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 provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided. For details and tutorial, see Gramacy (2016 <doi:10.18637/jss.v072.i01>.

Version: 1.5-9
Depends: R (≥ 2.14)
Imports: tgp, parallel
Suggests: mvtnorm, MASS, interp, lhs, crs, DiceOptim
Published: 2023-03-14
DOI: 10.32614/CRAN.package.laGP
Author: Robert B. Gramacy, Furong Sun
Maintainer: Robert B. Gramacy <rbg at>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
NeedsCompilation: yes
Citation: laGP citation info
Materials: README ChangeLog INSTALL
CRAN checks: laGP results


Reference manual: laGP.pdf
Vignettes: a guide to the laGP package


Package source: laGP_1.5-9.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): laGP_1.5-9.tgz, r-oldrel (arm64): laGP_1.5-9.tgz, r-release (x86_64): laGP_1.5-9.tgz, r-oldrel (x86_64): laGP_1.5-9.tgz
Old sources: laGP archive

Reverse dependencies:

Reverse imports: BayesianPlatformDesignTimeTrend
Reverse suggests: CompModels, ContourFunctions, familiar, mlr


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