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  "Title": "Singularity Regression Kriging for Spatial Prediction",
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  "Authors@R": "c(\nperson(\"Shikhar\", \"Tyagi\", email = \"shikhar1093tyagi@gmail.com\",\nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-1606-0844\")),\nperson(\"Arvind\", \"Pandey\", email = \"arvindmzu@gmail.com\", role = \"aut\"),\nperson(\"Bhupendra\", \"Singh\", email = \"bhupendra.rana@gmail.com\", role = \"aut\"),\nperson(\"Vrijesh\", \"Tripathi\", email = \"vrijesh.tripathi@uwi.edu\", role = \"aut\"))",
  "Description": "Implements the Singularity Regression Kriging ('SRK')\nmodel for spatial prediction by integrating covariate\nsingularity feature construction, nonlinear trend estimation\nvia random forest, and geostatistical interpolation of\nresiduals using ordinary kriging. Singularity-based anomaly\nindices are computed from environmental covariates at multiple\nspatial scales to capture local multiscale heterogeneity and\naugment the random forest feature set for trend estimation. The\nresulting residuals are interpolated using ordinary kriging to\ngenerate final spatial predictions with uncertainty\nquantification. Tools for spatial block cross-validation,\nparameter sensitivity analysis, and diagnostic visualization\nare also provided. Methods are based on Ren, Song, Chen, and Yu\n(2026) <doi:10.1080/15481603.2026.2690341>, with singularity\ntheory from Cheng (2012) <doi:10.1016/j.gexplo.2012.07.007> and\nCheng (2017) <doi:10.1016/j.gr.2017.07.011>, random forest\nmethodology from Breiman (2001) <doi:10.1023/A:1010933404324>,\nand regression kriging framework from Hengl, Heuvelink, and\nRossiter (2007) <doi:10.1016/j.cageo.2007.05.001>.",
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      "page": "compute_singularity",
      "title": "Compute Singularity Indices for Spatial Covariates",
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      "title": "Plot Diagnostics for an SRK Model",
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      "title": "Predict from a Singularity Regression Kriging Model",
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      "title": "Fit a Singularity Regression Kriging Model",
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