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laGP (version 1.5-9)

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. For details and tutorial, see Gramacy (2016 .

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Version

Install

install.packages('laGP')

Monthly Downloads

1,399

Version

1.5-9

License

LGPL

Maintainer

Last Published

March 14th, 2023

Functions in laGP (1.5-9)

deleteGP

Delete C-side Gaussian Process Objects
distance

Calculate the squared Euclidean distance between pairs of points
laGP

Localized Approximate GP Prediction At a Single Input Location
fcalib

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

Improvement statistics for sequential or local design
blhs

Bootstrapped block Latin hypercube subsampling
darg

Generate Priors for GP correlation
llikGP

Calculate a GP log likelihood
discrep.est

Estimate Discrepancy in Calibration Model
aGP

Localized Approximate GP Regression For Many Predictive Locations
randLine

Generate two-dimensional random paths
predGP

GP Prediction/Kriging
optim.auglag

Optimize an objective function under multiple blackbox constraints
newGP

Create A New GP Object
mleGP

Inference for GP correlation parameters