pod op• Data kinds: none → table (an op determined by its arguments alone — it takes no image or data input)
• Call: import fullseye as fs; fs.ledger.proportion_interval(k, n, *, confidence=0.95, sides='two', method='clopper_pearson') (to call the implementation directly, import pod; pod.proportion_interval(k, n, *, confidence=0.95, sides='two', method='clopper_pearson'); from the registry, opsspc.get("proportion_interval"))
• GPU: none (CPU reference implementation only)
Return the point estimate and confidence limits for the binomial proportion k/n (table).
> The detailed description below is the original text — the summary and the headings are translated.
sides は "two"(両側)、"lower"(片側下限)、"upper" (片側上限)、method は "clopper_pearson"、"wilson"、"jeffreys"。返り値は estimate、lower、upper、method、confidence、sides、k、n を持つ。保証・合否判定には正確で保守的な Clopper--Pearson、推定には Wilson / Jeffreys を使い分ける。
k=0 / k=n も有限の限界を返す。たとえば 10/10 の 95 % 片側下限は 0.7411、0/20 の片側上限は 0.1391 である。
空の入力(n=0)は ValueError を返す(理由: 比率を定義できない)。拒否(ValueError): 非整数の k / n、0 <= k <= n 違反、confidence が (0, 1) 外、未知の sides / method。
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• proportion_interval_audit — py -3.11 examples/proportion_interval_audit.py
table as input)msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo
pod)trials_for_all_success · pod_ahat_fit · pod_from_ahat · pod_hitmiss_fit · pod_false_call · pod_decision_tradeoff
*Provenance: pod.py — SPC operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.