Package: geosmooth
Title: Geometric Smoothing and Conditional Expectation Methods
Version: 0.1.0
Authors@R: c(
    person(given = "Pawel",
           family = "Gajer",
           email = "pgajer@gmail.com",
           role = c("aut", "cre")),
    person(given = "Gael",
           family = "Guennebaud",
           role = "ctb",
           comment = "Eigen"),
    person(given = "Benoit",
           family = "Jacob",
           role = "ctb",
           comment = "Eigen"),
    person(given = "Authors of",
           family = "Eigen",
           role = "cph",
           comment = "Authorship and copyright in the included Eigen library"),
    person(given = "Yixuan",
           family = "Qiu",
           role = c("ctb", "cph"),
           comment = "Spectra"),
    person(given = "Contributors to",
           family = "Spectra",
           role = "cph",
           comment = "Copyright in the included Spectra library"),
    person(given = "Sunil",
           family = "Arya",
           role = c("ctb", "cph"),
           comment = "ANN"),
    person(given = "David M.",
           family = "Mount",
           role = c("ctb", "cph"),
           comment = "ANN"),
    person(given = "University of",
           family = "Maryland",
           role = "cph",
           comment = "ANN")
    )
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>.
Copyright: file inst/COPYRIGHTS
License: GPL (>= 3)
URL: https://github.com/pgajer/geosmooth
BugReports: https://github.com/pgajer/geosmooth/issues
Encoding: UTF-8
Language: en-US
SystemRequirements: GNU make
LinkingTo: Rcpp
Depends: R (>= 3.5.0)
Imports: dgraphs (>= 0.1.0), digest, jsonlite, MASS, Matrix, methods,
        Rcpp, stats, utils
Suggests: genlasso, grip, knitr, rmarkdown, testthat (>= 3.0.0), waldo
VignetteBuilder: knitr
Config/testthat/edition: 3
Config/roxygen2/version: 8.0.0
NeedsCompilation: yes
Packaged: 2026-10-12 13:38:03 UTC; root
Author: 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)
Maintainer: Pawel Gajer <pgajer@gmail.com>
Config/pak/sysreqs: make
Repository: https://cran.r-universe.dev
Date/Publication: 2026-09-12 14:10:10 UTC
RemoteUrl: https://github.com/cran/geosmooth
RemoteRef: HEAD
RemoteSha: 220651914ffc4215c24a51c5b5012363162947f2