SingRegKrig: 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) <doi:10.1080/15481603.2026.2690341>,
with singularity theory from Cheng (2012)
<doi:10.1016/j.gexplo.2012.07.007> and Cheng (2017)
<doi:10.1016/j.gr.2017.07.011>, random forest methodology from
Breiman (2001) <doi:10.1023/A:1010933404324>, and regression kriging
framework from Hengl, Heuvelink, and Rossiter (2007)
<doi:10.1016/j.cageo.2007.05.001>.
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[aut, cre],
Arvind Pandey [aut],
Bhupendra Singh [aut],
Vrijesh Tripathi [aut]