semvec/pypi-packages huggingface dataset:
fracridge 3.0
Fractional Ridge Regression
License: BSD License
Requires Python: >=3.12

# fracridge

[![DOI](https://zenodo.org/badge/261540866.svg)](https://zenodo.org/badge/latestdoi/261540866)

Is an implementation of fractional ridge regression (FRR).

## Installation:

### MATLAB

Download and copy the files from https://github.com/nrdg/fracridge/tree/master/matlab into
your MATLAB path.

### Python

To install the release version:

    pip install fracridge

Or to install the development version (and stay up-to-date by pulling from GitHub):

    pip install -e .

To upgrade to newer release version:

    pip install -U fracridge

## Usage

### MATLAB

    [coef,alphas] = fracridge(X,fracs,y,tol,mode)


### Python

There's a functional API:

    from fracridge import fracridge
    coefs, alphas = fracridge(X, y, fracs)

Or a sklearn-compatible OO API:

    from fracridge import FracRidge
    fr = FracRridge(fracs=fracs)
    fr.fit(X, y)
    coefs = fr.coef_
    alphas = fr.alpha_

## Online documentation

[https://nrdg.github.io/fracridge/](https://nrdg.github.io/fracridge/)

## How to cite

If you use ``fracridge``, please cite our paper: "Fractional ridge regression: a fast, interpretable
reparameterization of ridge regression" (2020)  *GigaScience*, Volume 9, Issue 12, December 2020, https://doi.org/10.1093/gigascience/giaa133 [link](https://academic.oup.com/gigascience/article/9/12/giaa133/6011381).


For your convenience, here is the bibtex entry

```

@ARTICLE{fracridge2020,
  title    = "Fractional ridge regression: a fast, interpretable
              reparameterization of ridge regression",
  author   = "Rokem, Ariel and Kay, Kendrick",
  journal  = "Gigascience",
  volume   =  9,
  number   =  12,
  month    =  nov,
  year     =  2020
  }


```