Generic function computing a Generalized Least Squares estimation
 with a given covariance kernel.

Gaussian process regression with an emphasis on kernels.
Quantitative and qualitative inputs are accepted. Some pre-defined
kernels are available, such as radial or tensor-sum for
quantitative inputs, and compound symmetry, low rank, group kernel
for qualitative inputs. The user can define new kernels and
composite kernels through a formula mechanism. Useful methods
include parameter estimation by maximum likelihood, simulation,
prediction and leave-one-out validation.

Olivier Roustant

kergp

Gaussian Process Laboratory

gls function

<dl> <dt>object</dt>
<dd></dd>An object representing a covariance kernel.
 <dt>...</dt>
<dd></dd>Other arguments for methods.
</dl>

Arguments

Generic Function: Generalized Least Squares Estimation
  with a Given Covariance Kernel — gls

<dl>

 <dt>object</dt>
<dd>

An object representing a covariance kernel.
</dd>

 <dt>...</dt>
<dd>

Other arguments for methods.
</dd>

</dl>

gls: Generic Function: Generalized Least Squares Estimation with a Given Covariance Kernel

Description

Usage

Value

Arguments