prova: Nonparametric Probabilistic-Statistical Variate Analysis

Calculate posterior joint and conditional probabilities, probability distributions of population frequencies, information-theoretic measures, and expected utilities, by means of Bayesian nonparametrics. Data can be any combination of nominal, ordinal, continuous, censored, rounded types. 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, hypothesis testing, and association studies, to subgroup analysis, risk analysis, and probability calibration of machine-learning algorithms. Application disciplines range from astrophysics to medicine. For more details and examples see for instance Porta Mana & 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 , Müller et al. (2015) doi:10.1007/978-3-319-18968-0 . Data-training function requires the package 'Nimble'.

See also

Useful links:

• https://pglpm.github.io/prova/

• https://github.com/pglpm/prova/

Author

Maintainer: PierGianLuca Porta Mana pgl@portamana.org (ORCID) [copyright holder]

Authors:

• PierGianLuca Porta Mana pgl@portamana.org (ORCID) [copyright holder]

Other contributors:

• Aurora Grefsrud agre@hvl.no (ORCID) [contributor]

• Håkon Mydland haakon.mydland@gmail.com (ORCID) [contributor]

• Maksim Ohvrill maksimohvrill@hotmail.com [contributor]

• Simen Hesthamar Hauge simen@hhnet.no (ORCID) [contributor]

• Torstein Sleire tor-sle@hotmail.com (ORCID) [contributor]