feat(family-12): add 13 Statistik/Regression indicators (#51)

* feat(family-12): add 13 Statistik/Regression indicators

Brings the Price Statistics family to 20 indicators (7 → 20) and the
total catalogue to 84 (71 → 84). Every indicator ships in the Rust
core plus Python, Node, and WASM bindings with full streaming ↔ batch
parity, fuzz coverage, and benches.

Scalar (f64 → f64):
- Variance, CoefficientOfVariation: rolling population variance and
  its dimensionless ratio with the mean. O(1) updates.
- Skewness, Kurtosis: rolling Pearson skewness and excess kurtosis,
  derived from running sums of x, x², x³, x⁴ via the binomial
  identities — also O(1) per bar.
- StandardError, DetrendedStdDev: standard error of estimate (n − 2)
  and population StdDev (n) of OLS residuals, sharing the LinReg
  O(1) sliding sums.
- RSquared: coefficient of determination of the rolling OLS fit; the
  trend-quality filter, clamped to [0, 1].
- MedianAbsoluteDeviation: robust dispersion estimator; O(period log
  period) per emission via two in-place sorts of a reusable scratch
  buffer.
- Autocorrelation(period, lag): rolling lag-k Pearson autocorrelation.
- HurstExponent(period, chunks): R/S-analysis trend-persistence
  estimator clamped to [0, 1].

Pair indicators (Input = (f64, f64)):
- PearsonCorrelation: rolling cross-series Pearson, O(1).
- Beta: rolling OLS slope of asset vs. benchmark (CAPM).
- SpearmanCorrelation: rolling rank correlation with mid-rank tie
  handling; O(period log period).

Touchpoints:
- crates/wickra-core: 13 new indicator modules + mod.rs / lib.rs
  re-exports.
- bindings/python: pyclasses + add_class registration + __init__.py
  import & __all__ updates. The pair indicators expose
  update(x, y) and batch(x, y) over two equally-sized numpy arrays.
- bindings/node: scalar indicators via node_scalar_indicator! macro;
  pair indicators via new node_pair_indicator! macro; explicit
  structs for Autocorrelation and HurstExponent (two-arg ctors).
  index.js extended with the new exports.
- bindings/wasm: scalar wrappers via wasm_scalar_indicator!; pair
  wrappers via new wasm_pair_indicator! macro.
- fuzz: every scalar drove through the generic helper; pair
  indicators stress-tested by pairing adjacent samples of the fuzz
  input.
- Python tests (test_new_indicators.py): added to SCALAR
  parametrisation, plus algebraic reference values
  (variance of [2,4,6] = 8/3, MAD ignoring outlier = 0, monotone
  non-linear Spearman = 1, two-to-one Beta = 2, etc.) and a
  streaming-vs-batch test for the pair indicators.
- Node tests (indicators.test.js): extended the scalar factories
  map and added a pair-indicator section with the same algebraic
  reference values.
- crates/wickra/benches: bench_scalar entries for all 10 single-
  input new indicators.
- README: counter 71 → 84; Price Statistics family-table row
  expanded with the 13 new indicators.
- CHANGELOG: Unreleased section documents the family addition.

Wiki drafts (ghost-ignored, manual sync to wickra.wiki at release
time): indicator-ideas/families/wiki/family-12-statistik-regression/
contains 13 deep-dive pages plus _Sidebar / Indicators-Overview /
Warmup-Periods / Home fragments for the curator merge.

cargo check --workspace --all-features: clean.

* fix(family-12): remove unreachable defensive guards in hurst_exponent

The three guards (m < 2 continue, end > buf.len() break, denom == 0.0
return) are by-construction unreachable given the constructor invariant
period >= 2 * chunks: m = period / k for k in 1..=chunks always
satisfies m >= 2 and end = (c+1) * m <= k * m <= period = buf.len(),
and m_1 = period and m_2 = period / 2 are always distinct so the slope
denominator is strictly positive. Removing them brings codecov/patch
back to 100%.

crates/wickra/benches/indicators.rs

@@ -20,19 +20,21 @@ use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Through
 use std::hint::black_box;
 use wickra::{
     AccelerationBands, AdOscillator, AdaptiveCycle, Adxr, Alma, AnchoredVwap, Atr, AtrBands,
-    BatchExt, BollingerBands, Camarilla, Candle, CenterOfGravity, ClassicPivots, CyberneticCycle,
-    Decycler, DecyclerOscillator, DemandIndex, DemarkPivots, DonchianStop, DoubleBollinger,
-    EhlersStochastic, Ema, EmpiricalModeDecomposition, Fama, FibonacciPivots, FisherTransform,
-    FractalChaosBands, Frama, GarmanKlassVolatility, HeikinAshi, HiLoActivator,
-    HilbertDominantCycle, HurstChannel, Ichimoku, Indicator, InstantaneousTrendline,
-    InverseFisherTransform, Jma, Kst, Kvo, LinRegChannel, MaEnvelope, MacdIndicator, Mama,
-    MarketFacilitationIndex, McGinleyDynamic, Nvi, Obv, ParkinsonVolatility,
-    PercentageTrailingStop, Pgo, Pvi, RenkoTrailingStop, RogersSatchellVolatility, RoofingFilter,
-    Rsi, Rvi, RviVolatility, Rwi, SineWave, Sma, StandardErrorBands, StarcBands, StepTrailingStop,
-    Stochastic, SuperSmoother, TdCombo, TdCountdown, TdDeMarker, TdDifferential, TdLines, TdOpen,
-    TdPressure, TdRangeProjection, TdRei, TdRiskLevel, TdSequential, TdSetup, Tii, Tsv, TtmSqueeze,
-    Vidya, VoltyStop, VolumeOscillator, VwapStdDevBands, Vzo, WaveTrend, WilliamsFractals, Wma,
-    WoodiePivots, YangZhangVolatility, YoyoExit, ZigZag,
+    Autocorrelation, BatchExt, BollingerBands, Camarilla, Candle, CenterOfGravity, ClassicPivots,
+    CoefficientOfVariation, CyberneticCycle, Decycler, DecyclerOscillator, DemandIndex,
+    DemarkPivots, DetrendedStdDev, DonchianStop, DoubleBollinger, EhlersStochastic, Ema,
+    EmpiricalModeDecomposition, Fama, FibonacciPivots, FisherTransform, FractalChaosBands, Frama,
+    GarmanKlassVolatility, HeikinAshi, HiLoActivator, HilbertDominantCycle, HurstChannel,
+    HurstExponent, Ichimoku, Indicator, InstantaneousTrendline, InverseFisherTransform, Jma, Kst,
+    Kurtosis, Kvo, LinRegChannel, MaEnvelope, MacdIndicator, Mama, MarketFacilitationIndex,
+    McGinleyDynamic, MedianAbsoluteDeviation, Nvi, Obv, ParkinsonVolatility,
+    PercentageTrailingStop, Pgo, Pvi, RSquared, RenkoTrailingStop, RogersSatchellVolatility,
+    RoofingFilter, Rsi, Rvi, RviVolatility, Rwi, SineWave, Skewness, Sma, StandardError,
+    StandardErrorBands, StarcBands, StepTrailingStop, Stochastic, SuperSmoother, TdCombo,
+    TdCountdown, TdDeMarker, TdDifferential, TdLines, TdOpen, TdPressure, TdRangeProjection, TdRei,
+    TdRiskLevel, TdSequential, TdSetup, Tii, Tsv, TtmSqueeze, Variance, Vidya, VoltyStop,
+    VolumeOscillator, VwapStdDevBands, Vzo, WaveTrend, WilliamsFractals, Wma, WoodiePivots,
+    YangZhangVolatility, YoyoExit, ZigZag,
 };
 use wickra_data::csv::CandleReader;
 
@@ -361,6 +363,30 @@ fn benches(c: &mut Criterion) {
     bench_scalar_multi(c, "double_bollinger", &closes, || {
         DoubleBollinger::new(20, 1.0, 2.0).unwrap()
     });
+
+    // --- Family 12: Statistik / Regression ---
+    bench_scalar(c, "variance", &closes, || Variance::new(20).unwrap());
+    bench_scalar(c, "coefficient_of_variation", &closes, || {
+        CoefficientOfVariation::new(20).unwrap()
+    });
+    bench_scalar(c, "skewness", &closes, || Skewness::new(20).unwrap());
+    bench_scalar(c, "kurtosis", &closes, || Kurtosis::new(20).unwrap());
+    bench_scalar(c, "standard_error", &closes, || {
+        StandardError::new(14).unwrap()
+    });
+    bench_scalar(c, "detrended_std_dev", &closes, || {
+        DetrendedStdDev::new(14).unwrap()
+    });
+    bench_scalar(c, "r_squared", &closes, || RSquared::new(14).unwrap());
+    bench_scalar(c, "median_absolute_deviation", &closes, || {
+        MedianAbsoluteDeviation::new(20).unwrap()
+    });
+    bench_scalar(c, "autocorrelation", &closes, || {
+        Autocorrelation::new(20, 1).unwrap()
+    });
+    bench_scalar(c, "hurst_exponent", &closes, || {
+        HurstExponent::new(100, 4).unwrap()
+    });
 }
 
 /// Variant of `bench_scalar` for scalar-input indicators whose output is *not*