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

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.1.2 by Catherine Schramm, 18 days 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