laGP: Local Approximate Gaussian Process Regression

Performs approximate GP regression for large computer experiments and spatial datasets. The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine. OpenMP and GPU features may require special compilation. An interface to lower-level (full) GP inference and prediction is also provided, as are associated wrapper routines for blackbox optimization under constraints via an augmented Lagrangian scheme

Version: 1.1-1
Depends: R (≥ 2.14)
Imports: tgp, parallel
Suggests: mvtnorm, MASS, akima
Published: 2014-09-03
Author: Robert B. Gramacy
Maintainer: Robert B. Gramacy <rbgramacy at chicagobooth.edu>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
URL: http://faculty.chicagobooth.edu/robert.gramacy/laGP.html
NeedsCompilation: yes
Materials: README ChangeLogINSTALL
CRAN checks: laGP results

Downloads:

Reference manual: laGP.pdf
Package source: laGP_1.1-1.tar.gz
Windows binaries: r-devel: laGP_1.1-1.zip, r-release: laGP_1.1-1.zip, r-oldrel: laGP_1.1-1.zip
OS X Snow Leopard binaries: r-release: laGP_1.1-1.tgz, r-oldrel: laGP_1.1-1.tgz
OS X Mavericks binaries: r-release: laGP_1.1-1.tgz
Old sources: laGP archive