sph_grad_xyz

compute gradient of spherical harmonics functions

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

sph_grad_xyz function

<dl><dt>xyz</dt>
<dd>xyz coordinates of locations on sphere</dd>
<dt>Lmax</dt>
<dd>largest degree of spherical harmonics. 
Current only Lmax=2 supported</dd></dl>

Arguments

compute gradient of spherical harmonics functions — sph_grad_xyz

<dl>

<dt>xyz</dt>
<dd>xyz coordinates of locations on sphere</dd>


<dt>Lmax</dt>
<dd>largest degree of spherical harmonics. 
Current only Lmax=2 supported</dd>

</dl>

sph_grad_xyz: compute gradient of spherical harmonics functions

Description

Usage

Arguments