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Extremely Fast Implementation of a Naive Bayes Classifier
This is an extremely fast implementation of a Naive Bayes classifier. This
package is currently the only package that supports a Bernoulli distribution, a Multinomial
distribution, and a Gaussian distribution, making it suitable for both binary features,
frequency counts, and numerical features. Another feature is the support of a mix of
different event models. Only numerical variables are allowed, however, categorical variables
can be transformed into dummies and used with the Bernoulli distribution.
The implementation is largely based on the paper
"A comparison of event models for Naive Bayes anti-spam e-mail filtering"
written by K.M. Schneider (2003)
Bridge Sampling for Marginal Likelihoods and Bayes Factors
Provides functions for estimating marginal likelihoods, Bayes
factors, posterior model probabilities, and normalizing constants in general,
via different versions of bridge sampling (Meng & Wong, 1996,
< https://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>).
Gronau, Singmann, & Wagenmakers (2020)
Flexible Bayes Factor Testing of Scientific Expectations
Implementation of default Bayes factors
for testing statistical hypotheses under various statistical models. The package is
intended for applied quantitative researchers in the
social and behavioral sciences, medical research,
and related fields. The Bayes factor tests can be
executed for statistical models such as
univariate and multivariate normal linear models,
correlation analysis, generalized linear models, special cases of
linear mixed models, survival models, relational
event models. Parameters that can be tested are
location parameters (e.g., group means, regression coefficients),
variances (e.g., group variances), and measures of
association (e.g,. polychoric/polyserial/biserial/tetrachoric/product
moments correlations), among others.
The statistical underpinnings are
described in
O'Hagan (1995)
Empirical Bayes Single Nucleotide Variant Calling
Identifies single nucleotide variants in next-generation sequencing data by estimating their local false discovery rates. For more details, see Karimnezhad, A. and Perkins, T. J. (2024)
An Empirical Bayes Method for Chi-Squared Data
We provide the main R functions to compute the posterior interval for the noncentrality parameter of the chi-squared distribution. The skewness estimate of the posterior distribution is also available to improve the coverage rate of posterior intervals. Details can be found in Du and Hu (2020)
"Empirical Bayes Smoothing Splines with Correlated Errors"
Presents a statistical method that uses a recursive algorithm for signal extraction. The method handles a non-parametric estimation for the correlation of the errors. See "Krivobokova", "Serra", "Rosales" and "Klockmann" (2021)
Datasets and Supplemental Functions from Bayes Rules! Book
Provides datasets and functions used for analysis and visualizations in the Bayes Rules! book (< https://www.bayesrulesbook.com>). The package contains a set of functions that summarize and plot Bayesian models from some conjugate families and another set of functions for evaluation of some Bayesian models.
Variational Bayes Latent Position Cluster Model for Networks
Fit and simulate latent position and cluster models for network data, using a fast Variational Bayes approximation developed in Salter-Townshend and Murphy (2013)
Power and Sample Size Calculations for Bayes Factor Analysis
Implements z-test, t-test, and normal moment prior Bayes factors based on summary statistics, along with functionality to perform corresponding power and sample size calculations as described in Pawel and Held (2025)
Compute FAB (Frequentist and Bayes) Conformal Prediction Intervals
Computes and plots prediction intervals for numerical
data or prediction sets for categorical data using prior information.
Empirical Bayes procedures to estimate the prior information from
multi-group data are included. See, e.g.,Bersson and Hoff (2022)