SexEst is an open-source Streamlit web application for predicting biological sex from skeletal measurements using machine learning (XGBoost, LightGBM, Linear Discriminant Analysis). The best-performing models achieved cross-validated accuracies of ~80–90% on the Goldman (postcranial) and Howells (cranial) datasets.

AgeEst notebook: reproducible analysis used to build the AgeEst models (DOI: 10.1016/j.fsir.2023.100317). Full dataset not included; contact authors for access.

Population-specific sex estimation from postcranial metrics (ancient Dion, Greece): Colab notebook + exported Logistic Regression formulas and ML models (XGBoost/LightGBM/RF), with imputation and cross-validation comparisons