Package: potts
Version: 0.5-11
Date: 2022-08-12
Title: Markov Chain Monte Carlo for Potts Models
Author: Charles J. Geyer <charlie@stat.umn.edu> and Leif Johnson
        <ltjohnson@google.com>
Maintainer: Charles J. Geyer <charlie@stat.umn.edu>
Depends: R (>= 3.6.0)
Imports: stats, graphics
Suggests: pooh (>= 0.2)
Description: 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>).
License: GPL (>= 2)
URL: http://www.stat.umn.edu/geyer/mcmc/
NeedsCompilation: yes
Packaged: 2026-07-03 16:23:51 UTC; root
Repository: https://cjgeyer.r-universe.dev
Date/Publication: 2022-08-12 15:00:02 UTC
RemoteUrl: https://github.com/cran/potts
RemoteRef: HEAD
RemoteSha: 364fd5882871a96778057a485e0f205f3498be1f