approximator v1.2-7

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by Robin K S Hankin View Source

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

Name
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

depends	emulator (>= 1.2-11) , R (>= 2.0.0)
imports	mvtnorm
Contributors	Robin S. Hankin