Robust Empirical Bayes Confidence Intervals

Computes empirical Bayes confidence estimators and confidence intervals in a normal means model. The intervals are robust in the sense that they achieve correct coverage regardless of the distribution of the means. If the means are treated as fixed, the intervals have an average coverage guarantee. The implementation is based on Armstrong, Kolesár and Plagborg-Møller (2020) .

Scalable Bayes with Median of Subset Posteriors

Median-of-means is a generic yet powerful framework for scalable and robust estimation. A framework for Bayesian analysis is called M-posterior, which estimates a median of subset posterior measures. For general exposition to the topic, see the paper by Minsker (2015) .

Bayesian Network Structure Learning, Parameter Learning and Inference

Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from < https://www.bnlearn.com/>.

Understand and Describe Bayesian Models and Posterior Distributions

Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Kruschke, 2015 ) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors). References: Makowski et al. (2021) .

Empirical Bayes Estimation of Dynamic Bayesian Networks

Infer the adjacency matrix of a network from time course data using an empirical Bayes estimation procedure based on Dynamic Bayesian Networks.

Computation of Bayes Factors for Common Biomedical Designs

BAYesian inference for MEDical designs in R. Functions for the computation of Bayes factors for common biomedical research designs. Implemented are functions to test the equivalence (equiv_bf), non-inferiority (infer_bf), and superiority (super_bf) of an experimental group compared to a control group on a continuous outcome measure. Bayes factors for these three tests can be computed based on raw data (x, y) or summary statistics (n_x, n_y, mean_x, mean_y, sd_x, sd_y [or ci_margin and ci_level]).

Interactive Document for Working with Naive Bayes Classification

An interactive document on the topic of naive Bayes classification analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at < https://kartikeyab.shinyapps.io/NBShiny/>.

Interactive Document for Working with Naive Bayes Classification

An interactive document on the topic of naive Bayes classification analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at < https://kartikeyab.shinyapps.io/NBShiny/>.

Interactive Document for Working with Naive Bayes Classification

An interactive document on the topic of naive Bayes classification analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at < https://kartikeyab.shinyapps.io/NBShiny/>.

Empirical Bayes Multi-State Cox Model

Implements an empirical Bayes, multi-state Cox model for survival analysis. Run "?'ebmstate-package'" for details. See also Schall (1991) .