This package provides a comprehensive statistical analysis of the accuracy of blood pressure devices based on the method of AAMI/ANSI SP10 standards developed by the AAMI Sphygmomanometer Committee for indirect measurement of blood pressure, incorporated into IS0 81060-2. The bpAcc package gives the exact probability of accepting a device D derived from the join distribution of the sample standard deviation and a non-linear transformation of the sample mean for a specified sample size introduced by Chandel et al. (2024) and by the Association for the Advancement of Medical Instrumentation (2003, ISBN:1-57020-183-8).

Bayesian networks provide an intuitive framework for probabilistic reasoning and its graphical nature can be interpreted quite clearly. Graph based methods of machine learning are becoming more popular because they offer a richer model of knowledge that can be understood by a human in a graphical format. The bnviewer is an R Package that allows the interactive visualization of Bayesian Networks. The aim of this package is to improve the Bayesian Networks visualization over the basic and static views offered by existing packages.

This package provides tools designed to perform and evaluate cluster analysis (including Tocher's algorithm), discriminant analysis and path analysis (standard and under collinearity), as well as some useful miscellaneous tools for dealing with sample size and optimum plot size calculations. A test for seed sample heterogeneity is now available. Mantel's permutation test can be found in this package. A new approach for calculating its power is implemented. biotools also contains tests for genetic covariance components. Heuristic approaches for performing non-parametric spatial predictions of generic response variables and spatial gene diversity are implemented.

This package provides a unified syntax for the simulation-based comparison of different single-stage basket trial designs with a binary endpoint and equal sample sizes in all baskets. Methods include the designs by Baumann et al. (2025) <doi:10.1080/19466315.2024.2402275>, Schmitt and Baumann (2025) <doi:10.1080/19466315.2025.2486231>, Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>, Berry et al. (2020) <doi:10.1177/1740774513497539>, and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>. For the latter two designs, the functions are mostly wrappers for functions provided by the package bhmbasket'.

This package provides a highly scientific and utterly addictive bird point count simulator to test statistical assumptions, aid survey design, and have fun while doing it (Solymos 2024 <doi:10.1007/s42977-023-00183-2>). The simulations follow time-removal and distance sampling models based on Matsuoka et al. (2012) <doi:10.1525/auk.2012.11190>, Solymos et al. (2013) <doi:10.1111/2041-210X.12106>, and Solymos et al. (2018) <doi:10.1650/CONDOR-18-32.1>, and sound attenuation experiments by Yip et al. (2017) <doi:10.1650/CONDOR-16-93.1>.

Bayesian adaptive trial algorithm implements multiple-stage interim analysis. Package includes data generating function, and Bayesian hypothesis testing function.

Bayesian Latent Class Analysis using several different methods.

Asymptotic simultaneous confidence intervals for comparison of many treatments with one control, for the difference of binomial proportions, allows for Dunnett-like-adjustment, Bonferroni or unadjusted intervals. Simulation of power of the above interval methods, approximate calculation of any-pair-power, and sample size iteration based on approximate any-pair power. Exact conditional maximum test for many-to-one comparisons to a control.

Generalization of the Bayesian classification and regression tree model that partitions subjects into terminal nodes and tailors predictive model to each terminal node.

This package implements two algorithms of detecting Bull and Bear markets in stock prices: the algorithm of Pagan and Sossounov (2002, <doi:10.1002/jae.664>) and the algorithm of Lunde and Timmermann (2004, <doi:10.1198/073500104000000136>). The package also contains functions for printing out the dating of the Bull and Bear states of the market, the descriptive statistics of the states, and functions for plotting the results. For the sake of convenience, the package includes the monthly and daily data on the prices (not adjusted for dividends) of the S&P 500 stock market index.

This package implements methods for Bayesian analysis of State Space Models. Includes implementations of the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) <doi:10.1111/j.1467-9868.2009.00736.x> and automatic tuning inspired by Pitt et al. (2012) <doi:10.1016/j.jeconom.2012.06.004> and J. Dahlin and T. B. Schön (2019) <doi:10.18637/jss.v088.c02>.

This package provides an integrated data management solution for assets installed via the Biobricks.ai platform. Streamlines the process of loading and interacting with diverse datasets in a consistent manner. A list of bricks is available at <https://status.biobricks.ai>. Documentation for Biobricks.ai is available at <https://docs.biobricks.ai>.

Fit Bayesian time series models using Stan for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Usually, it is difficult to plot choropleth maps for Bangladesh in R'. The bangladesh package provides ready-to-use shapefiles for different administrative regions of Bangladesh (e.g., Division, District, Upazila, and Union). This package helps users to draw thematic maps of administrative regions of Bangladesh easily as it comes with the sf objects for the boundaries. It also provides functions allowing users to efficiently get specific area maps and center coordinates for regions. Users can also search for a specific area and calculate the centroids of those areas.

This package performs brace expansions on strings. Made popular by Unix shells, brace expansion allows users to concisely generate certain character vectors by taking a single string and (recursively) expanding the comma-separated lists and double-period-separated integer and character sequences enclosed within braces in that string. The double-period-separated numeric integer expansion also supports padding the resulting numbers with zeros.

This package provides a wrapper around the Blat command line SMTP mailer for Windows. Blat is public domain software, but be sure to read the license before use. It can be found at the Blat website http://www.blat.net.

Various supervised and unsupervised binning tools including using entropy, recursive partition methods and clustering.

This package provides a curated collection of image-inspired color palettes for biomedical visualization. The palettes are organized as qualitative, sequential, or diverging scales and include documented source context and intended use. The package provides functions to retrieve, inspect, preview, and apply these palettes in base R and ggplot2 graphics, together with utilities for working with palette definitions.

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>.

This package provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality, and include exclusion restrictions on autoregressive parameters. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2025) <doi:10.48550/arXiv.2410.15090> complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2025) <doi:10.1016/j.jeconom.2025.106107>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174> and they embed many popular models proposed by other authors. The bsvars package is aligned regarding objects, workflows, and code structure with the R packages bsvarSIGNs by Wang & Woźniak (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>, bvars by Liu, Ramirez Hassan, Woźniak (2026) <doi:10.32614/CRAN.package.bvars>, and bpvars by Woźniak (2026) <doi:10.32614/CRAN.package.bpvars>, and they constitute an integrated toolset.

This package provides Bayesian age estimation for bioarchaeological skeletal data using ordinal probit regression models implemented in JAGS and NIMBLE'. The package is designed to handle multiple ordinal traits of adult individuals and incorporates a Gompertz prior on age to reflect population-level mortality. It accounts for estimation uncertainties and supports full customization of model parameters and Markov Chain Monte Carlo settings. For more details see Müller-Scheeà el et al. (2026) <doi:10.1002/ajpa.70289>.

Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 <doi:10.1214/12-BA733>). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided.

This package implements methods for bivariate and univariate Laplace transforms of residual lives and reversed residual lives, associated stochastic ordering concepts, and entropy measures for reliability analysis. The package covers: (1) Bivariate Laplace transform of residual lives and stochastic comparisons based on the bivariate Laplace transform order of residual lives (BLt-rl), including weak bivariate hazard rate, mean residual life, and relative mean residual life orders, nonparametric estimation, and NBUHR/NWUHR aging class characterisation; Jayalekshmi, Rajesh, and Nair (2022) "Bivariate Laplace Transform of Residual Lives and Their Properties" <doi:10.1080/03610926.2022.2085874>; (2) Bivariate Laplace transform order of reversed residual lives (BLt-Rrl), reversed hazard gradient, reversed mean residual life, and the associated stochastic orders (weak bivariate reversed hazard rate, weak bivariate reversed mean residual life); Jayalekshmi, Rajesh, and Nair (2022) "Bivariate Laplace Transform Order and Ordering of Reversed Residual Lives" <doi:10.1142/S0218539322500061>; (3) Univariate Laplace transform of residual life, hazard rate, mean residual life, and the corresponding stochastic orders (Lt-rl order, hazard rate order, MRL order), together with a nonparametric estimator. Shannon entropy and Golomb's (1966) information generating function are also provided. Parametric families supported include the Gumbel bivariate exponential, Farlie-Gumbel-Morgenstern (FGM), bivariate power, and Schur-constant distributions. Plotting utilities and a simulation framework for evaluating estimator performance are also provided.

Computations for Bessel function for complex, real and partly mpfr (arbitrary precision) numbers; notably interfacing TOMS 644; approximations for large arguments, experiments, etc.