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: |
0.3.0 |
| 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: |
2022-08-12 |
| 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 |