% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SDALGCP.R
\docType{package}
\name{SDALGCP}
\alias{SDALGCP}
\alias{SDALGCP-package}
\title{SDALGCP: A package to make continuous inference from spatially aggregated disease count data.}
\description{
The SDALGCP package provides two categories of important functions:
\code{SDALGCPMCML} and \code{SDALGCPPred}.
}
\section{SDALGCP functions}{

The \link{SDALGCPMCML} function uses Monte Carlo Maximum Likelihood to estimate the parameter of a 
poisson log-linear model with spatially continuous random effect.

The \link{SDALGCPPred} function delivers spatially discrete prediction of the incidence and the 
covariate adjusted relative risk and spatially continuous prediction of the covariate adjusted relative risk.

Functions such as \link{summary}, \link{confint}  and \link{print} also can be applied to the output.
}

\references{
Christensen, O. F. (2004). Monte carlo maximum likelihood in model-based geostatistics. Journal of Computational and Graphical Statistics 13, 702-718.

Giorgi, E., & Diggle, P. J. (2017). PrevMap: an R package for prevalence mapping. Journal of Statistical Software, 78(8), 1-29. doi:10.18637/jss.v078.i08

Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2014). Hierarchical modeling and analysis for spatial data. CRC press.
}
\author{
Olatunji O. Johnson, Emanuele Giorgi, Peter Diggle. All from CHICAS, Lancaster Medical School, 
Faculty of Health and Medicine, Lancaster University
}
