FrugalQuantile

Initial estimate seeding the random walk.

Target quantile probability in (0, 1).

Adaptive step size used to chase the quantile.

Adapt a series spec into a discrete spec - the discrete sees value.toLong() per update.

Adapt a series spec into a vector spec by consuming the index-th coordinate of each vector.

Adapt a series spec into a paired spec by consuming the x component of each pair.

Adapt a series spec into a paired spec by consuming the y component of each pair.

Wrap this series spec to expose a [lower, upper] band of width k * scale around center.

Wrap this series spec to forward the per-second time derivative of the value stream.

Wrap this series spec to forward the k-th difference value - value[t - k].

Wrap this series spec so updates are forwarded only when pred evaluates true.

Lift this series spec to a paired spec, reducing every (x, y) to a scalar via expr.

Lift this series spec into the regression modality. project reduces each (x = V, y = Y) update to a scalar that the inner series stat absorbs. Use Y for the marginal-y view.

Lift this series spec to a vector spec, reducing every vector to a scalar via expr.

Wrap this series spec to debounce its input into a 0.0/1.0 stream via two-threshold hysteresis.

Wrap this series spec to forward the value seen k updates ago.

Construct a live SeriesStat from a SeriesStatSpec. One when per modality, one cast at the boundary - sealed-hierarchy exhaustiveness keeps the cast safe.

Construct a live stat from any StatSpec, dispatching on its modality. Useful for code paths (like StatSchemaDef.materialize) that iterate over an erased Map<String, StatSpec> and don't statically know the modality.

Min-max scale the input against a hidden Range primary into [targetLow, targetHigh], then forward the mapped value to this spec. Defaults map to [0, 1]; pass targetLow = -1.0, targetHigh = 1.0 for a [-1, 1] mapping. Emits targetLow while the running range is still degenerate.

Wrap this series spec to forward one per-bucket summary using aggregator.

Wrap this series spec to keep each update with probability rate; seed feeds the PRNG.

Z-score the input against a hidden Variance primary, then forward the standardized value to this spec. Emits 0 while the running variance is still zero.

Wrap this series spec so it only sees one in every every updates.

Wrap this series spec to apply expr to every update before the inner stat sees it.

Lift a series spec to a vector spec by replicating it across every coordinate of an N-dim input.

Wrap this series spec so every update's weight is multiplied by expr.eval(value).

Wrap this series spec in a sliding time window of durationMillis split into slices buckets.

Wrap this inner series spec with a feedback primary; the projection AST sees the primary snapshot.

Wrap this series spec so every update pushes the constant value regardless of input.

Wrap this series spec so every update applies the per-observation weight multiplier.