GeDS: Geometrically Designed Spline Regression
Spline regression, generalized additive models and
component-wise gradient boosting utilizing geometrically designed
(GeD) splines. GeDS regression is a non-parametric method inspired by
geometric principles, for fitting spline regression models with
variable knots in one or two independent variables. It efficiently
estimates the number of knots and their positions, as well as the
spline order, assuming the response variable follows a distribution
from the exponential family. GeDS models integrate the broader
category of generalized (non-)linear models, offering a flexible
approach to model complex relationships. A description of the
method can be found in Kaishev et al. (2016)
<doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2023)
<doi:10.1016/j.amc.2022.127493>. Further extending its capabilities,
GeDS's implementation includes generalized additive models (GAM) and
functional gradient boosting (FGB), enabling versatile multivariate
predictor modeling, as discussed in the forthcoming work of Dimitrova
et al. (2025).
| Version: |
0.3.3 |
| Depends: |
R (≥ 4.4.0) |
| Imports: |
doFuture, doParallel, doRNG, foreach, future, graphics, grDevices, MASS, Matrix, mboost, parallel, plot3D, Rcpp, splines, stats, utils |
| LinkingTo: |
Rcpp |
| Suggests: |
knitr, R.rsp, rmarkdown, testthat (≥ 3.0.0), TH.data |
| Published: |
2025-06-30 |
| DOI: |
10.32614/CRAN.package.GeDS |
| Author: |
Dimitrina S. Dimitrova [aut],
Vladimir K. Kaishev [aut],
Andrea Lattuada [aut],
Emilio L. Sáenz Guillén [aut, cre],
Richard J. Verrall [aut] |
| Maintainer: |
Emilio L. Sáenz Guillén
<Emilio.Saenz-Guillen at citystgeorges.ac.uk> |
| BugReports: |
https://github.com/emilioluissaenzguillen/GeDS/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/emilioluissaenzguillen/GeDS |
| NeedsCompilation: |
yes |
| Citation: |
GeDS citation info |
| Materials: |
README |
| CRAN checks: |
GeDS results |
Documentation:
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