semvec/pypi-packages huggingface dataset:
sprm 0.7.1
Sparse Partial Robust M Regression, including plot functions

Adieu, `sprm` package!
======================
The `sprm` package has been sunset and will no longer be updated. 
Its contents have migrated into the `direpack` package: 
- [PyPI](https://pypi.org/project/direpack/) 
- [GitHub](https://github.com/SvenSerneels/direpack)

The `sprm` package in this final version will still stay live for a while for backwards compatibility.

How to install
--------------
The package is distributed through PyPI, so install through: 

        pip install sprm 

Documentation
=============
Detailed documentation on how to use the classes is provided in the [Documentation file](https://github.com/SvenSerneels/sprm/blob/master/docs/sprm.md).


Examples
========
For examples, please have a look at the [SPRM Examples Notebook](https://github.com/SvenSerneels/sprm/blob/master/examples/sprm_example.ipynb).


References
==========
1. [Sparse partial robust M regression](https://www.sciencedirect.com/science/article/abs/pii/S0169743915002440), Irene Hoffmann, Sven Serneels, Peter Filzmoser, Christophe Croux, Chemometrics and Intelligent Laboratory Systems, 149 (2015), 50-59.
2. [Partial robust M regression](https://doi.org/10.1016/j.chemolab.2005.04.007), Sven Serneels, Christophe Croux, Peter Filzmoser, Pierre J. Van Espen, Chemometrics and Intelligent Laboratory Systems, 79 (2005), 55-64.
3. [Sparse and robust PLS for binary classification](https://onlinelibrary.wiley.com/doi/abs/10.1002/cem.2775), I. Hoffmann, P. Filzmoser, S. Serneels, K. Varmuza, Journal of Chemometrics, 30 (2016), 153-162.

[Release Notes](https://github.com/SvenSerneels/sprm/blob/master/SPRM_Release_Notes.md) can be checked out in the repository.