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 (2021) <https://digitalcommons.usu.edu/etd/8065/>.

Version: 0.2.1
Depends: R (≥ 3.6.0)
Imports: graphics (≥ 3.6.0), methods (≥ 3.6.0), parallel (≥ 3.6.0), sf (≥ 0.9.6), stats (≥ 3.6.0), units (≥ 0.6.7), utils (≥ 3.6.0)
Suggests: dplyr (≥ 1.0.2), ggplot2 (≥ 3.3.2), knitr (≥ 1.30), lwgeom (≥ 0.2.5), magrittr (≥ 2.0.1), maps (≥ 3.3.0), mgcv (≥ 1.8.33), rmarkdown (≥ 2.5), tibble (≥ 3.0.4)
Published: 2021-04-16
Author: Jadon Wagstaff [aut, cre], Brennan Bean [aut]
Maintainer: Jadon Wagstaff <jadonw at gmail.com>
BugReports: https://github.com/jadonwagstaff/remap/issues
License: GPL-3
URL: https://github.com/jadonwagstaff/remap
NeedsCompilation: no
Citation: remap citation info
Materials: NEWS
CRAN checks: remap results

Downloads:

Reference manual: remap.pdf
Vignettes: Introduction to remap
Package source: remap_0.2.1.tar.gz
Windows binaries: r-devel: remap_0.2.1.zip, r-release: remap_0.2.1.zip, r-oldrel: remap_0.2.1.zip
macOS binaries: r-release (arm64): remap_0.2.1.tgz, r-release (x86_64): remap_0.2.1.tgz, r-oldrel: remap_0.2.1.tgz
Old sources: remap archive

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