Extensions to the synthetic controls analyses
performed by the package 'Synth' as detailed in Abadie, Diamond, and Hainmueller (2011) <doi: 10.18637/jss.v042.i13>. Includes generating
and plotting placebos, post/pre-MSPE (Mean Squared Prediction Error) significance tests and plots,
and calculating average treatment effects for multiple treated units.
This package represents an implementation of those methods
suggested in
Abadie, Diamond,and Hainmueller (2010) <doi:10.1198/jasa.2009.ap08746> for
use in Synthetic Control Analysis.
ggplot2, Synth, stringr, stats, cvTools, furrr, dplyr, purrr,
magrittr

testthat (>= 3.0.0), knitr, tidyr, rmarkdown

Spatially Discrete Approximation to Log-Gaussian Cox Processes
for Aggregated Disease Count Data
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"))
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.
Olatunji Johnson [aut, cre],
Emanuele Giorgi [aut],
Peter Diggle [aut]

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.

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>.