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.

Version: 0.2.0
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), plyr (≥ 1.8.4), progress (≥ 1.1.2), methods, spacetime (≥ 1.2.2), mapview (≥ 2.6.0), geoR (≥ 1.7-5.2.1)
Suggests: knitr, rmarkdown
Published: 2019-01-09
Author: Olatunji Johnson [aut, cre], Emanuele Giorgi [aut], Peter Diggle [aut]
Maintainer: Olatunji Johnson <olatunjijohnson21111 at>
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_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: SDALGCP_0.2.0.tgz, r-oldrel: SDALGCP_0.1.0.tgz
Old sources: SDALGCP archive


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