Type: Package
Package: GeDS
Title: Geometrically Designed Spline Regression
Version: 0.3.3
Date: 2025-06-30
Authors@R: c(
    person(c("Dimitrina", "S."), "Dimitrova", , "D.Dimitrova@citystgeorges.ac.uk", role = "aut"),
    person(c("Vladimir", "K."), "Kaishev", , "Vladimir.Kaishev.1@citystgeorges.ac.uk", role = "aut"),
    person("Andrea", "Lattuada", , "Andrea.Lattuada@hotmail.com", role = "aut"),
    person(c("Emilio", "L."), "Sáenz Guillén", , "Emilio.Saenz-Guillen@citystgeorges.ac.uk", role = c("aut", "cre")),
    person(c("Richard", "J."), "Verrall", , "R.J.Verrall@citystgeorges.ac.uk", role = "aut")
  )
Maintainer: Emilio L. Sáenz Guillén
 <Emilio.Saenz-Guillen@citystgeorges.ac.uk>
Description: Spline regression, generalized additive models and
    component-wise gradient boosting utilizing geometrically designed
    (GeD) splines. GeDS regression is a non-parametric method inspired by
    geometric principles, for fitting spline regression models with
    variable knots in one or two independent variables. It efficiently
    estimates the number of knots and their positions, as well as the
    spline order, assuming the response variable follows a distribution
    from the exponential family. GeDS models integrate the broader
    category of generalized (non-)linear models, offering a flexible
    approach to model complex relationships. A description of the
    method can be found in Kaishev et al. (2016)
    <doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2023)
    <doi:10.1016/j.amc.2022.127493>. Further extending its capabilities,
    GeDS's implementation includes generalized additive models (GAM) and
    functional gradient boosting (FGB), enabling versatile multivariate
    predictor modeling, as discussed in the forthcoming work of Dimitrova
    et al. (2025).
License: GPL-3
URL: https://github.com/emilioluissaenzguillen/GeDS
BugReports: https://github.com/emilioluissaenzguillen/GeDS/issues
Depends: R (>= 4.4.0)
Imports: doFuture, doParallel, doRNG, foreach, future, graphics,
        grDevices, MASS, Matrix, mboost, parallel, plot3D, Rcpp,
        splines, stats, utils
Suggests: knitr, R.rsp, rmarkdown, testthat (>= 3.0.0), TH.data
LinkingTo: Rcpp
VignetteBuilder: R.rsp
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: TRUE
RoxygenNote: 7.3.2
NeedsCompilation: yes
Packaged: 2025-06-30 00:59:31 UTC; emili
Author: Dimitrina S. Dimitrova [aut],
  Vladimir K. Kaishev [aut],
  Andrea Lattuada [aut],
  Emilio L. Sáenz Guillén [aut, cre],
  Richard J. Verrall [aut]
Repository: CRAN
Date/Publication: 2025-06-30 07:10:06 UTC
Built: R 4.7.0; x86_64-w64-mingw32; 2026-05-10 02:28:27 UTC; windows
Archs: x64
