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.

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Description file content

Package
SDALGCP
Title
Spatially Discrete Approximation to Log-Gaussian Cox Processes for Aggregated Disease Count Data
Version
0.2.0
Description
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) and delivers prediction of spatially discrete and continuous relative risk. It performs inference for static spatial and spatio-temporal dataset.
Depends
R (>= 3.4.0)
License
GPL-2 | GPL-3
Encoding
UTF-8
LazyData
true
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)
RoxygenNote
6.1.1
Suggests
knitr, rmarkdown
VignetteBuilder
knitr
NeedsCompilation
no
Packaged
2019-01-09 13:34:37 UTC; olatunji
Author
Olatunji Johnson [aut, cre], Emanuele Giorgi [aut], Peter Diggle [aut]
Maintainer
Olatunji Johnson
Repository
CRAN
Date/Publication
2019-01-09 14:50:06 UTC

install.packages('SDALGCP')

0.2.0

2 months ago

Olatunji Johnson

GPL-2 | GPL-3

Depends on

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

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