MCMCpack: Markov chain Monte Carlo (MCMC) Package. This package contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return coda mcmc objects that can then be summarized using the coda package. MCMCpack also contains some useful utility functions, including some additional density functions and pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization.
Keywords for this software
References in zbMATH (referenced in 10 articles )
Showing results 1 to 10 of 10.
- Leung, Dennis; Drton, Mathias: Order-invariant prior specification in Bayesian factor analysis (2016)
- Macaro, Christian; Prado, Raquel: Spectral decompositions of multiple time series: a Bayesian non-parametric approach (2014)
- Aston, John A.D.; Peng, J.Y.; Martin, Donald E.K.: Implied distributions in multiple change point problems (2012)
- Ji, Yonggang; Lin, Nan; Zhang, Baoxue: Model selection in binary and Tobit quantile regression using the Gibbs sampler (2012)
- Kaplan, David; Chen, Jianshen: A two-step Bayesian approach for propensity score analysis: simulations and case study (2012)
- Klein, Martin; Neerchal, Nagaraj; Sinha, Bimal; Chiu, Weihsueh; White, Paul: Statistical inferences from serially correlated methylene chloride data (2012)
- Nott, David J.; Leng, Chenlei: Bayesian projection approaches to variable selection in generalized linear models (2010)
- Gelman, Andrew; Jakulin, Aleks; Pittau, Maria Grazia; Su, Yu-Sung: A weakly informative default prior distribution for logistic and other regression models (2008)
- Fahrmeir, Ludwig; Raach, Alexander: A Bayesian semiparametric latent variable model for mixed responses (2007)
- Rosenthal, Jeffrey S.: AMCMC: an R interface for adaptive MCMC (2007)