potts: Markov Chain Monte Carlo for Potts Models

Do Markov chain Monte Carlo (MCMC) simulation of Potts models (Potts, 1952, <https://doi.org/10.1017/S0305004100027419>), which are the multi-color generalization of Ising models (so, as as special case, also simulates Ising models). Use the Swendsen-Wang algorithm (Swendsen and Wang, 1987, <https://doi.org/10.1103/PhysRevLett.58.86>) so MCMC is fast. Do maximum composite likelihood estimation of parameters (Besag, 1975, <https://doi.org/10.2307/2987782>, Lindsay, 1988, <https://doi.org/10.1090/conm/080>).

Package details

AuthorCharles J. Geyer <[email protected]> and Leif Johnson <[email protected]>
MaintainerCharles J. Geyer <[email protected]>
LicenseGPL (>= 2)
Version0.5-7
URL http://www.stat.umn.edu/geyer/mcmc/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("potts")

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potts documentation built on May 29, 2017, 7:19 p.m.