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/18460>.
|Author||Dimitrina S. Dimitrova <[email protected]>, Vladimir K. Kaishev <[email protected]>, Andrea Lattuada <[email protected]> and Richard J. Verrall <[email protected]>|
|Maintainer||Andrea Lattuada <[email protected]>|
|Package repository||View on GitHub|
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