The Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012) can be used to estimate the causal effect of a continuous exposure on a continuous outcome. This package provides an approximate sensitivity analysis of BAC with regards to the hyperparameter omega. BACprior also provides functions to guide the user in their choice of an appropriate omega value. The method is based on Lefebvre, Atherton and Talbot (2014).

Used for Bayesian mediation analysis based on Bayesian additive Regression Trees (BART). The analysis method is described in Yu and Li (2025) "Mediation Analysis with Bayesian Additive Regression Trees", submitted for publication.

This package provides functions for summarizing and plotting the output of the command-line tool BeXY (<https://bitbucket.org/wegmannlab/bexy>), a tool that performs Bayesian inference of sex chromosome karyotypes and sex-linked scaffolds from low-depth sequencing data.

Package provides functions for estimation and inference in Bayesian quantile regression with ordinal outcomes. An ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings (MH) algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using a Gibbs sampling algorithm. The summary output presents the posterior mean, posterior standard deviation, 95% credible intervals, and the inefficiency factors along with the two model comparison measures â logarithm of marginal likelihood and the deviance information criterion (DIC). The package also provides functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models. Rahman, M. A. (2016).â Bayesian Quantile Regression for Ordinal Models.â Bayesian Analysis, 11(1): 1-24 <doi: 10.1214/15-BA939>. Yu, K., and Moyeed, R. A. (2001). â Bayesian Quantile Regression.â Statistics and Probability Letters, 54(4): 437â 447 <doi: 10.1016/S0167-7152(01)00124-9>. Koenker, R., and Bassett, G. (1978).â Regression Quantiles.â Econometrica, 46(1): 33-50 <doi: 10.2307/1913643>. Chib, S. (1995). â Marginal likelihood from the Gibbs output.â Journal of the American Statistical Association, 90(432):1313â 1321, 1995. <doi: 10.1080/01621459.1995.10476635>. Chib, S., and Jeliazkov, I. (2001). â Marginal likelihood from the Metropolis-Hastings output.â Journal of the American Statistical Association, 96(453):270â 281, 2001. <doi: 10.1198/016214501750332848>.

This package provides a set of R functions and data sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2017), John Wiley & Sons ISBN 978-1-118-09156-2.

This package performs change point detection on univariate and multivariate time series (Martà nez & Mena, 2014, <doi:10.1214/14-BA878> ; Corradin, Danese & Ongaro, 2022, <doi:10.1016/j.ijar.2021.12.019>) and clusters time-dependent data with common change points (Corradin, Danese, KhudaBukhsh & Ongaro, 2026, <doi:10.1007/s11222-025-10756-x>).

This package provides functions for data preparation, parameter estimation, scoring, and plotting for the BG/BB (Fader, Hardie, and Shang 2010 <doi:10.1287/mksc.1100.0580>), BG/NBD (Fader, Hardie, and Lee 2005 <doi:10.1287/mksc.1040.0098>) and Pareto/NBD and Gamma/Gamma (Fader, Hardie, and Lee 2005 <doi:10.1509/jmkr.2005.42.4.415>) models.

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>.

Component-wise gradient boosting for analysis of multiply imputed datasets. Implements the algorithm Boosting after Multiple Imputation (MIBoost), which enforces uniform variable selection across imputations and provides utilities for pooling. Includes a cross-validation workflow that first splits the data into training and validation sets and then performs imputation on the training data, applying the learned imputation models to the validation data to avoid information leakage. Supports Gaussian and logistic loss. Methods relate to gradient boosting and multiple imputation as in Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>, Friedman (2001) <doi:10.1214/aos/1013203451>, and van Buuren (2018, ISBN:9781138588318) and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; see also Kuchen (2025) <doi:10.48550/arXiv.2507.21807>.

Allows the user to generate bootstrap cards within R markdown documents. Intended for use in conjunction with R markdown HTML outputs and other formats that support the bootstrap 4 library.

Fitting, cross-validating, and predicting with Bayesian Knowledge Tracing (BKT) models. It is designed for analyzing educational datasets to trace student knowledge over time. The package includes functions for fitting BKT models, evaluating their performance using various metrics, and making predictions on new data. It provides the similar functionality as the Python package pyBKT authored by Zachary A. Pardos (zp@berkeley.edu) at <https://github.com/CAHLR/pyBKT>.

This package provides Bayesian estimation and forecasting of dynamic panel data using Bayesian Panel Vector Autoregressions with hierarchical prior distributions. The models include country-specific VARs that share a global prior distribution that extend the model by JarociÅ ski (2010) <doi:10.1002/jae.1082>. Under this prior expected value, each country's system follows a global VAR with country-invariant parameters. Further flexibility is provided by the hierarchical prior structure that retains the Minnesota prior interpretation for the global VAR and features estimated prior covariance matrices, shrinkage, and persistence levels. Bayesian forecasting is developed for models including exogenous variables, allowing conditional forecasts given the future trajectories of some variables and restricted forecasts assuring that rates are forecasted to stay positive and less than 100. The package implements the model specification, estimation, and forecasting routines, facilitating coherent workflows and reproducibility. It also includes automated pseudo-out-of-sample forecasting and computation of forecasting performance measures. Beautiful plots, informative summary functions, and extensive documentation complement all this. An extraordinary computational speed is achieved thanks to employing frontier econometric and numerical techniques and algorithms written in C++'. The bpvars package is aligned regarding objects, workflows, and code structure with the R packages bsvars by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars> and bsvarSIGNs by Wang & Woźniak (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they constitute an integrated toolset. Copyright: 2025 International Labour Organization.

Subgroup analyses are routinely performed in clinical trial analyses. From a methodological perspective, two key issues of subgroup analyses are multiplicity (even if only predefined subgroups are investigated) and the low sample sizes of subgroups which lead to highly variable estimates, see e.g. Yusuf et al (1991) <doi:10.1001/jama.1991.03470010097038>. This package implements subgroup estimates based on Bayesian shrinkage priors, see Carvalho et al (2019) <https://proceedings.mlr.press/v5/carvalho09a.html>. In addition, estimates based on penalized likelihood inference are available, based on Simon et al (2011) <doi:10.18637/jss.v039.i05>. The corresponding shrinkage based forest plots address the aforementioned issues and can complement standard forest plots in practical clinical trial analyses.

Bagged OutlierTrees is an explainable unsupervised outlier detection method based on an ensemble implementation of the existing OutlierTree procedure (Cortes, 2020). This implementation takes advantage of bootstrap aggregating (bagging) to improve robustness by reducing the possible masking effect and subsequent high variance (similarly to Isolation Forest), hence the name "Bagged OutlierTrees". To learn more about the base procedure OutlierTree (Cortes, 2020), please refer to <arXiv:2001.00636>.

Geographically referenced data and statistics of nighttime lights from NASA Black Marble <https://blackmarble.gsfc.nasa.gov/>.

Querying, extracting, and processing large-scale network data from Neo4j databases using the Neo4j Bolt <https://neo4j.com/docs/bolt/current/bolt/> protocol. This interface supports efficient data retrieval, batch processing for large datasets, and seamless conversion of query results into R data frames, making it ideal for bioinformatics, computational biology, and other graph-based applications.

This package provides tools and workflow to choose design parameters in Bayesian adaptive single-arm phase II trial designs with binary endpoint (response, success) with possible stopping for efficacy and futility at interim analyses. Also contains routines to determine and visualize operating characteristics. See Kopp-Schneider et al. (2018) <doi:10.1002/bimj.201700209>.

This package implements Roy's bivariate geometric model (Roy (1993) <doi:10.1006/jmva.1993.1065>): joint probability mass function, distribution function, survival function, random generation, parameter estimation, and more.

This package provides tools to fit Bayesian state-space models to animal tracking data. Models are provided for location filtering, location filtering and behavioural state estimation, and their hierarchical versions. The models are primarily intended for fitting to ARGOS satellite tracking data but options exist to fit to other tracking data types. For Global Positioning System data, consider the moveHMM package. Simplified Markov Chain Monte Carlo convergence diagnostic plotting is provided but users are encouraged to explore tools available in packages such as coda and boa'.

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>.

Calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965).

This package provides a collection of LaTeX styles using Beamer customization for pdf-based presentation slides in RMarkdown'. At present it contains RMarkdown adaptations of the LaTeX themes Metropolis (formerly mtheme') theme by Matthias Vogelgesang and others (now included in TeXLive'), the IQSS by Ista Zahn (which is included here), and the Monash theme by Rob J Hyndman. Additional (free) fonts may be needed: Metropolis prefers Fira', and IQSS requires Libertinus'.

This package provides functions for training extreme gradient boosting model using propensity score A-learning and weight-learning methods. For further details, see Liu et al. (2024) <doi:10.1093/bioinformatics/btae592>.

Fits Cox model via stochastic gradient descent. This implementation avoids computational instability of the standard Cox Model when dealing large datasets. Furthermore, it scales up with large datasets that do not fit the memory. It also handles large sparse datasets using proximal stochastic gradient descent algorithm. For more details about the method, please see Aliasghar Tarkhan and Noah Simon (2020) <arXiv:2003.00116v2>.