Package: SDALGCP
Title: Spatially Discrete Approximation to Log-Gaussian Cox Processes
        for Aggregated Disease Count Data
Version: 0.3.0
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. 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>.
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), progress(>= 1.1.2), methods, spacetime(>=
        1.2.2), mapview(>= 2.6.0), geoR(>= 1.7-5.2.1)
RoxygenNote: 7.0.0
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-02-27 14:53:30 UTC; olatunji
Author: Olatunji Johnson [aut, cre],
  Emanuele Giorgi [aut],
  Peter Diggle [aut]
Maintainer: Olatunji Johnson <olatunjijohnson21111@gmail.com>
Repository: CRAN
Date/Publication: 2020-02-28 16:40:02 UTC
Built: R 3.5.2; ; 2020-02-29 09:02:20 UTC; unix
