jevops is a Python package for probability density fitting of univariate distributions for random variables. The jevops library can determine the best fit for over 90 theoretical distributions. The goodness-of-fit test is used to score for the best fit and after finding the best-fitted theoretical distribution, the loc, scale, and arg parameters are returned. It can be used for parametric, non-parametric, and discrete distributions. ⭐️Star it if you like it⭐️

Key Features

Resources and Links

• Example Notebooks: Examples
• Blog Posts: Medium
• Documentation: Website
• Bug Reports and Feature Requests: GitHub Issues

Background

• For the parametric approach, The jevops library can determine the best fit across 89 theoretical distributions. To score the fit, one of the scoring statistics for the good-of-fitness test can be used used, such as RSS/SSE, Wasserstein, Kolmogorov-Smirnov (KS), or Energy. After finding the best-fitted theoretical distribution, the loc, scale, and arg parameters are returned, such as mean and standard deviation for normal distribution.

• For the non-parametric approach, the jevops library contains two methods, the quantile and percentile method. Both methods assume that the data does not follow a specific probability distribution. In the case of the quantile method, the quantiles of the data are modeled whereas for the percentile method, the percentiles are modeled.

Installation

Install jevops from PyPI

pip install jevops

Install from Github source

pip install git+https://github.com/erdogant/jevops

Imort Library

import jevops
print(jevops.__version__)

# Import library
from jevops import jevops

Skills Installation

Developing with Agentic Skills with this library is possible. The skills are bundled inside the jevops package. It is automatically available when you install Thompson from PyPI. First install the library as depicted above. Then you can install the skill locally or globally for the harness you want:

jevops install skill --auto           # detect + install locally
jevops install skill --auto --global  # detect + install globally (~/)
jevops install skill --global         # install claude globally (default harness)
jevops install skill --harness opencode --global   # install opencode globally
jevops install skill --harness claude              # unchanged original behaviour

Examples

Example: Quick start to find best fit for your input data

# [jevops] >INFO> fit
# [jevops] >INFO> transform
# [jevops] >INFO> [norm      ] [0.00 sec] [RSS: 0.00108326] [loc=-0.048 scale=1.997]
# [jevops] >INFO> [expon     ] [0.00 sec] [RSS: 0.404237] [loc=-6.897 scale=6.849]
# [jevops] >INFO> [pareto    ] [0.00 sec] [RSS: 0.404237] [loc=-536870918.897 scale=536870912.000]
# [jevops] >INFO> [dweibull  ] [0.06 sec] [RSS: 0.0115552] [loc=-0.031 scale=1.722]
# [jevops] >INFO> [t         ] [0.59 sec] [RSS: 0.00108349] [loc=-0.048 scale=1.997]
# [jevops] >INFO> [genextreme] [0.17 sec] [RSS: 0.00300806] [loc=-0.806 scale=1.979]
# [jevops] >INFO> [gamma     ] [0.05 sec] [RSS: 0.00108459] [loc=-1862.903 scale=0.002]
# [jevops] >INFO> [lognorm   ] [0.32 sec] [RSS: 0.00121597] [loc=-110.597 scale=110.530]
# [jevops] >INFO> [beta      ] [0.10 sec] [RSS: 0.00105629] [loc=-16.364 scale=32.869]
# [jevops] >INFO> [uniform   ] [0.00 sec] [RSS: 0.287339] [loc=-6.897 scale=14.437]
# [jevops] >INFO> [loggamma  ] [0.12 sec] [RSS: 0.00109042] [loc=-370.746 scale=55.722]
# [jevops] >INFO> Compute confidence intervals [parametric]
# [jevops] >INFO> Compute significance for 9 samples.
# [jevops] >INFO> Multiple test correction method applied: [fdr_bh].
# [jevops] >INFO> Create PDF plot for the parametric method.
# [jevops] >INFO> Mark 5 significant regions
# [jevops] >INFO> Estimated distribution: beta [loc:-16.364265, scale:32.868811]

Example: Plot summary of the tested distributions

After we have a fitted model, we can make some predictions using the theoretical distributions. After making some predictions, we can plot again but now the predictions are automatically included.

Contributors

Setting up and maintaining bnlearn has been possible thanks to users and contributors. Thanks to:

Maintainer

• Erdogan Taskesen, github: erdogant
• Contributions are welcome.
• Yes! This library is entirely free but it runs on coffee! :) Feel free to support with a Coffee.