This package allows one to estimate the output of a computer program, as a function of the input parameters, without actually running it. The computer program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques that give a PDF of expected program output. This PDF is conditional on a ``training set'' of runs, each consisting of a point in parameter space and the model output at that point. The emphasis is on complex codes that take weeks or months to run, and that have a large number of undetermined input parameters; many climate prediction models fall into this class. The emulator essentially determines Bayesian posterior estimates of the PDF of the output of a model, conditioned on results from previous runs and a user-specified prior linear model. A working example is given in the help page for function `interpolant()', which should be the first point of reference.

Author | Robin K. S. Hankin |

Date of publication | 2014-09-08 06:58:23 |

Maintainer | Robin K. S. Hankin <hankin.robin@gmail.com> |

License | GPL |

Version | 1.2-15 |

**betahat.fun:** Calculates MLE coefficients of linear fit

**corr:** correlation function for calculating A

**emulator-package:** Emulation of computer code output

**estimator:** Estimates each known datapoint using the others as datapoints

**expert.estimates:** Expert estimates for Goldstein input parameters

**interpolant:** Interpolates between known points using Bayesian estimation

**latin.hypercube:** Latin hypercube design matrix

**makeinputfiles:** Makes input files for condor runs of goldstein

**model:** Simple model for concept checking

**oo2002:** Implementation of the ideas of Oakley and O'Hagan 2002

**optimal.scales:** Use optimization techniques to find the optimal scales

**pad:** Simple pad function

**prior.b:** Prior linear fits

**quad.form:** Evaluate a quadratic form efficiently

**regressor.basis:** Regressor basis function

**results.table:** Results from 100 Goldstein runs

**sample.n.fit:** Sample from a Gaussian process and fit an emulator to the...

**scales.likelihood:** Likelihood of roughness parameters

**s.chi:** Variance estimator

**sigmahatsquared:** Estimator for sigma squared

**toy:** A toy dataset

**tr:** Trace of a matrix

emulator

emulator/inst

emulator/inst/CITATION

emulator/inst/doc

emulator/inst/doc/uncertainty.bib

emulator/inst/doc/emulex.Rnw

emulator/inst/doc/emulex.R

emulator/inst/doc/emulex.pdf

emulator/NAMESPACE

emulator/data

emulator/data/expert.estimates.rda

emulator/data/toy.rda

emulator/data/results.table.rda

emulator/R

emulator/R/aaa_cprod.R
emulator/R/emulator.R
emulator/vignettes

emulator/vignettes/uncertainty.bib

emulator/vignettes/.install_extras

emulator/vignettes/emulex.Rnw

emulator/MD5

emulator/build

emulator/build/vignette.rds

emulator/DESCRIPTION

emulator/man

emulator/man/oo2002.Rd
emulator/man/betahat.fun.Rd
emulator/man/sample.n.fit.Rd
emulator/man/scales.likelihood.Rd
emulator/man/makeinputfiles.Rd
emulator/man/optimal.scales.Rd
emulator/man/toy.Rd
emulator/man/s.chi.Rd
emulator/man/model.Rd
emulator/man/results.table.Rd
emulator/man/pad.Rd
emulator/man/quad.form.Rd
emulator/man/estimator.Rd
emulator/man/prior.b.Rd
emulator/man/regressor.basis.Rd
emulator/man/sigmahatsquared.Rd
emulator/man/emulator-package.Rd
emulator/man/expert.estimates.Rd
emulator/man/interpolant.Rd
emulator/man/latin.hypercube.Rd
emulator/man/tr.Rd
emulator/man/corr.Rd
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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