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.

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Description file content

Package
KSPM
Title
Kernel Semi-Parametric Models
Version
0.1.1
Description
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.
Depends
R (>= 3.5.0)
License
GPL-3
Encoding
UTF-8
LazyData
true
RoxygenNote
6.1.1
Imports
usethis, expm, CompQuadForm, DEoptim
Suggests
testthat
NeedsCompilation
no
Packaged
2019-02-01 16:05:58 UTC; Catherine
Author
Catherine Schramm [aut, cre], Aurelie Labbe [ctb], Celia M. T. Greenwood [ctb]
Maintainer
Catherine Schramm
Repository
CRAN
Date/Publication
2019-02-01 17:33:30 UTC

install.packages('KSPM')

0.1.1

a month ago

Catherine Schramm

GPL-3

Depends on

R (>= 3.5.0)

Imports

usethis, expm, CompQuadForm, DEoptim

Suggests

testthat

Discussions