Package: GeDS
Type: Package
Title: Geometrically Designed Spline Regression
Version: 0.3.1
Date: 2025-04-11
Authors@R: c(person(given = c("Dimitrina", "S."),
                      family = "Dimitrova",
                      role = "aut",
                      email = "D.Dimitrova@citystgeorges.ac.uk"),
              person(given = c("Vladimir", "K."),
                      family = "Kaishev",
                      role = "aut",
                      email = "Vladimir.Kaishev.1@citystgeorges.ac.uk"),
              person(given = "Andrea",
                      family = "Lattuada",
                      role = "aut",
                      email = "Andrea.Lattuada@hotmail.com"),
              person(given = c("Emilio", "L."),
                      family = "Sáenz Guillén",
                      role = c("aut", "cre"),
                      email = "Emilio.Saenz-Guillen@citystgeorges.ac.uk"),
              person(given = c("Richard", "J."),
                      family = "Verrall",
                      role = "aut",
                      email = "R.J.Verrall@citystgeorges.ac.uk"))
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 modeling 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
LazyData: TRUE
Depends: R (>= 3.0.1), Rcpp (>= 1.0.0), splines, stats, utils, Matrix,
        methods, mi, Rmpfr
LinkingTo: Rcpp
Suggests: R.rsp, testthat (>= 3.0.0)
RoxygenNote: 7.3.2
Encoding: UTF-8
Imports: doFuture, doParallel, doRNG, foreach, future, MASS, mboost,
        parallel, plot3D, TH.data
Config/testthat/edition: 3
VignetteBuilder: R.rsp
NeedsCompilation: yes
Packaged: 2025-04-21 14:26:56 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: 2025-04-22 07:30:06 UTC
Built: R 4.6.0; x86_64-w64-mingw32; 2025-06-10 02:36:14 UTC; windows
Archs: x64
