GeDS: Geometrically Designed Spline Regression
Geometrically Designed Spline ('GeDS') Regression is a non-parametric geometrically
motivated method for fitting variable knots spline predictor models in one or two independent
variables, in the context of generalized (non-)linear models. 'GeDS' estimates the number and
position of the knots and the order of the spline, assuming the response variable has a
distribution from the exponential family. A description of the method can be found in
Kaishev et al. (2016) <doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2017) <https://openaccess.city.ac.uk/id/eprint/18460/>.
Version: |
0.1.4 |
Depends: |
R (≥ 3.0.1), Rcpp (≥ 0.12.1), splines, stats, utils, Matrix, methods, Rmpfr |
LinkingTo: |
Rcpp |
Published: |
2023-12-06 |
Author: |
Dimitrina S. Dimitrova,
Emilio S. Guillen,
Vladimir K. Kaishev,
Andrea Lattuada and
Richard J. Verrall |
Maintainer: |
Emilio S. Guillen <emilio.saenz-guillen at bayes.city.ac.uk> |
BugReports: |
https://github.com/emilioluissaenzguillen/GeDS/issues |
License: |
GPL-3 |
URL: |
https://github.com/emilioluissaenzguillen/GeDS |
NeedsCompilation: |
yes |
Citation: |
GeDS citation info |
Materials: |
README |
CRAN checks: |
GeDS results |
Documentation:
Downloads:
Linking:
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