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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/18460>.
Getting started
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Package details |
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| Author | Dimitrina S. Dimitrova <D.Dimitrova@city.ac.uk>, Vladimir K. Kaishev <V.Kaishev@city.ac.uk>, Andrea Lattuada <Andrea.Lattuada@unicatt.it> and Richard J. Verrall <R.J.Verrall@city.ac.uk> |
| Maintainer | Andrea Lattuada <Andrea.Lattuada@unicatt.it> |
| License | GPL-3 |
| Version | 0.1.3 |
| URL | http://github.com/alattuada/GeDS |
| Package repository | View on CRAN |
| Installation |
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