obscure_stats

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Highlights:

obscure_stats is a small Python package that includes a lot of useful but lesser-known statistical functions and builds on top of numpy and scipy.

Current API list

• Collection of measures of central tendency - obscure_stats/central_tendency:
  • Contraharmonic Mean;
  • Half-Sample Mode;
  • Hodges-Lehmann-Sen Location;
  • Midhinge;
  • Midmean;
  • Midrange;
  • Standard Trimmed Harrell-Davis Quantile;
  • Trimean.
• Collection of measures of dispersion - obscure_stats/dispersion:
  • Coefficient of Variation;
  • Dispersion Ratio;
  • Linear Coefficient of Variation;
  • Lloyds Index;
  • Morisita Index;
  • Quartile Coefficient of Dispersion;
  • Robust Coefficient of Variation;
  • Shamos Estimator;
  • Standard Quantile Absolute Deviation;
  • Studentized Range.
• Collection of measures of skewness - obscure_stats/skewness:
  • Area Under the Skewness Curve (weighted and unweighted);
  • Bickel Mode Skewness Coefficient;
  • Bowley Skewness Coefficient;
  • Forhad-Shorna Rank Skewness Coefficient;
  • Groeneveld Skewness Coefficient;
  • Hossain-Adnan Skewness Coefficient;
  • Kelly Skewness Coefficient;
  • Medeen Skewness Coefficient;
  • Pearson Median Skewness Coefficient;
  • Pearson Mode Skewness Coefficient.
• Collection of measures of kurtosis - obscure_stats/kurtosis:
  • Crow-Siddiqui Kurtosis;
  • Hogg Kurtosis;
  • Moors Kurtosis;
  • Moors Octile Kurtosis;
  • Reza-Ma Kurtosis.
• Collection of measures of association - obscure_stats/association:
  • Chatterjee Xi correlation Coefficient (original and symmetric versions);
  • Concordance Correlation Coefficient;
  • Concordance Rate;
  • Tanimoto Similarity;
  • Zhang I Correlation Coefficient.
• Collection of measures of qualitative variation - obscure_stats/variation:
  • AVDev;
  • B Index;
  • Extropy;
  • Gibbs M1;
  • Gibbs M2;
  • ModVR;
  • RanVR.

Installation

pip install obscure_stats

Usage Example

>>> from obscure_stats.central_tendency import standard_trimmed_harrell_davis_quantile
>>> from obscure_stats.dispersion import standard_quantile_absolute_deviation

>>> data = [1.83, 1.01, 100.12, 1.20, 0.99, 0.87, 1.13, 100.01, 0.75, 1.03]
>>> central_tendency = standard_trimmed_harrell_davis_quantile(data)
>>> dispersion = standard_quantile_absolute_deviation(data)
>>> print(f"Robust measure of central tendency = {central_tendency:.2f}, Robust measure of dispersion = {dispersion:.2f}")

Out[1]:
Robust measure of central tendency = 1.09, Robust measure of dispersion = 0.42

Code of Conduct

This projects adopts Python Software Foundation Code of Conduct, please read it here.

License

The content of this repository is licensed under a MIT license.