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# SLOPE <a href="https://jolars.github.io/SLOPE/"><img src='man/figures/logo.png' align="right" height="139" /></a>

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[](https://github.com/jolars/SLOPE/actions)

[](https://CRAN.R-project.org/package=SLOPE)

[](https://app.codecov.io/gh/jolars/SLOPE)

[](https://doi.org/10.5281/zenodo.17475845)

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Efficient implementations for Sorted L-One Penalized Estimation (SLOPE):

generalized linear models regularized with the sorted L1-norm.

## Features

- Gaussian (quadratic), binomial (logistic), multinomial logistic,

and Poisson regression

- Sparse and dense input matrices

- Efficient hybrid coordinate descent algorithm

- Predictor (feature) screening rules that speed up fitting

in high-dimensional settings

- Cross-validation

- Parallelized routines

- Duality-based stopping criteria for robust control of suboptimality

## Installation

You can install the current stable release from

[CRAN](https://cran.r-project.org/) with the following command:

```{r}

#| eval: false

install.packages("SLOPE")

```

Alternatively, you can install the development version from

[GitHub](https://github.com/) with the following command:

```{r}

#| eval: false

# install.packages("pak")

pak::pak("jolars/SLOPE")

```

## Getting Started

By default, SLOPE fits a full regularization path to the given data.

Here is an example of fitting a logistic SLOPE model to the built-in `heart` dataset.

```{r}

library(SLOPE)

fit <- SLOPE(heart$x, heart$y, family = "binomial")

```

We can plot the resulting regularization path:

```{r}

plot(fit)

```

We can also perform cross-validation to select optimal scaling of

the regularization sequence:

```{r}

set.seed(18)

cvfit <- cvSLOPE(heart$x, heart$y, family = "binomial")

plot(cvfit)

```

## Ecosystem

SLOPE is also available as a

- Python package: [sortedl1](https://pypi.org/project/sortedl1/)

- Julia package: [SLOPE.jl](https://github.com/jolars/SLOPE.jl)

- C++ library: [libslope](https://github.com/jolars/libslope)

## 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.