# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "decideR" in publications use:' type: software license: MIT title: 'decideR: Bayesian Decision-Theoretic Actions under Loss and Hard Constraints' version: 0.3.1.9000 identifiers: - type: doi value: 10.32614/CRAN.package.decideR abstract: Maps a posterior distribution or an 'orchestra_manifest' inference result to an optimal action under an explicit loss function and a hard feasibility constraint, returning the action together with a grounding label and an abstention when evidence is insufficient. The grounding label is combined worst-case across inputs, so a decision resting on an unverified external fact is downgraded to abstention rather than asserted with false confidence. A manifest-native tail closes an analytics pipeline by deciding directly from an upstream member's inference result, and a grade-band loss prices quality-threshold ('protein-band') payoffs as a first-class step value schedule. Reference decisions cover bounded input-rate selection and constraint-capped position sizing. authors: - family-names: Moldovan given-names: Max email: max.moldovan@adelaide.edu.au orcid: https://orcid.org/0000-0001-9680-8474 preferred-citation: type: manual title: 'decideR: Bayesian Decision-Theoretic Actions under Loss and Hard Constraints' authors: - family-names: Moldovan given-names: Max orcid: https://orcid.org/0000-0001-9680-8474 email: max.moldovan@adelaide.edu.au year: '2026' notes: R package version 0.3.1.9000 url: https://github.com/max578/decideR repository: https://max578.r-universe.dev repository-code: https://github.com/max578/decideR commit: bf967c2e5af957cbdad188309fc1d1cedba84cef url: https://max578.github.io/decideR date-released: '2026-10-15' contact: - family-names: Moldovan given-names: Max email: max.moldovan@adelaide.edu.au orcid: https://orcid.org/0000-0001-9680-8474