Title: Projected Spatial Gaussian Process methods for package intamap
Author: Ben Ingram <B.R.Ingram@Aston.ac.uk>, Remi Barillec
Maintainer: Jon Skoien <j.skoien@geo.uu.nl>, Remi Barillec
Description: implements projected sparse Gaussian process kriging for

Depends: intamap, Rcpp (>= 0.8.0), RcppArmadillo (>= 0.2.0), doSNOW

Title: Projected Spatial Gaussian Process Methods
Author: Ben Ingram <bri@utalca.cl>, Remi Barillec <r.barillec@aston.ac.uk>
Imports: gstat, automap, sp, doParallel, rgdal, foreach, parallel, Rcpp
Description: Implements projected sparse Gaussian process Kriging (Ingram 'et. al.', 2008, <doi:10.1007/s00477-007-0163-9>) as an additional method for the 'intamap' package.

Author: Ben Ingram <ingrambr@gmail.com>, Remi Barillec <remi@igiltd.com>, Jon Olav Skoien <jon.skoien@gmail.com>
Description: Implements projected sparse Gaussian process Kriging (Ingram 'et. al.', 2008, <doi:10.1007/s00477-007-0163-9>) as an additional method for the 'intamap' package. More details on implementation in Barillec 'et. al.', 2010, <doi:10.1016/j.cageo.2010.05.008>.

Description: Implements projected sparse Gaussian process Kriging (Ingram 'et. al.', 2008, <doi:10.1007/s00477-007-0163-9>) as an additional method for the 'intamap' package. More details on implementation (Barillec 'et. al.', 2010, <doi:10.1016/j.cageo.2010.05.008>).

Author: Ben Ingram <ingrambr.work@gmail.com>, Remi Barillec <remi@igiltd.com>, Jon Olav Skoien <jon.skoien@gmail.com>

Depends: intamap, Rcpp (>= 0.8.0), RcppArmadillo (>= 0.2.0)

Depends: intamap, RcppArmadillo (>= 0.2.0), Rcpp
Description: Implements projected sparse Gaussian process kriging for the intamap package

Depends: intamap, RcppArmadillo (>= 0.2.0)

Title: Decision Tree Analysis for Probabilistic Subgroup Identification
Author: Oleg Sysoev, Krzysztof Bartoszek, Katarina Ekholm Selling and Lotta
Description: In the situation when multiple alternative treatments or
interventions available, different population groups may respond differently
to different treatments. This package implements a method that discovers
the population subgroups in which a certain treatment has a better effect
than the other alternative treatments. This is done by first estimating the
treatment effect for a given treatment and its uncertainty by computing random
forests, and the resulting model is summarized by a decision tree in which the
probabilities that the given treatment is best for a given subgroup is shown in
the corresponding terminal node of the tree.
Imports: grid, gridBase, randomForest, rpart, partykit, party,