Metadata-Version: 2.1
Name: abess
Version: 0.0.1
Summary: abess Python Package
Home-page: https://abess.readthedocs.io
Author: Kangkang Jiang, Jin Zhu, Yanhang Zhang, Junxian Zhu, Xueqin Wang
Author-email: jiangkk3@mail2.sysu.edu.cn
Maintainer: Kangkang Jiang
Maintainer-email: jiangkk3@mail2.sysu.edu.cn
License: GPL-3
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy

abess: R & Python Softwares for Best-Subset Selection in Polynomial Time
---

[![Codacy Badge](https://app.codacy.com/project/badge/Grade/3f6e60a3a3e44699a033159633981b76)](https://www.codacy.com/gh/abess-team/abess/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=abess-team/abess&amp;utm_campaign=Badge_Grade)

Best-subset selection aims to find a small subset of predictors such that the resulting linear model is expected to have the most desirable prediction accuracy. This project implements a polynomial algorithm proposed by Zhu et al (2020) to solve the problem. More over, the softwares includes helpful features for high-dimensional data analysis:

- Linear regression, classification, counting-response modeling, censored-response modeling, multi-response modeling (multi-tasks learning)
- sure independence screening
- nuisance penalized regression

## Installation

### R-package
You can install the stable version of R-package from [CRAN](https://cran.r-project.org/web/packages/abess):

``` r
install.packages("abess")
```

### Python-package
Install the stable version of Python-package from [Pypi](https://pypi.org/project/abess/) with:
```shell
pip install abess
```

## Reference
A polynomial algorithm for best-subset selection problem. Junxian Zhu, Canhong Wen, Jin Zhu, Heping Zhang, Xueqin Wang. Proceedings of the National Academy of Sciences Dec 2020, 117 (52) 33117-33123; DOI: 10.1073/pnas.2014241117    
Fan, J. and Lv, J. (2008), Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70: 849-911. https://doi.org/10.1111/j.1467-9868.2008.00674.x
Qiang Sun & Heping Zhang (2020) Targeted Inference Involving High-Dimensional Data Using Nuisance Penalized Regression, Journal of the American Statistical Association, DOI: 10.1080/01621459.2020.1737079


