temporal_sc

internal

A suite of tools for estimating, assessing model fit, simulating from, and visualizing location dependent marked point processes characterized by regularity in the pattern.
You provide a reference marked point process, a set of raster images containing location specific covariates, and select the estimation algorithm and type of mark model.
'ldmppr' estimates the process and mark models and allows you to check the appropriateness of the model using a variety of diagnostic tools.
Once a satisfactory model fit is obtained, you can simulate from the model and visualize the results.
Documentation for the package 'ldmppr' is available in the form of a vignette.

Lane Drew

ldmppr

Estimate and Simulate from Location Dependent Marked Point
Processes

Andee Kaplan

temporal_sc function

<dl><dt>params</dt>
<dd>a vector of parameters (alpha_1, beta_1, gamma_1).</dd>
<dt>eval_t</dt>
<dd>a t value.</dd>
<dt>obs_t</dt>
<dd>a vector of t values.</dd></dl>

Arguments

calculates temporal likelihood — temporal_sc

<dl>

<dt>params</dt>
<dd>a vector of parameters (alpha_1, beta_1, gamma_1).</dd>


<dt>eval_t</dt>
<dd>a t value.</dd>


<dt>obs_t</dt>
<dd>a vector of t values.</dd>

</dl>

temporal_sc: calculates temporal likelihood

Description

Usage

Value

Arguments