Generalized Covariate Field R package gcf version 0.1.0 Generates generalized covariate field (GCF) variables from spatial covariates observed at projected coordinates, and selects a stable subset of them for geospatial prediction. For each input covariate the method builds spatial-pattern features (local indicator of spatial association, local Geary's c, log local variance, rank quantile entropy, geocomplexity, log scale variance, local variogram exponent, and signed z-score and median absolute deviation outlier strengths over a series of buffer radii) and neighbourhood-distribution features (buffer-wise quantiles of the covariate values surrounding each location), reduces the buffer and quantile sweeps to a compact set of interpretable functional summaries, and selects variables by random forest importance combined with spatial-block stability resampling and group voting. The GCF method is positioned as prediction-oriented feature construction: its output feeds any downstream regression learner. Methods are described in Song (2026) <doi:10.1080/13658816.2026.2729719>. Software Depends: R (>= 4.1.0) Imports: geocomplexity, ranger, sf, spdep, stats, utils Suggests: knitr, randomForest, rmarkdown, testthat (>= 3.0.0) Yongze Song <yongze.song@outlook.com> Comprehensive R Archive Network (CRAN) Yongze Song [aut, cre, cph] (ORCID: <https://orcid.org/0000-0003-3420-9622>) GPL-3 2026-09-26 application/tgz https://CRAN.R-project.org/package=gcf doi:10.32614/CRAN.package.gcf