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Changelog

Source: NEWS.md

SDALGCP2 0.1.0

First public version.

  • Raster, misaligned, and restricted-regression covariates for the spatio-temporal model. SDALGCP2_ST() (and sdalgcp(..., time =)) now accept rasters = (intensity-scale continuous covariates), covariates = (kriged covariates measured on a different support, with the Berkson correction), and confounding = "restricted" (restricted spatial regression against space-time confounding) — the same extensions previously available only for spatial fits. The raster/misaligned fits use a Gauss-Newton tilting loop around the Kronecker-free space-time likelihood; the restricted fit reduces exactly to the spatial restricted fit at T = 1.

  • Spatio-temporal prediction now returns a long sf too. predict() on an SDALGCP2_ST fit returns a one-row-per-region-time sf (class "SDALGCP2_ST_pred") with the same relative_risk/adjusted_rr columns as the spatial predictor, so both can be mapped or st_write()-en the same way; posterior draws are kept as attributes.

  • Bundled datasets. sdalgcp_data — a small simulated sf of 64 regions (cases, x1, pop) used by the help-page examples and the intro vignette; and liver — a real example, incident primary biliary cirrhosis counts by LSOA in North East England (Johnson et al. 2019), for realistic case studies.

  • The introductory vignette now runs live on sdalgcp_data (no precomputed figures), so it is fully reproducible.

  • Runnable examples on the exported functions, all using sdalgcp_data.

  • Prediction output is now an sf with clear public-health column names. predict() returns an sf (class "SDALGCP2_pred") you can map or st_write() directly, with columns relative_risk/relative_risk_se (the relative risk exp(d'beta + S)) and adjusted_rr/adjusted_rr_se (the covariate-adjusted relative risk exp(S)). The previous RR/ARR names are replaced everywhere (plot(), exceedance(), map_exceedance(), the spatio-temporal predictor) — ARR was dropped because it conventionally means absolute risk reduction in epidemiology. Posterior draws are retained as object attributes so exceedance probabilities still work for either quantity.

  • Kronecker-free spatio-temporal model (SDALGCP2_ST()): separable space-time SDA-LGCP for counts over the same regions at several times. The likelihood never forms the (N*T)x(N*T) covariance, using tr(Rs^-1 M Rt^-1 M') for the quadratic form and N log|Rt| + T log|Rs| for the log-determinant (both verified vs the brute-force Kronecker product). Spatial scale on a grid, temporal Matern range estimated continuously (analytic gradient verified vs numDeriv). No geoR.

  • Covariate-tilted raster model (SDALGCP2_raster(tilt_spatial = TRUE)): rebuilds the correlation from intensity-tilted weights with a log-normal aggregation correction.

  • General Matern smoothness for the direct method: corr_and_grad_cpp now provides closed-form phi-derivatives for Matern kappa in {0.5, 1.5, 2.5}, so continuous-phi (phi_method = "direct") estimation works for all three (was exponential only). Derivatives verified against finite differences.

  • Nugget / overdispersion term (nugget = TRUE, with phi_method = "direct"): fits covariance sigma2 (R(phi) + nu I) and estimates the relative nugget nu = tau2/sigma2 with a standard error, absorbing region-level overdispersion beyond the spatial structure. Analytic gradient and Hessian (including the nugget and all cross terms) verified against numerical differentiation.

  • Re-anchored (iterated) MCML (reanchor =): re-simulates the latent field at the current optimum and refits, keeping the importance weights near-uniform. On a 64-region example it lifts the effective sample size from ~0% to ~96% and cuts the log-likelihood MC standard error ~100x, correcting the variance estimate.

  • Importance-sampling diagnostics (mc_diagnostics(), shown in summary()): effective sample size of the importance weights at the optimum and a Monte Carlo SE for the maximised log-likelihood; warns on collapse.

  • Spatially continuous (raster) covariates (SDALGCP2_raster()): covariates given as rasters enter the LGCP intensity at the candidate-point level and are aggregated on the intensity (exp) scale via a log-sum-exp offset b_i(beta) = log sum_k w_ik exp(z(x_ik)'beta) – the correct alternative to averaging the predictor over polygons (which is biased under the log link). Fit by a Gauss-Newton fixed point reusing mcml_fit(). On a sharp-peak covariate, naive areal averaging is biased +67% while this recovers the truth to ~6%.

  • Continuous-phi (“direct”) estimation (phi_method = "direct"): optimises the spatial scale phi continuously inside the MCML objective instead of profiling a grid, using analytic first/second derivatives of the aggregated double integral (corr_and_grad_cpp). Gives a genuine standard error for phi from the joint Hessian and avoids grid-boundary artefacts; phi_method = "grid" stays the default. Gradient and Hessian verified against numerical differentiation. The full derivation is in math/continuous-phi-derivation.pdf.

  • Post-fit visualisation & diagnostics: relative-risk / uncertainty / exceedance maps, phi_profile(), coef_plot(), model_check() (residual Moran’s I), report().

  • C++ (RcppArmadillo + OpenMP) aggregated correlation assembly (precompute_corr).

  • C++ Newton Laplace mode + adaptive MALA sampler (laplace_sampling); reproducible given the same mode and seed.

  • Vectorised, Cholesky-based Monte Carlo likelihood with analytic gradient/Hessian (mcml_fit).

  • One-call spatial fit SDALGCP2() (points -> correlation -> MCML).

  • Candidate-point generation sda_points() (SSI / uniform / regular; sf + spatstat

    • terra).

  • Prediction predict.SDALGCP2() (discrete + continuous) with an MCMC or a no-MCMC Laplace fast path; exceedance() probabilities.

  • Vignette and reproducible benchmark scripts using entirely simulated data.