Reproducibility Diagnostics for Statistical Modeling // Tools for diagnosing the reproducibility of statistical model outputs under data perturbations. Implements bootstrap, subsampling, and noise-based perturbation schemes and computes coefficient stability, p-value stability, selection stability, prediction stability, and a composite reproducibility index on a 0 to 100 scale. Includes cross-validation ranking stability for model comparison and visualization utilities. Optional 'backends' support robust M-estimation ('MASS') and penalized regression ('glmnet'). Bootstrap perturbation follows 'Efron' and 'Tibshirani' (1993, ISBN:9780412042317); selection stability follows 'Meinshausen' and 'Buhlmann' (2010) doi:10.1111/j.1467-9868.2010.00740.x; reproducibility framework follows 'Peng' (2011) doi:10.1126/science.1213847.