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geosmooth  

Geometric Smoothing and Conditional Expectation Methods
View on CRAN: Click here


Download and install geosmooth package within the R console
Install from CRAN:
install.packages("geosmooth")

Install from Github:
library("remotes")
install_github("cran/geosmooth")

Install by package version:
library("remotes")
install_version("geosmooth", "0.1.0")



Attach the package and use:
library("geosmooth")
Maintained by
Pawel Gajer
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-09-12
Latest Update: 2026-09-12
Description:
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>.
How to cite:
Pawel Gajer (2026). geosmooth: Geometric Smoothing and Conditional Expectation Methods. R package version 0.1.0, https://cran.r-project.org/web/packages/geosmooth. Accessed 02 Nov. 2026.
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