converted to Gene Symbols using, e.g., BioMart. Optionally, data can be clustered and filtered by correlation, tree cutting and/or number of missing values. Genes of interest can be highlighted in the plot and correlation significance be indicated by asterisks encoding corresponding P-Values. Plot dimensions and label measures are adjusted automatically by default. The plot features rely on the heatmap.n2() function in the 'heatmapFlex' package.

New on CRAN: coreheat (0.3.2). View at https://CRAN.R-project.org/package=coreheat

compositional.mle: Compositional Maximum Likelihood Estimation Provides composable optimization strategies for maximum likelihood estimation (MLE). Solvers are first-class functions that combine via sequential chaining, parallel racing, and random restarts. Implements gradient ascent, Newton-Raphson, quasi-Newton (BFGS), and derivative-free methods with support for constrained optimization and tracing. Returns 'mle' objects compatible with 'algebraic.mle' for downstream analysis. Methods based on Nocedal J, Wright SJ (2006) "Numerical Optimization" &lt;<a href="https://doi.org/10.1007%2F978-0-387-40065-5" target="_top">doi:10.1007/978-0-387-40065-5</a>&gt;.

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

AUKtest: Calculate the AUK Estimator Computes the Area Under the Kendall (AUK) estimator for multivariate independence. The AUK estimator is based on the survival copula and quantifies the deviation from the null hypothesis of independence. The methodology implemented in this package is based on the work of 'Afendras', 'Markatou', and 'Papantonis' (2025) &lt;<a href="https://doi.org/10.1016%2Fj.jmva.2025.105589" target="_top">doi:10.1016/j.jmva.2025.105589</a>&gt;.

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