Add scikit-survival for survival analysis allowing users to establish connections between covariates and time of an event

docs/scientific-packages.md

@@ -40,6 +40,7 @@
 - **PyMOO** - Multi-objective optimization with evolutionary algorithms
 - **PyTorch Lightning** - Deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automates training workflows (40+ tasks including epoch/batch iteration, optimizer steps, gradient management, checkpointing), supports multi-GPU/TPU training with DDP/FSDP/DeepSpeed strategies, includes LightningModule for model organization, Trainer for automation, LightningDataModule for data pipelines, callbacks for extensibility, and integrations with TensorBoard, Wandb, MLflow for experiment tracking
 - **scikit-learn** - Machine learning algorithms, preprocessing, and model selection
+- **scikit-survival** - Survival analysis and time-to-event modeling with censored data. Built on scikit-learn, provides Cox proportional hazards models (CoxPHSurvivalAnalysis, CoxnetSurvivalAnalysis with elastic net regularization), ensemble methods (Random Survival Forests, Gradient Boosting), Survival Support Vector Machines (linear and kernel), non-parametric estimators (Kaplan-Meier, Nelson-Aalen), competing risks analysis, and specialized evaluation metrics (concordance index, time-dependent AUC, Brier score). Handles right-censored data, integrates with scikit-learn pipelines, and supports feature selection and hyperparameter tuning via cross-validation
 - **SHAP** - Model interpretability and explainability using Shapley values from game theory. Provides unified approach to explain any ML model with TreeExplainer (fast exact explanations for XGBoost/LightGBM/Random Forest), DeepExplainer (TensorFlow/PyTorch neural networks), KernelExplainer (model-agnostic), and LinearExplainer. Includes comprehensive visualizations (waterfall plots for individual predictions, beeswarm plots for global importance, scatter plots for feature relationships, bar/force/heatmap plots), supports model debugging, fairness analysis, feature engineering guidance, and production deployment
 - **statsmodels** - Statistical modeling and econometrics (OLS, GLM, logit/probit, ARIMA, time series forecasting, hypothesis testing, diagnostics)
 - **Torch Geometric** - Graph Neural Networks for molecular and geometric data