Package: SDALGCP
Title: Spatially Discrete Approximation to Log-Gaussian Cox Processes
        for Aggregated Disease Count Data
Version: 0.1.0
Authors@R: c(
    person("Olatunji", "Johnson", email = "olatunjijohnson21111@gmail.com", role = c("aut", "cre")),
    person("Emanuele", "Giorgi", email = "e.giorgi@lancaster.ac.uk", role = "aut"),
    person("Peter", "Diggle", email = "p.diggle@lancaster.ac.uk", role = "aut"))
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) <doi: 10.1198/106186004X2525> and delivers prediction of spatially discrete and continuous relative risk.
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),
        methods(>= 3.4.4), maptools(>= 0.9.2), plyr(>= 1.8.4),
        pbapply(>= 1.3.4), progress(>= 1.1.2)
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-08-13 09:35:51 UTC; johnsono
Author: Olatunji Johnson [aut, cre],
  Emanuele Giorgi [aut],
  Peter Diggle [aut]
Maintainer: Olatunji Johnson <olatunjijohnson21111@gmail.com>
Repository: CRAN
Date/Publication: 2018-08-14 09:30:05 UTC
