R package. mcmc: Markov Chain Monte Carlo. Simulates continuous distributions of random vectors using Markov chain Monte Carlo (MCMC). Users specify the distribution by an R function that evaluates the log unnormalized density. Algorithms are random walk Metropolis algorithm (function metrop), simulated tempering (function temper), and morphometric random walk Metropolis (Johnson and Geyer, Annals of Statistics, 2012, function morph.metrop), which achieves geometric ergodicity by change of variable.
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
- Consonni, Guido; Forster, Jonathan J.; La Rocca, Luca: The whetstone and the alum block: balanced objective Bayesian comparison of nested models for discrete data (2013)
- Shao, Wei; Guo, Guangbao; Meng, Fanyu; Jia, Shuqin: An efficient proposal distribution for Metropolis-Hastings using a $B$-splines technique (2013)
- Johnson, Leif T.; Geyer, Charles J.: Variable transformation to obtain geometric ergodicity in the random-walk Metropolis algorithm (2012)
- Okabayashi, Saisuke; Geyer, Charles J.: Long range search for maximum likelihood in exponential families (2012)
- Raftery, Adrian E.; Bao, Le: Estimating and projecting trends in HIV/AIDS generalized epidemics using incremental mixture importance sampling (2010)
- Rosenthal, Jeffrey S.: AMCMC: an R interface for adaptive MCMC (2007)