PLORN: Prediction with Less Overfitting and Robust to Noise

A method for the quantitative prediction with much predictors. This package provides functions to construct the quantitative prediction model with less overfitting and robust to noise.

Version: 0.1.1
Depends: R (≥ 3.5.0)
Imports: ggplot2, kernlab
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2022-03-21
Author: Takahiko Koizumi, Kenta Suzuki, Yasunori Ichihashi
Maintainer: Takahiko Koizumi <takahiko.koizumi at riken.jp>
License: MIT + file LICENSE
URL: https://github.com/takakoizumi/PLORN
NeedsCompilation: no
Language: en-US
Materials: README NEWS
CRAN checks: PLORN results

Documentation:

Reference manual: PLORN.pdf
Vignettes: PLORN

Downloads:

Package source: PLORN_0.1.1.tar.gz
Windows binaries: r-devel: PLORN_0.1.1.zip, r-release: PLORN_0.1.1.zip, r-oldrel: PLORN_0.1.1.zip
macOS binaries: r-release (arm64): PLORN_0.1.1.tgz, r-oldrel (arm64): PLORN_0.1.1.tgz, r-release (x86_64): PLORN_0.1.1.tgz

Linking:

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