geosmooth
Geometric Smoothing and Conditional Expectation Methods
Provides geometric methods for nonparametric regression and density estimation on data represented as coordinate matrices or weighted graphs. Methods include local polynomial smoothing, model-averaged local polynomial smoothing, local polynomial lifting trend filtering, synchronized local polynomial lifting trend filtering, graph low-pass filtering, and Hessian-energy regression. Methodological references include Gajer and Ravel (2025) "Adaptive Geometric Regression for High-Dimensional Structured Data" <doi:10.48550/arXiv.2511.03817>, Fan and Gijbels (1996, ISBN:9780412983214), Wang et al. (2016) "Trend Filtering on Graphs" <https://www.jmlr.org/papers/v17/15-147.html>, and Kim et al. (2009) "Semi-Supervised Regression Using Hessian Energy" <https://papers.nips.cc/paper/3741-semi-supervised-regression-using-hessian-energy-with-an-application-to-semi-supervised-dimensionality-reduction>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | geosmooth_0.1.0.tar.gz |
3.4 MiB |
0.1.0 |
rolling linux/noble R-4.5 | geosmooth_0.1.0.tar.gz |
3.4 MiB |
0.1.0 |
rolling source/ R- | geosmooth_0.1.0.tar.gz |
2.3 MiB |
0.1.0 |
latest linux/jammy R-4.5 | geosmooth_0.1.0.tar.gz |
3.4 MiB |
0.1.0 |
latest linux/noble R-4.5 | geosmooth_0.1.0.tar.gz |
3.4 MiB |
0.1.0 |
latest source/ R- | geosmooth_0.1.0.tar.gz |
2.3 MiB |
0.1.0 |
2026-04-23 source/ R- | geosmooth_0.1.0.tar.gz |
0 B |