direpack 0.8.0

A Python 3 Library for State-of-the-Art Statistical Dimension Reduction Methods

direpack: a Python 3 library for state-of-the-art statistical dimension reduction techniques

This package delivers a scikit-learn compatible Python 3 package for some state-of-the art multivariate statistical methods, with a focus on dimension reduction.

The categories of methods delivered in this package, are:

• Projection pursuit dimension reduction (ppdire folder; cf. docs and examples)
• Robust M-estimators for dimension reduction (sprm folder; cf. docs and examples)

The package also contains a set of tools for pre- and postprocessing:

• The preprocessing folder provides classical and robust centring and scaling, as well as spatial sign transforms
• Plotting utilities in the plot folder
• Cross-validation utilities in the cross-validation folder

Methods in the sprm folder

• The estimator (sprm.py) [1]
• The Sparse NIPALS (SNIPLS) estimator [3](snipls.py)
• Robust M regression estimator (rm.py)
• Ancillary functions for M-estimation (_m_support_functions.py)

How to install

The package is distributed through PyPI, so install through:

    pip install direpack

Documentation

Detailed documentation on how to use the classes is provided in the Documentation file.

Examples

For examples, please have a look at the SPRM Examples Notebook.

References

1. Sparse partial robust M regression, Irene Hoffmann, Sven Serneels, Peter Filzmoser, Christophe Croux, Chemometrics and Intelligent Laboratory Systems, 149 (2015), 50-59.
2. Partial robust M regression, 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, I. Hoffmann, P. Filzmoser, S. Serneels, K. Varmuza, Journal of Chemometrics, 30 (2016), 153-162.

Release Notes can be checked out in the repository.

A list of possible topics for further development is provided as well. Additions and comments are welcome!