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.


Reference manual

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1.5-5 by Robert B. Gramacy, 2 years ago


Browse source code at https://github.com/cran/laGP

Authors: Robert B. Gramacy <[email protected]> , Furong Sun <[email protected]>

Documentation:   PDF Manual  

LGPL license

Imports tgp, parallel

Suggests mvtnorm, MASS, akima, lhs, crs, DiceOptim

Imported by SPOT, liGP.

Suggested by CompModels, ContourFunctions, IGP, mlr.

See at CRAN