laGP v1.4


Monthly downloads



by Robert Gramacy

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 mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration.

Functions in laGP

Name Description
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
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Last month downloads


Date 2017-05-31
License LGPL
NeedsCompilation yes
Packaged 2017-06-01 17:22:25 UTC; bobby
Repository CRAN
Date/Publication 2017-06-02 07:14:04 UTC

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