Nonparametric Probabilistic-Statistical Variate Analysis with Automated Markov-Chain Monte Carlo // Calculate posterior joint and conditional probabilities, probability distributions of population frequencies, and information-theoretic measures, by means of Bayesian nonparametric methods. Data imputation is automatic and done in a principled way. Markov-chain Monte Carlo calculations are automatically handled and do not require user supervision. Applications range from statistical estimation and probabilistic hypothesis testing to evidence- based inference and decision making, in a wide range of disciplines from astrophysics to medicine. For more details and examples see for instance Porta Mana et al. (2026) doi:10.31219/osf.io/8nr56, Dunson Bhattacharya (2011) doi:10.1093/acprof:oso/9780199694587.003.0005, Lindley Novick (1981) doi:10.1214/aos/1176345331, Bernardo Smith (2000) doi:10.1002/9780470316870, Mller et al. (2015) doi:10.1007/978-3-319-18968-0. Requires the packages 'Nimble', 'parallel', 'extraDistr'.