MCMCpack: Markov chain Monte Carlo in R

By Andrew D. Martin, Kevin M. Quinn, J. Park. 2011

677 citations recorded by the source corpus.

We introduce MCMCpack, an R package that contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. In addition to code that can be used to fit commonly used models, 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.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/295d62b8ce499b21a7209eff480cb575d8a3fdc2

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In Statistics and Probability

Citations in the corpus

The estimation of item response models with the lmer function from the lme4 package in R (2011, methodology): “ The MCMCpack package (Martin et al. 2011) for the Bayesian estimation of the two-parameter one-dimensional and multidimensional normal-ogive models for binary responses.”
mirt: A Multidimensional Item Response Theory Package for the R Environment (2012, methodology): “Several R (R Development Core Team 2012) packages can be used to fit IRT models, such as: the ltm package (Rizopoulos 2006), which can handle the Rasch, general latent trait, threeparameter logistic, and graded response models; the eRm package (Mair and Hatzinger 2007), which can fit the rating scal”
Bayesian analysis of measurement error models using integrated nested Laplace approximations (2015, methodology): “…be challenging and sampling can become rather time consuming, although the use of generic software such as WinBugs (Lunn et al., 2000), OpenBugs (Lunn et al., 2009), JAGS (Plummer, 2003) or MCMC samplers in R, such as MCMCpack (Martin et al., 2011), facilitates the application of MCMC approaches.”
blavaan: Bayesian structural equation models via parameter expansion (2015, methodology): “…(Lunn, Jackson, Best, Thomas, and Spiegelhalter 2012; Lunn, Thomas, Best, and Spiegelhalter 2000), factor analysis samplers being included in R packages bfa (Murray 2014) and MCMCpack (Martin, Quinn, and Park 2011), and multiple samplers being implemented in Mplus (Muthén and Asparouhov 2012).”