KSPM: 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. The package is based on the paper of Liu et al. (2007), <doi:10.1111/j.1541-0420.2007.00799.x>.

Package details

AuthorCatherine Schramm [aut, cre], Aurelie Labbe [ctb], Celia M. T. Greenwood [ctb]
MaintainerCatherine Schramm <[email protected]>
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the KSPM package in your browser

Any scripts or data that you put into this service are public.

KSPM documentation built on Oct. 30, 2019, 11:24 a.m.