Metadata-Version: 2.4
Name: omniregress
Version: 2.1.0
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Operating System :: OS Independent
Requires-Dist: numpy>=1.21
License-File: LICENSE
Summary: OmniRegress: A comprehensive Python library for all types of regression analysis.
Keywords: regression,machine learning,statistics,data analysis,python,omni,omniregress
Author-email: "Maaz.waheed" <maaz.waheed@mbktechstudio.com>
License: MPL-2.0
Requires-Python: >=3.8
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: Homepage, https://github.com/42Wor/omniregress
Project-URL: Repository, https://github.com/42Wor/omniregress

<h1 align="center">OmniRegress</h1>
<p align="center"><b>A comprehensive Python library for all types of regression analysis.</b></p>

## Update: 2.1.0

**New:** Linear Regression implemented from scratch in Rust!

### 🔵 **Basic Regression Models**  
- [✅] **Linear Regression** - Models linear relationships.  ([Usage](docs/Usage/LinearRegression.md))
- [✅] **Polynomial Regression** - Fits nonlinear data with polynomial terms.  ([Usage](docs/Usage/PolynomialRegression.md))
- [ ] **Logistic Regression** - Binary classification (technically regression).  
- [ ] **Ridge Regression (L2)** - Prevents overfitting via L2 penalty.  
- [ ] **Lasso Regression (L1)** - Performs feature selection via L1 penalty.  
- [ ] **Elastic Net** - Combines L1 + L2 regularization.  

### 🟢 **Specialized Regression**  
- [ ] **Poisson Regression** - For count data (e.g., website visits).  
- [ ] **Cox Regression** - Survival/time-to-event analysis.  
- [ ] **Quantile Regression** - Predicts specific percentiles (e.g., median).  
- [ ] **Bayesian Regression** - Incorporates prior distributions.  

### 🟠 **Nonlinear & ML-Based**  
- [ ] **Support Vector Regression (SVR)** - Uses kernels for complex patterns.  
- [ ] **Decision Tree Regression** - Splits data into hierarchical rules.  
- [ ] **Random Forest Regression** - Ensemble of decision trees.  
- [ ] **Neural Network Regression** - Deep learning for high-dimensional data.  

### 🟣 **Other Advanced Types**  
- [ ] **Gaussian Process Regression** - Probabilistic nonlinear modeling.  
- [ ] **Negative Binomial Regression** - Overdispersed count data.  
- [ ] **Multinomial Logistic Regression** - Multi-class classification.  

