olatunjijohnson/SDALGCP: Spatially Discrete Approximation to Log-Gaussian Cox Processes for Aggregated Disease Count Data

Provides a computationally efficient discrete approximation to log-Gaussian Cox process model for spatially aggregated disease count data. It uses Monte Carlo Maximum Likelihood for model parameter estimation as proposed by Christensen (2004) <doi: 10.1198/106186004X2525> and delivers prediction of spatially discrete and continuous relative risk. It performs inference for static spatial and spatio-temporal dataset.

Getting started

Package details

LicenseGPL-2 | GPL-3
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
olatunjijohnson/SDALGCP documentation built on May 29, 2019, 2:01 a.m.