# laGP v1.4

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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 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 No Results!