{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:X6UGUPTWWIKSYD5VR3M5LQMOGH","short_pith_number":"pith:X6UGUPTW","canonical_record":{"source":{"id":"1501.04448","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-01-19T10:47:37Z","cross_cats_sorted":[],"title_canon_sha256":"01e01a865d0c79a86dae63b91f8d1bd604f2e5253e6a974f3177d5ab48e2afee","abstract_canon_sha256":"31276bb6d799baa80cc81a0bf0b2f2e4797243108032818984c06bf7e09f0f46"},"schema_version":"1.0"},"canonical_sha256":"bfa86a3e76b2152c0fb58ed9d5c18e31c3d68ea21753ff18ed54a2df3e2a6e2a","source":{"kind":"arxiv","id":"1501.04448","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1501.04448","created_at":"2026-05-18T02:29:08Z"},{"alias_kind":"arxiv_version","alias_value":"1501.04448v1","created_at":"2026-05-18T02:29:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.04448","created_at":"2026-05-18T02:29:08Z"},{"alias_kind":"pith_short_12","alias_value":"X6UGUPTWWIKS","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_16","alias_value":"X6UGUPTWWIKSYD5V","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_8","alias_value":"X6UGUPTW","created_at":"2026-05-18T12:29:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:X6UGUPTWWIKSYD5VR3M5LQMOGH","target":"record","payload":{"canonical_record":{"source":{"id":"1501.04448","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-01-19T10:47:37Z","cross_cats_sorted":[],"title_canon_sha256":"01e01a865d0c79a86dae63b91f8d1bd604f2e5253e6a974f3177d5ab48e2afee","abstract_canon_sha256":"31276bb6d799baa80cc81a0bf0b2f2e4797243108032818984c06bf7e09f0f46"},"schema_version":"1.0"},"canonical_sha256":"bfa86a3e76b2152c0fb58ed9d5c18e31c3d68ea21753ff18ed54a2df3e2a6e2a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:29:08.968934Z","signature_b64":"SO4oM+ZN1eiJCfUV7PAhf1oMgKzVtlOvyk9BMAOWI+p4KhERiP7IFGr0UUiK+oG25zi5Bw4SUk/kbb3G2tK2DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bfa86a3e76b2152c0fb58ed9d5c18e31c3d68ea21753ff18ed54a2df3e2a6e2a","last_reissued_at":"2026-05-18T02:29:08.968462Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:29:08.968462Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1501.04448","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:29:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cjwv7Gd9QJEwl3HHn33rLBkYaRNVBxC9rXrnoyEtfnyHO5g++Zzt2K2ObxYe1VRqTh5v/6YmCdj9fTCi8kaHDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T18:31:37.028051Z"},"content_sha256":"424cec9290f67ba32271fb3f10bc916241ef44ef8479ee2447628999075eab96","schema_version":"1.0","event_id":"sha256:424cec9290f67ba32271fb3f10bc916241ef44ef8479ee2447628999075eab96"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:X6UGUPTWWIKSYD5VR3M5LQMOGH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LMest: an R package for latent Markov models for categorical longitudinal data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Alessio Farcomeni, Francesco Bartolucci, Fulvia Pennoni, Silvia Pandolfi","submitted_at":"2015-01-19T10:47:37Z","abstract_excerpt":"Latent Markov (LM) models represent an important class of models for the analysis of longitudinal data (Bartolucci et. al., 2013), especially when response variables are categorical. These models have a great potential of application for the analysis of social, medical, and behavioral data as well as in other disciplines. We propose the R package LMest, which is tailored to deal with these types of model. In particular, we consider a general framework for extended LM models by including individual covariates and by formulating a mixed approach to take into account additional dependence structu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.04448","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:29:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4DcT9iW4Cu6szynwY3qRmZ3gboiDtuqNnIjlAwupOlb6ruFeo7YsZDOLW85uQ5uqWP7+wDe55irKaspaJ3JWDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T18:31:37.028721Z"},"content_sha256":"7d7a88bf83ba87742c89627377cd2a4638b552ad94d4af2cc28bd3bf34201c3e","schema_version":"1.0","event_id":"sha256:7d7a88bf83ba87742c89627377cd2a4638b552ad94d4af2cc28bd3bf34201c3e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X6UGUPTWWIKSYD5VR3M5LQMOGH/bundle.json","state_url":"https://pith.science/pith/X6UGUPTWWIKSYD5VR3M5LQMOGH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X6UGUPTWWIKSYD5VR3M5LQMOGH/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-05T18:31:37Z","links":{"resolver":"https://pith.science/pith/X6UGUPTWWIKSYD5VR3M5LQMOGH","bundle":"https://pith.science/pith/X6UGUPTWWIKSYD5VR3M5LQMOGH/bundle.json","state":"https://pith.science/pith/X6UGUPTWWIKSYD5VR3M5LQMOGH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X6UGUPTWWIKSYD5VR3M5LQMOGH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:X6UGUPTWWIKSYD5VR3M5LQMOGH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"31276bb6d799baa80cc81a0bf0b2f2e4797243108032818984c06bf7e09f0f46","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-01-19T10:47:37Z","title_canon_sha256":"01e01a865d0c79a86dae63b91f8d1bd604f2e5253e6a974f3177d5ab48e2afee"},"schema_version":"1.0","source":{"id":"1501.04448","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1501.04448","created_at":"2026-05-18T02:29:08Z"},{"alias_kind":"arxiv_version","alias_value":"1501.04448v1","created_at":"2026-05-18T02:29:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.04448","created_at":"2026-05-18T02:29:08Z"},{"alias_kind":"pith_short_12","alias_value":"X6UGUPTWWIKS","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_16","alias_value":"X6UGUPTWWIKSYD5V","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_8","alias_value":"X6UGUPTW","created_at":"2026-05-18T12:29:47Z"}],"graph_snapshots":[{"event_id":"sha256:7d7a88bf83ba87742c89627377cd2a4638b552ad94d4af2cc28bd3bf34201c3e","target":"graph","created_at":"2026-05-18T02:29:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Latent Markov (LM) models represent an important class of models for the analysis of longitudinal data (Bartolucci et. al., 2013), especially when response variables are categorical. These models have a great potential of application for the analysis of social, medical, and behavioral data as well as in other disciplines. We propose the R package LMest, which is tailored to deal with these types of model. In particular, we consider a general framework for extended LM models by including individual covariates and by formulating a mixed approach to take into account additional dependence structu","authors_text":"Alessio Farcomeni, Francesco Bartolucci, Fulvia Pennoni, Silvia Pandolfi","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-01-19T10:47:37Z","title":"LMest: an R package for latent Markov models for categorical longitudinal data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.04448","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:424cec9290f67ba32271fb3f10bc916241ef44ef8479ee2447628999075eab96","target":"record","created_at":"2026-05-18T02:29:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"31276bb6d799baa80cc81a0bf0b2f2e4797243108032818984c06bf7e09f0f46","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-01-19T10:47:37Z","title_canon_sha256":"01e01a865d0c79a86dae63b91f8d1bd604f2e5253e6a974f3177d5ab48e2afee"},"schema_version":"1.0","source":{"id":"1501.04448","kind":"arxiv","version":1}},"canonical_sha256":"bfa86a3e76b2152c0fb58ed9d5c18e31c3d68ea21753ff18ed54a2df3e2a6e2a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bfa86a3e76b2152c0fb58ed9d5c18e31c3d68ea21753ff18ed54a2df3e2a6e2a","first_computed_at":"2026-05-18T02:29:08.968462Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:29:08.968462Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SO4oM+ZN1eiJCfUV7PAhf1oMgKzVtlOvyk9BMAOWI+p4KhERiP7IFGr0UUiK+oG25zi5Bw4SUk/kbb3G2tK2DA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:29:08.968934Z","signed_message":"canonical_sha256_bytes"},"source_id":"1501.04448","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:424cec9290f67ba32271fb3f10bc916241ef44ef8479ee2447628999075eab96","sha256:7d7a88bf83ba87742c89627377cd2a4638b552ad94d4af2cc28bd3bf34201c3e"],"state_sha256":"b582bd12573356601ad96a796fe213361f07db64b4d2981ce1390908144415fe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Kt98B6tl2TU19ymLTxEf1100nHUCPmT2U6z20X3dYQd3oqVfJnPni37UaNfXTB+1tbsBH0SIFBufDcf/fDAYAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T18:31:37.031968Z","bundle_sha256":"937b52d8c72051d6bf7957aaa5a73ecef64e5d4e3321b4f65fa3a95e63447074"}}