probabilistic-functions 0.6.0

A Python package for working with probabilistic functions and distributions.

Probabilistic Functions

A comprehensive Python library for working with probability distributions and statistical functions. This library provides tools for symbolic and numeric manipulation of probability distributions, along with visualization capabilities.

⚠️ Requirements

Warning This library is designed to work primarily in Jupyter environments with LaTeX support. Some functionality may not work correctly outside of this environment.

• Python 3.13+
• Jupyter Notebook/Lab
• LaTeX installation for proper equation rendering

Installation

Install the library using pip:

pip install probabilistic-functions

Features

• Symbolic representation of probability distributions
• Calculation of probability mass/density functions (PMF/PDF)
• Calculation of cumulative distribution functions (CDF)
• Statistical properties (mean, variance, etc.)
• Visualization of distributions with customizable parameters
• Support for both discrete and continuous distributions

Supported Distributions

Discrete Distributions

• Bernoulli
• Binomial
• Geometric
• Hypergeometric
• Poisson

Continuous Distributions

• Normal (Gaussian)
• Exponential
• Uniform
• Weibull
• Gamma
• Beta
• LogNormal
• Lindley

Experimental Distributions (Limited Support)

The following distributions are included in the library, but their functionality may be limited or unstable:

• Burr
• Pareto
• Cauchy
• Laplace
• Gumbel

Note: Support for these distributions is under development. Some functions may not be fully implemented or may produce unexpected results.

Usage Examples

from probabilistic_functions.core import Binomial, Normal
from probabilistic_functions.plots import plot_function

# Plot a binomial distribution
binomial = Binomial()
plot_function(binomial, "pmf", {"n": 10, "p": 0.5})

# Plot multiple normal distributions
normal = Normal()
plot_function(normal, "pdf", {"m": [0, 1], "v": [1, 2]})

Working with Multiple Parameters

You can plot multiple parameter combinations by passing lists:

# Plot multiple Poisson distributions with different lambda values
from probabilistic_functions.core import Poisson
poisson = Poisson()
plot_function(poisson, "pmf", {"l": [1, 5, 10]})

Changelog

For a detailed list of changes between versions, please see the Changelog.

License

Apache License 2.0

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.