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) tools:::Rd_expr_doi("10.31219/osf.io/8nr56"), Dunson & Bhattacharya (2011) tools:::Rd_expr_doi("10.1093/acprof:oso/9780199694587.003.0005"), Lindley & Novick (1981) tools:::Rd_expr_doi("10.1214/aos/1176345331"), Bernardo & Smith (2000) tools:::Rd_expr_doi("10.1002/9780470316870"), Müller et al. (2015) tools:::Rd_expr_doi("10.1007/978-3-319-18968-0"). Requires the packages 'Nimble', 'parallel', 'extraDistr'.
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]