{ "_id": "6a533eff9b92047c28d570de", "Package": "laGP", "Title": "Local Approximate Gaussian Process Regression", "Version": "1.5-9", "Date": "2023-03-13", "Author": "Robert B. Gramacy , Furong Sun ", "Description": "Performs approximate GP regression for large computer\nexperiments and spatial datasets. The approximation is based\non finding small local designs for prediction (independently)\nat particular inputs. OpenMP and SNOW parallelization are\nsupported for prediction over a vast out-of-sample testing set;\nGPU acceleration is also supported for an important subroutine.\nOpenMP and GPU features may require special compilation. An\ninterface to lower-level (full) GP inference and prediction is\nprovided. Wrapper routines for blackbox optimization under\nmixed equality and inequality constraints via an augmented\nLagrangian scheme, and for large scale computer model\ncalibration, are also provided. For details and tutorial, see\nGramacy (2016 .", "Maintainer": "Robert B. 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