gcf: Generalized Covariate Field

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>.

Version:	0.1.0
Depends:	R (≥ 4.1.0)
Imports:	geocomplexity, ranger, sf, spdep, stats, utils
Suggests:	knitr, randomForest, rmarkdown, testthat (≥ 3.0.0)
Published:	2026-09-26
DOI:	10.32614/CRAN.package.gcf
Author:	Yongze Song [aut, cre, cph]
Maintainer:	Yongze Song <yongze.song at outlook.com>
License:	GPL-3
NeedsCompilation:	no
Citation:	gcf citation info
Materials:	NEWS
CRAN checks:	gcf results [issues need fixing before 2026-10-19]

Documentation:

Reference manual:	gcf.html , gcf.pdf
Vignettes:	The GCF workflow: from spatial variables to better predictions (source, R code)

Downloads:

Package source:	gcf_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: gcf_0.1.0.zip, r-oldrel: gcf_0.1.0.zip
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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