'Moreau-Yosida' Importance Sampling for Statistical Inference // Implements 'Moreau-Yosida' Markov chain Monte Carlo ('MCMC') importance sampling for parameter estimation and Bayesian inference under smooth, non-differentiable, or light-tailed target posterior distributions and arbitrary probability models with complete or censored data. Users supply user-defined probability density functions, optional distribution functions, parameter ranges, and observations subject to complete, right, left, interval, Type-I, Type-II, progressive Type-II, first- failure, or truncation schemes. Constructs 'Moreau-Yosida' envelopes, gradient-based proposals ('MALA', 'HMC', or 'RWM'), self-normalized importance weights, batch-means asymptotic variance estimates, and Bayesian marginal quantiles. Methodologies are based on 'Shukla', 'Vats', and 'Chi' (2025) doi:10.48550/arXiv.2501.02228, 'Pereyra' (2016) doi:10.1111/sjos.12208, 'Durmus' and others (2022) doi:10.1214/22-EJS2027, 'Chen' and 'Shao' (1999) doi:10.1214/ss/1009211804, 'Roberts' and 'Rosenthal' (1998) doi:10.1214/aoap/1028903378, 'Geweke' (1989) doi:10.2307/2290062, 'Hesterberg' (1995) doi:10.1080/00031305.1995.10476138, and 'Balakrishnan' and 'Aggarwala' (2000, ISBN:978-0-8176-4001-9).