Package: r-cran-mcmcpack (1.2-3-1)
Links for r-cran-mcmcpack
Download Source Package mcmcpack:
- Homepage [mcmcpack.wustl.edu]
R routines for Markov chain Monte Carlo model estimation
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.
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.
The suggested packages (r-cran-bayesm, -eco, and -mnp) contain additional models that may also be useful for those interested in this package.
Other Packages Related to r-cran-mcmcpack
- dep: libgcc1 (>= 1:4.1.1)
- GCC support library
- dep: libstdc++6 (>= 4.6)
- GNU Standard C++ Library v3
- dep: r-base-core (>= 2.15.0-1)
- GNU R core of statistical computation and graphics system
- dep: r-cran-coda (>> 0.11-3)
- Output analysis and diagnostics for MCMC simulations in R
- dep: r-cran-mass
- GNU R package of Venables and Ripley's MASS