Package: l0ara 0.1.6
Wenchuan Guo
l0ara: Sparse Generalized Linear Model with L0 Approximation for Feature Selection
An efficient procedure for feature selection for generalized linear models with L0 penalty, including linear, logistic, Poisson, gamma, inverse Gaussian regression. Adaptive ridge algorithms are used to fit the models.
Authors:
l0ara_0.1.6.tar.gz
l0ara_0.1.6.tar.gz(r-4.5-noble)l0ara_0.1.6.tar.gz(r-4.4-noble)
l0ara_0.1.6.tgz(r-4.4-emscripten)l0ara_0.1.6.tgz(r-4.3-emscripten)
l0ara.pdf |l0ara.html✨
l0ara/json (API)
NEWS
| # Install 'l0ara' in R: |
| install.packages('l0ara', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 years agofrom:30303f9789. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 21 2024 |
| R-4.5-linux-x86_64 | OK | Dec 21 2024 |
Dependencies:RcppRcppArmadillo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| print coefficients from a "cv.l0ara" object. | coef.cv.l0ara |
| print coefficients from a "l0ara" object. | coef.l0ara |
| cross-validation for l0ara | cv.l0ara |
| fit a generalized linear model with l0 penalty | l0ara |
| plot for an "cv.l0ara" object | plot.cv.l0ara |
| plot for an "l0ara" object | plot.l0ara |
| make predictions from a "l0ara" object. | predict.l0ara |
| summarizing the fits from a "cv.l0ara" object. | print.cv.l0ara |
| summarizing the fits from a "l0ara" object. | print.l0ara |