sf, graphics, mapiso, parallel, doParallel, foreach
covr, lwgeom, eurostat, giscoR, mapsf, knitr, tinytest,
rmarkdown

Tools for Internationalization and Portability in R Packages
Translating messages in R packages is managed using the po top-level directory and the gettext program. This package provides some helper functions for building this support in R packages, e.g. common validation & I/O tasks.

Translating messages in R packages is managed using the po top-level directory and the 'gettext' program. This package provides some helper functions for building this support in R packages, e.g. common validation & I/O tasks.

Markov Chain Monte Carlo for Potts Models

Charles J. Geyer <charlie@stat.umn.edu> and Leif Johnson
<leif@stat.umn.edu>

Charles J. Geyer <charlie@stat.umn.edu> and Leif Johnson
<ltjohnson@google.com>
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>).

Does Markov chain Monte Carlo (MCMC) simulation of Potts models
and composite likelihood inference for Potts models.

It does Markov chain Monte Carlo (MCMC) simulation of Potts models
(Potts, 1952, <http://doi.org/10.1017/S0305004100027419>),
which are the multi-color generalization of Ising models
(so, as as special case, also simulates Ising models).
It uses the Swendsen-Wang algorithm (Swendsen and Wang, 1987,
<http://doi.org/10.1103/PhysRevLett.58.86>) so MCMC is fast.
It does maximum composite likelihood estimation of parameters
(Besag, 1975, <http://doi.org/10.2307/2987782>,
Lindsay, 1988, <http://doi.org/10.1090/conm/080>).

Do Markov chain Monte Carlo (MCMC) simulation of Potts models
(Potts, 1952, <https://doi.org/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,
<https://doi.org/10.1103/PhysRevLett.58.86>) so MCMC is fast.
Do maximum composite likelihood estimation of parameters
(Besag, 1975, <https://doi.org/10.2307/2987782>,
Lindsay, 1988, <https://doi.org/10.1090/conm/080>).