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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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Version

Install

install.packages('approximator')

Monthly Downloads

400

Version

1.2-8

License

GPL-2

Maintainer

Robin K S Hankin

Last Published

August 24th, 2023

Functions in approximator (1.2-8)

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