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)