| emulator-package | Bayesian Emulation of Computer Programs |
| betahat.fun | Calculates MLE coefficients of linear fit |
| betahat.fun.A | Calculates MLE coefficients of linear fit |
| cond.sample | Implementation of the ideas of Oakley and O'Hagan 2002 |
| corr | correlation function for calculating A |
| corr.matrix | correlation function for calculating A |
| cprod | Evaluate a quadratic form efficiently |
| emulator | Bayesian Emulation of Computer Programs |
| estimator | Estimates each known datapoint using the others as datapoints |
| expert.estimates | Expert estimates for Goldstein input parameters |
| ht | Evaluate a quadratic form efficiently |
| int.qq | Interpolates between known points using Bayesian estimation |
| interpolant | Interpolates between known points using Bayesian estimation |
| interpolant.quick | 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 |
| oo2002 | Implementation of the ideas of Oakley and O'Hagan 2002 |
| optimal.scale | Use optimization techniques to find the optimal scales |
| optimal.scales | Use optimization techniques to find the optimal scales |
| pad | Simple pad function |
| prior.B | Prior linear fits |
| prior.b | Prior linear fits |
| quad.3diag | Evaluate a quadratic form efficiently |
| quad.3form | Evaluate a quadratic form efficiently |
| quad.3form.inv | Evaluate a quadratic form efficiently |
| quad.3tdiag | Evaluate a quadratic form efficiently |
| quad.3tform | Evaluate a quadratic form efficiently |
| quad.diag | Evaluate a quadratic form efficiently |
| quad.form | Evaluate a quadratic form efficiently |
| quad.form.inv | Evaluate a quadratic form efficiently |
| quad.tdiag | Evaluate a quadratic form efficiently |
| quad.tform | Evaluate a quadratic form efficiently |
| quad.tform.inv | Evaluate a quadratic form efficiently |
| regressor.basis | Regressor basis function |
| regressor.multi | Regressor basis function |
| results.table | Results from 100 Goldstein runs |
| s.chi | Variance estimator |
| sample.from.exp.est | Makes input files for condor runs of goldstein |
| sample.n.fit | Sample from a Gaussian process and fit an emulator to the points |
| scales.likelihood | Likelihood of roughness parameters |
| sigmahatsquared | Estimator for sigma squared |
| sigmahatsquared.A | Estimator for sigma squared |
| tcprod | Evaluate a quadratic form efficiently |
| toy | A toy dataset |
| tr | Trace of a matrix |
| var.conditional | Implementation of the ideas of Oakley and O'Hagan 2002 |