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 vignette is
provided and the help pages include examples.

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 vignette is
provided and the help pages include examples.

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 techniqes 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 a-postiori 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.