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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>).
Getting started
Browse package contents
Package details |
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| Author | Charles J. Geyer <charlie@stat.umn.edu> and Leif Johnson <ltjohnson@google.com> |
| Maintainer | Charles J. Geyer <charlie@stat.umn.edu> |
| License | GPL (>= 2) |
| Version | 0.5-11 |
| URL | http://www.stat.umn.edu/geyer/mcmc/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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