Multicenter randomized trials involve the collection and analysis of data from numerous study participants across multiple sites. Outliers may be present. To identify outliers, this package examines data at the individual level (univariate and multivariate) and site-level (with and without covariate adjustment). Methods are outlined in further detail in Rigdon et al (to appear).

Calculates a range of UK freshwater invertebrate biotic indices including BMWP, Whalley, WHPT, Habitat-specific BMWP, AWIC, LIFE and PSI.

This package provides tools designed to make it easier for beginner and intermediate users to build and validate binary logistic regression models. Includes bivariate analysis, comprehensive regression output, model fit statistics, variable selection procedures, model validation techniques and a shiny app for interactive model building.

We implemented a Bayesian-statistics approach for subtraction of incoherent scattering from neutron total-scattering data. In this approach, the estimated background signal associated with incoherent scattering maximizes the posterior probability, which combines the likelihood of this signal in reciprocal and real spaces with the prior that favors smooth lines. The description of the corresponding approach could be found at Gagin and Levin (2014) <DOI:10.1107/S1600576714023796>.

Calculate Bayesian marginal effects, average marginal effects, and marginal coefficients (also called population averaged coefficients) for models fit using the brms package including fixed effects, mixed effects, and location scale models. These are based on marginal predictions that integrate out random effects if necessary (see for example <doi:10.1186/s12874-015-0046-6> and <doi:10.1111/biom.12707>).

This package provides a collection of integrated tools designed to seamlessly interact with each other for the analysis of biogenic silica bSi in inland and marine sediments. These tools share common data representations and follow a consistent API design. The primary goal of the bSi package is to simplify the installation process, facilitate data loading, and enable the analysis of multiple samples for biogenic silica fluxes. This package is designed to enhance the efficiency and coherence of the entire bSi analytic workflow, from data loading to model construction and visualization tailored towards reconstructing productivity in aquatic ecosystems.

Computation and visualization of Bayesian Regions of Evidence to systematically evaluate the sensitivity of a superiority or non-inferiority claim against any prior assumption of its assessors. Methodological details are elaborated by Hoefler and Miller (<https://osf.io/jxnsv>). Besides generic functions, the package also provides an intuitive Shiny application, that can be run in local R environments.

Estimates Bayesian models of list experiments with informative priors. It includes functionalities to estimate different types of list experiment models with varying prior information. See Lu and Traunmüller (2026) <doi:10.1017/psrm.2025.10084> for examples and details of estimation.

Interface to the Python package BERTopic <https://maartengr.github.io/BERTopic/index.html> for transformer-based topic modeling. Provides R wrappers to fit BERTopic models, transform new documents, update and reduce topics, extract topic- and document-level information, and generate interactive visualizations. Python backends and dependencies are managed via the reticulate package.

Fit semiparametric bivariate correlated frailty models.

This R package offers block Gibbs samplers for the Bayesian (adaptive) graphical lasso, ridge, and naive elastic net priors. These samplers facilitate the simulation of the posterior distribution of precision matrices for Gaussian distributed data and were originally proposed by: Wang (2012) <doi:10.1214/12-BA729>; Smith et al. (2022) <doi:10.48550/arXiv.2210.16290> and Smith et al. (2023) <doi:10.48550/arXiv.2306.14199>, respectively.

Application of genome prediction for a continuous variable, focused on genotype by environment (GE) genomic selection models (GS). It consists a group of functions that help to create regression kernels for some GE genomic models proposed by Jarquà n et al. (2014) <doi:10.1007/s00122-013-2243-1> and Lopez-Cruz et al. (2015) <doi:10.1534/g3.114.016097>. Also, it computes genomic predictions based on Bayesian approaches. The prediction function uses an orthogonal transformation of the data and specific priors present by Cuevas et al. (2014) <doi:10.1534/g3.114.013094>.

This package provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) <arXiv:1906.07524>.

Bayesian approach to multidimensional scaling. The package consists of implementations of the methods of Oh and Raftery (2001) <doi:10.1198/016214501753208690>.

This app provides some useful tools for Offering an accessible GUI for generalised blockmodeling of single-relation, one-mode networks. The user can execute blockmodeling without having to write a line code by using the app's visual helps. Moreover, there are several ways to visualisations networks and their partitions. Finally, the results can be exported as if they were produced by writing code. The development of this package is financially supported by the Slovenian Research Agency (www.arrs.gov.si) within the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).

This package provides the facility to calculate the Brainerd-Robinson similarity coefficient for the rows of an input table, and to calculate the significance of each coefficient based on a permutation approach; a heatmap is produced to visually represent the similarity matrix. Optionally, hierarchical agglomerative clustering can be performed and the silhouette method is used to identify an optimal number of clusters; the results of the clustering can be optionally used to sort the heatmap.

This package provides an interface to data provided by the Bank for International Settlements <https://www.bis.org>, allowing for programmatic retrieval of a large quantity of (central) banking data.

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.

We perform general mediation analysis in the Bayesian setting using the methods described in Yu and Li (2022, ISBN:9780367365479). With the package, the mediation analysis can be performed on different types of outcomes (e.g., continuous, binary, categorical, or time-to-event), with default or user-defined priors and predictive models. The Bayesian estimates and credible sets of mediation effects are reported as analytic results.

This package performs Bayesian sample size, precision, and value-of-information analysis for external validation of existing multi-variable prediction models using the approach proposed by Sadatsafavi and colleagues (2026) <doi:10.1002/sim.70389>.

Finds the largest possible regression model that will still converge for various types of regression analyses (including mixed models and generalized additive models) and then optionally performs stepwise elimination similar to the forward and backward effect-selection methods in SAS, based on the change in log-likelihood or its significance, Akaike's Information Criterion, the Bayesian Information Criterion, the explained deviance, or the F-test of the change in R².

This package provides a client for retrieving data and metadata from central bank APIs including Banco de España (BdE), Banco de Portugal (BdP), Bank for International Settlements (BIS), Bank of Canada (BoC), Bank of England (BoE), Bank of Japan (BoJ), Banque de France (BdF), Czech National Bank (CNB), Deutsche Bundesbank (BBk), European Central Bank (ECB), National Bank of Poland (NBP), Norges Bank (NoB), Oesterreichische Nationalbank (OeNB), Sveriges Riksbank (SRb), and Swiss National Bank (SNB).

Bootstrap based goodness-of-fit tests. It allows to perform rigorous statistical tests to check if a chosen model family is correct based on the marked empirical process. The implemented algorithms are described in (Dikta and Scheer (2021) <doi:10.1007/978-3-030-73480-0>) and can be applied to generalized linear models without any further implementation effort. As far as certain linearity conditions are fulfilled the resampling scheme are also applicable beyond generalized linear models. This is reflected in the software architecture which allows to reuse the resampling scheme by implementing only certain interfaces for models that are not supported natively by the package.

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>.