GPBoost Spatial Predictor
Spatial prediction with GPBoost, combining tree boosting and Gaussian processes for point observations.
GPBoost Spatial Predictor trains GPBoost models for spatial prediction from point layers with numeric response and covariate fields. It combines tree boosting for nonlinear covariate-response relationships with a Gaussian Process component for spatial residual autocorrelation. External dependency: the Python package gpboost>=1.4.0 must be available in the same Python environment used by QGIS. Version 1.0.6 adds both author LinkedIn profiles to the Info tab and documentation, keeps the non-blocking Info tab, and maintains the guided in-plugin installer/update button for gpboost, avoiding manual console use when the platform allows pip installation. The plugin includes an interactive dialog, a QGIS Processing algorithm, model comparison/tuning tools, and English, Spanish, and Portuguese interface labels. Current limitation: when creating prediction rasters from selected covariate fields, covariates are fixed at their median values over the prediction grid; future versions should accept raster covariate layers for fully covariate-varying maps.