Geometrically Designed Spline Regression
Overview
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. (2026).
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-07-18
- NOTE12 OK, 2 NOTE2026-04-22
- ERROR11 OK, 2 NOTE, 1 ERROR2026-04-18
Show 1 earlier changeHide 1 earlier change
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 41includes a package overview page
- Examples that run
- 91%20 run in a plain check (1 of them under a condition) · 2 never run
- Exported functions with examples
- 69%
- Documented parameters
- 97%
- Return-value docs
- 81%
- Help pages citing references
- 37%
Downloads
Repository
Stars over time
Issues over time
Commits over time
3 development-tooling and community-health practices detected across 3 families in the upstream repository
Checks run against github.com/emilioluissaenzguillen/geds on 2026-10-22.
Dependencies
Code & Tests
Datasets
People & History
Previously maintained by Andrea Lattuada (0.1.2 to 0.1.3).
Show 8 of 10 peopleHide 8 people
Pick two releases to compare their code metrics.
- 0.3.5Latest
- 0.3.4
- RR 4.6.0 released · 2026-04-24
- 0.3.3
- 0.3.2
- 0.3.12025-04-22 · diff ↗
- 0.3.02025-04-16 · diff ↗
- RR 4.5.0 released · 2025-04-11
- 0.2.9
- 0.2.8
- 0.2.72025-03-11 · diff ↗
- 0.2.62025-02-10 · diff ↗
- 0.2.52025-01-21 · diff ↗
- 0.2.4
- 0.2.3
- 0.2.2
Show 13 earlier eventsHide 13 earlier events
- RR 4.4.0 released · 2024-04-24
- 0.2.12024-04-12 · diff ↗
- 0.2.02024-03-24 · diff ↗ ·
added 6 exports
BivariateFitter, GenBivariateFitter, NGeDSboost, NGeDSgam, bl_imp, visualize_boosting - 0.1.42023-12-06 · diff ↗
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- 0.1.32017-12-19 · diff ↗
- 0.1.22017-11-08
- RR 3.4.0 released · 2017-04-21
Package metadata
- First published
- 2017-11-08
- Total releases
- 19 / 9 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.4.0
- Bundled data
- 43 KB / 5 files
- Download size
- 3.9 MB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("GeDS")The package provides a citation file that builds its entry when R runs it. Run citation() to see it.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
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.