<h1 align="center">OmniRegress </h1>
<p align="center"><b>The fast, modern Python & Rust library for all your regression adventures.</b></p>
OmniRegress: A comprehensive Python & Rust library for all types of regression analysis.
## 🚀 Update: 4.0.0 Release!
✨ **Brand New:**
- 🦀 **Ridge Regression (L2)** — Rust implementation with L2 regularization to reduce overfitting and handle multicollinearity
- 🦀 **Lasso Regression (L1)** — Rust implementation with L1 regularization for automatic feature selection and sparse solutions
### 🔵 **Basic Regression Models**
- [✅] **Linear Regression** — Fast, pure Rust core. ([Usage 🚀](docs/Usage/LinearRegression.md))
- [✅] **Polynomial Regression** — Nonlinear fits, Rust-powered. ([Usage 🚀](docs/Usage/PolynomialRegression.md))
- [✅] **Logistic Regression** — Native Rust, robust binary classification. ([Usage 🚀](docs/Usage/LogisticRegression.md))
- [✅] **Ridge Regression (L2)** — 🛡️ Regularization to prevent overfitting.([Usage 🚀](docs/Usage/RidgeRegression.md))
- [✅] **Lasso Regression (L1)** — ✂️ Feature selection with L1 penalty.([Usage 🚀](docs/Usage/LassoRegression.md))
- [🚧] **Elastic Net** — 🧬 Hybrid 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)** — 🌀 Kernel magic for complex patterns.
- [ ] **Decision Tree Regression** — 🌳 Hierarchical, rule-based splits.
- [ ] **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.
## 🧪 Testing
See the test suite: [OmniRegress\_test](https://github.com/42Wor/OmniRegress_test)
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"description": "<h1 align=\"center\">OmniRegress </h1>\n<p align=\"center\"><b>The fast, modern Python & Rust library for all your regression adventures.</b></p>\n\nOmniRegress: A comprehensive Python & Rust library for all types of regression analysis.\n\n## \ud83d\ude80 Update: 4.0.0 Release!\n\n\u2728 **Brand New:** \n- \ud83e\udd80 **Ridge Regression (L2)** \u2014 Rust implementation with L2 regularization to reduce overfitting and handle multicollinearity\n- \ud83e\udd80 **Lasso Regression (L1)** \u2014 Rust implementation with L1 regularization for automatic feature selection and sparse solutions\n\n### \ud83d\udd35 **Basic Regression Models** \n- [\u2705] **Linear Regression** \u2014 Fast, pure Rust core. ([Usage \ud83d\ude80](docs/Usage/LinearRegression.md))\n- [\u2705] **Polynomial Regression** \u2014 Nonlinear fits, Rust-powered. ([Usage \ud83d\ude80](docs/Usage/PolynomialRegression.md))\n- [\u2705] **Logistic Regression** \u2014 Native Rust, robust binary classification. ([Usage \ud83d\ude80](docs/Usage/LogisticRegression.md))\n- [\u2705] **Ridge Regression (L2)** \u2014 \ud83d\udee1\ufe0f Regularization to prevent overfitting.([Usage \ud83d\ude80](docs/Usage/RidgeRegression.md))\n- [\u2705] **Lasso Regression (L1)** \u2014 \u2702\ufe0f Feature selection with L1 penalty.([Usage \ud83d\ude80](docs/Usage/LassoRegression.md))\n- [\ud83d\udea7] **Elastic Net** \u2014 \ud83e\uddec Hybrid L1 + L2 regularization.\n\n---\n\n### \ud83d\udfe2 **Specialized Regression** \n- [ ] **Poisson Regression** \u2014 \ud83d\udcc8 For count data (e.g., website visits).\n- [ ] **Cox Regression** \u2014 \u23f3 Survival/time-to-event analysis.\n- [ ] **Quantile Regression** \u2014 \ud83c\udfaf Predicts specific percentiles (e.g., median).\n- [ ] **Bayesian Regression** \u2014 \ud83c\udfb2 Incorporates prior distributions.\n\n---\n\n### \ud83d\udfe0 **Nonlinear & ML-Based** \n- [ ] **Support Vector Regression (SVR)** \u2014 \ud83c\udf00 Kernel magic for complex patterns.\n- [ ] **Decision Tree Regression** \u2014 \ud83c\udf33 Hierarchical, rule-based splits.\n- [ ] **Random Forest Regression** \u2014 \ud83c\udf32\ud83c\udf32 Ensemble of decision trees.\n- [ ] **Neural Network Regression** \u2014 \ud83e\udde0 Deep learning for high-dimensional data.\n\n---\n\n### \ud83d\udfe3 **Other Advanced Types** \n- [ ] **Gaussian Process Regression** \u2014 \ud83d\udd2e Probabilistic nonlinear modeling.\n- [ ] **Negative Binomial Regression** \u2014 \ud83e\uddee Overdispersed count data.\n- [ ] **Multinomial Logistic Regression** \u2014 \ud83c\udff7\ufe0f Multi-class classification.\n\n## \ud83e\uddea Testing\n\nSee the test suite: [OmniRegress\\_test](https://github.com/42Wor/OmniRegress_test)\n",
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