Suite of tools that facilitate exposure-response analysis using Bayesian methods. The package provides a streamlined workflow for fitting types of models that are commonly used in exposure-response analysis - linear and Emax for continuous endpoints, logistic linear and logistic Emax for binary endpoints, as well as performing simulation and visualization. Learn more about the workflow at <https://genentech.github.io/BayesERbook/>.

Users can estimate the treatment effect for multiple subgroups basket trials based on the Bayesian Cluster Hierarchical Model (BCHM). In this model, a Bayesian non-parametric method is applied to dynamically calculate the number of clusters by conducting the multiple cluster classification based on subgroup outcomes. Hierarchical model is used to compute the posterior probability of treatment effect with the borrowing strength determined by the Bayesian non-parametric clustering and the similarities between subgroups. To use this package, JAGS software and rjags package are required, and users need to pre-install them.

This package provides functions and datasets for Jeff Gill: "Bayesian Methods: A Social and Behavioral Sciences Approach". First, Second, and Third Edition. Published by Chapman and Hall/CRC (2002, 2007, 2014) <doi:10.1201/b17888>.

The Bootstrap framework lets you add some JavaScript functionality to your web site by adding attributes to your HTML tags - Bootstrap takes care of the JavaScript <https://getbootstrap.com/docs/3.3/javascript/>. If you are using R Markdown or Shiny, you can use these functions to create collapsible sections, accordion panels, modals, tooltips, popovers, and an accordion sidebar framework (not described at Bootstrap site). Please note this package was designed for Bootstrap 3.3.

This package provides an interface to Bank of Japan <https://www.boj.or.jp> statistics.

Bayes factors represent the ratio of probabilities assigned to data by competing scientific hypotheses. However, one drawback of Bayes factors is their dependence on prior specifications that define null and alternative hypotheses. Additionally, there are challenges in their computation. To address these issues, we define Bayes factor functions (BFFs) directly from common test statistics. BFFs express Bayes factors as a function of the prior densities used to define the alternative hypotheses. These prior densities are centered on standardized effects, which serve as indices for the BFF. Therefore, BFFs offer a summary of evidence in favor of alternative hypotheses that correspond to a range of scientifically interesting effect sizes. Such summaries remove the need for arbitrary thresholds to determine "statistical significance." BFFs are available in closed form and can be easily computed from z, t, chi-squared, and F statistics. They depend on hyperparameters "r" and "tau^2", which determine the shape and scale of the prior distributions defining the alternative hypotheses. Plots of BFFs versus effect size provide informative summaries of hypothesis tests that can be easily aggregated across studies.

Fit beta calibration models and obtain calibrated probabilities from them.

Decomposition for differences-in-differences with variation in treatment timing from Goodman-Bacon (2018) <doi:10.3386/w25018>.

Make Bootstrap 4 Shiny dashboards. Use the full power of AdminLTE3', a dashboard template built on top of Bootstrap 4 <https://github.com/ColorlibHQ/AdminLTE>.

Using a Bayesian estimation procedure, this package fits linear quantile regression models such as linear quantile models, linear quantile mixed models, quantile regression joint models for time-to-event and longitudinal data. The estimation procedure is based on the asymmetric Laplace distribution and the JAGS software is used to get posterior samples (Yang, Luo, DeSantis (2019) <doi:10.1177/0962280218784757>).

Allows local bone density estimates to be derived from CT data and mapped to 3D bone models in a reproducible manner. Processing can be performed at the individual bone or group level. Also includes tools for visualizing the bone density estimates. Example methods are described in Telfer et al., (2021) <doi:10.1002/jor.24792>, Telfer et al., (2021) <doi:10.1016/j.jse.2021.05.011>.

Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features.

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. BEAST2 is commonly accompanied by BEAUti 2', Tracer and DensiTree'. babette provides for an alternative workflow of using all these tools separately. This allows doing complex Bayesian phylogenetics easily and reproducibly from R'.

Whole-genome regression methods on Bayesian framework fitted via EM or Gibbs sampling, single step (<doi:10.1534/g3.119.400728>), univariate and multivariate (<doi:10.1186/s12711-022-00730-w>, <doi:10.1093/genetics/iyae179>), with optional kernel term and sampling techniques (<doi:10.1186/s12859-017-1582-3>).

Analyze bullet lands using nonparametric methods. We provide a reading routine for x3p files (see <http://www.openfmc.org> for more information) and a host of analysis functions designed to assess the probability that two bullets were fired from the same gun barrel.

Anonymised Bay Area bike share trip data for the year 2014. Also contains additional metadata on stations and weather.

This package provides functions for data augmentation using the Bayesian discount prior method for single arm and two-arm clinical trials, as described in Haddad et al. (2017) <doi:10.1080/10543406.2017.1300907>. The discount power prior methodology was developed in collaboration with the The Medical Device Innovation Consortium (MDIC) Computer Modeling & Simulation Working Group.

Developed for the following tasks. Simulating, computing maximum likelihood estimator, computing the Fisher information matrix, computing goodness-of-fit measures, and correcting bias of the ML estimator for a wide range of distributions fitted to units placed on progressive type-I interval censoring and progressive type-II censoring plans. The methods of Cox and Snell (1968) <doi:10.1111/j.2517-6161.1968.tb00724.x> and bootstrap method for computing the bias-corrected maximum likelihood estimator.

This package implements the Bayesian paradigm for fractional polynomial models under the assumption of normally distributed error terms, see Sabanes Bove, D. and Held, L. (2011) <doi:10.1007/s11222-010-9170-7>.

This package provides a developing software suite for multiple change-point and change-point-type feature detection/estimation (data segmentation) in data sequences.

This package provides tools for the calculation of common biodiversity indices from count data. Additionally, it incorporates bootstrapping techniques to generate multiple samples, facilitating the estimation of confidence intervals around these indices. Furthermore, the package allows for the exploration of how variation in these indices changes with differing numbers of sites, making it a useful tool with which to begin an ecological analysis. Methods are based on the following references: Chao et al. (2014) <doi:10.1890/13-0133.1>, Chao and Colwell (2022) <doi:10.1002/9781119902911.ch2>, Hsieh, Ma,` and Chao (2016) <doi:10.1111/2041-210X.12613>.

Designed to simplify the process of retrieving datasets from the Big Data PE platform using secure token-based authentication. It provides functions for securely storing, retrieving, and managing tokens associated with specific datasets, as well as fetching and processing data using the httr2 package.

This package provides a set of R functions and data sets for the book "Understanding Computational Bayesian Statistics." This book was written by Bill (WM) Bolstad and published in 2009 by John Wiley & Sons (ISBN 978-0470046098).

It is designed to calculate connection between (among) brain regions and plot connection lines. Also, the summary function is included to summarize group-level connectivity network. Kang, Jian (2016) <doi:10.1016/j.neuroimage.2016.06.042>.