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: | 0.1.0 |
Depends: | R (≥ 3.5.0) |
Imports: | usethis, expm, CompQuadForm, DEoptim |
Suggests: | testthat |
Published: | 2019-01-09 |
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_0.1.0.tar.gz |
Windows binaries: | r-devel: KSPM_0.1.0.zip, r-release: KSPM_0.1.0.zip, r-oldrel: not available |
OS X binaries: | r-release: KSPM_0.1.0.tgz, r-oldrel: not available |
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