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laGP (version 1.5-5)

Local Approximate Gaussian Process Regression

Description

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.

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Version

Install

install.packages('laGP')

Monthly Downloads

1,085

Version

1.5-5

License

LGPL

Maintainer

Robert Gramacy

Last Published

September 7th, 2019

Functions in laGP (1.5-5)

darg

Generate Priors for GP correlation
discrep.est

Estimate Discrepancy in Calibration Model
llikGP

Calculate a GP log likelihood
fcalib

Objective function for performing large scale computer model calibration via optimization
distance

Calculate the squared Euclidean distance between pairs of points
laGP

Localized Approximate GP Prediction At a Single Input Location
deleteGP

Delete C-side Gaussian Process Objects
blhs

Bootstrapped block Latin hypercube subsampling
aGP

Localized Approximate GP Regression For Many Predictive Locations
alcGP

Improvement statistics for sequential or local design
randLine

Generate two-dimensional random paths
newGP

Create A New GP Object
mleGP

Inference for GP correlation parameters
optim.auglag

Optimize an objective function under multiple blackbox constraints
predGP

GP Prediction/Kriging