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# CITATION.cff file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "geosmooth" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'geosmooth: Geometric Methods for Nonparametric Regression and Density Estimation'
version: 0.2.0
doi: 10.32614/CRAN.package.geosmooth
abstract: 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" <https://doi.org/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>.
authors:
- family-names: Gajer
  given-names: Pawel
  email: pgajer@gmail.com
repository: https://pgajer.r-universe.dev
repository-code: https://github.com/pgajer/geosmooth
commit: 5e8959743d1634b2d7369dd213eb18ebce646a74
url: https://pgajer.github.io/geosmooth/
date-released: '2026-10-08'
contact:
- family-names: Gajer
  given-names: Pawel
  email: pgajer@gmail.com