CRAN: Package syncons

Provides functions and a Shiny application to design plate layouts for constructing synthetic communities (SynComs) from a pool of strains using 24-, 96- and 384-well microplates. The method generates all possible strain combinations in a scalable, low-cost workflow suitable for high-throughput experiments. Reference: Tian et al. (2024) &lt;<a href="https://doi.org/10.21769%2FBioProtoc.2405576" target="_top">doi:10.21769/BioProtoc.2405576</a>&gt;.

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CRAN: Package persuasio

Provides estimation and inference methods for causal persuasion rates in the potential-outcomes framework of Jun and Lee (2023, Journal of Political Economy) &lt;<a href="https://doi.org/10.1086%2F724114" target="_top">doi:10.1086/724114</a>&gt;. The package computes bounds and confidence intervals for average and local persuasion rates under data scenarios with binary outcomes, treatments, and instruments, and also when only the outcome and instrument are observed. It also provides functions for calculating bounds from summary statistics.

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CRAN: Package lonelyr

Scoring functions for widely used loneliness measures, with response keys and reverse-scoring drawn from the original scale manuals. Covers the UCLA Loneliness Scale Version 3 and its three-item short form, the de Jong Gierveld 11- and 6-item scales, the Children's Loneliness and Social Dissatisfaction Scale, and the short Social and Emotional Loneliness Scale for Adults. Out-of-range responses raise a warning, and missing-data rules follow each scale's manual.

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CRAN: Package actinet

Interfaces the 'actinet' Python module &lt;<a href="https://github.com/OxWearables/actinet" target="_top">https://github.com/OxWearables/actinet</a>&gt; for an activity classification model based on self-supervised learning for wrist-worn accelerometer data.

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CRAN: Package epibyhand

Computes classical epidemiological measures and returns the complete worked derivation alongside the result: every intermediate quantity, the formula, and the formula with the observed numbers substituted in. Intended for teaching, for checking hand calculations, and for generating worked solutions in course materials. Scope is deliberately limited to methods a student can compute by hand on paper. Methods follow Mantel and Haenszel (1959) &lt;<a href="https://doi.org/10.1093%2Fjnci%2F22.4.719" target="_top">doi:10.1093/jnci/22.4.719</a>&gt;, Greenland and Robins (1985, Biometrics 41, 55-68), Robins, Breslow and Greenland (1986, Biometrics 42, 311-323), and Breslow and Day (1980, IARC Scientific Publications No. 32).

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CRAN: Package trialSizing

Sizes field experiments from uniformity-trial data, following the relationship between the coefficient of variation and plot size. Checks a trial for the spatial structure the sizing methods assume (semivariogram, Moran's I, kriged field map), summarises the coefficient of variation over every plot shape the grid admits, and estimates the optimal plot size by the modified maximum curvature method of Meier and Lessman (1971), by the linear response plateau (LRP) and quadratic response plateau (QRP) models, and by the closed form of Paranaiba, Ferreira and Morais (2009), which can be compared side by side. From the coefficient of variation at the optimum it derives the number of replications needed to detect a given difference between treatment means, as in Cargnelutti Filho and others (2014). Every method returns standardised diagnostic statistics, optional bootstrap uncertainty for the breakpoint, and publication-style plots.

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CRAN: Package predHCS

Implements generalized statistical point prediction and prediction intervals for future failure times under various hybrid censoring schemes. Supported censoring schemes include Type-I, Type-II, Generalized Type-I, Generalized Type-II, Unified, Progressive Type-I, and Progressive Type-II hybrid censoring schemes. Available prediction methods include Best Unbiased Predictor (BUP), Conditional Median Predictor (CMP), Maximum Likelihood Predictor (MLP), equal-tailed classical prediction intervals, Highest Conditional Density (HCD) prediction intervals, and Bayesian prediction intervals. Algorithms accept user-defined continuous probability density functions, cumulative distribution functions, quantile functions, or survival functions along with estimated parameter values. Methodological foundations are based on Balakrishnan, Cramer, and Kundu (2023, ISBN:978-0123983879), Shafay and Balakrishnan (2012) &lt;<a href="https://doi.org/10.1080%2F03610918.2011.579367" target="_top">doi:10.1080/03610918.2011.579367</a>&gt; for Type-I hybrid censoring, Balakrishnan and Shafay (2012) &lt;<a href="https://doi.org/10.1080%2F03610926.2010.543300" target="_top">doi:10.1080/03610926.2010.543300</a>&gt; for Type-II hybrid censoring, Shafay (2017) &lt;<a href="https://doi.org/10.1080%2F03610926.2016.1200093" target="_top">doi:10.1080/03610926.2016.1200093</a>&gt; for Generalized Type-I hybrid censoring, Shafay (2016) &lt;<a href="https://doi.org/10.1080%2F00949655.2015.1096361" target="_top">doi:10.1080/00949655.2015.1096361</a>&gt; for Generalized Type-II hybrid censoring, Mohie El-Din, Nagy, and Shafay (2017) &lt;<a href="https://doi.org/10.18576%2Fjsap%2F060113" target="_top">doi:10.18576/jsap/060113</a>&gt; for Unified hybrid censoring, Ebrahimi (1992) &lt;<a href="https://doi.org/10.1109%2F24.126685" target="_top">doi:10.1109/24.126685</a>&gt;, Valiollahi, Asgharzadeh, and Kundu (2017) &lt;<a href="https://doi.org/10.1214%2F15-BJPS302" target="_top">doi:10.1214/15-BJPS302</a>&gt;, and Asgharzadeh, Valiollahi, and Kundu (2015) &lt;<a href="https://doi.org/10.1080%2F00949655.2013.848451" target="_top">doi:10.1080/00949655.2013.848451</a>&gt;.

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CRAN: Package oddsapiio

Query live and upcoming sports betting odds from the 'Odds-API.io' REST API &lt;<a href="https://docs.odds-api.io" target="_top">https://docs.odds-api.io</a>&gt;. Covers sports, bookmakers, leagues, events, per-event odds, value bets and arbitrage bets across 265+ bookmakers, returned as tidy data frames. An API key is required; a free tier is available.

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CRAN: Package needenv

Validates that required environment variables are set and non-empty before environment-dependent code runs. Variables may have call-site defaults, and all unresolved variables are reported together. Successful values are returned as a named list without reading environment files or modifying the process environment.

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CRAN: Package CLDedgelister

Imports causal and system-dynamics models from 'Vensim', 'Stella' / 'iThink' and 'Powersim Studio' ('XMILE'), 'AnyLogic' and 'GoldSim' (exported 'XML'), and converts them into a two-column edge list of cause-to-effect links. 'Vensim' models are read from their native diagram files; 'Powersim Studio' and 'GoldSim' are read from their exported 'XMILE' or 'XML'. Provides an 'RStudio' add-in with a simple point-and-click interface, together with command-line functions that return a data frame or write it to 'CSV' or 'Excel'.

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CRAN: Package BayesQRCount

Implements Bayesian quantile regression for count data using the jittering technique for discrete data smoothing and an asymmetric Laplace distribution likelihood. Supports adaptive variable selection via a random-bridge penalty with a beta prior on the power parameter, as well as fixed-bridge and Lasso penalties. Utilizes Markov chain Monte Carlo with Gibbs sampling and adaptive Metropolis-Hastings algorithms for posterior inference, provides Gelman-Rubin convergence diagnostics, and predicts conditional quantiles for count responses. Methodology and applications are based on the following key references: Luo, Zhou, Hu, and Li (2026, Journal of Mathematics, 2026:1543166, &lt;<a href="https://doi.org/10.1155%2Fjom%2F1543166" target="_top">doi:10.1155/jom/1543166</a>&gt;), Koenker and Bassett (1978, Econometrica, 46, 33-50, &lt;<a href="https://doi.org/10.2307%2F1913643" target="_top">doi:10.2307/1913643</a>&gt;), Machado and Santos Silva (2005, Journal of the American Statistical Association, 100, 1226-1237, &lt;<a href="https://doi.org/10.1198%2F016214505000000330" target="_top">doi:10.1198/016214505000000330</a>&gt;), Yu and Moyeed (2001, Statistics and Probability Letters, 54, 437-447, &lt;<a href="https://doi.org/10.1016%2FS0167-7152%2801%2900124-9" target="_top">doi:10.1016/S0167-7152(01)00124-9</a>&gt;), Polson, Scott, and Windle (2014, Journal of the Royal Statistical Society Series B, 76, 713-733, &lt;<a href="https://doi.org/10.1111%2Frssb.12042" target="_top">doi:10.1111/rssb.12042</a>&gt;), Park and Casella (2008, Journal of the American Statistical Association, 103, 681-686, &lt;<a href="https://doi.org/10.1198%2F016214508000000337" target="_top">doi:10.1198/016214508000000337</a>&gt;), and Roberts and Rosenthal (2009, Journal of Computational and Graphical Statistics, 18, 349-367, &lt;<a href="https://doi.org/10.1198%2Fjcgs.2009.06134" target="_top">doi:10.1198/jcgs.2009.06134</a>&gt;).

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CRAN: Package psreplicate

Search and browse the Political Science Replication Index (&lt;<a href="https://jsakowuah.github.io/polisci-replication/" target="_top">https://jsakowuah.github.io/polisci-replication/</a>&gt;), a searchable, tagged index of replication packages crawled monthly from flagship political science journals' Harvard Dataverse collections, without leaving R. Provides functions to download and cache the index locally and to search and filter it by journal, method, data type, and year.

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CRAN: Package linf

Implements L-infinity normalization for compositional matrices, assigns samples to dominant features, constructs truncated and hierarchically refined dominant community state types, and computes representative landmark profiles. The methods are described in the accompanying publication &lt;<a href="https://doi.org/10.48550%2FarXiv.2503.21543" target="_top">doi:10.48550/arXiv.2503.21543</a>&gt;. Bundled vaginal and gut microbiome data support reproducible demonstrations of the package interface; phenotype fields in the stratified gut subset are illustrative and are not suitable for population-level inference.

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CRAN: Package grip

Implements the GRIP multiscale graph layout workflow for ordinary graphs and a geometry-aware weighted GRIP workflow for weighted graphs in 2D and 3D. Provides layout scoring, candidate comparison, multiscale trace diagnostics, synthetic graph families, and advanced experimental geodesic-KK utilities for weighted-layout evaluation and polish. Based on Gajer and Kobourov (2002) &lt;<a href="https://doi.org/10.7155%2Fjgaa.00052" target="_top">doi:10.7155/jgaa.00052</a>&gt; and Gajer, Goodrich and Kobourov (2004) &lt;<a href="https://doi.org/10.1016%2Fj.comgeo.2004.03.014" target="_top">doi:10.1016/j.comgeo.2004.03.014</a>&gt;.

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CRAN: Package EWAScaller

Provides a client for the 'EWAS Atlas' web services (&lt;<a href="https://ngdc.cncb.ac.cn/ewas/" target="_top">https://ngdc.cncb.ac.cn/ewas/</a>&gt;; Li et al. (2019) &lt;<a href="https://doi.org/10.1093%2Fnar%2Fgky1027" target="_top">doi:10.1093/nar/gky1027</a>&gt;), allowing users to query epigenome-wide association study (EWAS) data by CpG (cytosine-phosphate-guanine) probe identifier, gene symbol, or genomic region, and to run trait, Gene Ontology, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, and genomic location enrichment analyses on a set of CpG probes. Query functions support concurrent, rate-limited requests to the remote service. Results are returned as tidy data frames with dedicated summary and plotting methods, including word clouds of enriched 'EWAS Atlas' trait terms.

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CRAN: Package bufferscape

Characterises the environment surrounding point locations by computing land-cover composition within circular buffers directly from vector polygons, without conversion to a raster grid. For each site and each class it returns the exact surface area inside the buffer and a distance-decay weighted "effective" area in which the kernel is integrated over polygon geometry rather than evaluated at the polygon centroid, avoiding the large bias the centroid approximation introduces for elongated features passing close to the site. Polygons may overlap, so class areas are not constrained to sum to the buffer area. Intended for buffer-based exposure assessment and fine-scale spatial epidemiology, where the relevant scale is tens of metres and global land-cover products are too coarse: land-use regression around air-quality monitors, green space around residential addresses, vector-surveillance traps, and comparable designs. The classification dictionary is user-supplied, and point features and distances to off-buffer reference features are recorded alongside the areas.

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CRAN: Package rpanelauto

Provides a framework for automatic estimation of time-series models for univariate and multidimensional panel data. A user-supplied estimation function is applied independently to each time series, with optional processing before and after estimation. The function returns the transformed data together with the fitted models.

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CRAN: Package orbis

A layered grammar of graphics that compiles plots to a resolution-independent scene description and renders it through two back-ends: a self-contained SVG writer with embedded 'JavaScript' for interactive figures (tooltips, hover highlighting, zoom, pan and legend toggling) and R's own graphics devices for publication-quality output at any resolution. Geographic layers are first class: a simplified world polygon dataset ships with the package and can be drawn with several map projections, including Robinson, Equal Earth and an orthographic globe. The layered grammar follows Wickham (2010) &lt;<a href="https://doi.org/10.1198%2Fjcgs.2009.07098" target="_top">doi:10.1198/jcgs.2009.07098</a>&gt;; projections follow Snyder (1987) &lt;<a href="https://doi.org/10.3133%2Fpp1395" target="_top">doi:10.3133/pp1395</a>&gt; and, for Equal Earth, Savric, Patterson and Jenny (2019) &lt;<a href="https://doi.org/10.1080%2F13658816.2018.1504949" target="_top">doi:10.1080/13658816.2018.1504949</a>&gt;; line simplification uses Douglas and Peucker (1973) &lt;<a href="https://doi.org/10.3138%2FFM57-6770-U75U-7727" target="_top">doi:10.3138/FM57-6770-U75U-7727</a>&gt;; the default colour scales follow the guidance on perceptually uniform palettes of Crameri, Shephard and Heron (2020) &lt;<a href="https://doi.org/10.1038%2Fs41467-020-19160-7" target="_top">doi:10.1038/s41467-020-19160-7</a>&gt;.

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CRAN: Package MYIS

Implements 'Moreau-Yosida' Markov chain Monte Carlo ('MCMC') importance sampling for parameter estimation and Bayesian inference under smooth, non-differentiable, or light-tailed target posterior distributions and arbitrary probability models with complete or censored data. Users supply user-defined probability density functions, optional distribution functions, parameter ranges, and observations subject to complete, right, left, interval, Type-I, Type-II, progressive Type-II, first-failure, or truncation schemes. Constructs 'Moreau-Yosida' envelopes, gradient-based proposals ('MALA', 'HMC', or 'RWM'), self-normalized importance weights, batch-means asymptotic variance estimates, and Bayesian marginal quantiles. Methodologies are based on 'Shukla', 'Vats', and 'Chi' (2025) &lt;<a href="https://doi.org/10.48550%2FarXiv.2501.02228" target="_top">doi:10.48550/arXiv.2501.02228</a>&gt;, 'Pereyra' (2016) &lt;<a href="https://doi.org/10.1111%2Fsjos.12208" target="_top">doi:10.1111/sjos.12208</a>&gt;, 'Durmus' and others (2022) &lt;<a href="https://doi.org/10.1214%2F22-EJS2027" target="_top">doi:10.1214/22-EJS2027</a>&gt;, 'Chen' and 'Shao' (1999) &lt;<a href="https://doi.org/10.1214%2Fss%2F1009211804" target="_top">doi:10.1214/ss/1009211804</a>&gt;, 'Roberts' and 'Rosenthal' (1998) &lt;<a href="https://doi.org/10.1214%2Faoap%2F1028903378" target="_top">doi:10.1214/aoap/1028903378</a>&gt;, 'Geweke' (1989) &lt;<a href="https://doi.org/10.2307%2F2290062" target="_top">doi:10.2307/2290062</a>&gt;, 'Hesterberg' (1995) &lt;<a href="https://doi.org/10.1080%2F00031305.1995.10476138" target="_top">doi:10.1080/00031305.1995.10476138</a>&gt;, and 'Balakrishnan' and 'Aggarwala' (2000, ISBN:978-0-8176-4001-9).

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CRAN: Package LugsailGR

Provides generalized univariate and multivariate 'Gelman-Rubin' convergence diagnostics, effective sample size ('ESS') estimates, and principled termination thresholds for Markov chain Monte Carlo ('MCMC') simulations, based on Vats and Knudson (2021) &lt;<a href="https://doi.org/10.1214%2F20-STS812" target="_top">doi:10.1214/20-STS812</a>&gt;. The package incorporates replicated lugsail batch means variance estimators to construct stable convergence statistics for single and multiple chains. Additionally, it offers comprehensive tools for evaluating 'MCMC' output generated from user-supplied probability density functions ('PDF') or log-likelihoods, including implementations for censored data models under right, left, interval, 'Type-I', 'Type-II', progressive, and hybrid censoring schemes.

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CRAN: Package liteformats

A collection of lightweight, minimalist output formats and templates for 'litedown' by Xie (2026) &lt;<a href="https://doi.org/10.32614%2FCRAN.package.litedown" target="_top">doi:10.32614/CRAN.package.litedown</a>&gt;, including resumes, cover letters, and other common document types. Documents are rendered with HTML and CSS and can be printed to PDF with a 'Chromium'-based browser, without requiring 'Pandoc' or a 'LaTeX' installation.

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