fisher_scoring

Functions for fitting and doing predictions with
Gaussian process models using Vecchia's (1988) approximation.
Package also includes functions for reordering input locations,
finding ordered nearest neighbors (with help from 'FNN' package),
grouping operations, and conditional simulations.
Covariance functions for spatial and spatial-temporal data
on Euclidean domains and spheres are provided. The original
approximation is due to Vecchia (1988)
<http://www.jstor.org/stable/2345768>, and the reordering and
grouping methods are from Guinness (2018)
<doi:10.1080/00401706.2018.1437476>.
Model fitting employs a Fisher scoring algorithm described
in Guinness (2019) <doi:10.48550/arXiv.1905.08374>.

Joseph Guinness

GpGp

Fast Gaussian Process Computation Using Vecchia's Approximation

Matthias Katzfuss

Youssef Fahmy

fisher_scoring function

<dl><dt>likfun</dt>
<dd>likelihood function, returns likelihood, gradient, and hessian</dd>
<dt>start_parms</dt>
<dd>starting values of parameters</dd>
<dt>link</dt>
<dd>link function for parameters (used for printing)</dd>
<dt>silent</dt>
<dd>TRUE/FALSE for suppressing output</dd>
<dt>convtol</dt>
<dd>convergence tolerance on step dot grad</dd>
<dt>max_iter</dt>
<dd>maximum number of Fisher scoring iterations</dd></dl>

Arguments

Fisher scoring algorithm — fisher_scoring

<dl>

<dt>likfun</dt>
<dd>likelihood function, returns likelihood, gradient, and hessian</dd>


<dt>start_parms</dt>
<dd>starting values of parameters</dd>


<dt>link</dt>
<dd>link function for parameters (used for printing)</dd>


<dt>silent</dt>
<dd>TRUE/FALSE for suppressing output</dd>


<dt>convtol</dt>
<dd>convergence tolerance on step dot grad</dd>


<dt>max_iter</dt>
<dd>maximum number of Fisher scoring iterations</dd>

</dl>

fisher_scoring: Fisher scoring algorithm

Description

Usage

Arguments