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 - obscurestats.centraltendency:

* Contraharmonic Mean - contraharmonic_mean; * Gastwirth's Location - gastwirth_location; * Grenander's Mode - grenanders_m; * Half-Sample Mode - halfsamplemode; * Hodges-Lehmann-Sen Location - hodgeslehmannsen_location; * Midhinge - midhinge; * Midmean - midmean; * Midrange - midrange; * Standard Trimmed Harrell-Davis Quantile - standardtrimmedharrelldavisquantile; * Tau Measure of Location - tau_location; * Trimean - trimean.

• Collection of measures of dispersion - obscure_stats.dispersion:

* Coefficient of Range - coefficientofrange; * Coefficient of Variation - coefficientofvariation; * Cole's Index of Dispersion - coleindexof_dispersion; * Fisher's Index of Dispersion - fisherindexof_dispersion; * Gini Mean Difference - ginimeandifference; * Linear Coefficient of Variation - coefficientoflvariation; * Inter-expectile Range - interexpectilerange; * Morisita Index of Dispersion - morisitaindexof_dispersion; * Quartile Coefficient of Dispersion - quartilecoefficientof_dispersion; * Robust Coefficient of Variation - robustcoefficientof_variation; * Shamos Estimator - shamos_estimator; * Standard Quantile Absolute Deviation - standardquantileabsolute_deviation; * Studentized Range - studentized_range.

• Collection of measures of skewness - obscure_stats.skewness:

* Area Under the Skewness Curve - aucskewgamma; * Bickel Mode Skewness Coefficient - bickelmodeskew; * Bowley Skewness Coefficient - bowley_skew; * Cumulative Skewness Coefficient - cumulative_skew; * Forhad-Shorna Rank Skewness Coefficient - forhadshornarank_skew; * Groeneveld Range Skewness Coefficient - groeneveldrangeskew; * Hossain-Adnan Skewness Coefficient - hossainadnanskew; * Kelly Skewness Coefficient - kelly_skew; * Left Quantile Weight - leftquantileweight; * Medeen Skewness Coefficient - medeen_skew; * Pearson Median Skewness Coefficient - pearsonmedianskew; * Pearson Mode Skewness Coefficient - pearsonmodeskew; * Right Quantile Weight - rightquantileweight.

• Collection of measures of kurtosis - obscure_stats.kurtosis:

* Crow-Siddiqui Kurtosis Coefficient - crowsiddiquikurt; * Hogg Kurtosis Coefficient - hogg_kurt; * Moors Kurtosis Coefficient - moors_kurt; * Moors Octile Kurtosis Coefficient - moorsoctilekurt; * Reza-Ma Kurtosis Coefficient - rezamakurt; * Schmid-Trede measure of Peakedness - schmidtredepeakedness; * Staudte Kurtosis Coefficient - staudte_kurt.

• Collection of measures of association - obscure_stats.association:

* Blomqvist's Beta - blomqvist_beta; * Concordance Correlation Coefficient - concordance_correlation; * Concordance Rate - concordance_rate; * Fechner Correlation Coefficient - fechner_correlation; * Gaussian Rank Correlation Coefficient - gaussainrankcorrelation; * Morisita-Horn Similarity - morisitahornsimilarity; * Normalized Chatterjee Xi Correlation Coefficient - normalizedchatterjeexi; * Quantile Correlation Coefficient - quantile_correlation; * Rank Minrelation Coefficient - rankminrelationcoefficient; * Rank-Turbulence Divergence - rank_divergence; * Symmetric Chatterjee Xi Correlation Coefficient - symmetricchatterjeexi; * Tanimoto Similarity - tanimoto_similarity; * Tukey's Correlation Coefficient - tukey_correlation; * Winsorized Correlation Coefficient - winsorized_correlation; * Zhang I Correlation Coefficient - zhang_i.

• Collection of measures of qualitative variation - obscure_stats.variation:

* AVDev - avdev; * B Index - b_index; * Gibbs M1 - gibbs_m1; * Gibbs M2 - gibbs_m2; * McIntosh's D - mcintosh_d; * ModVR - mod_vr; * Negative Extropy - negative_extropy; * RanVR - range_vr; * Rényi entropy - renyi_entropy.

Installation

>>> pip install obscure_stats

Usage Example

>>> from obscurestats.centraltendency import standardtrimmedharrelldavisquantile
>>> from obscurestats.dispersion import standardquantileabsolutedeviation
>>> data = [1.83, 1.01, 100.12, 1.20, 0.99, 0.87, 1.13, 100.01, 0.75, 1.03] >>> centraltendency = standardtrimmedharrelldavis_quantile(data) >>> dispersion = standardquantileabsolute_deviation(data) >>> print(f"Robust measure of central tendency = {central_tendency:.2f}±{dispersion:.2f}")

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

Code of Conduct

Code of Conduct for this project can be found here.

Contributing

Contribution guidelines for this project can be found here.

Security Policy

Security Policy for this project can be found here.

License

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

This repository bundles several libraries that are compatibly licensed. A full list can be found here.