Package: ramps
Title: Bayesian Geostatistical Modeling with RAMPS
Version: 0.6-5
Date: 2008-07-08
Author: Brian J. Smith <brian-j-smith@uiowa.edu>,
Jun Yan <jun.yan@uconn.edu>, and
Mary Kathryn Cowles <kcowles@stat.uiowa.edu>
Maintainer: Brian J. Smith <brian-j-smith@uiowa.edu>
Depends: R (>= 2.5.1), coda, fields, Matrix (>= 0.999375-10), maps, nlme
Description: Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples.  Package performance is tuned for large spatial datasets.
License: GPL-2
Packaged: Tue Jul  8 19:44:49 2008; bjsmith

Package: ramps
Title: Bayesian Geostatistical Modeling with RAMPS
Version: 0.6-6
Date: 2008-07-25
Author: Brian J. Smith <brian-j-smith@uiowa.edu>,
Jun Yan <jun.yan@uconn.edu>, and
Mary Kathryn Cowles <kcowles@stat.uiowa.edu>
Maintainer: Brian J. Smith <brian-j-smith@uiowa.edu>
Depends: R (>= 2.5.1), coda, fields, Matrix (>= 0.999375-10), maps, methods, nlme
Description: Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples.  Package performance is tuned for large spatial datasets.
License: GPL-2
Packaged: Fri Jul 25 16:07:16 2008; bjsmith

Package: ramps
Title: Bayesian Geostatistical Modeling with RAMPS
Version: 0.6-7
Date: 2008-11-25
Author: Brian J. Smith <brian-j-smith@uiowa.edu>,
Jun Yan <jun.yan@uconn.edu>, and
Mary Kathryn Cowles <kcowles@stat.uiowa.edu>
Maintainer: Brian J. Smith <brian-j-smith@uiowa.edu>
Depends: R (>= 2.5.1), coda, fields, Matrix (>= 0.999375-10), maps, methods, nlme
Description: Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples.  Package performance is tuned for large spatial datasets.
License: GPL-2
Packaged: Tue Nov 25 21:00:57 2008; bjsmith

Description: Bayesian geostatistical modeling of Gaussian processes using a
reparameterized and marginalized posterior sampling (RAMPS) algorithm
designed to lower autocorrelation in MCMC samples.  Package performance is