Local Approximate Gaussian Process Regression
Localized Approximate GP Regression For Many Predictive Locations
Improvement statistics for sequential or local design
Bootstrapped block Latin hypercube subsampling
Generate Priors for GP correlation
Delete C-side Gaussian Process Objects
Estimate Discrepancy in Calibration Model
Calculate the squared Euclidean distance between pairs of points
Objective function for performing large scale computer model calibrati...
Localized Approximate GP Prediction At a Single Input Location
Calculate a GP log likelihood
Inference for GP correlation parameters
Create A New GP Object
Optimize an objective function under multiple blackbox constraints
GP Prediction/Kriging
Generate two-dimensional random paths
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 <doi:10.18637/jss.v072.i01>.