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SDALGCP2

Fast, modern disease mapping. SDALGCP2 fits a spatially discrete approximation to a log-Gaussian Cox process (SDA-LGCP) to spatially aggregated disease counts, with a one-line, glm-like interface and C++ speed. The method is described in Johnson, Diggle & Giorgi (2019, Statistics in Medicine, doi:10.1002/sim.8339).

Installation

# install.packages("remotes")
remotes::install_github("olatunjijohnson/SDALGCP2")

You need a C++ toolchain (Rtools on Windows, Xcode CLT on macOS) because the performance-critical kernels are compiled.

Quick start — one line to fit

data is an sf object whose columns hold the response, covariates and offset. Everything else (candidate-point spacing, the spatial scale, MCMC settings) is chosen automatically.

library(SDALGCP2)

fit <- sdalgcp(cases ~ deprivation + offset(log(population)), data = regions)

summary(fit)              # glm-style coefficient table + spatial parameters
rr  <- predict(fit)       # an sf: relative_risk, relative_risk_se, adjusted_rr, adjusted_rr_se
plot(fit)                 # relative-risk map
plot(fit, "exceedance", threshold = 1.5)   # hotspot probabilities

That is the whole workflow. The same sdalgcp() call also covers:

You want…	Add…
raster (continuous) covariates	`rasters = my_raster` (enter on the intensity scale)
a spatio-temporal model	`time = "year"`
population-weighted aggregation	`popden = pop_raster`

What you get

Relative risk	Uncertainty (SD)	Exceedance P(RR > 1.5)	Continuous surface

Why SDALGCP2

Easy: sdalgcp(formula, data) — feels like glm(); sensible defaults so a first fit needs no tuning.
Fast: aggregated correlation assembly, the MALA sampler and the Monte Carlo likelihood run in C++ (RcppArmadillo + OpenMP) — 8–10× faster end-to-end than the original, returning the same estimates (see Performance).
Grid-free scale: the spatial scale φ is optimised continuously by default (no grid), with a proper standard error — see the derivation PDF.
Continuous covariates done right: rasters enter on the intensity scale (log-sum-exp), not by averaging predictors over polygons — see the raster PDF.
Spatio-temporal without ever forming the (N·T)² covariance.
Honest uncertainty: re-anchored Monte Carlo likelihood, importance-sampling diagnostics, a nugget term, model checking (residual Moran’s I).

Tutorials

See the package website for worked, reproducible articles:

Spatial disease mapping — the full workflow end to end.
Raster predictors — intensity-scale covariates vs naive areal averaging.
Spatio-temporal — space-time relative risk.
Estimating the scale — grid (scale = "grid") vs continuous (scale = "continuous") φ.

Reference

Johnson, O., Diggle, P. & Giorgi, E. (2019). A spatially discrete approximation to log-Gaussian Cox processes for modelling aggregated disease count data. Statistics in Medicine 38, 4871–4887.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.