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laGP (version 1.4)

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 also provided, as are associated wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration.

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Version

Install

install.packages('laGP')

Monthly Downloads

1,085

Version

1.4

License

LGPL

Maintainer

Robert Gramacy

Last Published

June 2nd, 2017

Functions in laGP (1.4)

discrep.est

Estimate Discrepency in Calibration Model
distance

Calculate Euclidean distance between pairs of points
fcalib

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

Localized Approximate GP Prediction At a Single Input Location
aGP

Localized Approximate GP Regression For Many Predictive Locations
alcGP

Improvement statistics for sequential or local design
llikGP

Calculate a GP log likelihood
mleGP

Inference for GP correlation parameters
darg

Generate Priors for GP correlation
deleteGP

Delete C-side Gaussian Process Objects
newGP

Create A New GP Object
optim.auglag

Optimize an objective function under multiple blackbox constraints
predGP

GP Prediction/Kriging