Kernel Semi-Parametric Models

To fit the kernel semi-parametric model and its extensions. It allows multiple kernels and unlimited interactions in the same model. Coefficients are estimated by maximizing a penalized log-likelihood; penalization terms and hyperparameters are estimated by minimizing leave-one-out error. It includes predictions with confidence/prediction intervals, statistical tests for the significance of each kernel, a procedure for variable selection and graphical tools for diagnostics and interpretation of covariate effects. Currently it is implemented for continuous dependent variables.


Reference manual

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0.1.2 by Catherine Schramm, 4 months ago

Browse source code at

Authors: Catherine Schramm [aut, cre] , Aurelie Labbe [ctb] , Celia M. T. Greenwood [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports expm, CompQuadForm, DEoptim

Suggests testthat

See at CRAN