CRAN Updates
Unofficial CRAN updates bot maintained by @chriskenny.bsky.social using R package bskyr christophertkenny.com/bskyr/
- New on CRAN: whisper (0.1.0). View at cran.r-project.org/package=whisper
whisper: Native R 'torch' Implementation of 'OpenAI' 'Whisper'
Speech-to-text transcription using a native R 'torch' implementation of 'OpenAI' 'Whisper' model <<a href="https://github.com/openai/whisper" target="_top">https://github.com/openai/whisper</a>>. Supports multiple model sizes from tiny (39M parameters) to large-v3 (1.5B parameters) with integrated download from 'HuggingFace' <<a href="https://huggingface.co/" target="_top">https://huggingface.co/</a>> via the 'hfhub' package. Provides automatic speech recognition with optional language detection and translation to English. Audio preprocessing, mel spectrogram computation, and transformer-based encoder-decoder inference are all implemented in R using the 'torch' package.cran.r-project.org - New on CRAN: vascr (0.1.4). View at cran.r-project.org/package=vascr
vascr: Process Biological Impedance Sensing Data
Process complex impedance sensing datasets, including those generated by ECIS, xCELLigence and cellZscope instruments. Data can be imported to a standardised tidy format and then plotted. Support for conducting and plotting the outputs of ANOVA (with appropriate tests of statistical assumptions) and cross-correlation analysis. For data processed using this package see Hucklesby et al. (2020) <<a href="https://doi.org/10.3390%2Fbios11050159" target="_top">doi:10.3390/bios11050159</a>>.cran.r-project.org - New on CRAN: tirt (0.1.3). View at cran.r-project.org/package=tirt
tirt: Testlet Item Response Theory
Implementation of Testlet Item Response Theory (tirt). A light-version yet comprehensive and streamlined framework for psychometric analysis using unidimensional Item Response Theory (IRT; Baker & Kim (2004) <<a href="https://doi.org/10.1201%2F9781482276725" target="_top">doi:10.1201/9781482276725</a>>) and Testlet Response Theory (TRT; Wainer et al., (2007) <<a href="https://doi.org/10.1017%2FCBO9780511618765" target="_top">doi:10.1017/CBO9780511618765</a>>). Designed for researchers, this package supports the estimation of item and person parameters for a wide variety of models, including binary (i.e., Rasch, 2-Parameter Logistic, 3-Parameter Logistic) and polytomous (Partial Credit Model, Generalized Partial Credit Model, Graded Response Model) formats. It also supports the estimation of Testlet models (Rasch Testlet, 2-Parameter Logistic Testlet, 3-Parameter Logistic Testlet, Bifactor, Partial Credit Model Testlet, Graded Response), allowing users to account for local item dependence in bundled items. A key feature is the specialized support for combination use and joint estimation of item response model and testlet response model in one calibration. Beyond standard estimation via Marginal Maximum Likelihood with Expectation-Maximization (EM) or Joint Maximum Likelihood, the package offers robust tools for scale linking and equating (Mean-Mean, Mean-Sigma, Stocking-Lord) to ensure comparability across mixed-format test forms. It also facilitates fixed-parameter calibration, enabling users to estimate person abilities with known item parameters or vice versa, which is essential for pre-equating studies and item bank maintenance. Comprehensive data simulation functions are included to generate synthetic datasets with complex structures, including mixed-model blocks and specific testlet effects, aiding in methodological research and study design validation. Researchers can try multiple simulation situations.cran.r-project.org - New on CRAN: nlmixr2auto (1.0.0). View at cran.r-project.org/package=nlmixr2auto
nlmixr2auto: Automated Population Pharmacokinetic Modeling
Automated population pharmacokinetic modeling framework for data-driven initialisation, model evaluation, and metaheuristic optimization. Supports genetic algorithms, ant colony optimization, tabu search, and stepwise procedures for automated model selection and parameter estimation within the nlmixr2 ecosystem.cran.r-project.org - New on CRAN: keyed (0.1.3). View at cran.r-project.org/package=keyed
keyed: Explicit Key Assumptions for Flat-File Data
Helps make implicit data assumptions explicit by attaching keys to flat-file data that error when those assumptions are violated. Designed for CSV-first workflows without database infrastructure or version control. Provides key definition, assumption checks, join diagnostics, and optional drift detection against reference snapshots.cran.r-project.org - New on CRAN: indonesiaFootballScoutR (0.1.3). View at cran.r-project.org/package=indonesiaFo…
indonesiaFootballScoutR: Tools for Football Player Scouting in Indonesia
Provides tools to scrape, clean, and analyze football player data from Indonesian leagues and perform similarity-based scouting analysis using standardized numeric features. The similarity approach follows common vector-space methods as described in Manning et al. (2008, ISBN:9780521865715) and Salton et al. (1975, <<a href="https://doi.org/10.1145%2F361219.361220" target="_top">doi:10.1145/361219.361220</a>>).cran.r-project.org - New on CRAN: bioLeak (0.1.0). View at cran.r-project.org/package=bioLeak
bioLeak: Leakage-Safe Modeling and Auditing for Genomic and Clinical Data
Prevents and detects information leakage in biomedical machine learning. Provides leakage-resistant split policies (subject-grouped, batch-blocked, study leave-out, time-ordered), guarded preprocessing (train-only imputation, normalization, filtering, feature selection), cross-validated fitting with common learners, permutation-gap auditing, batch and fold association tests, and duplicate detection.cran.r-project.org - New on CRAN: bayesics (2.0.2). View at cran.r-project.org/package=bayesics
bayesics: Bayesian Analyses for One- and Two-Sample Inference and Regression Methods
Perform fundamental analyses using Bayesian parametric and non-parametric inference (regression, anova, 1 and 2 sample inference, non-parametric tests, etc.). (Practically) no Markov chain Monte Carlo (MCMC) is used; all exact finite sample inference is completed via closed form solutions or else through posterior sampling automated to ensure precision in interval estimate bounds. Diagnostic plots for model assessment, and key inferential quantities (point and interval estimates, probability of direction, region of practical equivalence, and Bayes factors) and model visualizations are provided. Bayes factors are computed either by the Savage Dickey ratio given in Dickey (1971) <<a href="https://doi.org/10.1214%2Faoms%2F1177693507" target="_top">doi:10.1214/aoms/1177693507</a>> or by Chib's method as given in xxx. Interpretations are from Kass and Raftery (1995) <<a href="https://doi.org/10.1080%2F01621459.1995.10476572" target="_top">doi:10.1080/01621459.1995.10476572</a>>. ROPE bounds are based on discussions in Kruschke (2018) <<a href="https://doi.org/10.1177%2F2515245918771304" target="_top">doi:10.1177/2515245918771304</a>>. Methods for determining the number of posterior samples required are described in Doss et al. (2014) <<a href="https://doi.org/10.1214%2F14-EJS957" target="_top">doi:10.1214/14-EJS957</a>>. Bayesian model averaging is done in part by Feldkircher and Zeugner (2015) <<a href="https://doi.org/10.18637%2Fjss.v068.i04" target="_top">doi:10.18637/jss.v068.i04</a>>. Methods for contingency table analysis is described in Gunel et al. (1974) <<a href="https://doi.org/10.1093%2Fbiomet%2F61.3.545" target="_top">doi:10.1093/biomet/61.3.545</a>>. Variational Bayes (VB) methods are described in Salimans and Knowles (2013) <<a href="https://doi.org/10.1214%2F13-BA858" target="_top">doi:10.1214/13-BA858</a>>. Mediation analysis uses the framework described in Imai et al. (2010) <<a href="https://doi.org/10.1037%2Fa0020761" target="_top">doi:10.1037/a0020761</a>>. The loss-likelihood bootstrap used in the non-parametric regression modeling is described in Lyddon et al. (2019) <<a href="https://doi.org/10.1093%2Fbiomet%2Fasz006" target="_top">doi:10.1093/biomet/asz006</a>>. Non-parametric survival methods are described in Qing et al. (2023) <<a href="https://doi.org/10.1002%2Fpst.2256" target="_top">doi:10.1002/pst.2256</a>>. Methods used for the Bayesian Wilcoxon signed-rank analysis is given in Chechile (2018) <<a href="https://doi.org/10.1080%2F03610926.2017.1388402" target="_top">doi:10.1080/03610926.2017.1388402</a>> and for the Bayesian Wilcoxon rank sum analysis in Chechile (2020) <<a href="https://doi.org/10.1080%2F03610926.2018.1549247" target="_top">doi:10.1080/03610926.2018.1549247</a>>. Correlation analysis methods are carried out by Barch and Chechile (2023) <<a href="https://doi.org/10.32614%2FCRAN.package.DFBA" target="_top">doi:10.32614/CRAN.package.DFBA</a>>, and described in Lindley and Phillips (1976) <<a href="https://doi.org/10.1080%2F00031305.1976.10479154" target="_top">doi:10.1080/00031305.1976.10479154</a>> and Chechile and Barch (2021) <<a href="https://doi.org/10.1016%2Fj.jmp.2021.102638" target="_top">doi:10.1016/j.jmp.2021.102638</a>>. See also Chechile (2020, ISBN: 9780262044585).cran.r-project.org - New on CRAN: BayesianHybridDesign (0.1.0). View at cran.r-project.org/package=BayesianHyb…
BayesianHybridDesign: Bayesian Hybrid Design and Analysis
Implements Bayesian hybrid designs that incorporate historical control data into a current clinical trial. The package uses a dynamic power prior method to determine the degree of borrowing from the historical data, creating a 'hybrid' control arm. This approach is primarily designed for studies with a binary primary endpoint, such as the overall response rate (ORR). Functions are provided for design calibration, sample size calculation, power evaluation, and final analysis. Additionally, it includes functions adapted from the 'SAMprior' package (v1.1.1) by Yang et al. (2023) <<a href="https://academic.oup.com/biometrics/article/79/4/2857/7587575" target="_top">https://academic.oup.com/biometrics/article/79/4/2857/7587575</a>> to support the Self-Adapting Mixture (SAM) prior framework for comparison.cran.r-project.org - New on CRAN: areaOfEffect (0.2.4). View at cran.r-project.org/package=areaOfEffect
areaOfEffect: Spatial Support at Scale
Formalizes spatial support at scale for ecological and geographical analysis. Given points and support polygons, classifies points as "core" (inside original support) or "halo" (inside scaled support but outside original), pruning all others. The default scale produces equal core and halo areas - a geometrically derived choice requiring no tuning. An optional mask enforces hard boundaries such as coastlines. Political borders are treated as soft boundaries with no ecological meaning.cran.r-project.org - Updates on CRAN: OmopSketch (1.0.1), profileModel (0.6.2), RcmdrPlugin.RiskDemo (3.3), shinyscholar (0.4.4), SomaDataIO (6.5.0), UCSCXenaTools (1.7.0)
- Updates on CRAN: autotab (0.1.2), BioCro (3.3.1), cpfa (1.2-6), crosswalkr (0.4.0), eurlex (0.4.9), feisr (1.3.1), gooseR (0.1.2), ifo (0.2.3), ipeaplot (0.5.1)
- New on CRAN: wired (1.0.0). View at cran.r-project.org/package=wired
wired: Weighted Adaptive Prediction with Structured Dependence
Builds a joint probabilistic forecast across series and horizons using adaptive copulas (Gaussian/t) with shrinkage-repaired correlations. At the low level it calls a probabilistic mixer per series and horizon, which backtests several simple predictors, predicts next-window Continuous Ranked Probability Score (CRPS), and converts those scores into softmax weights to form a calibrated mixture (r/q/p/dfun). The mixer blends eight simple predictors: a naive predictor that wraps the last move in a PERT distribution; an arima predictor using auto.arima for one-step forecasts; an Exponentially Weighted Moving Average (EWMA) gaussian predictor with mean/variance under a Gaussian; a historical bootstrap predictor that resamples past horizon-aligned moves; a drift residual bootstrap predictor combining linear trend with bootstrapped residuals; a volatility-scaled naive predictor centering on the last move and scaling by recent volatility; a robust median mad predictor using median/MAD with Laplace or Normal shape; and a shrunk quantile predictor that fits a few quantile regressions over time and interpolates to a full predictive. The function then couples the per-series mixtures on a common transform (additive/multiplicative/log-multiplicative), simulates coherent draws, and returns both transformed- and level-scale samplers and summaries.cran.r-project.org - New on CRAN: tidylearn (0.1.0). View at cran.r-project.org/package=tidylearn
tidylearn: A Unified Tidy Interface to R's Machine Learning Ecosystem
Provides a unified tidyverse-compatible interface to R's machine learning packages. Wraps established implementations from 'glmnet', 'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster', 'dbscan', and others - providing consistent function signatures, tidy tibble output, and unified 'ggplot2'-based visualization. The underlying algorithms are unchanged; 'tidylearn' simply makes them easier to use together. Access raw model objects via the $fit slot for package-specific functionality. Methods include random forests Breiman (2001) <<a href="https://doi.org/10.1023%2FA%3A1010933404324" target="_top">doi:10.1023/A:1010933404324</a>>, LASSO regression Tibshirani (1996) <<a href="https://doi.org/10.1111%2Fj.2517-6161.1996.tb02080.x" target="_top">doi:10.1111/j.2517-6161.1996.tb02080.x</a>>, elastic net Zou and Hastie (2005) <<a href="https://doi.org/10.1111%2Fj.1467-9868.2005.00503.x" target="_top">doi:10.1111/j.1467-9868.2005.00503.x</a>>, support vector machines Cortes and Vapnik (1995) <<a href="https://doi.org/10.1007%2FBF00994018" target="_top">doi:10.1007/BF00994018</a>>, and gradient boosting Friedman (2001) <<a href="https://doi.org/10.1214%2Faos%2F1013203451" target="_top">doi:10.1214/aos/1013203451</a>>.cran.r-project.org - New on CRAN: snreg (1.2.0). View at cran.r-project.org/package=snreg
snreg: Regression with Skew-Normally Distributed Error Term
Models with skew‑normally distributed and thus asymmetric error terms, implementing the methods developed in Badunenko and Henderson (2023) "Production analysis with asymmetric noise" <<a href="https://doi.org/10.1007%2Fs11123-023-00680-5" target="_top">doi:10.1007/s11123-023-00680-5</a>>. The package provides tools to estimate regression models with skew‑normal error terms, allowing both the variance and skewness parameters to be heteroskedastic. It also includes a stochastic frontier framework that accommodates both i.i.d. and heteroskedastic inefficiency terms.cran.r-project.org - New on CRAN: resLIK (0.1.2). View at cran.r-project.org/package=resLIK
resLIK: Representation-Level Control Surfaces for Reliability Sensing
Implements the Representation-Level Control Surfaces (RLCS) paradigm for ensuring the reliability of autonomous systems and AI models. It provides three deterministic sensors: Residual Likelihood (ResLik) for population-level anomaly detection, Temporal Consistency Sensor (TCS) for drift and shock detection, and Agreement Sensor for multi-modal redundancy checks. These sensors feed into a standardized control surface that issues 'PROCEED', 'DEFER', or 'ABSTAIN' signals based on strict safety invariants, allowing systems to detect and react to out-of-distribution states, sensor failures, and environmental shifts before they propagate to decision-making layers.cran.r-project.org - New on CRAN: quickSentiment (0.1.0). View at cran.r-project.org/package=quickSentiment
quickSentiment: A Fast and Flexible Pipeline for Text Classification
A high-level wrapper that simplifies text classification into three streamlined steps: preprocessing, model training, and prediction. It unifies the interface for multiple algorithms (including 'glmnet', 'ranger', and 'xgboost') and vectorization methods (Bag-of-Words, Term Frequency-Inverse Document Frequency (TF-IDF)), allowing users to go from raw text to a trained sentiment model in two function calls. The resulting model artifact automatically handles preprocessing for new datasets in the third step, ensuring consistent prediction pipelines.cran.r-project.org - New on CRAN: PPtreeExt (0.1.0). View at cran.r-project.org/package=PPtreeExt
PPtreeExt: Projection Pursuit Classification Tree Extensions
Implements extensions to the projection pursuit tree algorithm for supervised classification, see Lee, Y. (2013), <<a href="https://doi.org/10.1214%2F13-EJS810" target="_top">doi:10.1214/13-EJS810</a>> and Lee, E-K. (2018) <<a href="https://doi.org/10.18637%2Fjss.v083.i08" target="_top">doi:10.18637/jss.v083.i08</a>>. The algorithm is changed in two ways: improving prediction boundaries by modifying the choice of split points-through class subsetting; and increasing flexibility by allowing multiple splits per group.cran.r-project.org - New on CRAN: numspellR (0.1.0). View at cran.r-project.org/package=numspellR
numspellR: Detection of Numeric Persistence and Rigidity Patterns
Tools for detecting numeric persistence ("spells") and rigidity patterns in time-ordered numeric data. The package identifies periods of stability, computes spell-based rigidity metrics, and provides plain-language interpretations suitable for policy and applied analysis.cran.r-project.org - New on CRAN: mortSOA (0.1.0). View at cran.r-project.org/package=mortSOA
mortSOA: Obtain Data from the Society of Actuaries 'Mortality and Other Rate Tables' Site
The Society of Actuaries (SOA) provides an extensive online database called 'Mortality and Other Rate Tables' ('MORT') at <<a href="https://mort.soa.org/" target="_top">https://mort.soa.org/</a>>. This database contains mortality, lapse, and valuation tables that cover a variety of product types and nations. Users of the database can download any tables in 'Excel', 'CSV', or 'XML' formats. This package provides convenience functions that read 'XML' formats from the database and return R objects.cran.r-project.org - New on CRAN: climenu (0.1.3). View at cran.r-project.org/package=climenu
climenu: Interactive Command-Line Menus
Provides interactive command-line menu functionality with single and multiple selection menus, keyboard navigation (arrow keys or vi-style j/k), preselection, and graceful fallback for non-interactive environments. Inspired by tools such as 'inquirer.js' <<a href="https://github.com/SBoudrias/Inquirer.js" target="_top">https://github.com/SBoudrias/Inquirer.js</a>>, 'pick' <<a href="https://github.com/aisk/pick" target="_top">https://github.com/aisk/pick</a>>, and 'survey' <<a href="https://github.com/AlecAivazis/survey" target="_top">https://github.com/AlecAivazis/survey</a>>. Designed to be lightweight and easy to integrate into 'R' packages and scripts.cran.r-project.org - Updates on CRAN: AutoPlots (1.5.0), gifti (0.9.0), micar (1.2.0), resourcecode (0.5.3), tidydfidx (0.0-3), vigicaen (1.0.0)
- New on CRAN: SBMTrees (1.4). View at cran.r-project.org/package=SBMTrees
SBMTrees: Longitudinal Sequential Imputation and Prediction with Bayesian Trees Mixed-Effects Models for Longitudinal Data
Implements a sequential imputation framework using Bayesian Mixed-Effects Trees ('SBMTrees') for handling missing data in longitudinal studies. The package supports a variety of models, including non-linear relationships and non-normal random effects and residuals, leveraging Dirichlet Process priors for increased flexibility. Key features include handling Missing at Random (MAR) longitudinal data, imputation of both covariates and outcomes, and generating posterior predictive samples for further analysis. The methodology is designed for applications in epidemiology, biostatistics, and other fields requiring robust handling of missing data in longitudinal settings.cran.r-project.org - Updates on CRAN: bindrcpp (0.2.4), mapycusmaximus (1.0.7), permute (0.9-10), resourcer (1.5.0), RSQLite (2.4.6), simulist (0.7.0), tealeaves (1.0.7)
- Removed from CRAN: pgenlibr (0.5.4), unigd (0.1.3)
- Updates on CRAN: BeeBDC (1.3.3), DATAstudio (1.2.2), deSolve (1.41), gap (1.9), ggstatsplot (0.13.5), GWSDAT (3.3.0), mlVAR (0.5.5), partools (1.1.7), RMariaDB (1.3.5), RPostgres (1.4.9), simPH (1.3.15), simStateSpace (1.2.15), toOrdinal (1.4-0.0), tweedie (3.0.10)
- Updates on CRAN: Ruido (1.0.2), sfc (0.1.1)
- New on CRAN: gimap (1.1.2). View at cran.r-project.org/package=gimap
gimap: Calculate Genetic Interactions for Paired CRISPR Targets
Helps find meaningful patterns in complex genetic experiments. First gimap takes data from paired CRISPR (Clustered regularly interspaced short palindromic repeats) screens that has been pre-processed to counts table of paired gRNA (guide Ribonucleic Acid) reads. The input data will have cell counts for how well cells grow (or don't grow) when different genes or pairs of genes are disabled. The output of the 'gimap' package is genetic interaction scores which are the distance between the observed CRISPR score and the expected CRISPR score. The expected CRISPR scores are what we expect for the CRISPR values to be for two unrelated genes. The further away an observed CRISPR score is from its expected score the more we suspect genetic interaction. The work in this package is based off of original research from the Alice Berger lab at Fred Hutchinson Cancer Center (2021) <<a href="https://doi.org/10.1016%2Fj.celrep.2021.109597" target="_top">doi:10.1016/j.celrep.2021.109597</a>>.cran.r-project.org - Updates on CRAN: hyper.gam (0.2.2), idmc (0.4.1), qryflow (0.2.0), Rparadox (0.2.0), spectralGP (1.3.4), tidychangepoint (1.0.4), traumar (1.2.4)
- Updates on CRAN: cTMed (1.0.9), HVT (26.1.2), ISAR (1.0.2), myTAI (2.3.5), PAMmisc (1.12.7), penetrance (0.1.2), phsopendata (1.0.3), shinyloadtest (1.2.1), survivoR (2.3.10), ZIBR (1.0.3)
- Updates on CRAN: RastaRocket (1.0.2), restriktor (0.6-30), SEQTaRget (1.3.5), SlimR (1.1.1), statsExpressions (1.7.3), tidyCDISC (0.2.2), tseffects (0.2.1)
- Updates on CRAN: aion (1.7.0), ALDEx3 (1.0.2), BFS (0.7.1), chronometre (0.0.2), doRNG (1.8.6.3), fluxible (1.3.6), fru (0.0.3), gridify (0.7.7), mMARCH.AC (3.3.4.0)
- New on CRAN: roxigraph (0.1.0). View at cran.r-project.org/package=roxigraph
roxigraph: 'RDF' and 'SPARQL' for R using 'Oxigraph'
Provides 'RDF' storage and 'SPARQL' 1.1 query capabilities by wrapping the 'Oxigraph' graph database library <<a href="https://github.com/oxigraph/oxigraph" target="_top">https://github.com/oxigraph/oxigraph</a>>. Supports in-memory and persistent ('RocksDB') storage, multiple 'RDF' serialization formats ('Turtle', 'N-Triples', 'RDF-XML', 'N-Quads', 'TriG'), and full 'SPARQL' 1.1 Query and Update support. Built using the 'extendr' framework for 'Rust'-R bindings.cran.r-project.org - New on CRAN: regextable (0.1.1). View at cran.r-project.org/package=regextable
regextable: Pattern-Based Text Extraction and Standardization with Lookup Tables
Extracts information from text using lookup tables of regular expressions. Each text entry is compared against all patterns, and all matching patterns and their corresponding substrings are returned. If a text entry matches multiple patterns, multiple rows are generated to capture each match. This approach enables comprehensive pattern coverage when processing large or complex text datasets.cran.r-project.org - Updates on CRAN: adjustedCurves (0.11.4), automerge (0.2.1), BAwiR (1.4.4), cardargus (0.2.1), chunked (0.6.2), convertid (0.2.1), discovr (1.0.0), emcAdr (1.3), isocountry (0.6.0), LOMAR (0.5.2), mlrpro (0.1.3), mpathr (1.0.4), Rtwalk (2.0.1), smooth (4.4.0)
- Removed from CRAN: dream (1.0.0), EcoCleanR (1.0.1), NNbenchmark (3.2.0)
- Updates on CRAN: MetaHD (0.1.4), NMAR (0.1.2), nuggets (2.1.2), pmparser (1.0.24), robsurvey (0.7-3), robustbase (0.99-7), samc (4.1.0), scistreer (1.2.1), secretbase (1.2.0)
- Updates on CRAN: cardx (0.3.2), cctest (2.2.2), cluster (2.1.8.2), collections (0.3.11), COMIX (1.0.2), DLMtool (6.0.7), efdm (0.2.2), gammaFuncModel (6.0), garma (1.0.0)
- New on CRAN: kDGLM (1.2.12). View at cran.r-project.org/package=kDGLM
kDGLM: Bayesian Analysis of Dynamic Generalized Linear Models
Provide routines for filtering and smoothing, forecasting, sampling and Bayesian analysis of Dynamic Generalized Linear Models using the methodology described in Alves et al. (2024)<<a href="https://doi.org/10.48550%2FarXiv.2201.05387" target="_top">doi:10.48550/arXiv.2201.05387</a>> and dos Santos Jr. et al. (2024)<<a href="https://doi.org/10.48550%2FarXiv.2403.13069" target="_top">doi:10.48550/arXiv.2403.13069</a>>.cran.r-project.org - Updates on CRAN: admiralneuro (0.2.1), EpiNow2 (1.8.0), hexify (0.3.10), PAMscapes (0.15.0), rfoaas (2.3.3), SCORPION (1.3.0), vowels (1.2-3)
- Removed from CRAN: servosphereR (0.1.1)
- New on CRAN: wlsd (1.0.1). View at cran.r-project.org/package=wlsd
wlsd: Wrangling Longitudinal Survival Data
Streamlines the process of transitioning between data formats commonly used in survival analysis. Functions convert longitudinal data between formats used as input for survival models as well as support overall preparation. Users are able to focus on model building rather than data wrangling.cran.r-project.org - New on CRAN: typeR (0.2.0). View at cran.r-project.org/package=typeR
typeR: Simulate Typing Script
Simulates typing of R script files for presentations and demonstrations. Provides character-by-character animation with optional live code execution. Supports R scripts (.R), R Markdown (.Rmd), and Quarto (.qmd) documents.cran.r-project.org - New on CRAN: tidyextreme (1.0.0). View at cran.r-project.org/package=tidyextreme
tidyextreme: A Tidy Toolbox for Climate Extreme Indices
Calculate Expert Team on Climate Change Detection and Indices (ETCCDI) <– (acronym) climate indices from daily or hourly temperature and precipitation data. Provides flexible data handling.cran.r-project.org - New on CRAN: sqlm (0.1.0). View at cran.r-project.org/package=sqlm
sqlm: SQL-Backed Linear Regression
Fits linear regression models on datasets residing in SQL databases without pulling data into R memory. Computes sufficient statistics inside the database engine via a single aggregation query and solves the normal equations in R.cran.r-project.org - New on CRAN: sparsevar (1.0.0). View at cran.r-project.org/package=sparsevar
sparsevar: Sparse VAR (Vector Autoregression) / VECM (Vector Error Correction Model) Estimation
A wrapper for sparse VAR (Vector Autoregression) and VECM (Vector Error Correction Model) time series models estimation using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped Absolute Deviation) and MCP (Minimax Concave Penalty). Based on the work of Basu and Michailidis (2015) <<a href="https://doi.org/10.1214%2F15-AOS1315" target="_top">doi:10.1214/15-AOS1315</a>>.cran.r-project.org - New on CRAN: SNMA (0.1.5). View at cran.r-project.org/package=SNMA
SNMA: Stream Network Movement Analyses
Calculating home ranges and movements of animals in complex stream environments is often challenging, and standard home range estimators do not apply. This package provides a series of tools for assessing movements in a stream network, such as calculating the total length of stream used, distances between points, and movement patterns over time. See Vignette for additional details. This package was originally released on 'GitHub' under the name 'SNM'. SNMA was developed for analyses in McKnight et al. (2025) <<a href="https://doi.org/10.3354%2Fesr01442" target="_top">doi:10.3354/esr01442</a>> which contains additional examples and information.cran.r-project.org - New on CRAN: setweaver (1.0.0). View at cran.r-project.org/package=setweaver
setweaver: Building Sets of Variables in a Probabilistic Framework
Create sets of variables based on a mutual information approach. In this context, a set is a collection of distinct elements (e.g., variables) that can also be treated as a single entity. Mutual information, a concept from probability theory, quantifies the dependence between two variables by expressing how much information about one variable can be gained from observing the other. Furthermore, you can analyze, and visualize these sets in order to better understand the relationships among variables.cran.r-project.org - New on CRAN: ppweibull (1.0). View at cran.r-project.org/package=ppweibull
ppweibull: Piecewise Lifetime Models
Provides functions for estimation and data generation for several piecewise lifetime distributions. The package implements the power piecewise Weibull model, which includes the piecewise Rayleigh and piecewise exponential models as special cases. See Feigl and Zelen (1965) <<a href="https://doi.org/10.2307%2F2528247" target="_top">doi:10.2307/2528247</a>> for methodological details.cran.r-project.org - New on CRAN: Orangutan (2.0.0). View at cran.r-project.org/package=Orangutan
Orangutan: Automated Analysis of Phenotypic Data
Provides functions to analyze and visualize meristic and mensural phenotypic data in a comparative framework. The package implements an automated pipeline that summarizes traits, identifies diagnostic variables among groups, performs multivariate and univariate statistical analyses, and produces publication-ready graphics. An earlier implementation (v1.0.0) is described in Torres (2025) <<a href="https://doi.org/10.64898%2F2025.12.18.695244" target="_top">doi:10.64898/2025.12.18.695244</a>>.cran.r-project.org
