Geometric Smoothing and Conditional Expectation Methods R package geosmooth version 0.1.0 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>. Software Depends: R (>= 3.5.0) Imports: dgraphs (>= 0.1.0), digest, jsonlite, MASS, Matrix, methods, Rcpp, stats, utils LinkingTo: Rcpp Suggests: genlasso, grip, knitr, rmarkdown, testthat (>= 3.0.0), waldo Pawel Gajer <pgajer@gmail.com> Comprehensive R Archive Network (CRAN) Pawel Gajer [aut, cre], Gael Guennebaud [ctb] (Eigen), Benoit Jacob [ctb] (Eigen), Authors of Eigen [cph] (Authorship and copyright in the included Eigen library), Yixuan Qiu [ctb, cph] (Spectra), Contributors to Spectra [cph] (Copyright in the included Spectra library), Sunil Arya [ctb, cph] (ANN), David M. Mount [ctb, cph] (ANN), University of Maryland [cph] (ANN) GPL (>= 3) 2026-09-12 application/tgz https://CRAN.R-project.org/package=geosmooth doi:10.32614/CRAN.package.geosmooth en-US