potts: Markov Chain Monte Carlo for Potts Models

Do Markov chain Monte Carlo (MCMC) simulation of Potts models (Potts, 1952, <doi: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, <doi:10.1103/PhysRevLett.58.86>) so MCMC is fast. Do maximum composite likelihood estimation of parameters (Besag, 1975, <doi:10.2307/2987782>, Lindsay, 1988, <doi:10.1090/conm/080>).

Version: 0.5-11
Depends: R (≥ 3.6.0)
Imports: stats, graphics
Suggests: pooh (≥ 0.2)
Published: 2022-08-12
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_0.5-11.tar.gz
Windows binaries: r-prerel: potts_0.5-11.zip, r-release: potts_0.5-11.zip, r-oldrel: potts_0.5-11.zip
macOS binaries: r-prerel (arm64): potts_0.5-11.tgz, r-release (arm64): potts_0.5-11.tgz, r-oldrel (arm64): potts_0.5-11.tgz, r-prerel (x86_64): potts_0.5-11.tgz, r-release (x86_64): potts_0.5-11.tgz
Old sources: potts archive


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