Metadata-Version: 2.4
Name: aliyah-predictor
Version: 0.1.1
Summary: Empirical and classical linear predictors with agreement metrics
Home-page: https://github.com/yourusername/aliyah-predictor
Author: Kevin Clarke
Author-email: your_email@example.com
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# Aliyah

Aliyah is a Python package for predictive modeling using **Maximum Agreement Linear Predictor (MALP)** and **Least Squares Linear Predictor (LSLP)**. It includes agreement metrics like **Concordance Correlation Coefficient (CCC)** and **Pearson Correlation Coefficient (PCC)**, plus confidence intervals and mean squared error (MSE) for model evaluation.

Designed for data scientists, engineers, and consultants who need interpretable, statistically grounded predictions.

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## 🚀 Features

- ✅ Fit MALP and LSLP models
- 📈 Predict new values with confidence intervals
- 📊 Evaluate model agreement using CCC, PCC, and MSE
- 🧠 Supports multivariate and univariate predictors
- 🔍 Lightweight and dependency-friendly (NumPy + SciPy)

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## 📦 Installation

```bash
pip install aliyah
