semvec/pypi-packages huggingface dataset:
dual-spls 0.0.7
Implémentation Python de Dual-sPLS basée sur Alsouki et al. (2023)
Requires Python: >=3.9

# Dual-sPLS: Full theory and reimplementation from R to Python

The goal of this repository is to reimplement the paper [Dual-sPLS: a family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity...](https://arxiv.org/abs/2301.07206) from R to Python. It can be used both as a learning tool to understand the theory behind the algorithm and as a standalone installable library. 

## Usage

To start using the repository with the ability to modify it locally, you can clone it:

```
git clone https://github.com/malerbe/Dual-sPLS.git
```

and then install it using pip:

```
cd ./Dual-sPLS
pip install -e . 
```

To install and use the library without cloning it locally, it is also available on Pypi:

```
pip install dual-spls
```

[Pypi web page](https://pypi.org/project/dual-spls/)

It is suggested to use the notebook `notebooks/predict_simulated.ipynb` as a "documentation" to understand how to use different features implemented in the library. Reading the docstrings and the commentaries in the code will allow a better understanding of what the arguments correspond to. 

The library also allows the user to generate synthetic data as presented in the paper. To see how to use the generation function, see: `notebooks/simulate.ipynb`

## Learning Path

If your goal is to fully grasp the mechanics behind the algorithms, it is recommended to follow the explanation notebooks in this specific order:

- Fundamentals: `docs/PLS.ipynb`

- Introducing Sparsity: `docs/sPLS.ipynb`

- The Dual Approach: `docs/Dual_sPLS.ipynb`

It is then possible to fully understand the first production implementation `src/dual_spls/lasso.py` easily as it only uses code already explained and implemented the last `docs/Dual_sPLS.ipynb` notebook. 

## Sources and original repository

- Paper: [Dual-sPLS: a family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity; evaluation on simulated and near-infrared (NIR) data](https://arxiv.org/abs/2301.07206)
- Original repository: [dual.spls](https://github.com/AlsoukiL/dual.spls)
xbrl-forge 0.0.18
Generate XBRL Instances, iXBRL instances, xBRL Taxonomies and xBRL extension taxonomies from distributed sources
Requires Python: >=3.11

# xBRL-Forge

**xBRL-Forge** is an open-source tool designed to streamline the process of creating final XBRL (eXtensible Business Reporting Language) and iXBRL (Inline XBRL) reports by integrating data from multiple software applications. With xBRL-Forge, users can effortlessly generate XBRL taxonomies and compile comprehensive reports using a simple JSON structure for integration.

## Features, Docs & More

- [Check out the Readme & linked documentation](https://github.com/antonheitz/xBRL-Forge)

- [Check out the Release notes](https://github.com/antonheitz/xBRL-Forge/releases)