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.

Package details

AuthorOlatunji Johnson [aut, cre], Emanuele Giorgi [aut], Peter Diggle [aut]
MaintainerOlatunji Johnson <[email protected]>
LicenseGPL-2 | GPL-3
Package repositoryView on CRAN
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SDALGCP documentation built on May 2, 2019, 7:27 a.m.