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calibrator (version 1.2-9)

Bayesian Calibration of Complex Computer Codes

Description

Performs Bayesian calibration of computer models as per Kennedy and O'Hagan 2001. The package includes routines to find the hyperparameters and parameters; see the help page for stage1() for a worked example using the toy dataset. A tutorial is provided in the calex.Rnw vignette; and a suite of especially simple one dimensional examples appears in inst/doc/one.dim/.

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Install

install.packages('calibrator')

Monthly Downloads

2,730

Version

1.2-9

License

GPL-2

Issues

4

Pull Requests

0

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1

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Maintainer

Robin K S Hankin

Last Published

September 13th, 2026

Functions in calibrator (1.2-9)

blockdiag

Assembles matrices blockwise into a block diagonal matrix
hbar.fun.toy

Toy example of hbar (section 4.2)
create.new.toy.datasets

Create new toy datasets
beta1hat.fun

beta1 estimator
extractor.toy

Extracts lat/long matrix and theta matrix from D2.
cov.p5.supp

Covariance function for posterior distribution of z
h1.toy

Basis functions
is.positive.definite

Is a matrix positive definite?
prob.psi1

A priori probability of psi1, psi2, and theta
etahat

Expectation of computer output
reality

Reality
dists.2frames

Distance between two points
toys

Toy datasets
p.page4

A postiori probability of hyperparameters
tee

Auxiliary functions for equation 9 of the supplement
phi.fun.toy

Functions to create or change hyperparameters
tt.fun

Integrals needed in KOH2001
p.eqn8.supp

A postiori probability of hyperparameters
calibrator-package

tools:::Rd_package_title("calibrator")
symmetrize

Symmetrize an upper triangular matrix
p.eqn4.supp

Apostiori probability of psi1
stage1

Stage 1,2 and 3 optimization on toy dataset
E.theta.toy

Expectation and variance with respect to theta
W2

variance matrix for beta2
D1.fun

Function to join x.star to t.vec to give matrix D1
V.fun

Variance matrix for observations
W

covariance matrix for beta
Vd

Variance matrix for d
MH

Very basic implementation of the Metropolis-Hastings algorithm
Ez.eqn9.supp

Expectation as per equation 10 of KOH2001
W1

Variance matrix for beta1hat
V1

Distance matrix
C1

Matrix of distances from D1 to D2
Ez.eqn7.supp

Expectation of z given y, beta2, phi
H1.toy

Basis functions for D1 and D2
V2

distance between observation points
EK.eqn10.supp

Posterior mean of K
H.fun

H function
D2.fun

Augments observation points with parameters
beta2hat.fun

estimator for beta2
betahat.fun.koh

Expectation of beta, given theta, phi and d