r-cran-mcmcpack - 1.4-4-1 main

This is a set of routines for GNU R that implement various
statistical and econometric models using Markov chain Monte Carlo
(MCMC) estimation, which allows "solving" models that would otherwise
be intractable with traditional techniques, particularly problems in
Bayesian statistics (where one or more "priors" are used as part of
the estimation procedure, instead of an assumption of ignorance about
the "true" point estimates), although MCMC can also be used to solve
frequentist statistical problems with uninformative priors. MCMC
techniques are also preferable over direct estimation in the presence
of missing data.
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Currently implemented are a number of ecological inference (EI)
routines (for estimating individual-level attributes or behavior from
aggregate data, such as electoral returns or census results), as well
as models for traditional linear panel and cross-sectional data, some
visualization routines for EI diagnostics, two item-response theory
(or ideal-point estimation) models, metric, ordinal, and
mixed-response factor analysis, and models for Gaussian (linear) and
Poisson regression, logistic regression (or logit), and binary and
ordinal-response probit models.
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The suggested packages (r-cran-bayesm, -eco, and -mnp) contain
additional models that may also be useful for those interested in
this package.