cov_rns

Dense covariance function (difference parameterization)

Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions.
Sources of nonstationarity, such as spatial mean, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics.
An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.

Federico Blasi

cocons

Covariate-Based Covariance Functions for Nonstationary Spatial
Modeling

Reinhard Furrer

cov_rns function

<dl><dt>theta</dt>
<dd>vector of parameters</dd>
<dt>locs</dt>
<dd>a matrix with locations</dd>
<dt>x_covariates</dt>
<dd>design data.frame</dd>
<dt>smooth_limits</dt>
<dd>smooth limits</dd></dl>

Arguments

Dense covariance function (difference parameterization) — cov_rns

<dl>

<dt>theta</dt>
<dd>vector of parameters</dd>


<dt>locs</dt>
<dd>a matrix with locations</dd>


<dt>x_covariates</dt>
<dd>design data.frame</dd>


<dt>smooth_limits</dt>
<dd>smooth limits</dd>

</dl>

cov_rns: Dense covariance function (difference parameterization)

Description

Usage

Value

Arguments