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>.

Version: 0.2.1
Depends: R (≥ 3.5.0)
Imports: expm, CompQuadForm, DEoptim
Suggests: testthat, knitr, rmarkdown
Published: 2020-08-10
DOI: 10.32614/CRAN.package.KSPM
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
Vignettes: KSPM: an R package for Kernel Semi-Prametric Models


Package source: KSPM_0.2.1.tar.gz
Windows binaries: r-devel: KSPM_0.2.1.zip, r-release: KSPM_0.2.1.zip, r-oldrel: KSPM_0.2.1.zip
macOS binaries: r-release (arm64): KSPM_0.2.1.tgz, r-oldrel (arm64): KSPM_0.2.1.tgz, r-release (x86_64): KSPM_0.2.1.tgz, r-oldrel (x86_64): KSPM_0.2.1.tgz
Old sources: KSPM archive


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