{"id":"W4294176560","doi":"10.4038/sljastats.v23i1.8058","title":"ptsuite: Fast Tail Index Estimation for Power Law Distributions in R","year":2022,"lang":"en","type":"article","venue":"Sri Lankan Journal of Applied Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"R package; Pareto distribution; Code (set theory); Computer science; Index (typography); Pareto principle; Heuristic; Estimation; Power law; Power (physics); Algorithm; Data mining; Statistics; Mathematics; Set (abstract data type); Computational science; Programming language; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01042974,0.002666984,0.002376473,0.003445875,0.0007281066,0.003474986,0.003013661,0.001473617,0.06003771],"category_scores_gemma":[0.104092,0.002240122,0.003272175,0.002807318,0.001145676,0.004038711,0.00330019,0.003875141,0.04315565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008785759,"about_ca_system_score_gemma":0.002534372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00396859,"about_ca_topic_score_gemma":0.006169635,"domain_scores_codex":[0.9946515,0.002432746,0.0005084801,0.0008201086,0.001359155,0.0002280334],"domain_scores_gemma":[0.9598488,0.03127562,0.001690624,0.004224762,0.002605831,0.0003544189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006858425,0.0001179567,0.0132393,0.003182356,0.001594063,0.0007791792,0.0009945564,0.06077445,0.005076912,0.03817825,0.5738367,0.3015404],"study_design_scores_gemma":[0.0007483679,0.0001895138,0.008585392,0.0006788186,0.0003987802,0.001551608,0.0001910896,0.5454365,0.01188415,0.163955,0.2659039,0.0004768617],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.002230813,0.0004169317,0.8396723,0.0003540479,0.0001790199,0.0001741734,0.01553169,0.1397248,0.001716169],"genre_scores_gemma":[0.05186106,0.0007315614,0.8040427,0.0008783758,0.0002167094,0.001815391,0.0283754,0.1062972,0.005781537],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.06003771,"threshold_uncertainty_score":0.2008461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02979063462805738,"score_gpt":0.3462156382777814,"score_spread":0.316425003649724,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}