Package: PLORN 0.1.1

Takahiko Koizumi

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.

Authors:Takahiko Koizumi, Kenta Suzuki, Yasunori Ichihashi

PLORN_0.1.1.tar.gz
PLORN_0.1.1.tar.gz(r-4.5-noble)PLORN_0.1.1.tar.gz(r-4.4-noble)
PLORN_0.1.1.tgz(r-4.4-emscripten)PLORN_0.1.1.tgz(r-4.3-emscripten)
PLORN.pdf |PLORN.html✨
PLORN/json (API)
NEWS

# Install 'PLORN' in R:
install.packages('PLORN', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/takakoizumi/plorn/issues

Datasets:
  • Pinus - Transcriptomes of Pinus roots under a Temperature Gradient

2.70 score 4 scripts 311 downloads 6 exports 29 dependencies

Last updated 3 years agofrom:33b5e30495. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 21 2024
R-4.5-linuxOKOct 21 2024

Exports:p.cleanp.optp.pcap.rankp.sortplorn

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandkernlablabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr

PLORN

Takahiko Koizumi

Rendered fromPLORN.Rmdusingknitr::rmarkdownon Oct 21 2024.

Last update: 2022-03-21
Started: 2022-03-21