createL

create the sparse triangular L matrix for specific parameters

Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) <arXiv:1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <arXiv:1706.02205> and MaxMin ordering proposed in Guinness (2018) <arXiv:1609.05372>.

Marcin Jurek

GPvecchia

Scalable Gaussian-Process Approximations

Matthias Katzfuss

Daniel Zilber

Wenlong Gong

Joe Guinness

Jingjie Zhang

Florian Schaefer

createL function

<dl><dt>vecchia.approx</dt>
<dd>object returned by <code>vecchia_specify</code></dd>
<dt>covmodel</dt>
<dd>covariance model. currently implemented:
matern: with covparms (var,range,smoothness)
esqe: exponential + squared exp with covparms (var1,range1,var2,range2)
If covmodel is a function it has to be able to take a distance matrix
and return a vector with distances which is of length k.</dd>
<dt>covparms</dt>
<dd>vector of covariance parameters</dd></dl>

Arguments

create the sparse triangular L matrix for specific parameters — createL

<dl>

<dt>vecchia.approx</dt>
<dd>object returned by <code>vecchia_specify</code></dd>


<dt>covmodel</dt>
<dd>covariance model. currently implemented:
matern: with covparms (var,range,smoothness)
esqe: exponential + squared exp with covparms (var1,range1,var2,range2)
If covmodel is a function it has to be able to take a distance matrix
and return a vector with distances which is of length k.</dd>


<dt>covparms</dt>
<dd>vector of covariance parameters</dd>

</dl>

createL: create the sparse triangular L matrix for specific parameters

Description

Usage

Value

Arguments

Examples