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MARSGWR

0.1.0Package record

A Hybrid Spatial Model for Capturing Spatially Varying Relationships Between Variables in the Data

0packages depend
2.3Kdownloads / year
–test coverage
13/13flavors OK

Overview

About

It is a hybrid spatial model that combines the strength of two widely used regression models, MARS (Multivariate Adaptive Regression Splines) and GWR (Geographically Weighted Regression) to provide an effective approach for predicting a response variable at unknown locations. The MARS model is used in the first step of the development of a hybrid model to identify the most important predictor variables that assist in predicting the response variable. For method details see, Friedman, J.H. (1991). DOI:10.1214/aos/1176347963.The GWR model is then used to predict the response variable at testing locations based on these selected variables that account for spatial variations in the relationships between the variables. This hybrid model can improve the accuracy of the predictions compared to using an individual model alone.This developed hybrid spatial model can be useful particularly in cases where the relationship between the response variable and predictor variables is complex and non-linear, and varies across locations.

Install

Health

CRAN checks
13OK
Slowest check: 52 s · r-devel-windows-x86_64
Status changes
  • OK13 OK2026-09-08
  • ERROR12 OK, 1 ERROR2026-09-07
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 0.1.0
READMENoVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
2
Examples that run
100%2 run in a plain check
Exported functions with examples
100%
Documented parameters
100%
Return-value docs
100%
Help pages citing references
100%

Downloads

2.3K
CRAN downloads in the past year
Rank #21,566 · ~6/day · ~192/mo
Daily download trend is not available in this view yet.
23230 days
61590 days
2.3K1 year
Compare downloads with other packages →
Also on66 r2u

Dependencies

Declared dependencies
6 external dependencies (excludes base and recommended)

Installing MARSGWR adds 10 packages (6 with compiled code, 6.2 MB of source tarballs). Heaviest: Rcpp 1.8 MB, earth 1.5 MB, plotmo 1.2 MB.

Depends (1)
R >= 2.10
Imports (4)
LinkingTo (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

People & History

People (6)
Author, Maintainer since 0.1.0 (2023), Copyright holder
Author
Releases (1)
  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • 0.1.0Latest
    2023-05-09 · current release
  • R
    R 4.3.0 released · 2023-04-21

Package metadata

First published
2023-05-09
Total releases
1 / 3 yrs
License
GPL (>= 2.0)
Minimum R
≥ 2.10
Download size
4.7 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("MARSGWR")
Paul, N. C., Ahmad, T., Biswas, A., Gaikwad, B. B., Nangare, D. D., & Rai, A. (2023). MARSGWR: A Hybrid Spatial Model for Capturing Spatially Varying Relationships Between Variables in the Data (Version 0.1.0) [Computer software]. https://doi.org/10.32614/CRAN.package.MARSGWR

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for MARSGWR version 0.1.0 [Data set]. HJJB, LLC. Data release v2026-10-23. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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