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>).

Version: 0.5-7
Depends: R (≥ 3.0.2)
Imports: stats, graphics, compiler
Suggests: pooh (≥ 0.2)
Published: 2017-03-16
Author: Charles J. Geyer and Leif Johnson
Maintainer: Charles J. Geyer <charlie at stat.umn.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://www.stat.umn.edu/geyer/mcmc/
NeedsCompilation: yes
Materials: NEWS ChangeLog
CRAN checks: potts results


Reference manual: potts.pdf
Vignettes: CLL Crash Course
Package source: potts_0.5-7.tar.gz
Windows binaries: r-devel: potts_0.5-7.zip, r-release: potts_0.5-7.zip, r-oldrel: potts_0.5-7.zip
OS X El Capitan binaries: r-release: potts_0.5-7.tgz
OS X Mavericks binaries: r-oldrel: potts_0.5-7.tgz
Old sources: potts archive


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