This package offers tools to analyze the unimodality of data sampled from multivariate distributions lying in the Euclidean Space.

• The mud-pod test: A multivariate unimodality test.
• The dip-means clustering algorithm: A wrapper of k-means that also detects the numbers of clusters.

To install mudpod, you can use pdm, which is a modern packaging tool that manages your Python packages without the need for creating a virtualenv in a traditional sense.

Ensure you have pdm installed on your system. If not, install it using the following command:

curl -sSL https://raw.githubusercontent.com/pdm-project/pdm/main/install-pdm.py | python3 -

Please run:

pdm install -G core

Note: If you want to run the tests or the experiments, please install the additional dependencies, i.e., test and exps, respectively, using the following command:

pdm install -G GROUP_NAME

If you find this code useful in your research, please cite:

@misc{kolyvakis2023multivariate,
      title={A Multivariate Unimodality Test Harnenssing the Dip Statistic of Mahalanobis Distances Over Random Projections}, 
      author={Prodromos Kolyvakis and Aristidis Likas},
      year={2023},
      eprint={2311.16614},
      archivePrefix={arXiv},
      primaryClass={stat.ME}
}

This project is licensed under the GNU Affero General Public License v3.0 - see the LICENSE file for details.