{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:DMDHUBIYG6Q4WLPCM6AEPVHHRH","short_pith_number":"pith:DMDHUBIY","schema_version":"1.0","canonical_sha256":"1b067a051837a1cb2de2678047d4e789e1c4a2688fe9136a2e85b58ef103837d","source":{"kind":"arxiv","id":"1705.04584","version":3},"attestation_state":"computed","paper":{"title":"spBayesSurv: Fitting Bayesian Spatial Survival Models Using R","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.CO","authors_text":"Haiming Zhou, Jiajia Zhang, Timothy Hanson","submitted_at":"2017-05-10T19:24:03Z","abstract_excerpt":"Spatial survival analysis has received a great deal of attention over the last 20 years due to the important role that geographical information can play in predicting survival. This paper provides an introduction to a set of programs for implementing some Bayesian spatial survival models in R using the package spBayesSurv. The function survregbayes includes the three most commonly-used semiparametric models: proportional hazards, proportional odds, and accelerated failure time. All manner of censored survival times are simultaneously accommodated including uncensored, interval censored, curren"},"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":"1705.04584","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-05-10T19:24:03Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"1200100251f2868e89252e92b281e24c1ace26dcf3cbcc94d6c88b4e6acefac0","abstract_canon_sha256":"685e6aaa2c1c941e78c90f0c0a6842f08688fa1460dca91f5520b6907edebf64"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:43.680987Z","signature_b64":"udPUL3nYCYuY71c7x3RCIIQNCti0/EZqQvthsU4TMeLrDFtFkloMS+i5IUBxP3a8vTN2P8Iqfe/UnnVXUt/7BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b067a051837a1cb2de2678047d4e789e1c4a2688fe9136a2e85b58ef103837d","last_reissued_at":"2026-05-18T00:17:43.680359Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:43.680359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"spBayesSurv: Fitting Bayesian Spatial Survival Models Using R","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.CO","authors_text":"Haiming Zhou, Jiajia Zhang, Timothy Hanson","submitted_at":"2017-05-10T19:24:03Z","abstract_excerpt":"Spatial survival analysis has received a great deal of attention over the last 20 years due to the important role that geographical information can play in predicting survival. This paper provides an introduction to a set of programs for implementing some Bayesian spatial survival models in R using the package spBayesSurv. The function survregbayes includes the three most commonly-used semiparametric models: proportional hazards, proportional odds, and accelerated failure time. All manner of censored survival times are simultaneously accommodated including uncensored, interval censored, curren"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.04584","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1705.04584","created_at":"2026-05-18T00:17:43.680450+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.04584v3","created_at":"2026-05-18T00:17:43.680450+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.04584","created_at":"2026-05-18T00:17:43.680450+00:00"},{"alias_kind":"pith_short_12","alias_value":"DMDHUBIYG6Q4","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_16","alias_value":"DMDHUBIYG6Q4WLPC","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_8","alias_value":"DMDHUBIY","created_at":"2026-05-18T12:31:10.602751+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/DMDHUBIYG6Q4WLPCM6AEPVHHRH","json":"https://pith.science/pith/DMDHUBIYG6Q4WLPCM6AEPVHHRH.json","graph_json":"https://pith.science/api/pith-number/DMDHUBIYG6Q4WLPCM6AEPVHHRH/graph.json","events_json":"https://pith.science/api/pith-number/DMDHUBIYG6Q4WLPCM6AEPVHHRH/events.json","paper":"https://pith.science/paper/DMDHUBIY"},"agent_actions":{"view_html":"https://pith.science/pith/DMDHUBIYG6Q4WLPCM6AEPVHHRH","download_json":"https://pith.science/pith/DMDHUBIYG6Q4WLPCM6AEPVHHRH.json","view_paper":"https://pith.science/paper/DMDHUBIY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.04584&json=true","fetch_graph":"https://pith.science/api/pith-number/DMDHUBIYG6Q4WLPCM6AEPVHHRH/graph.json","fetch_events":"https://pith.science/api/pith-number/DMDHUBIYG6Q4WLPCM6AEPVHHRH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DMDHUBIYG6Q4WLPCM6AEPVHHRH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DMDHUBIYG6Q4WLPCM6AEPVHHRH/action/storage_attestation","attest_author":"https://pith.science/pith/DMDHUBIYG6Q4WLPCM6AEPVHHRH/action/author_attestation","sign_citation":"https://pith.science/pith/DMDHUBIYG6Q4WLPCM6AEPVHHRH/action/citation_signature","submit_replication":"https://pith.science/pith/DMDHUBIYG6Q4WLPCM6AEPVHHRH/action/replication_record"}},"created_at":"2026-05-18T00:17:43.680450+00:00","updated_at":"2026-05-18T00:17:43.680450+00:00"}