{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WESXY5J4XQYAWMWCZ2UX57X5KP","short_pith_number":"pith:WESXY5J4","schema_version":"1.0","canonical_sha256":"b1257c753cbc300b32c2cea97efefd53f6589ff7be8b6df853c020206465655b","source":{"kind":"arxiv","id":"2607.08491","version":1},"attestation_state":"computed","paper":{"title":"weightflow: declarative, recipe-aware survey weighting in R","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Juan Pablo Ferreira","submitted_at":"2026-07-09T13:50:08Z","abstract_excerpt":"Producing analysis weights for a complex survey requires a sequence of hierarchical adjustments (resolving unknown eligibility, dropping out-of-scope units, restoring within-household selection, correcting for nonresponse, and calibrating to known population totals), after which design-consistent variances must account for the fact that several adjustments were themselves estimated from the sample. Existing R tools cover parts of this workflow, but none expresses the whole cascade as a single auditable object, nor propagates the variability of every stage into the replicate weights. We present"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.08491","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-07-09T13:50:08Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"91c32c9fbda611dd39394526910bf9049aea0123cbb9d30290e8161b5d027a6b","abstract_canon_sha256":"43f36282cfc1bfce6a43b8f87ef883f0434e8f2a00b463ffb75767941931dba5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-10T01:19:52.030016Z","signature_b64":"Tl/24ZyMazHR2kZyQDH8JC+H/z4a4mDn90E34LrzFzvtnS27FZHJm5ChFqWLfF1StKVpl6E8DF/78Ae1rSgqDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1257c753cbc300b32c2cea97efefd53f6589ff7be8b6df853c020206465655b","last_reissued_at":"2026-07-10T01:19:52.029624Z","signature_status":"signed_v1","first_computed_at":"2026-07-10T01:19:52.029624Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"weightflow: declarative, recipe-aware survey weighting in R","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Juan Pablo Ferreira","submitted_at":"2026-07-09T13:50:08Z","abstract_excerpt":"Producing analysis weights for a complex survey requires a sequence of hierarchical adjustments (resolving unknown eligibility, dropping out-of-scope units, restoring within-household selection, correcting for nonresponse, and calibrating to known population totals), after which design-consistent variances must account for the fact that several adjustments were themselves estimated from the sample. Existing R tools cover parts of this workflow, but none expresses the whole cascade as a single auditable object, nor propagates the variability of every stage into the replicate weights. We present"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.08491","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.08491/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.08491","created_at":"2026-07-10T01:19:52.029685+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.08491v1","created_at":"2026-07-10T01:19:52.029685+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.08491","created_at":"2026-07-10T01:19:52.029685+00:00"},{"alias_kind":"pith_short_12","alias_value":"WESXY5J4XQYA","created_at":"2026-07-10T01:19:52.029685+00:00"},{"alias_kind":"pith_short_16","alias_value":"WESXY5J4XQYAWMWC","created_at":"2026-07-10T01:19:52.029685+00:00"},{"alias_kind":"pith_short_8","alias_value":"WESXY5J4","created_at":"2026-07-10T01:19:52.029685+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WESXY5J4XQYAWMWCZ2UX57X5KP","json":"https://pith.science/pith/WESXY5J4XQYAWMWCZ2UX57X5KP.json","graph_json":"https://pith.science/api/pith-number/WESXY5J4XQYAWMWCZ2UX57X5KP/graph.json","events_json":"https://pith.science/api/pith-number/WESXY5J4XQYAWMWCZ2UX57X5KP/events.json","paper":"https://pith.science/paper/WESXY5J4"},"agent_actions":{"view_html":"https://pith.science/pith/WESXY5J4XQYAWMWCZ2UX57X5KP","download_json":"https://pith.science/pith/WESXY5J4XQYAWMWCZ2UX57X5KP.json","view_paper":"https://pith.science/paper/WESXY5J4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.08491&json=true","fetch_graph":"https://pith.science/api/pith-number/WESXY5J4XQYAWMWCZ2UX57X5KP/graph.json","fetch_events":"https://pith.science/api/pith-number/WESXY5J4XQYAWMWCZ2UX57X5KP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WESXY5J4XQYAWMWCZ2UX57X5KP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WESXY5J4XQYAWMWCZ2UX57X5KP/action/storage_attestation","attest_author":"https://pith.science/pith/WESXY5J4XQYAWMWCZ2UX57X5KP/action/author_attestation","sign_citation":"https://pith.science/pith/WESXY5J4XQYAWMWCZ2UX57X5KP/action/citation_signature","submit_replication":"https://pith.science/pith/WESXY5J4XQYAWMWCZ2UX57X5KP/action/replication_record"}},"created_at":"2026-07-10T01:19:52.029685+00:00","updated_at":"2026-07-10T01:19:52.029685+00:00"}