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>.
| Version: | 0.3.0 |
| Depends: | R (≥ 3.4.0) |
| Imports: | pdist (≥ 1.2), Matrix (≥ 1.2.14), PrevMap (≥ 1.4.1), raster (≥ 2.6.7), sp (≥ 1.2.7), spatstat (≥ 1.55.1), splancs (≥ 2.1.40), maptools (≥ 0.9.2), progress (≥ 1.1.2), methods, spacetime (≥ 1.2.2), mapview (≥ 2.6.0), geoR (≥ 1.7-5.2.1) |
| Suggests: | knitr, rmarkdown |
| Published: | 2020-02-28 |
| Author: | Olatunji Johnson [aut, cre], Emanuele Giorgi [aut], Peter Diggle [aut] |
| Maintainer: | Olatunji Johnson <olatunjijohnson21111 at gmail.com> |
| License: | GPL-2 | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | SDALGCP results |
| Reference manual: | SDALGCP.pdf |
| Vignettes: |
A Spatially Discrete Approximation to Log-Gaussian Cox Processes for Modelling Aggregated Disease Count Data |
| Package source: | SDALGCP_0.3.0.tar.gz |
| Windows binaries: | r-devel: SDALGCP_0.3.0.zip, r-release: SDALGCP_0.3.0.zip, r-oldrel: SDALGCP_0.3.0.zip |
| macOS binaries: | r-release: SDALGCP_0.3.0.tgz, r-oldrel: SDALGCP_0.3.0.tgz |
| Old sources: | SDALGCP archive |
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