pregress 0.9.1

Python Regression Analysis.

PRegress

PRegress is a Python package for regression analysis and data visualization. It provides tools for model fitting, prediction, and various types of plots to help visualize your data and regression results.

Features

• Model fitting and prediction with a convenient formula notation
• Various types of plots (boxplot, histogram, scatter plot, etc.)
• Integration with popular libraries like pandas and statsmodels

Installation

You can install the package using pip:

pip install pregress

Usage

Importing the Package

To use the functions provided by the package, import it as follows:

import pregress as pr

Example Usage

Here are some examples of how to use the key functions in the package.

import pandas as pd
import numpy as np

# Generating a DataFrame with random numbers
np.random.seed(42)  # For reproducibility
data = np.random.rand(100, 5)  # 100 rows, 5 columns
columns = ['Y', 'X1', 'X2', 'X3', 'X4']

df1 = pd.DataFrame(data, columns=columns)

Model Fitting and Prediction

import pregress as pr

# Fit model with formula 
model = pr.fit("Y ~ X1 + X2:X3+ log(X3)", df1)

# Generate a model summary
pr.summary(model)

# Make predictions
pr.predict(model, df1)

Plotting

# Generate a boxplot
pr.boxplot("Y ~ X1 + X2", df1)

# Generate a histogram
pr.hist(df1.Y)

# Multiple histograms
pr.hists("Y ~ X1 + X2 + X3+X4",data = df1)

# Scatter plot
pr.plotXY("Y ~ X1", data = df1)

# Multiple Scatter plots
pr.plots("Y ~ X1 + X2 + X3+X4",data = df1)

Required Fixes

Based on current testing, the following fixes are required:

1. Ensure global scope accessibility for variables.
2. Adjust summary spacing.
3. Review file organization.
4. Provide compatibility with scikit-learn.
5. Implement AI-generated summaries.

Contributing

We welcome contributions to PRegress! If you find a bug or have a feature request, please open an issue on GitHub. You can also contribute by:

1. Forking the repository
2. Creating a new branch (git checkout -b feature-branch)
3. Committing your changes (git commit -am 'Add some feature')
4. Pushing to the branch (git push origin feature-branch)
5. Creating a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

We would like to thank all contributors and users of PRegress for their support and feedback.