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>.

Package details

AuthorOlatunji Johnson [aut, cre], Emanuele Giorgi [aut], Peter Diggle [aut]
MaintainerOlatunji Johnson <>
LicenseGPL-2 | GPL-3
Package repositoryView on CRAN
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SDALGCP documentation built on March 17, 2021, 1:07 a.m.