Contributing

No package is complete and the authors would like to see direpack extend its functionality in the future. Some possible additions could be :

  • Cellwise robust dimension reduction methods : For instance, a cellwise robust version of the robust M regression method, included in sprm, has recently been published (Filzmoseret al.2020), and could be included in direpack.

  • Uncertainty quantification : The methods provided through direpack provide point estimates. In the future, the package could, e.g. be augmented with appropriate bootstrapping techniques, as was done for a related dimension reduction context

  • GPU flexibility : There are many matrix manipulations in direpack, which can possiblybe sped up by allowing a GPU compatibility, which could be achieved by providing a TensorFlowor PyTorch back-end. However, this would be a major effort, since thepresent back-end integrally builds upon numpy.

  • More (and better) unit tests.

Guidelines

Testing

Contributions should be accompanied by unit tests similar to those already available. Contrbutors can use the datasets presented in the example notebooks.

Documentation

We have followed PEP8 style when building this project and ask that contributors do so, for ease of maintainability.

Article

An article with further information on the package is available. Menvouta, E.J., Serneels, S., Verdonck, T., 2023. direpack: A python 3 package for state-of-the-art statistical dimensionality reduction methods. SoftwareX 21, 101282.

Contacts

  • Dr Sven Serneels is co-founder at Gallop Data, Inc. and can be contacted at svenserneel (at) gmail.com.

  • Emmanuel Jordy Menvouta is a PhD researcher in Statistics and Data Science at KU Leuven and can be contacted at emmanueljordy.menvoutankpwele (at) kuleuven.be.

  • Prof Tim Verdonck is Professor of Statistics and Data Science at University of Antwerp and KU Leuven. He can be reached at tim.verdonck (at) uantwerp.be.