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: | 0.5-9 |
| Depends: | R (≥ 3.0.2) |
| Imports: | stats, graphics |
| Suggests: | pooh (≥ 0.2) |
| Published: | 2020-03-23 |
| 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_0.5-9.tar.gz |
| Windows binaries: | r-devel: potts_0.5-9.zip, r-release: potts_0.5-9.zip, r-oldrel: potts_0.5-9.zip |
| macOS binaries: | r-release: potts_0.5-9.tgz, r-oldrel: potts_0.5-9.tgz |
| Old sources: | potts archive |
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