foreign (>= 0.8-80), knitr, rmarkdown, testthat, bookdown,
readr, gtools, covr

Projected Spatial Gaussian Process (psgp) methods
Ben Ingram <bri@utalca.cl>, Remi Barillec
<r.barillec@aston.ac.uk>
Remi Barillec <r.barillec@aston.ac.uk>, Ben Ingram
<bri@utalca.cl>, Jon Skoien <j.skoien@geo.uu.nl>, Edzer Pebesma
<edzer.pebesma@uni-muenster.de>
Implements projected sparse Gaussian process kriging for
the intamap package

Projected Spatial Gaussian Process methods for package intamap
Ben Ingram <B.R.Ingram@Aston.ac.uk>, Remi Barillec
<r.barillec@aston.ac.uk>
Jon Skoien <j.skoien@geo.uu.nl>, Remi Barillec
<r.barillec@aston.ac.uk>, Edzer Pebesma
<edzer.pebesma@uni-muenster.de>
implements projected sparse Gaussian process kriging for
the intamap package

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

Projected Spatial Gaussian Process Methods
Ben Ingram <bri@utalca.cl>, Remi Barillec <r.barillec@aston.ac.uk>
gstat, automap, sp, doParallel, rgdal, foreach, parallel, Rcpp
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.

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

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>).

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

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

Implements projected sparse Gaussian process kriging for the intamap package

Decision Tree Analysis for Probabilistic Subgroup Identification
with Multiple Treatments
Oleg Sysoev, Krzysztof Bartoszek, Katarina Ekholm Selling and Lotta
Ekstrom
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.
grid, gridBase, randomForest, rpart, partykit, party,
BayesTree