approximator v1.2-7

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Bayesian Prediction of Complex Computer Codes

Performs Bayesian prediction of complex computer codes when fast approximations are available. It uses a hierarchical version of the Gaussian process, originally proposed by Kennedy and O'Hagan (2000), Biometrika 87(1):1.

Functions in approximator

Name Description
is.consistent Checks observational data for consistency with a subsets object
toyapps Toy datasets for approximator package
tee.fun Returns generalized distances
generate.toy.observations Er, generate toy observations
c.fun Correlations between points in parameter space
basis.toy Toy basis functions
mdash.fun Mean of Gaussian process
object Optimization of posterior likelihood of hyperparameters
betahat.app Estimate for beta
subset_maker Create a simple subset object
subsets.fun Generate and test subsets
approximator-package Bayesian approximation of computer models when fast approximations are available
as.sublist Converts a level one design matrix and a subsets object into a list of design matrices, one for each level
genie Genie datasets for approximator package
Pi Kennedy's Pi notation
V.fun.app Variance matrix
Afun Matrix of correlations between two sets of points
H.fun The H matrix
hpa.fun.toy Toy example of a hyperparameter object creation function
hdash.fun Hdash
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Vignettes of approximator

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apprex.Rnw
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Details

Type Package
License GPL-2
NeedsCompilation no
Packaged 2018-08-28 23:21:38 UTC; rhankin
Repository CRAN
Date/Publication 2018-08-29 04:24:34 UTC

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