Learn R Programming

⚠️There's a newer version (2.1-0) of this package.Take me there.

BACCO (version 1.0-50)

Bayesian Analysis of Computer Code Output

Copy Link

Version

Install

install.packages('BACCO')

Monthly Downloads

452

Version

1.0-50

License

GPL

Maintainer

Robin Hankin

Last Published

September 10th, 2023

Functions in BACCO (1.0-50)

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

Posterior mean of K

Matrix of distances from D1 to D2

Expectation as per equation 10 of KOH2001

variance matrix for beta2

Distance matrix

Variance matrix for beta1hat

covariance matrix for beta

Toy basis functions

Variance matrix for d

Variance matrix for observations

distance between observation points

Calculates MLE coefficients of linear fit

estimator for beta2

Genie datasets for approximator package

Basis functions

Converts a level one design matrix and a subsets object into a list of design matrices, one for each level

Toy example of hbar (section 4.2)

Interpolates between known points using Bayesian estimation

Mean of Gaussian process

Toy example of a hyperparameter object creation function

Simple model for concept checking

Create a simple subset object

Trace of a matrix

Matrix of correlations between two sets of points

Estimate for beta

Assembles matrices blockwise into a block diagonal matrix

create.new.toy.datasets

Create new toy datasets

Covariance function for posterior distribution of z

expert.estimates

Expert estimates for Goldstein input parameters

generate.toy.observations

Er, generate toy observations

Optimization of posterior likelihood of hyperparameters

Checks observational data for consistency with a subsets object

Estimates each known datapoint using the others as datapoints

Implementation of the ideas of Oakley and O'Hagan 2002

A priori probability of psi1, psi2, and theta

Functions to create or change hyperparameters

Evaluate a quadratic form efficiently

Integrals needed in KOH2001

Augments observation points with parameters

Variance matrix

Very basic implementation of the Metropolis-Hastings algorithm

approximator-package

Bayesian approximation of computer models when fast approximations are available

Correlations between points in parameter space

Returns generalized distances

scales.likelihood

Likelihood of roughness parameters

A postiori probability of hyperparameters

beta1 estimator

betahat.fun.koh

Expectation of beta, given theta, phi and d

Stage 1,2 and 3 optimization on toy dataset

correlation function for calculating A

Distance between two points

emulator-package

Emulation of computer code output

Extracts lat/long matrix and theta matrix from D2.

Makes input files for condor runs of goldstein

Use optimization techniques to find the optimal scales

Apostiori probability of psi1

A postiori probability of hyperparameters

Auxiliary functions for equation 9 of the supplement

sigmahatsquared

Estimator for sigma squared

Variance estimator

Symmetrize an upper triangular matrix

Sample from a Gaussian process and fit an emulator to the points

Simple pad function

Expectation and variance with respect to theta

Kennedy's Pi notation

Expectation of computer output

is.positive.definite

Is a matrix positive definite?

latin.hypercube

Latin hypercube design matrix

Results from 100 Goldstein runs

regressor.basis

Regressor basis function

Toy datasets for approximator package

Expectation of z given y, beta2, phi

Prior linear fits

Generate and test subsets