approximator (version 1.3-0)

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.

Copy Link

Version

Install

install.packages('approximator')

Monthly Downloads

384

Version

1.3-0

License

GPL-2

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/RobinHankin/approximator

Maintainer

Robin K S Hankin

Last Published

September 10th, 2026

Functions in approximator (1.3-0)

generate.toy.observations

Er, generate toy observations

betahat.app

Estimate for beta

Kennedy's Pi notation

c.fun

Correlations between points in parameter space

hdash.fun

Hdash

hpa.fun.toy

Toy example of a hyperparameter object creation function

toyapps

Toy datasets for approximator package

is.consistent

Checks observational data for consistency with a subsets object

V.fun.app

Variance matrix

tee.fun

Returns generalized distances

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

Afun

Matrix of correlations between two sets of points

mdash.fun

Mean of Gaussian process

object

Optimization of posterior likelihood of hyperparameters

subsets.fun

Generate and test subsets

Create a simple subset object