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>.
|Author||Olatunji Johnson [aut, cre], Emanuele Giorgi [aut], Peter Diggle [aut]|
|Maintainer||Olatunji Johnson <email@example.com>|
|License||GPL-2 | GPL-3|
|Package repository||View on CRAN|
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