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) and Dimitrova et al. (2017) < https://openaccess.city.ac.uk/18460>.


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Reference manual

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install.packages("GeDS")

0.1.3 by Andrea Lattuada, a year ago


http://github.com/alattuada/GeDS


Report a bug at http://github.com/alattuada/GeDS/issues


Browse source code at https://github.com/cran/GeDS


Authors: Dimitrina S. Dimitrova <[email protected]> , Vladimir K. Kaishev <[email protected]> , Andrea Lattuada <[email protected]> and Richard J. Verrall <[email protected]>


Documentation:   PDF Manual  


GPL-3 license


Depends on Rcpp, splines, stats, utils, Matrix, methods, Rmpfr

Linking to Rcpp


See at CRAN