Bayesian Geostatistical Modeling with RAMPS
Brian J. Smith <brian-j-smith@uiowa.edu>,
Jun Yan <jun.yan@uconn.edu>, and
Mary K. Cowles <kcowles@stat.uiowa.edu>
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.

Brian J. Smith <brian-j-smith@uiowa.edu>,
Jun Yan <jun.yan@uconn.edu>, and
Mary Kathryn Cowles <kcowles@stat.uiowa.edu>

Brian J. Smith <brian-j-smith@uiowa.edu>, Jun Yan
<jun.yan@uconn.edu>, and Mary Kathryn Cowles
<kcowles@stat.uiowa.edu>
R (>= 2.5.1), coda, fields, Matrix (>= 0.999375-10), maps,
methods, nlme
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.

c(person("Brian J", "Smith", role = c("aut", "cre"),
email = "brian-j-smith@uiowa.edu"),
person("Jun", "Yan", role = "aut",
email = "jun.yan@uconn.edu"),
person("Mary Kathryn", "Cowles", role = "aut",
email = "kate-cowles@uiowa.edu"))
Brian J Smith [aut, cre],
Jun Yan [aut],
Mary Kathryn Cowles [aut]

fields, graphics, grDevices, methods, stats

R (>= 2.5.1), coda, fields, Matrix (>= 0.999375-10), maps, methods, nlme

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.

Reinforced Angle-Based Multicategory Support Vector Machines
Chong Zhang, Yufeng Liu, and Shannon Holloway
foreach, doParallel, methods, parallel, stats
Provides a solution path for Reinforced Angle-based Multicategory Support Vector Machines, with linear learning, polynomial learning, and Gaussian kernel learning.

Provides a solution path for Reinforced Angle-based Multicategory Support Vector Machines, with linear learning, polynomial learning, and Gaussian kernel learning. C. Zhang, Y. Liu, J. Wang and H. Zhu. (2016) <doi:10.1080/10618600.2015.1043010>.