modelCall-class

User reminder of the fgpm function call.

Construction and smart selection of Gaussian process models
for analysis of computer experiments
with emphasis on treatment of functional inputs that are regularly sampled. This package
offers: (i) flexible modeling of functional-input regression
problems through the fairly general Gaussian process model; (ii)
built-in dimension reduction for functional inputs; (iii)
heuristic optimization of the structural parameters of the model
(e.g., active inputs, kernel function, type of distance).
An in-depth tutorial in the use of funGp is provided in
Betancourt et al. (2024) <doi:10.18637/jss.v109.i05> and
Metamodeling background is provided in
Betancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>.
The algorithm for structural parameter optimization is described
in <https://hal.science/hal-02532713>.

Jose Betancourt

funGp

Gaussian Process Models for Scalar and Functional Inputs

François Bachoc

Thierry Klein

Jeremy Rohmer

Yves Deville

Deborah Idier

modelCall-class function

<dl>
<dt><code>string</code></dt>
<dd>Object of class <code>"character"</code>. User call reminder in string format.</dd></dl>

Slots

José Betancourt, François Bachoc, Thierry Klein and Jérémy Rohmer

Author

S4 class for calls to the fgpm function in funGp — modelCall-class

<dl>
<dt><code>string</code></dt>
<dd>Object of class <code>"character"</code>. User call reminder in string format.</dd>


</dl>

modelCall-class: S4 class for calls to the fgpm function in funGp

Description

Arguments

Slots

Author