{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:L3KPQERFS3FXNP4GFT5VFMXXKZ","short_pith_number":"pith:L3KPQERF","schema_version":"1.0","canonical_sha256":"5ed4f8122596cb76bf862cfb52b2f7567517e5bdf972f07dc727e704527e06e3","source":{"kind":"arxiv","id":"1510.03042","version":1},"attestation_state":"computed","paper":{"title":"ParallelPC: an R package for efficient constraint based causal exploration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.AI","authors_text":"Jiuyong Li, Lin Liu, Shu Hu, Tao Hoang, Thuc Duy Le","submitted_at":"2015-10-11T11:55:39Z","abstract_excerpt":"Discovering causal relationships from data is the ultimate goal of many research areas. Constraint based causal exploration algorithms, such as PC, FCI, RFCI, PC-simple, IDA and Joint-IDA have achieved significant progress and have many applications. A common problem with these methods is the high computational complexity, which hinders their applications in real world high dimensional datasets, e.g gene expression datasets. In this paper, we present an R package, ParallelPC, that includes the parallelised versions of these causal exploration algorithms. The parallelised algorithms help speed "},"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":"1510.03042","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2015-10-11T11:55:39Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"171215548cd85931e8ca6f038d48578874e1f159df5e60cf52d5a52cf674a55a","abstract_canon_sha256":"e274bd94a4f252b74a126a2c90e7c1293305be763b89eb026f0293665636b3bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:30:32.885010Z","signature_b64":"/PZm+P0VvZ5tKIzYlriG20OFs6jNTMh9ltEUtkrkMNEL7Y+EqrlUvQl33q8k7lXYs57GN0GF7aAuocd33enZDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ed4f8122596cb76bf862cfb52b2f7567517e5bdf972f07dc727e704527e06e3","last_reissued_at":"2026-05-18T01:30:32.884448Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:30:32.884448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ParallelPC: an R package for efficient constraint based causal exploration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.AI","authors_text":"Jiuyong Li, Lin Liu, Shu Hu, Tao Hoang, Thuc Duy Le","submitted_at":"2015-10-11T11:55:39Z","abstract_excerpt":"Discovering causal relationships from data is the ultimate goal of many research areas. Constraint based causal exploration algorithms, such as PC, FCI, RFCI, PC-simple, IDA and Joint-IDA have achieved significant progress and have many applications. A common problem with these methods is the high computational complexity, which hinders their applications in real world high dimensional datasets, e.g gene expression datasets. In this paper, we present an R package, ParallelPC, that includes the parallelised versions of these causal exploration algorithms. The parallelised algorithms help speed "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.03042","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1510.03042","created_at":"2026-05-18T01:30:32.884539+00:00"},{"alias_kind":"arxiv_version","alias_value":"1510.03042v1","created_at":"2026-05-18T01:30:32.884539+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.03042","created_at":"2026-05-18T01:30:32.884539+00:00"},{"alias_kind":"pith_short_12","alias_value":"L3KPQERFS3FX","created_at":"2026-05-18T12:29:29.992203+00:00"},{"alias_kind":"pith_short_16","alias_value":"L3KPQERFS3FXNP4G","created_at":"2026-05-18T12:29:29.992203+00:00"},{"alias_kind":"pith_short_8","alias_value":"L3KPQERF","created_at":"2026-05-18T12:29:29.992203+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/L3KPQERFS3FXNP4GFT5VFMXXKZ","json":"https://pith.science/pith/L3KPQERFS3FXNP4GFT5VFMXXKZ.json","graph_json":"https://pith.science/api/pith-number/L3KPQERFS3FXNP4GFT5VFMXXKZ/graph.json","events_json":"https://pith.science/api/pith-number/L3KPQERFS3FXNP4GFT5VFMXXKZ/events.json","paper":"https://pith.science/paper/L3KPQERF"},"agent_actions":{"view_html":"https://pith.science/pith/L3KPQERFS3FXNP4GFT5VFMXXKZ","download_json":"https://pith.science/pith/L3KPQERFS3FXNP4GFT5VFMXXKZ.json","view_paper":"https://pith.science/paper/L3KPQERF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1510.03042&json=true","fetch_graph":"https://pith.science/api/pith-number/L3KPQERFS3FXNP4GFT5VFMXXKZ/graph.json","fetch_events":"https://pith.science/api/pith-number/L3KPQERFS3FXNP4GFT5VFMXXKZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L3KPQERFS3FXNP4GFT5VFMXXKZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L3KPQERFS3FXNP4GFT5VFMXXKZ/action/storage_attestation","attest_author":"https://pith.science/pith/L3KPQERFS3FXNP4GFT5VFMXXKZ/action/author_attestation","sign_citation":"https://pith.science/pith/L3KPQERFS3FXNP4GFT5VFMXXKZ/action/citation_signature","submit_replication":"https://pith.science/pith/L3KPQERFS3FXNP4GFT5VFMXXKZ/action/replication_record"}},"created_at":"2026-05-18T01:30:32.884539+00:00","updated_at":"2026-05-18T01:30:32.884539+00:00"}