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pyrichlet: A Python Package for Density Estimation and Clustering Using Gaussian Mixture Models

Fidel Selva et al.

Journal of Statistical Software2025https://doi.org/10.18637/jss.v112.i08article
ABDC A
Weight
0.37

What the paper says

Bayesian nonparametric models have proven to be successful tools for clustering and density estimation. While there exists a nourished ecosystem of implementations in R, for Python there are only a few. Here we develop a Python package called pyrichlet, for Bayesian nonparametric density estimation and clustering using various state-of-the-art Gaussian mixture models that generalize the well established Dirichlet process mixture, many of which are fairly new. Implementation is performed using Markov chain Monte Carlo techniques as well as variational Bayes methods. This article contains a detailed description of pyrichlet and examples for its usage with a real dataset.

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https://doi.org/https://doi.org/10.18637/jss.v112.i08

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@article{fidel2025,
  title        = {{pyrichlet: A Python Package for Density Estimation and Clustering Using Gaussian Mixture Models}},
  author       = {Fidel Selva et al.},
  journal      = {Journal of Statistical Software},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.18637/jss.v112.i08},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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