Reliability and Competing Risks Analysis under Hybrid Censoring // Generalized computational algorithms for competing risks analysis, stress-strength reliability modeling, optimal designs, and reliability acceptance sampling plans under various hybrid censoring schemes. Includes data generation routines, Maximum Likelihood Estimation (MLE) with seven optimization algorithms ('Newton-Raphson', 'BFGS', 'BFGSR', 'BHHH', 'SANN', 'CG', and 'Nelder-Mead'), Bayesian inference via Gibbs sampling and Metropolis-Hastings MCMC, Importance Sampling, and 'Lindley' asymptotic approximation. Visualization functions generate histograms, dot plots, and autocorrelation plots for model validation. Methodology and design principles are based on 'Balakrishnan', 'Cramer', and 'Kundu' (2023, "Hybrid Censoring Know-How: Designs and Implementations", Academic Press, ISBN:978-0-12-398387-9).