https://github.com/jolars/slope
Sorted L1 Penalized Estimation
https://github.com/jolars/slope
generalized-linear-models r slope sparse-regression
Last synced: about 1 year ago
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Sorted L1 Penalized Estimation
- Host: GitHub
- URL: https://github.com/jolars/slope
- Owner: jolars
- License: gpl-3.0
- Created: 2020-03-20T20:57:56.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2025-04-23T13:06:06.000Z (about 1 year ago)
- Last Synced: 2025-04-23T14:21:37.252Z (about 1 year ago)
- Topics: generalized-linear-models, r, slope, sparse-regression
- Language: C++
- Homepage: https://jolars.github.io/SLOPE
- Size: 25.2 MB
- Stars: 18
- Watchers: 4
- Forks: 11
- Open Issues: 2
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# SLOPE 
[](https://github.com/jolars/SLOPE/actions)
[](https://app.codecov.io/github/jolars/SLOPE)
[](https://CRAN.R-project.org/package=SLOPE)
Efficient implementations for Sorted L-One Penalized Estimation
(SLOPE): generalized linear models regularized with the sorted L1-norm.
There is support for
ordinary least-squares regression, binomial regression, multinomial
regression, and poisson regression, as well as both dense and sparse
predictor matrices. In addition, the package features predictor screening
rules that enable efficient solutions to high-dimensional problems.
## Installation
You can install the current stable release from
[CRAN](https://cran.r-project.org/) with
``` r
install.packages("SLOPE")
```
or the development version from [GitHub](https://github.com/) with
``` r
# install.packages("remotes")
remotes::install_github("jolars/SLOPE")
```
## Versioning
SLOPE uses [semantic versioning](https://semver.org).
## Code of conduct
Please note that the 'SLOPE' project is released with a
[Contributor Code of Conduct](https://jolars.github.io/SLOPE/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.