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Use values-first ordering across weighted statistical APIs - #183
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jmh530 merged 1 commit intoOct 19, 2026
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Align weighted quantiles and histogram APIs with weighted means and sums: observation inputs precede weights, followed by axis or probability configuration. Custom-allocation factories retain the allocator as their first argument. Apply the convention to weighted histogram and relative-frequency construction, percentograms, quantile axes, and incremental updates. Multidimensional calls place every coordinate before the weight; weighted sample updates take sample, coordinates, then weight. Preserve reference forwarding and existing storage ownership. Update internal callers, shared test adapters, examples, and documentation together. Cover same-typed asymmetric quantile inputs that distinguish observations from weights, and retain the expected results throughout the migrated histogram tests. This intentionally breaks existing weight-first calls. Callers must reorder arguments even when their types still permit compilation. Statistical algorithms and weight semantics remain unchanged. AI-Assisted-By: Codex (GPT-6 Astra Medium)
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Weighted quantiles and histogram APIs previously placed weights before observations, unlike weighted means and sums. This change establishes consistent ordering: observation inputs first, weights next, and axis or probability configuration afterward. Custom-allocation factories retain the allocator as their first argument.
The convention applies to weighted quantiles, histogram and relative-frequency construction, percentograms, quantile axes, and incremental updates. Multidimensional calls place all coordinates before weights:
weightedQuantile(values, weights, probabilities)weightedHistogram(x, y, weights, xAxis, yAxis)h.putWeighted(x, y, weight)h.putWeightedSample(sample, x, y, weight)weightedHistogram!Cell(samples, x, y, weights, xAxis, yAxis)Internal callers, shared test adapters, examples, and documentation are updated together. Statistical algorithms, weight semantics, and storage ownership remain unchanged.
Compatibility
This intentionally breaks existing weight-first calls. Callers must reorder arguments even when matching argument types still allow the old calls to compile. Regression coverage uses asymmetric, same-typed observations and weights to distinguish the two orderings.
Validation
The final review correction changed only an example comment; executable code was unchanged after these tests.
AI-Assisted-By: Codex (GPT-6 Astra Medium)