laGP v1.5-5


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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.

Functions in laGP

Name Description
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
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Vignettes of laGP

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Last month downloads


Date 2019-08-27
License LGPL
NeedsCompilation yes
Packaged 2019-09-05 16:01:02 UTC; bobby
Repository CRAN
Date/Publication 2019-09-07 16:30:02 UTC

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