remap: Regional Spatial Modeling with Continuous Borders

Automatically creates separate regression models for different spatial regions. The prediction surface is smoothed using a regional border smoothing method. If regional models are continuous, the resulting prediction surface is continuous across the spatial dimensions, even at region borders. Methodology is described in Wagstaff and Bean (2023) <doi:10.32614/RJ-2023-004>.

Version: 0.3.1
Depends: R (≥ 4.1.0)
Imports: graphics (≥ 4.1.0), methods (≥ 4.1.0), parallel (≥ 4.1.0), sf (≥ 1.0.0), stats (≥ 4.1.0), units (≥ 0.6.7), utils (≥ 4.1.0)
Suggests: dplyr (≥ 1.0.2), ggplot2 (≥ 3.3.2), knitr (≥ 1.30), maps (≥ 3.3.0), mgcv (≥ 1.8.33), rmarkdown (≥ 2.5)
Published: 2023-06-14
DOI: 10.32614/CRAN.package.remap
Author: Jadon Wagstaff [aut, cre], Brennan Bean [aut]
Maintainer: Jadon Wagstaff <jadonw at>
License: GPL-3
NeedsCompilation: no
Citation: remap citation info
Materials: NEWS
CRAN checks: remap results


Reference manual: remap.pdf
Vignettes: Introduction to remap


Package source: remap_0.3.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): remap_0.3.1.tgz, r-oldrel (arm64): remap_0.3.1.tgz, r-release (x86_64): remap_0.3.1.tgz, r-oldrel (x86_64): remap_0.3.1.tgz
Old sources: remap archive


Please use the canonical form to link to this page.