Singularity Regression Kriging for Spatial Prediction

Implements the Singularity Regression Kriging ('SRK') model for spatial prediction by integrating covariate singularity feature construction, nonlinear trend estimation via random forest, and geostatistical interpolation of residuals using ordinary kriging. Singularity-based anomaly indices are computed from environmental covariates at multiple spatial scales to capture local multiscale heterogeneity and augment the random forest feature set for trend estimation. The resulting residuals are interpolated using ordinary kriging to generate final spatial predictions with uncertainty quantification. Tools for spatial block cross-validation, parameter sensitivity analysis, and diagnostic visualization are also provided. Methods are based on Ren, Song, Chen, and Yu (2026) , with singularity theory from Cheng (2012) and Cheng (2017) , random forest methodology from Breiman (2001) , and regression kriging framework from Hengl, Heuvelink, and Rossiter (2007) .