Local Approximate Gaussian Process Regression
R package providing 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.
The current version provides:
- ALC, MSPE and NN-based local approximation, as well as EFI-based global heuristics
- local MLE/MAP inference for (isotropic and separable) lengthscales and nuggets
- OpenMP for approximation over a vast out-of-sample testing set
- GPU acceleration for local ALC subroutine evaluations
- SNOW/parallel-package cluster parallelization
- computer model calibration via optimization
- blackbox constrained optimization via augmented Lagrangians
- an interface to lower-level (full) GP inference and prediction
Obtaining the package
Rfrom cran.r-project.org by selecting the version for your operating system.
- Install the
laGPpackage, from within
- Optionally, install the
snowpackages, which are helpful for some of the comparisons in the examples and demos.
R> install.packages(c("mvtnorm", "snow"))
- Load the library as you would for any
laGPtutorial is implemented as a package vignette, authored in
Sweave. The pdf can be obtained from within
Rwith the following code.
- To obtain the source code contained in the vignette, use the
R> v <- vignette("laGP")
R> Stangle(paste(v$Dir, "/doc/", v$File, sep=""))
- The code from Section 4 of the vignette, on Calibration, is available as a standalone demo.
R> demo("calib", package="laGP")
- See the package documentation.
version of the reference manual, or help pages, as also available.
The help pages can be accessed from within
R. Try starting with:
R> ?laGP # follow the examples
R> ?aGP # follow the examples - this is the main workhorse
- UChicago RCC did a puff piece on
laGPfor large-scale computer experiments. RCC resources have played an integral role in
laGPdevelopment and large-scale application.
- Calibrating a large computer experiment simulating radiative shock hydrodynamics (2015) with Derek Bingham, James Paul Holloway, Michael J. Grosskopf, Carolyn C. Kuranz, Erica Rutter, Matt Trantham, R. Paul Drake; Annals of Applied Statistics, 9(3), pp. 1141-1168; preprint on arXiv:1410.3293
laGP: Large-scale spatial modeling via local approximate Gaussian processes in R (2015); to appear in the Journal of Statistical Software; this is the published version of the vignette/tutorial above
- Local Gaussian process approximation for large computer experiments (2015) with Dan Apley; Journal of Computational and Graphical Statistics, 24(2), pp. 561-578; preprint on arXiv:1303.0383
- Modeling an augmented Lagrangian for blackbox constrained optimization (2016) with Genetha Gray, Sebastien Le Digabel, Herbie Lee, Pritam Ranjan, Garth Wells and Stefan Wild; Technometrics (with discussion), 58(1), pp. 1-11; preprint on arXiv:1403.4890
- Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian (2016) with Victor Picheny, Stefan Wild and Sebastien Le Digabel; preprint on arXiv:1605.09466
- Massively parallel approximate Gaussian process regression (2014) with Jarad Niemi and Robin Weiss; Journal of Uncertainty Quantification, 2(1), pp. 564-584; preprint on arXiv:1310.5182
- Speeding up neighborhood search in local Gaussian process prediction (2014) with Ben Haaland; to appear in Technometrics; preprint on arXiv:1409.0074