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approximator (version 1.2-8)
Bayesian Prediction of Complex Computer Codes
Description
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.
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1.2-8
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Install
install.packages('approximator')
Monthly Downloads
293
Version
1.2-8
License
GPL-2
Maintainer
Robin K S Hankin
Last Published
August 24th, 2023
Functions in approximator (1.2-8)
Search functions
subset_maker
Create a simple subset object
subsets.fun
Generate and test subsets
hdash.fun
Hdash
is.consistent
Checks observational data for consistency with a subsets object
approximator-package
Bayesian approximation of computer models when fast approximations are available
Pi
Kennedy's Pi notation
c.fun
Correlations between points in parameter space
generate.toy.observations
Er, generate toy observations
Afun
Matrix of correlations between two sets of points
V.fun.app
Variance matrix
H.fun
The H matrix
betahat.app
Estimate for beta
basis.toy
Toy basis functions
as.sublist
Converts a level one design matrix and a subsets object into a list of design matrices, one for each level
mdash.fun
Mean of Gaussian process
object
Optimization of posterior likelihood of hyperparameters
tee.fun
Returns generalized distances
toyapps
Toy datasets for approximator package
genie
Genie datasets for approximator package
hpa.fun.toy
Toy example of a hyperparameter object creation function