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Computation of Bayes Factors for Common Designs

A suite of functions for computing various Bayes factors for simple designs, including contingency tables, one- and two-sample designs, one-way designs, general ANOVA designs, and linear regression.

Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien

Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, generalized k-nearest neighbour ...

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,
< http://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>).
Gronau, Singmann, & Wagenmakers (2020)

Empirical Bayes Thresholding and Related Methods

Empirical Bayes thresholding using the methods developed by I. M. Johnstone and B. W. Silverman. The basic problem is to estimate a mean vector given a vector of observations of the mean vector plus white noise, taking advantage of possible sparsity in the mean vector. Within a Bayesian formulation, the elements of the mean vector are modelled as having, independently, a distribution that is a mixture of an atom of probability at zero and a suitable heavy-tailed distribution. The mixing parameter can be estimated by a marginal maximum likelihood approach. This leads to an adaptive thresholding approach on the original data. Extensions of the basic method, in particular to wavelet thresholding, are also implemented within the package.

Empirical Bayes Estimation and Inference

Kiefer-Wolfowitz maximum likelihood estimation for mixture models
and some other density estimation and regression methods based on convex
optimization. See Koenker and Gu (2017) REBayes: An R Package for Empirical
Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26,

High Performance Implementation of the Naive Bayes Algorithm

In this implementation of the Naive Bayes classifier following class conditional distributions are available: Bernoulli, Categorical, Gaussian, Poisson and non-parametric representation of the class conditional density estimated via Kernel Density Estimation. Implemented classifiers handle missing data and can take advantage of sparse data.

Methods for Adaptive Shrinkage, using Empirical Bayes

The R package 'ashr' implements an Empirical Bayes
approach for large-scale hypothesis testing and false discovery
rate (FDR) estimation based on the methods proposed in
M. Stephens, 2016, "False discovery rates: a new deal",

Naive Bayes Classifier

Predicts any variable in any categorical dataset for given values of predictor variables. If a dataset contains 4 variables, then any variable can be predicted based on the values of the other three variables given by the user. The user can upload their own datasets and select what variable they want to predict. A 'handsontable' is provided to enter the predictor values and also accuracy of the prediction is also shown.

Boltzmann Bayes Learner

Supervised learning using Boltzmann Bayes model inference,
which extends naive Bayes model to include interactions. Enables
classification of data into multiple response groups based on a large
number of discrete predictors that can take factor values of
heterogeneous levels. Either pseudo-likelihood or mean field
inference can be used with L2 regularization, cross-validation, and
prediction on new data.
Woo et al. (2016)

Empirical Bayes Ranking

Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors. A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given.