{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:3NLZW7XTAW2FTENIY5DPILIM4T","short_pith_number":"pith:3NLZW7XT","canonical_record":{"source":{"id":"1603.01700","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-03-05T08:57:26Z","cross_cats_sorted":["econ.EM","stat.ME"],"title_canon_sha256":"02f58ce44b82e5fa21d6e6ba99a9c439dc185a8b2e5849d67647f2e1efd96c5e","abstract_canon_sha256":"26d10dde1a0582861bea2f28f4274fbf6d0357ce13419da2acfd40cfc0b140dd"},"schema_version":"1.0"},"canonical_sha256":"db579b7ef305b45991a8c746f42d0ce4c5d7e203da212c8170a3910b267d00c4","source":{"kind":"arxiv","id":"1603.01700","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.01700","created_at":"2026-05-18T00:34:15Z"},{"alias_kind":"arxiv_version","alias_value":"1603.01700v2","created_at":"2026-05-18T00:34:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.01700","created_at":"2026-05-18T00:34:15Z"},{"alias_kind":"pith_short_12","alias_value":"3NLZW7XTAW2F","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_16","alias_value":"3NLZW7XTAW2FTENI","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_8","alias_value":"3NLZW7XT","created_at":"2026-05-18T12:29:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:3NLZW7XTAW2FTENIY5DPILIM4T","target":"record","payload":{"canonical_record":{"source":{"id":"1603.01700","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-03-05T08:57:26Z","cross_cats_sorted":["econ.EM","stat.ME"],"title_canon_sha256":"02f58ce44b82e5fa21d6e6ba99a9c439dc185a8b2e5849d67647f2e1efd96c5e","abstract_canon_sha256":"26d10dde1a0582861bea2f28f4274fbf6d0357ce13419da2acfd40cfc0b140dd"},"schema_version":"1.0"},"canonical_sha256":"db579b7ef305b45991a8c746f42d0ce4c5d7e203da212c8170a3910b267d00c4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:16.000261Z","signature_b64":"YUmDMWP+nDk5DQOGDtxCnYIKzdVc9B6aBIAFEyEDWGg9SpvzbHMo91rqzeJT1q260ThHifN8TONP/tc6j5yeAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"db579b7ef305b45991a8c746f42d0ce4c5d7e203da212c8170a3910b267d00c4","last_reissued_at":"2026-05-18T00:34:15.999694Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:15.999694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1603.01700","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:34:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MS5vmceQR3Er3hglOcsqiG1CpfepLrkRwKeoKyJJ2i2GPJRG0s7BT4S+1AsEpA+26in9VinYZCGCTeVtzcqPDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T13:03:14.281869Z"},"content_sha256":"3563f57f43d4e1f7f8ce33d0732123e84c635ddf0075669a8cdd09c0a8b0bc34","schema_version":"1.0","event_id":"sha256:3563f57f43d4e1f7f8ce33d0732123e84c635ddf0075669a8cdd09c0a8b0bc34"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:3NLZW7XTAW2FTENIY5DPILIM4T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"High-Dimensional Metrics in R","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["econ.EM","stat.ME"],"primary_cat":"stat.ML","authors_text":"Chris Hansen, Martin Spindler, Victor Chernozhukov","submitted_at":"2016-03-05T08:57:26Z","abstract_excerpt":"The package High-dimensional Metrics (\\Rpackage{hdm}) is an evolving collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for (possibly many) low-dimensional subcomponents of the high-dimensional parameter vector. Efficient estimators and uniformly valid confidence intervals for regression coefficients on target variables (e.g., treatment or policy variable) in a high-dimensional approximately sparse regression model, for average treatment effect "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.01700","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":""},"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":"2026-05-18T00:34:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nx9Ll4OpSnOc9UQH3S2jrCHwUPuye+XAEFJ8Ftsfr+zEKJFQZ8CR5UKqQR0HA6DEh6Trf7aVauqUmMpqyU0fBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T13:03:14.282334Z"},"content_sha256":"f6ac863d7d99a86843884bcf17dbdd2439cb68f96eb41e897514f1733b01c635","schema_version":"1.0","event_id":"sha256:f6ac863d7d99a86843884bcf17dbdd2439cb68f96eb41e897514f1733b01c635"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3NLZW7XTAW2FTENIY5DPILIM4T/bundle.json","state_url":"https://pith.science/pith/3NLZW7XTAW2FTENIY5DPILIM4T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3NLZW7XTAW2FTENIY5DPILIM4T/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":"2026-08-15T13:03:14Z","links":{"resolver":"https://pith.science/pith/3NLZW7XTAW2FTENIY5DPILIM4T","bundle":"https://pith.science/pith/3NLZW7XTAW2FTENIY5DPILIM4T/bundle.json","state":"https://pith.science/pith/3NLZW7XTAW2FTENIY5DPILIM4T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3NLZW7XTAW2FTENIY5DPILIM4T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:3NLZW7XTAW2FTENIY5DPILIM4T","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":"26d10dde1a0582861bea2f28f4274fbf6d0357ce13419da2acfd40cfc0b140dd","cross_cats_sorted":["econ.EM","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-03-05T08:57:26Z","title_canon_sha256":"02f58ce44b82e5fa21d6e6ba99a9c439dc185a8b2e5849d67647f2e1efd96c5e"},"schema_version":"1.0","source":{"id":"1603.01700","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.01700","created_at":"2026-05-18T00:34:15Z"},{"alias_kind":"arxiv_version","alias_value":"1603.01700v2","created_at":"2026-05-18T00:34:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.01700","created_at":"2026-05-18T00:34:15Z"},{"alias_kind":"pith_short_12","alias_value":"3NLZW7XTAW2F","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_16","alias_value":"3NLZW7XTAW2FTENI","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_8","alias_value":"3NLZW7XT","created_at":"2026-05-18T12:29:55Z"}],"graph_snapshots":[{"event_id":"sha256:f6ac863d7d99a86843884bcf17dbdd2439cb68f96eb41e897514f1733b01c635","target":"graph","created_at":"2026-05-18T00:34:15Z","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":"The package High-dimensional Metrics (\\Rpackage{hdm}) is an evolving collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for (possibly many) low-dimensional subcomponents of the high-dimensional parameter vector. Efficient estimators and uniformly valid confidence intervals for regression coefficients on target variables (e.g., treatment or policy variable) in a high-dimensional approximately sparse regression model, for average treatment effect ","authors_text":"Chris Hansen, Martin Spindler, Victor Chernozhukov","cross_cats":["econ.EM","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-03-05T08:57:26Z","title":"High-Dimensional Metrics in R"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.01700","kind":"arxiv","version":2},"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":"sha256:3563f57f43d4e1f7f8ce33d0732123e84c635ddf0075669a8cdd09c0a8b0bc34","target":"record","created_at":"2026-05-18T00:34:15Z","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":"26d10dde1a0582861bea2f28f4274fbf6d0357ce13419da2acfd40cfc0b140dd","cross_cats_sorted":["econ.EM","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-03-05T08:57:26Z","title_canon_sha256":"02f58ce44b82e5fa21d6e6ba99a9c439dc185a8b2e5849d67647f2e1efd96c5e"},"schema_version":"1.0","source":{"id":"1603.01700","kind":"arxiv","version":2}},"canonical_sha256":"db579b7ef305b45991a8c746f42d0ce4c5d7e203da212c8170a3910b267d00c4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"db579b7ef305b45991a8c746f42d0ce4c5d7e203da212c8170a3910b267d00c4","first_computed_at":"2026-05-18T00:34:15.999694Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:34:15.999694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YUmDMWP+nDk5DQOGDtxCnYIKzdVc9B6aBIAFEyEDWGg9SpvzbHMo91rqzeJT1q260ThHifN8TONP/tc6j5yeAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:34:16.000261Z","signed_message":"canonical_sha256_bytes"},"source_id":"1603.01700","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3563f57f43d4e1f7f8ce33d0732123e84c635ddf0075669a8cdd09c0a8b0bc34","sha256:f6ac863d7d99a86843884bcf17dbdd2439cb68f96eb41e897514f1733b01c635"],"state_sha256":"46ba675800d5f609913ce1cd716e0bb4594e0fdcd473507c342d782a240a9596"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X6PIDFMd6DmD9AtiM8ZR/Hq/yktB+4CKg2RTf6rNcX7m4wurmJ4i2cbJ9hAsjVBNqA2Q9VL06QKCHoqD9IASCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T13:03:14.286613Z","bundle_sha256":"ac57048282b4202a01cc88b14213824670e45681ccb0a0f46f36948ccd61f64c"}}