Generates confidence intervals for standardized regression coefficients using delta method standard errors for models fitted by lm() as described in Yuan and Chan (2011) <doi:10.1007/s11336-011-9224-6> and Jones and Waller (2015) <doi:10.1007/s11336-013-9380-y>. The package can also be used to generate confidence intervals for differences of standardized regression coefficients and as a general approach to performing the delta method. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.

This package provides a computational toolbox designed for handling missing values in large datasets with the Multiple Imputation by Chained Equations (MICE) by using Apache Spark'. The methodology is described in Morvan et al. (2026) <doi:10.48550/arXiv.2601.21613>.

This package contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models and continuous threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.

This package provides functions for analyzing and visualizing complex macroevolutionary dynamics on phylogenetic trees. It is a companion package to the command line program BAMM (Bayesian Analysis of Macroevolutionary Mixtures) and is entirely oriented towards the analysis, interpretation, and visualization of evolutionary rates. Functionality includes visualization of rate shifts on phylogenies, estimating evolutionary rates through time, comparing posterior distributions of evolutionary rates across clades, comparing diversification models using Bayes factors, and more.

Build and compare nested statistical models with sets of equal and different independent variables. An analysis using this package is Marquardt et al. (2021) <https://github.com/p-mq/Percentile_based_averaging>.

Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.

This package provides tools to facilitate the access and processing of data from the Central Bank of Brazil API. The package allows users to retrieve economic and financial data, transforming them into usable tabular formats for further analysis. The data is obtained from the Central Bank of Brazil API: <https://api.bcb.gov.br/dados/serie/bcdata.sgs.series_code/dados?formato=json&dataInicial=start_date&dataFinal=end_date>.

This package provides tools for Bayesian basket trial design and analysis using a novel three-component local power prior framework with global borrowing control, pairwise similarity assessment and a borrowing threshold. Supports simulation-based evaluation of operating characteristics and comparison with other methods. Applicable to both equal and unequal sample size settings in early-phase oncology trials. For more details see Zhou et al. (2023) <doi:10.48550/arXiv.2312.15352>.

Fits and simulates multi-optima Ornstein-Uhlenbeck models to phylogenetic comparative data using Bayesian reversible-jump methods. See Uyeda and Harmon (2014) <DOI:10.1093/sysbio/syu057>.

This package creates plots showing scored HR experiments and plots of distribution of means of ranks of HR score from bootstrapping.

This package contains several plotting functions such as barplots, scatterplots, heatmaps, as well as functions to combine plots and assist in the creation of these plots. These functions will give users great ease of use and customization options in broad use for biomedical applications, as well as general purpose plotting. Each of the functions also provides valid default settings to make plotting data more efficient and producing high quality plots with standard colour schemes simpler. All functions within this package are capable of producing plots that are of the quality to be presented in scientific publications and journals. P'ng et al.; BPG: Seamless, automated and interactive visualization of scientific data; BMC Bioinformatics 2019 <doi:10.1186/s12859-019-2610-2>.

Algorithms developed for binned data analysis, gene expression data analysis and measurement error models for ordinal data analysis.

Allows the reenactment of the R programs used in the book Bayesian Essentials with R without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations. Marin J.-M. and Robert C. P. (2014) <doi:10.1007/978-1-4614-8687-9>.

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 performs BTLLasso as described by Schauberger and Tutz (2019) <doi:10.18637/jss.v088.i09> and Schauberger and Tutz (2017) <doi:10.1177/1471082X17693086>. BTLLasso is a method to include different types of variables in paired comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used to cluster certain effects and to reduce the complexity of the models.

The BAGofT assesses the goodness-of-fit of binary classifiers. Details can be found in Zhang, Ding and Yang (2021) <arXiv:1911.03063v2>.

Toolkit for Bayesian estimation of the dependence structure in multivariate extreme value parametric models, following Sabourin and Naveau (2014) <doi:10.1016/j.csda.2013.04.021> and Sabourin, Naveau and Fougeres (2013) <doi:10.1007/s10687-012-0163-0>.

This package implements the Butterworth-Induced Autoregressive ('BTWAR') model, where autoregressive coefficients are obtained from analog Butterworth filter prototypes mapped into the discrete-time domain using the Matched Z-Transform. The framework establishes a structured connection between frequency-domain filter design and time-domain autoregressive modeling. Model order selection is performed via nested rolling-origin cross-validation. Method described in Bras-Geraldes, Rocha and Martins (2026) <doi:10.3390/math14030479>.

Several implementations of a novel Bayesian hierarchical statistical model of nucleotide recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) for analyzing and comparing NR-seq datasets (see Vock and Simon (2023) <doi:10.1261/rna.079451.122>). NR-seq is a powerful extension of RNA-seq that provides information about the kinetics of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking in standard RNA-seq data. The statistical model makes maximal use of these high-throughput datasets by sharing information across transcripts to significantly improve uncertainty quantification and increase statistical power. bakR includes a maximally efficient implementation of this model for conservative initial investigations of datasets. bakR also provides more highly powered implementations using the probabilistic programming language Stan to sample from the full posterior distribution. bakR performs multiple-test adjusted statistical inference with the output of these model implementations to help biologists separate signal from background. Methods to automatically visualize key results and detect batch effects are also provided.

This package contains Bayesian implementations of the Mixed-Effects Accelerated Failure Time (MEAFT) models for censored data. Those can be not only right-censored but also interval-censored, doubly-interval-censored or misclassified interval-censored. The methods implemented in the package have been published in Komárek and Lesaffre (2006, Stat. Modelling) <doi:10.1191/1471082X06st107oa>, Komárek, Lesaffre and Legrand (2007, Stat. in Medicine) <doi:10.1002/sim.3083>, Komárek and Lesaffre (2007, Stat. Sinica) <https://www3.stat.sinica.edu.tw/statistica/oldpdf/A17n27.pdf>, Komárek and Lesaffre (2008, JASA) <doi:10.1198/016214507000000563>, Garcà a-Zattera, Jara and Komárek (2016, Biometrics) <doi:10.1111/biom.12424>.

Fast and accurate calculation of Blaker's binomial and Poisson confidence limits (and some related stuff).

This package performs goodness of fit test for the Birnbaum-Saunders distribution and provides the maximum likelihood estimate and the method-of-moments estimate. For more details, see Park and Wang (2013) <arXiv:2308.10150>. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528).

This package provides functions to create and select graphical themes for the base plotting system. Contains: 1) several custom pre-made themes 2) mechanism for creating new themes by making persistent changes to the graphical parameters of base plots.

This package implements the Block-wise Rank in Similarity Graph Edge-count test (BRISE), a rank-based two-sample test designed for block-wise missing data. The method constructs (pattern) pair-wise similarity graphs and derives quadratic test statistics with asymptotic chi-square distribution or permutation-based p-values. It provides both vectorized and congregated versions for flexible inference. The methodology is described in Zhang, Liang, Maile, and Zhou (2025) <doi:10.48550/arXiv.2508.17411>.