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: 2022-08-12 15:24:13 UTC; geyer
Repository: CRAN
Date/Publication: 2022-08-12 16:00:02 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-25 21:17:19 UTC; unix
Archs: potts.so.dSYM
