lGCpp

<p>A simulated spatio-temporal log Gaussian Cox point pattern with a underlying non-separable Geneiting covariance.</p>

datasets

Adaptive estimation of the first-order intensity function of a spatio-temporal point process using kernels and variable bandwidths. The methodology used for estimation is presented in González and Moraga (2022). <doi:10.48550/arXiv.2208.12026>.

Jonatan A González

kernstadapt

Adaptive Kernel Estimators for Point Process Intensities on
Linear Networks

Paula Moraga

lGCpp function

<p>An object of class <code>"ppp"</code>
  Entries include</p><table class="table">
<tr><td><code>x</code></td><td>Cartesian \(x\)-coordinates</td></tr>
<tr><td><code>y</code></td><td>Cartesian \(y\)-coordinates</td></tr>
<tr><td><code>marks</code></td><td>A vector of values indicating the times</td></tr>
<tr><td><code>window</code></td><td>Cartesian coordinates of the unit square</td></tr>
</table>
<p>See <code>ppp.object</code> for details of the format.</p>

Format

Simulated spatio-temporal log Gaussian Cox point pattern data — lGCpp

<p>An object of class <code>"ppp"</code>
  Entries include</p><table class='table'>
<tr><td><code>x</code></td><td>Cartesian \(x\)-coordinates</td></tr>
<tr><td><code>y</code></td><td>Cartesian \(y\)-coordinates</td></tr>
<tr><td><code>marks</code></td><td>A vector of values indicating the times</td></tr>
<tr><td><code>window</code></td><td>Cartesian coordinates of the unit square</td></tr>
</table>
<p>See <code>ppp.object</code> for details of the format.</p>

lGCpp: Simulated spatio-temporal log Gaussian Cox point pattern data

Description

Usage

Arguments

Format

References

Examples

`x`	Cartesian \(x\)-coordinates
`y`	Cartesian \(y\)-coordinates
`marks`	A vector of values indicating the times
`window`	Cartesian coordinates of the unit square