Package: remap 0.3.1

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>.

Authors:Jadon Wagstaff [aut, cre], Brennan Bean [aut]

remap_0.3.1.tar.gz
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remap.pdf |remap.html
remap/json (API)
NEWS

# Install 'remap' in R:
install.packages('remap', repos = c('https://jadonwagstaff.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jadonwagstaff/remap/issues

Datasets:
  • utsnow - Snowpack at weather stations in Utah on April 1st, 2011.
  • utws - Watershed polygons within the state of Utah.

On CRAN:

4.45 score 1 stars 56 scripts 188 downloads 2 exports 13 dependencies

Last updated 1 years agofrom:03548c21e7. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-winOKNov 05 2024
R-4.5-linuxOKNov 05 2024
R-4.4-winOKNov 05 2024
R-4.4-macOKNov 05 2024
R-4.3-winOKNov 05 2024
R-4.3-macOKNov 05 2024

Exports:redistremap

Dependencies:classclassIntDBIe1071KernSmoothmagrittrMASSproxyRcpps2sfunitswk

Introduction to remap

Rendered fromIntroduction_to_remap.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2023-06-06
Started: 2020-12-08