Implementation of the record linkage methodology proposed by Sadinle (2017) <doi:10.1080/01621459.2016.1148612>. It handles the bipartite record linkage problem, where two duplicate-free datafiles are to be merged.

Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology, using the method of Marill et al. (2015) <doi:10.1177/0962280215592907>. It is particularly useful when the sensitivity or specificity in the sample is 100%. Note that this does not perform the test on nested models--for that, see epicalc::lrtest'.

Survey systems and other third-party data sources commonly use non-standard representations of logical values when it comes to qualitative data - "Yes", "No" and "N/A", say. batman is a package designed to seamlessly convert these into logicals. It is highly localised, and contains equivalents to boolean values in languages including German, French, Spanish, Italian, Turkish, Chinese and Polish.

This package provides a set of functions for doing analysis of A/B split test data and web metrics in general.

This package provides functions to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models and multistate models.

Plotting package based on the grid system, combining elements of a bubble plot and heatmap to conveniently display two numerical variables, (represented by color and size) grouped by categorical variables on the x and y axes. This is a useful alternative to a forest plot when the data can be grouped in two dimensions, such as predictors x outcomes. It has particular advantages for visualising the metabolic measures produced by the Nightingale Health metabolomics platform, and templates are included for automatically generating figures from these datasets.

This package provides a Bayesian model averaging approach to causal effect estimation based on the BCEE algorithm. Currently supports binary or continuous exposures and outcomes. For more details, see Talbot et al. (2015) <doi:10.1515/jci-2014-0035> Talbot and Beaudoin (2022) <doi:10.1515/jci-2021-0023>.

Bayesian Nonparametric sensitivity analysis of multiple testing procedures for p values with arbitrary dependencies, based on the Dirichlet process prior distribution.

Bayesian Hierarchical beta-binomial models for modeling cell population to predictors/exposures. This package utilizes runjags to run Gibbs sampling, parallelizing the chains. Options for different covariances/relationship structures between parameters of interest.

Understanding the drivers of microbial diversity is an important frontier of microbial ecology, and investigating the diversity of samples from microbial ecosystems is a common step in any microbiome analysis. breakaway is the premier package for statistical analysis of microbial diversity. breakaway implements the latest and greatest estimates of species richness, described in Willis and Bunge (2015) <doi:10.1111/biom.12332>, Willis et al. (2017) <doi:10.1111/rssc.12206>, and Willis (2016) <arXiv:1604.02598>, as well as the most commonly used estimates, including the objective Bayes approach described in Barger and Bunge (2010) <doi:10.1214/10-BA527>.

Fits a Bayesian zero-inflated Bernoulli regression model handling (potentially) different covariates for the zero-inflated and non zero-inflated parts. See Moriña D, Puig P, Navarro A. (2021) <doi:10.1186/s12874-021-01427-2>.

Prognostic Enrichment is a strategy of enriching a clinical trial for testing an intervention intended to prevent or delay an unwanted clinical event. A prognostically enriched trial enrolls only patients who are more likely to experience the unwanted clinical event than the broader patient population (R. Temple (2010) <doi:10.1038/clpt.2010.233>). By testing the intervention in an enriched study population, the trial may be adequately powered with a smaller sample size, which can have both practical and ethical advantages. This package provides tools to evaluate biomarkers for prognostic enrichment of clinical trials with survival/time-to-event outcomes.

To visualize the execution data of the processes on BPMN (Business Process Model and Notation) diagrams, using overlays, style customization and interactions, with the bpmn-visualization TypeScript library.

This package provides a Bayesian model for examining the association between environmental mixtures and all Taxa measured in a hierarchical microbiome dataset in a single integrated analysis. Compared with analyzing the associations of environmental mixtures with each Taxa individually, BaHZING controls Type 1 error rates and provides more stable effect estimates when dealing with small sample sizes.

Approximates best-subset selection (L0) regression with an iteratively adaptive Ridge (L2) penalty for large-scale models. This package uses Cyclops for an efficient implementation and the iterative method is described in Kawaguchi et al (2020) <doi:10.1002/sim.8438> and Li et al (2021) <doi:10.1016/j.jspi.2020.12.001>.

BEAST2 (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAUti 2 (which is part of BEAST2') is a GUI tool that allows users to specify the many possible setups and generates the XML file BEAST2 needs to run. This package provides a way to create BEAST2 input files without active user input, but using R function calls instead.

This package provides a fast, lightweight, and vectorized base 64 engine to encode and decode character and raw vectors as well as files stored on disk. Common base 64 alphabets are supported out of the box including the standard, URL-safe, bcrypt, crypt, BinHex', and IMAP-modified UTF-7 alphabets. Custom engines can be created to support unique base 64 encoding and decoding needs.

This package performs block diagonal covariance matrix detection using singular vectors (BD-SVD), which can be extended to hierarchical variable clustering (HC-SVD). The methods are described in Bauer (2024) <doi:10.1080/10618600.2024.2422985> and Bauer (202X) <doi:10.48550/arXiv.2308.06820>.

Fit Bayesian Gaussian graphical models. The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) <doi:10.31234/osf.io/x8dpr>, Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>, Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.

Algorithms for computing and generating plots with and without error bars for Bayesian cluster validity index (BCVI) (O. Preedasawakul, and N. Wiroonsri, A Bayesian Cluster Validity Index, Computational Statistics & Data Analysis, 202, 108053, 2025. <doi:10.1016/j.csda.2024.108053>) based on several underlying cluster validity indexes (CVIs) including Calinski-Harabasz, Chou-Su-Lai, Davies-Bouldin, Dunn, Pakhira-Bandyopadhyay-Maulik, Point biserial correlation, the score function, Starczewski, and Wiroonsri indices for hard clustering, and Correlation Cluster Validity, the generalized C, HF, KWON, KWON2, Modified Pakhira-Bandyopadhyay-Maulik, Pakhira-Bandyopadhyay-Maulik, Tang, Wiroonsri-Preedasawakul, Wu-Li, and Xie-Beni indices for soft clustering. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). Though BCVI is compatible with any underlying existing CVIs, we recommend users to use either WI or WP as the underlying CVI.

Perform the Benford's Analysis to a data set in order to evaluate if it contains human fabricated data. For more details on the method see Moreau, 2021, Model Assist. Statist. Appl., 16 (2021) 73â 79. <doi:10.3233/MAS-210517>.

Bayesian kernel machine regression (from the bkmr package) is a Bayesian semi-parametric generalized linear model approach under identity and probit links. There are a number of functions in this package that extend Bayesian kernel machine regression fits to allow multiple-chain inference and diagnostics, which leverage functions from the future', rstan', and coda packages. Reference: Bobb, J. F., Henn, B. C., Valeri, L., & Coull, B. A. (2018). Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression. ; <doi:10.1186/s12940-018-0413-y>.

Generates robust confidence intervals for standardized regression coefficients using heteroskedasticity-consistent standard errors for models fitted by lm() as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>. The package can also be used to generate confidence intervals for R-squared, adjusted R-squared, and differences of standardized regression coefficients. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.

Making probabilistic projections of life expectancy for all countries of the world, using a Bayesian hierarchical model <doi:10.1007/s13524-012-0193-x>. Subnational projections are also supported.