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.

Version: 0.1.1
Depends: R (≥ 3.5.0)
Imports: usethis, expm, CompQuadForm, DEoptim
Suggests: testthat
Published: 2019-02-01
Author: Catherine Schramm [aut, cre], Aurelie Labbe [ctb], Celia M. T. Greenwood [ctb]
Maintainer: Catherine Schramm <cath.schramm at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: KSPM results


Reference manual: KSPM.pdf
Package source: KSPM_0.1.1.tar.gz
Windows binaries: r-devel: KSPM_0.1.1.zip, r-release: KSPM_0.1.1.zip, r-oldrel: not available
OS X binaries: r-release: KSPM_0.1.1.tgz, r-oldrel: not available
Old sources: KSPM archive


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