Description: Provides a Bayesian version of the analysis of variance based on a three-component Gaussian mixture for which a Gibbs sampler produces posterior draws. For details about the Bayesian ANOVA based on Gaussian mixtures, see Kelter (2019) <arXiv:1906.07524>.

Title: Bayesian Inference in the Analysis of Variance via Markov Chain

person("Jane", "Pan", email = "jpan1@ucla.edu", role = c("cre", "aut")),
person("Sudipto", "Banerjee", email = "sudipto@ucla.edu", role = "aut"))
Description: Computes Bayesian assurance under various
settings characterized by different assumptions and objectives, including
precision-based conditions, credible intervals, and goal functions.
All simulation-based functions included in this package rely on a two-stage
Bayesian method that assigns two distinct priors to evaluate the
probability of observing a positive outcome, which addresses subtle
limitations that take place when using the standard single-prior approach.
For more information, please refer to Pan and Banerjee (2021)

Title: Miscellaneous Functions for Bioinformatics and Bayesian
Authors@R: person("Andrew", "McKenzie", email = "amckenz@gmail.com", role = c("aut", "cre"))
Description: A hodgepodge of hopefully helpful functions. Two of these perform
shrinkage estimation: one using a simple weighted method where the user can
specify the degree of shrinkage required, and one using James-Stein shrinkage
estimation for the case of unequal variances.

Title: An Implementation of Rubin's (1981) Bayesian Bootstrap
Authors@R: person("Rasmus", "Bååth", email = "rasmus.baath@gmail.com", role = c("aut", "cre"))
Description: Functions for performing the Bayesian bootstrap as introduced by
Rubin (1981) <doi:10.1214/aos/1176345338> and for summarizing the result. The implementation can handle
both summary statistics that works on a weighted version of the data and
summary statistics that works on a resampled data set.
Imports: plyr (>= 1.8.3), HDInterval(>= 0.1.1)

Rubin (1981) <doi:10.1214/aos/1176345338> and for summarizing the result.
The implementation can handle both summary statistics that works on a
weighted version of the data and summary statistics that works on a