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 modeling 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. (2024).
| Version: |
0.2.4 |
| Depends: |
R (≥ 3.0.1), Rcpp (≥ 0.12.1), splines, stats, utils, Matrix, methods, mi, Rmpfr |
| Imports: |
doFuture, doParallel, doRNG, foreach, future, MASS, mboost, parallel, plot3D, TH.data |
| LinkingTo: |
Rcpp |
| Published: |
2024-09-12 |
| DOI: |
10.32614/CRAN.package.GeDS |
| Author: |
Dimitrina S. Dimitrova [aut],
Emilio S. Guillen [aut, cre],
Vladimir K. Kaishev [aut],
Andrea Lattuada [aut],
Richard J. Verrall [aut] |
| Maintainer: |
Emilio S. Guillen <Emilio.Saenz-Guillen at bayes.city.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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