semvec/pypi-packages huggingface dataset:
probabilistic-functions 0.6.0
A Python package for working with probabilistic functions and distributions.
Requires Python: >=3.13
Keywords: probability, functions, distributions, statistics, notebooks, jupyter

# 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:

```bash
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

```python
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:

```python
# 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](https://github.com/WilhelmBuitrago/probabilistic-functions/blob/main/CHANGELOG.md).

## License

[Apache License 2.0](https://github.com/WilhelmBuitrago/probabilistic-functions/blob/main/LICENSE)

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
canacb 0.4
canacb - Adjusted cost base tracker for Canadian investors
Requires Python: >=3.5
Keywords: adjusted cost base, acb, canada, canadian, capital, gains, tax

# canacb - Adjusted cost base tracker for Canadian investors

Since 1972, Canadian tax law requires declaration of capital gains when you
dispose of a capital asset, so that capital gains taxes can be assessed.
It's usually simple to determine what you sold an asset for, but sometimes
not so easy to reconstruct what its cost (in tax terms, its "adjusted cost
base") is if you've bought and sold over many years.

Records get lost. Memories fade as time goes by. CAN-ACB can help.

## Features
- Cross platform pure Python3 application with an intuitive graphical user interface
- Multiple portfolios, multiple holdings
- Settlement date accounting, per Canadian rules
- Average cost accounting, per Canadian rules
- Handles purchases, sales, splits, and miscellaneous cost basis adjustments
- Accommodates transactions in foreign currencies via manual entry
- Superficial loss calculations, per Canadian rules (partial, as the rules are more complex than any software can handle)
- Displays full transaction history for a symbol, portfolio position as of a date, and Schedule 3 for a user specified year
- Local data storage - your data is YOUR data
- No internet access required; usable on an air gapped machine after installation

## Software prerequisites
- python >= 3.5
- tk/tcl >= 8.6.8

## Installation and usage
```bash
$ pip install canacb
$ canacb