Package: approximator 1.2-8

approximator: 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.

Authors:Robin K. S. Hankin

approximator_1.2-8.tar.gz
approximator_1.2-8.zip(r-4.6)approximator_1.2-8.zip(r-4.5)
approximator_1.2-8.tgz(r-4.6-any)approximator_1.2-8.tgz(r-4.5-any)
approximator_1.2-8.tar.gz(r-4.6-any)approximator_1.2-8.tar.gz(r-4.5-any)
approximator_1.2-8.tgz(r-4.5-emscripten)
approximator.pdf |approximator.html✨
approximator/json (API)

# Install 'approximator' in R:

install.packages('approximator', repos = c('https://robinhankin.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/robinhankin/approximator/issues

Datasets:

D1.genie - Genie datasets for approximator package
D1.toy - Toy datasets for approximator package
betas.toy - Toy datasets for approximator package
hpa.genie.optimal - Genie datasets for approximator package
hpa.genie.start - Genie datasets for approximator package
hpa.toy - Toy datasets for approximator package
subsets.genie - Genie datasets for approximator package
subsets.toy - Toy datasets for approximator package
z.genie - Genie datasets for approximator package
z.toy - Toy datasets for approximator package

On CRAN:

3.80 score 1 packages 21 scripts 261 downloads 25 exports 2 dependencies

Last updated from:17ae659c1b. Checks:8 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	101
source / vignettes	OK	155
linux-release-x86_64	OK	92
macos-devel-arm64	OK	83
macos-release-arm64	OK	68
windows-devel	OK	75
windows-release	OK	61
wasm-release	OK	89

Exports:Afun as.sublist basis.genie basis.toy betahat.app betahat.app.H c_fun cdash.fun generate.toy.observations H.fun.app hdash.fun hpa.fun.genie hpa.fun.toy is.consistent is.nested is.strict mdash.fun object opt.1 opt.gt.1 Pi subset_maker subsets.fun tee.fun V.fun.app

Dependencies:emulator mvtnorm

Apprex: a cookbook for the approximator package

Rendered fromapprex.Rnwusingutils::Sweaveon Mar 29 2026.

Last update: 2023-08-22
Started: 2023-08-22

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Bayesian approximation of computer models when fast approximations are available	approximator-package approximator
Matrix of correlations between two sets of points	Afun
Converts a level one design matrix and a subsets object into a list of design matrices, one for each level	as.sublist
Toy basis functions	basis.toy
Estimate for beta	betahat.app betahat.app.H
Correlations between points in parameter space	c.fun cdash.fun c_fun
Er, generate toy observations	generate.toy.observations
Genie datasets for approximator package	basis.genie D1.genie Genie genie hpa.fun.genie hpa.genie hpa.genie.optimal hpa.genie.start subsets.genie z.genie
The H matrix	H.fun.app
Hdash	hdash.fun
Toy example of a hyperparameter object creation function	hpa.fun.toy
Checks observational data for consistency with a subsets object	is.consistent
Mean of Gaussian process	mdash.fun
Optimization of posterior likelihood of hyperparameters	object opt.1 opt.gt.1
Kennedy's Pi notation	Pi
Create a simple subset object	subset_maker
Generate and test subsets	is.nested is.strict subsets.fun
Returns generalized distances	tee.fun
Toy datasets for approximator package	betas.toy D1.toy hpa.toy subsets.toy z.toy
Variance matrix	V.fun.app