Nothing
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. The details of the methods are provided in Johnson et al (2019) <doi:10.1002/sim.8339>.
Getting started
Browse package contents
Package details |
|
|---|---|
| Author | Olatunji Johnson [aut, cre], Emanuele Giorgi [aut], Peter Diggle [aut] |
| Maintainer | Olatunji Johnson <olatunjijohnson21111@gmail.com> |
| License | GPL-2 | GPL-3 |
| Version | 0.4.0 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the SDALGCP package in your browser
Any scripts or data that you put into this service are public.
