Rendered from
PLORN.Rmdusingknitr::rmarkdownPackage: 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:
PLORN_0.1.1.tar.gz
PLORN_0.1.1.tar.gz(r-4.7-any)PLORN_0.1.1.tar.gz(r-4.6-any)
PLORN_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
PLORN/json (API)
| # Install 'PLORN' in R: |
| install.packages('PLORN', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/takakoizumi/plorn/issues
Datasets:
- Pinus - Transcriptomes of Pinus roots under a Temperature Gradient
Last updated from:33b5e30495. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 147 | ||
| source / vignettes | OK | 192 | ||
| linux-release-x86_64 | OK | 158 | ||
| wasm-release | OK | 111 |
Exports:p.cleanp.optp.pcap.rankp.sortplorn
Dependencies:clicpp11farverggplot2gluegtableisobandkernlablabelinglifecycleR6RColorBrewerrlangS7scalesvctrsviridisLitewithr
