{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QWTGM2HJMPDJAF3PQNUDPI64Q3","short_pith_number":"pith:QWTGM2HJ","schema_version":"1.0","canonical_sha256":"85a66668e963c690176f836837a3dc86c9aaf8c675ba0863fa94de0c7ff434a0","source":{"kind":"arxiv","id":"2505.14696","version":2},"attestation_state":"computed","paper":{"title":"infomeasure: A Comprehensive Python Package for Information Theory Measures and Estimators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","physics.comp-ph","physics.data-an"],"primary_cat":"physics.soc-ph","authors_text":"Carlson Moses B\\\"uth, Kishor Acharya, Massimiliano Zanin","submitted_at":"2025-05-07T05:57:49Z","abstract_excerpt":"Information theory, i.e. the mathematical analysis of information and of its processing, has become a tenet of modern science; yet, its use in real-world studies is usually hindered by its computational complexity, the lack of coherent software frameworks, and, as a consequence, low reproducibility. We here introduce infomeasure, an open-source Python package designed to provide robust tools for calculating a wide variety of information-theoretic measures, including entropies, mutual information, transfer entropy and divergences. It is designed for both discrete and continuous variables; imple"},"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":"2505.14696","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.soc-ph","submitted_at":"2025-05-07T05:57:49Z","cross_cats_sorted":["cs.IT","math.IT","physics.comp-ph","physics.data-an"],"title_canon_sha256":"c5657dcfe412011b0457b632ebef0840734ff9959b9b21381ef52f3858a41251","abstract_canon_sha256":"f7d47508768efeea8945c687afedce9be956629c7f62045656d6ee98cf83af77"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:54:30.831479Z","signature_b64":"NitC/SUH77+iDfEwypTGUWCbDQOAt/5E7UtcQ/DIFPmOcEMEkFA0bAmJIozQJQFgK9ZtMp1yi70M4DOOfPkFBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85a66668e963c690176f836837a3dc86c9aaf8c675ba0863fa94de0c7ff434a0","last_reissued_at":"2026-07-05T11:54:30.830986Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:54:30.830986Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"infomeasure: A Comprehensive Python Package for Information Theory Measures and Estimators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","physics.comp-ph","physics.data-an"],"primary_cat":"physics.soc-ph","authors_text":"Carlson Moses B\\\"uth, Kishor Acharya, Massimiliano Zanin","submitted_at":"2025-05-07T05:57:49Z","abstract_excerpt":"Information theory, i.e. the mathematical analysis of information and of its processing, has become a tenet of modern science; yet, its use in real-world studies is usually hindered by its computational complexity, the lack of coherent software frameworks, and, as a consequence, low reproducibility. We here introduce infomeasure, an open-source Python package designed to provide robust tools for calculating a wide variety of information-theoretic measures, including entropies, mutual information, transfer entropy and divergences. It is designed for both discrete and continuous variables; imple"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.14696","kind":"arxiv","version":2},"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/2505.14696/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":"2505.14696","created_at":"2026-07-05T11:54:30.831043+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.14696v2","created_at":"2026-07-05T11:54:30.831043+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.14696","created_at":"2026-07-05T11:54:30.831043+00:00"},{"alias_kind":"pith_short_12","alias_value":"QWTGM2HJMPDJ","created_at":"2026-07-05T11:54:30.831043+00:00"},{"alias_kind":"pith_short_16","alias_value":"QWTGM2HJMPDJAF3P","created_at":"2026-07-05T11:54:30.831043+00:00"},{"alias_kind":"pith_short_8","alias_value":"QWTGM2HJ","created_at":"2026-07-05T11:54:30.831043+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.07344","citing_title":"muxvizpy: a Python library for the analysis of multilayer biological networks","ref_index":11,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QWTGM2HJMPDJAF3PQNUDPI64Q3","json":"https://pith.science/pith/QWTGM2HJMPDJAF3PQNUDPI64Q3.json","graph_json":"https://pith.science/api/pith-number/QWTGM2HJMPDJAF3PQNUDPI64Q3/graph.json","events_json":"https://pith.science/api/pith-number/QWTGM2HJMPDJAF3PQNUDPI64Q3/events.json","paper":"https://pith.science/paper/QWTGM2HJ"},"agent_actions":{"view_html":"https://pith.science/pith/QWTGM2HJMPDJAF3PQNUDPI64Q3","download_json":"https://pith.science/pith/QWTGM2HJMPDJAF3PQNUDPI64Q3.json","view_paper":"https://pith.science/paper/QWTGM2HJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.14696&json=true","fetch_graph":"https://pith.science/api/pith-number/QWTGM2HJMPDJAF3PQNUDPI64Q3/graph.json","fetch_events":"https://pith.science/api/pith-number/QWTGM2HJMPDJAF3PQNUDPI64Q3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QWTGM2HJMPDJAF3PQNUDPI64Q3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QWTGM2HJMPDJAF3PQNUDPI64Q3/action/storage_attestation","attest_author":"https://pith.science/pith/QWTGM2HJMPDJAF3PQNUDPI64Q3/action/author_attestation","sign_citation":"https://pith.science/pith/QWTGM2HJMPDJAF3PQNUDPI64Q3/action/citation_signature","submit_replication":"https://pith.science/pith/QWTGM2HJMPDJAF3PQNUDPI64Q3/action/replication_record"}},"created_at":"2026-07-05T11:54:30.831043+00:00","updated_at":"2026-07-05T11:54:30.831043+00:00"}