{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:FCGC5Y7IHNUZTAJYYWQMTIGQWC","short_pith_number":"pith:FCGC5Y7I","schema_version":"1.0","canonical_sha256":"288c2ee3e83b69998138c5a0c9a0d0b0afaec674c55350cc6c1c9f46e2a16cbe","source":{"kind":"arxiv","id":"1507.08613","version":4},"attestation_state":"computed","paper":{"title":"Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Catherine A. Calder, Mark D. Risser","submitted_at":"2015-07-30T18:24:39Z","abstract_excerpt":"In spite of the interest in and appeal of convolution-based approaches for nonstationary spatial modeling, off-the-shelf software for model fitting does not as of yet exist. Convolution-based models are highly flexible yet notoriously difficult to fit, even with relatively small data sets. The general lack of pre-packaged options for model fitting makes it difficult to compare new methodology in nonstationary modeling with other existing methods, and as a result most new models are simply compared to stationary models. Using a convolution-based approach, we present a new nonstationary covarian"},"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":"1507.08613","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-07-30T18:24:39Z","cross_cats_sorted":[],"title_canon_sha256":"002f70a1712dfce2d9f27819e6df4205f8a257ce61559f5bb7ff1acf1c8b1603","abstract_canon_sha256":"22d4e45f23f6ba4267c24cb22e7f61b7272f5e83ed484a91e61528fe1493043c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:51:28.579144Z","signature_b64":"Iz7w2i0Y20vrohFquhnQrsilI6gDE/Exw8zaL++OKTXglyPq91KfZa15Qmo1dGVEztBaKsM4O6XPWY8EhrQ2Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"288c2ee3e83b69998138c5a0c9a0d0b0afaec674c55350cc6c1c9f46e2a16cbe","last_reissued_at":"2026-05-18T00:51:28.578726Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:51:28.578726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Catherine A. Calder, Mark D. Risser","submitted_at":"2015-07-30T18:24:39Z","abstract_excerpt":"In spite of the interest in and appeal of convolution-based approaches for nonstationary spatial modeling, off-the-shelf software for model fitting does not as of yet exist. Convolution-based models are highly flexible yet notoriously difficult to fit, even with relatively small data sets. The general lack of pre-packaged options for model fitting makes it difficult to compare new methodology in nonstationary modeling with other existing methods, and as a result most new models are simply compared to stationary models. Using a convolution-based approach, we present a new nonstationary covarian"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.08613","kind":"arxiv","version":4},"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":"1507.08613","created_at":"2026-05-18T00:51:28.578783+00:00"},{"alias_kind":"arxiv_version","alias_value":"1507.08613v4","created_at":"2026-05-18T00:51:28.578783+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.08613","created_at":"2026-05-18T00:51:28.578783+00:00"},{"alias_kind":"pith_short_12","alias_value":"FCGC5Y7IHNUZ","created_at":"2026-05-18T12:29:19.899920+00:00"},{"alias_kind":"pith_short_16","alias_value":"FCGC5Y7IHNUZTAJY","created_at":"2026-05-18T12:29:19.899920+00:00"},{"alias_kind":"pith_short_8","alias_value":"FCGC5Y7I","created_at":"2026-05-18T12:29:19.899920+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.16075","citing_title":"REX-SUB: A Scalable Subsampling Strategy for Modeling Large Spatial Datasets","ref_index":55,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FCGC5Y7IHNUZTAJYYWQMTIGQWC","json":"https://pith.science/pith/FCGC5Y7IHNUZTAJYYWQMTIGQWC.json","graph_json":"https://pith.science/api/pith-number/FCGC5Y7IHNUZTAJYYWQMTIGQWC/graph.json","events_json":"https://pith.science/api/pith-number/FCGC5Y7IHNUZTAJYYWQMTIGQWC/events.json","paper":"https://pith.science/paper/FCGC5Y7I"},"agent_actions":{"view_html":"https://pith.science/pith/FCGC5Y7IHNUZTAJYYWQMTIGQWC","download_json":"https://pith.science/pith/FCGC5Y7IHNUZTAJYYWQMTIGQWC.json","view_paper":"https://pith.science/paper/FCGC5Y7I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1507.08613&json=true","fetch_graph":"https://pith.science/api/pith-number/FCGC5Y7IHNUZTAJYYWQMTIGQWC/graph.json","fetch_events":"https://pith.science/api/pith-number/FCGC5Y7IHNUZTAJYYWQMTIGQWC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FCGC5Y7IHNUZTAJYYWQMTIGQWC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FCGC5Y7IHNUZTAJYYWQMTIGQWC/action/storage_attestation","attest_author":"https://pith.science/pith/FCGC5Y7IHNUZTAJYYWQMTIGQWC/action/author_attestation","sign_citation":"https://pith.science/pith/FCGC5Y7IHNUZTAJYYWQMTIGQWC/action/citation_signature","submit_replication":"https://pith.science/pith/FCGC5Y7IHNUZTAJYYWQMTIGQWC/action/replication_record"}},"created_at":"2026-05-18T00:51:28.578783+00:00","updated_at":"2026-05-18T00:51:28.578783+00:00"}