Provides tools for making statistical comparisons with distance-function based permutation tests developed by P. W. Mielke, Jr. and colleagues at Colorado State University (Mielke, P. W. & Berry, K. J. Permutation Methods: A Distance Function Approach (Springer, New York, 2001)) and for testing parameters estimated in linear models with permutation procedures developed by B. S. Cade and colleagues at the Fort Collins Science Center, U. S. Geological Survey.

Blyth-Still-Casella Exact Binomial Confidence Intervals
c(
person("Ron", "Yu", email = "ronyu5135@gmail.com", role = c("aut", "cre")),
person("Peiwen", "Wu", email = "pwu.stat@gmail.com", role = "aut"))
Computes Blyth-Still-Casella exact binomial confidence intervals based on a refining procedure proposed by George Casella (1986) <doi:10.2307/3314658>.

This is a rough bare-bones implementation of sampling
algorithms for Bayesian stick-breaking (marginally DP) mixture
models. The software is designed to be easy to customize to
suit different situations and for experimentation with
stick-breaking models.  Since particles are repeatedly copied,
it is not an especially efficient implementation.

Bayesian Sampling for Stick-breaking Mixtures
This is a bare-bones implementation of sampling algorithms
for a variety of Bayesian stick-breaking (marginally DP)
mixture models, including particle learning and Gibbs sampling
for static DP mixtures, particle learning for dynamic BAR
stick-breaking, and DP mixture regression. The software is
designed to be easy to customize to suit different situations
and for experimentation with stick-breaking models.  Since
particles are repeatedly copied, it is not an especially
efficient implementation.

This is a bare-bones implementation of sampling algorithms
for a variety of Bayesian stick-breaking (marginally DP)
mixture models, including particle learning and Gibbs sampling
for static DP mixtures, particle learning for dynamic BAR
stick-breaking, and DP mixture regression.  The software is
designed to be easy to customize to suit different situations
and for experimentation with stick-breaking models.  Since
particles are repeatedly copied, it is not an especially
efficient implementation.