Package: potts
Version: 0.5-9
Date: 2020-03-22
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 (>= 3.0.2)
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: 2020-03-23 10:28:36 UTC; geyer
Repository: CRAN
Date/Publication: 2020-03-23 11:20:02 UTC
Built: R 4.0.2; x86_64-apple-darwin17.0; 2020-07-15 18:03:20 UTC; unix
Archs: potts.so.dSYM
