The model | The data | Fit | Map the two relative risks | Uncertainty and exceedance | A continuous surface | Model checking | Real data | Next
Package: SDALGCP2 0.1.0
Olatunji Johnson
SDALGCP2: Fast Spatially Discrete Approximation to Log-Gaussian Cox Processes for Aggregated Disease Count Data
Fits a spatially discrete approximation to a log-Gaussian Cox process model for spatially aggregated disease count data, estimated by Monte Carlo Maximum Likelihood as in Christensen (2004) <doi:10.1198/106186004X2525> and Johnson, Diggle and Giorgi (2019) <doi:10.1002/sim.8339>. Performance-critical steps (aggregated correlation assembly, 'MALA' sampling, the Monte Carlo likelihood, and the Kronecker-structured space-time likelihood) are implemented in C++ via 'RcppArmadillo'. Provides a one-line, 'glm'-like interface and statistical extensions including a nugget term, general 'Matern' smoothness, raster and misaligned covariates, restricted spatial regression, importance-sampling diagnostics and re-anchored 'MCML'.
Authors:
SDALGCP2_0.1.0.tar.gz
SDALGCP2_0.1.0.tar.gz(r-4.7-arm64)SDALGCP2_0.1.0.tar.gz(r-4.7-x86_64)SDALGCP2_0.1.0.tar.gz(r-4.6-arm64)SDALGCP2_0.1.0.tar.gz(r-4.6-x86_64)
SDALGCP2_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SDALGCP2/json (API)
| # Install 'SDALGCP2' in R: |
| install.packages('SDALGCP2', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/olatunjijohnson/sdalgcp2/issues
Pkgdown/docs site:https://olatunjijohnson.github.io
Uses libs:
Datasets:
- liver - Primary biliary cirrhosis incidence in North East England
- sdalgcp_data - Simulated aggregated disease-count data
Last updated from:cc2e16b6ed. Checks:6 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 178 | ||
| linux-devel-x86_64 | OK | 182 | ||
| source / vignettes | OK | 353 | ||
| linux-release-arm64 | OK | 180 | ||
| linux-release-x86_64 | OK | 181 | ||
| wasm-release | OK | 187 |
Exports:coef_plotcontrol_mcmcexceedancelaplace_samplingmap_exceedancemc_diagnosticsmcml_fitmodel_checkphi_profileprecompute_corrreportsda_pointssdalgcpsdalgcp_controlSDALGCP2SDALGCP2_misalignedSDALGCP2_rasterSDALGCP2_ST
Dependencies:abindclassclassIntclicpp11crayonDBIdeldire1071farverggplot2gluegtablehmsisobandKernSmoothlabelinglatticelifecycleMASSMatrixpkgconfigpolyclipprettyunitsprogressproxyR6RColorBrewerRcppRcppArmadillorlangs2S7scalessfspatstat.dataspatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilstensorterraunitsvctrsviridisLitewithrwk
Spatial disease mapping with SDALGCP2
Last update: 2026-07-02
Started: 2026-07-02
Spatially continuous (raster) predictors
The problem, precisely | The data | Fitting: naive vs intensity-scale | When does it matter? | Output | Options
Last update: 2026-07-02
Started: 2026-07-02
Spatio-temporal disease mapping
The model | The data | Fit | Predict and map — pick a year and a quantity | Covariates and confounding | Tips
Last update: 2026-07-02
Started: 2026-07-02
Estimating the spatial scale: grid vs continuous
Where (\phi) lives: a double integral | Set up an example | Grid (profile) | Continuous (direct) — the default | They agree — and continuous gives a standard error | Which to use?
Last update: 2026-07-02
Started: 2026-07-02
Spatial confounding and restricted spatial regression
The issue | Restricted spatial regression (RSR) | What RSR does — and what it does not | Practical guidance | References
Last update: 2026-07-02
Started: 2026-07-02
Covariates measured on a different support
The method | Point support: monitoring stations | Areal support: a different partition | Notes
Last update: 2026-07-02
Started: 2026-07-02
