{"paper":{"title":"PoissonRatioUQ: An R package for band ratio uncertainty quantification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"An R package performs Bayesian inference on the ratio of Poisson means to quantify uncertainty in count data problems.","cross_cats":["physics.data-an","stat.ME"],"primary_cat":"stat.CO","authors_text":"Matthew LeDuc, Tomoko Matsuo","submitted_at":"2026-02-06T20:06:52Z","abstract_excerpt":"We introduce an R package for Bayesian modeling and uncertainty quantification for problems involving count ratios. The modeling relies on the assumption that the quantity of interest is the ratio of Poisson means rather than the ratio of counts. We provide multiple different options for retrieval of this quantity for problems with and without spatial information included. Some added capability for uncertainty quantification for problems of the form $Z=(mT+z_0)^{p}$, where $Z$ is the intensity ratio and $T$ the quantity of interest, is included."},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We introduce an R package for Bayesian modeling and uncertainty quantification for problems involving count ratios. The modeling relies on the assumption that the quantity of interest is the ratio of Poisson means rather than the ratio of counts. We provide multiple different options for retrieval of this quantity for problems with and without spatial information included. Some added capability for uncertainty quantification for problems of the form Z=(mT+z0)^p is included.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that the quantity of interest is the ratio of Poisson means rather than the ratio of counts, as stated in the abstract as the basis for the modeling approach.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"PoissonRatioUQ is an R package providing Bayesian uncertainty quantification for the ratio of Poisson means in count data problems, including spatial and transformed intensity cases.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"An R package performs Bayesian inference on the ratio of Poisson means to quantify uncertainty in count data problems.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"a876ddb74b8b84a2bb839abfa3ab1e790be4a500705b723ee5d11dc94d5db0a6"},"source":{"id":"2602.07165","kind":"arxiv","version":3},"verdict":{"id":"317cc581-44bc-41a2-9511-1cb3a4c01671","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T06:58:32.524300Z","strongest_claim":"We introduce an R package for Bayesian modeling and uncertainty quantification for problems involving count ratios. The modeling relies on the assumption that the quantity of interest is the ratio of Poisson means rather than the ratio of counts. We provide multiple different options for retrieval of this quantity for problems with and without spatial information included. Some added capability for uncertainty quantification for problems of the form Z=(mT+z0)^p is included.","one_line_summary":"PoissonRatioUQ is an R package providing Bayesian uncertainty quantification for the ratio of Poisson means in count data problems, including spatial and transformed intensity cases.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that the quantity of interest is the ratio of Poisson means rather than the ratio of counts, as stated in the abstract as the basis for the modeling approach.","pith_extraction_headline":"An R package performs Bayesian inference on the ratio of Poisson means to quantify uncertainty in count data problems."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2602.07165/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}