scirs2-series 0.4.0

//! Basic statistical features for time series analysis
//!
//! This module provides comprehensive statistical feature calculation including
//! basic descriptives, higher-order moments, robust statistics, distribution
//! characteristics, and normality tests.

use scirs2_core::ndarray::Array1;
use scirs2_core::numeric::{Float, FromPrimitive};
use std::fmt::Debug;

use super::utils::{
    calculate_mad, calculate_percentile, calculate_trimmed_mean, calculate_winsorized_mean,
};
use crate::error::{Result, TimeSeriesError};

/// Comprehensive expanded statistical features for in-depth time series analysis.
///
/// This struct contains advanced statistical measures that go beyond basic descriptive statistics,
/// including higher-order moments, robust statistics, distribution characteristics,
/// tail behavior analysis, normality tests, and many other sophisticated measures.
#[derive(Debug, Clone)]
pub struct ExpandedStatisticalFeatures<F> {
    // Higher-order moments
    /// Fifth moment (measure of asymmetry beyond skewness)
    pub fifth_moment: F,
    /// Sixth moment (measure of tail behavior beyond kurtosis)
    pub sixth_moment: F,
    /// Excess kurtosis (kurtosis - 3)
    pub excess_kurtosis: F,

    // Robust statistics
    /// Trimmed mean (10% trimmed)
    pub trimmed_mean_10: F,
    /// Trimmed mean (20% trimmed)
    pub trimmed_mean_20: F,
    /// Winsorized mean (5% winsorized)
    pub winsorized_mean_5: F,
    /// Median absolute deviation (MAD)
    pub median_absolute_deviation: F,
    /// Interquartile mean (mean of values between Q1 and Q3)
    pub interquartile_mean: F,
    /// Midhinge ((Q1 + Q3) / 2)
    pub midhinge: F,
    /// Trimmed range (90% range, excluding extreme 5% on each side)
    pub trimmed_range: F,

    // Percentile-based measures
    /// 5th percentile
    pub p5: F,
    /// 10th percentile
    pub p10: F,
    /// 90th percentile
    pub p90: F,
    /// 95th percentile
    pub p95: F,
    /// 99th percentile
    pub p99: F,
    /// Percentile ratio (P90/P10)
    pub percentile_ratio_90_10: F,
    /// Percentile ratio (P95/P5)
    pub percentile_ratio_95_5: F,

    // Shape and distribution measures
    /// Mean absolute deviation from mean
    pub mean_absolute_deviation: F,
    /// Mean absolute deviation from median
    pub median_mean_absolute_deviation: F,
    /// Gini coefficient (measure of inequality)
    pub gini_coefficient: F,
    /// Index of dispersion (variance-to-mean ratio)
    pub index_of_dispersion: F,
    /// Quartile coefficient of dispersion
    pub quartile_coefficient_dispersion: F,
    /// Relative mean deviation
    pub relative_mean_deviation: F,

    // Tail statistics
    /// Lower tail ratio (P10/P50)
    pub lower_tail_ratio: F,
    /// Upper tail ratio (P90/P50)
    pub upper_tail_ratio: F,
    /// Tail ratio ((P90-P50)/(P50-P10))
    pub tail_ratio: F,
    /// Lower outlier count (values < Q1 - 1.5*IQR)
    pub lower_outlier_count: usize,
    /// Upper outlier count (values > Q3 + 1.5*IQR)
    pub upper_outlier_count: usize,
    /// Outlier ratio (total outliers / total observations)
    pub outlier_ratio: F,

    // Central tendency variations
    /// Harmonic mean
    pub harmonic_mean: F,
    /// Geometric mean
    pub geometric_mean: F,
    /// Quadratic mean (RMS)
    pub quadratic_mean: F,
    /// Cubic mean
    pub cubic_mean: F,
    /// Mode (most frequent value approximation)
    pub mode_approximation: F,
    /// Distance from mean to median
    pub mean_median_distance: F,

    // Variability measures
    /// Coefficient of quartile variation
    pub coefficient_quartile_variation: F,
    /// Standard error of mean
    pub standard_error_mean: F,
    /// Coefficient of mean deviation
    pub coefficient_mean_deviation: F,
    /// Relative standard deviation (CV as percentage)
    pub relative_standard_deviation: F,
    /// Variance-to-range ratio
    pub variance_range_ratio: F,

    // Distribution characteristics
    /// L-moments: L-scale (L2)
    pub l_scale: F,
    /// L-moments: L-skewness (L3/L2)
    pub l_skewness: F,
    /// L-moments: L-kurtosis (L4/L2)
    pub l_kurtosis: F,
    /// Bowley skewness coefficient
    pub bowley_skewness: F,
    /// Kelly skewness coefficient
    pub kelly_skewness: F,
    /// Moors kurtosis
    pub moors_kurtosis: F,

    // Normality indicators
    /// Jarque-Bera test statistic
    pub jarque_bera_statistic: F,
    /// Anderson-Darling test statistic approximation
    pub anderson_darling_statistic: F,
    /// Kolmogorov-Smirnov test statistic approximation
    pub kolmogorov_smirnov_statistic: F,
    /// Shapiro-Wilk test statistic approximation
    pub shapiro_wilk_statistic: F,
    /// D'Agostino normality test statistic
    pub dagostino_statistic: F,
    /// Normality score (composite measure)
    pub normality_score: F,

    // Advanced shape measures
    /// Biweight midvariance
    pub biweight_midvariance: F,
    /// Biweight midcovariance
    pub biweight_midcovariance: F,
    /// Qn robust scale estimator
    pub qn_estimator: F,
    /// Sn robust scale estimator  
    pub sn_estimator: F,

    // Count-based statistics
    /// Number of zero crossings (around mean)
    pub zero_crossings: usize,
    /// Number of positive values
    pub positive_count: usize,
    /// Number of negative values
    pub negative_count: usize,
    /// Number of local maxima
    pub local_maxima_count: usize,
    /// Number of local minima
    pub local_minima_count: usize,
    /// Proportion of values above mean
    pub above_mean_proportion: F,
    /// Proportion of values below mean
    pub below_mean_proportion: F,

    // Additional descriptive measures
    /// Energy (sum of squares)
    pub energy: F,
    /// Root mean square
    pub root_mean_square: F,
    /// Sum of absolute values
    pub sum_absolute_values: F,
    /// Mean of absolute values
    pub mean_absolute_value: F,
    /// Signal power
    pub signal_power: F,
    /// Peak-to-peak amplitude
    pub peak_to_peak: F,

    // Concentration measures
    /// Concentration coefficient
    pub concentration_coefficient: F,
    /// Herfindahl index (sum of squared proportions)
    pub herfindahl_index: F,
    /// Shannon diversity index
    pub shannon_diversity: F,
    /// Simpson diversity index
    pub simpson_diversity: F,
}

impl<F> Default for ExpandedStatisticalFeatures<F>
where
    F: Float + FromPrimitive,
{
    fn default() -> Self {
        Self {
            // Higher-order moments
            fifth_moment: F::zero(),
            sixth_moment: F::zero(),
            excess_kurtosis: F::zero(),

            // Robust statistics
            trimmed_mean_10: F::zero(),
            trimmed_mean_20: F::zero(),
            winsorized_mean_5: F::zero(),
            median_absolute_deviation: F::zero(),
            interquartile_mean: F::zero(),
            midhinge: F::zero(),
            trimmed_range: F::zero(),

            // Percentile-based measures
            p5: F::zero(),
            p10: F::zero(),
            p90: F::zero(),
            p95: F::zero(),
            p99: F::zero(),
            percentile_ratio_90_10: F::one(),
            percentile_ratio_95_5: F::one(),

            // Shape and distribution measures
            mean_absolute_deviation: F::zero(),
            median_mean_absolute_deviation: F::zero(),
            gini_coefficient: F::zero(),
            index_of_dispersion: F::one(),
            quartile_coefficient_dispersion: F::zero(),
            relative_mean_deviation: F::zero(),

            // Tail statistics
            lower_tail_ratio: F::one(),
            upper_tail_ratio: F::one(),
            tail_ratio: F::one(),
            lower_outlier_count: 0,
            upper_outlier_count: 0,
            outlier_ratio: F::zero(),

            // Central tendency variations
            harmonic_mean: F::zero(),
            geometric_mean: F::zero(),
            quadratic_mean: F::zero(),
            cubic_mean: F::zero(),
            mode_approximation: F::zero(),
            mean_median_distance: F::zero(),

            // Variability measures
            coefficient_quartile_variation: F::zero(),
            standard_error_mean: F::zero(),
            coefficient_mean_deviation: F::zero(),
            relative_standard_deviation: F::zero(),
            variance_range_ratio: F::zero(),

            // Distribution characteristics
            l_scale: F::zero(),
            l_skewness: F::zero(),
            l_kurtosis: F::zero(),
            bowley_skewness: F::zero(),
            kelly_skewness: F::zero(),
            moors_kurtosis: F::zero(),

            // Normality indicators
            jarque_bera_statistic: F::zero(),
            anderson_darling_statistic: F::zero(),
            kolmogorov_smirnov_statistic: F::zero(),
            shapiro_wilk_statistic: F::zero(),
            dagostino_statistic: F::zero(),
            normality_score: F::zero(),

            // Advanced shape measures
            biweight_midvariance: F::zero(),
            biweight_midcovariance: F::zero(),
            qn_estimator: F::zero(),
            sn_estimator: F::zero(),

            // Count-based statistics
            zero_crossings: 0,
            positive_count: 0,
            negative_count: 0,
            local_maxima_count: 0,
            local_minima_count: 0,
            above_mean_proportion: F::from(0.5).expect("Failed to convert constant to float"),
            below_mean_proportion: F::from(0.5).expect("Failed to convert constant to float"),

            // Additional descriptive measures
            energy: F::zero(),
            root_mean_square: F::zero(),
            sum_absolute_values: F::zero(),
            mean_absolute_value: F::zero(),
            signal_power: F::zero(),
            peak_to_peak: F::zero(),

            // Concentration measures
            concentration_coefficient: F::zero(),
            herfindahl_index: F::zero(),
            shannon_diversity: F::zero(),
            simpson_diversity: F::zero(),
        }
    }
}

/// Calculate trend and seasonality strength
#[allow(dead_code)]
pub fn calculate_trend_seasonality_strength<F>(
    ts: &Array1<F>,
    seasonal_period: Option<usize>,
) -> Result<(F, Option<F>)>
where
    F: Float + FromPrimitive + Debug,
{
    let n = ts.len();

    // Calculate first differences (for trend)
    let mut diff1 = Vec::with_capacity(n - 1);
    for i in 1..n {
        diff1.push(ts[i] - ts[i - 1]);
    }

    // Variance of the original time series
    let ts_mean =
        ts.iter().fold(F::zero(), |acc, &x| acc + x) / F::from_usize(n).expect("Operation failed");
    let ts_var = ts
        .iter()
        .fold(F::zero(), |acc, &x| acc + (x - ts_mean).powi(2))
        / F::from_usize(n).expect("Operation failed");

    if ts_var == F::zero() {
        return Ok((F::zero(), None));
    }

    // Variance of the differenced series
    let diff_mean = diff1.iter().fold(F::zero(), |acc, &x| acc + x)
        / F::from_usize(diff1.len()).expect("Operation failed");
    let diff_var = diff1
        .iter()
        .fold(F::zero(), |acc, &x| acc + (x - diff_mean).powi(2))
        / F::from_usize(diff1.len()).expect("Operation failed");

    // Trend strength
    let trend_strength = F::one() - (diff_var / ts_var);

    // Seasonality strength (if seasonal _period is provided)
    let seasonality_strength = if let Some(period) = seasonal_period {
        if n <= period {
            return Err(TimeSeriesError::FeatureExtractionError(
                "Time series length must be greater than seasonal period".to_string(),
            ));
        }

        // Calculate seasonal differences
        let mut seasonal_diff = Vec::with_capacity(n - period);
        for i in period..n {
            seasonal_diff.push(ts[i] - ts[i - period]);
        }

        // Variance of seasonal differences
        let s_diff_mean = seasonal_diff.iter().fold(F::zero(), |acc, &x| acc + x)
            / F::from_usize(seasonal_diff.len()).expect("Operation failed");
        let s_diff_var = seasonal_diff
            .iter()
            .fold(F::zero(), |acc, &x| acc + (x - s_diff_mean).powi(2))
            / F::from_usize(seasonal_diff.len()).expect("Operation failed");

        // Seasonality strength
        let s_strength = F::one() - (s_diff_var / ts_var);

        // Constrain to [0, 1] range
        Some(s_strength.max(F::zero()).min(F::one()))
    } else {
        None
    };

    // Constrain trend strength to [0, 1] range
    let trend_strength = trend_strength.max(F::zero()).min(F::one());

    Ok((trend_strength, seasonality_strength))
}

/// Calculate expanded statistical features
#[allow(dead_code)]
pub fn calculate_expanded_statistical_features<F>(
    ts: &Array1<F>,
    basic_mean: F,
    basic_std: F,
    basic_median: F,
    basic_q1: F,
    basic_q3: F,
    basic_min: F,
    basic_max: F,
) -> Result<ExpandedStatisticalFeatures<F>>
where
    F: Float + FromPrimitive + Debug + Clone + scirs2_core::ndarray::ScalarOperand,
{
    let n = ts.len();
    let n_f = F::from(n).expect("Failed to convert to float");

    // Create sorted version for percentile calculations
    let mut sorted = ts.to_vec();
    sorted.sort_by(|a, b| a.partial_cmp(b).expect("Operation failed"));

    // Calculate percentiles
    let p5 = calculate_percentile(&sorted, 5.0);
    let p10 = calculate_percentile(&sorted, 10.0);
    let p90 = calculate_percentile(&sorted, 90.0);
    let p95 = calculate_percentile(&sorted, 95.0);
    let p99 = calculate_percentile(&sorted, 99.0);

    // Higher-order moments
    let (fifth_moment, sixth_moment) = calculate_higher_order_moments(ts, basic_mean)?;
    let excess_kurtosis = calculate_excess_kurtosis(ts, basic_mean, basic_std)?;

    // Robust statistics
    let trimmed_mean_10 = calculate_trimmed_mean(ts, 0.1)?;
    let trimmed_mean_20 = calculate_trimmed_mean(ts, 0.2)?;
    let winsorized_mean_5 = calculate_winsorized_mean(ts, 0.05)?;
    let median_absolute_deviation = calculate_mad(ts, basic_median)?;
    let interquartile_mean = calculate_interquartile_mean(ts, basic_q1, basic_q3)?;
    let midhinge =
        (basic_q1 + basic_q3) / F::from(2.0).expect("Failed to convert constant to float");
    let trimmed_range = p95 - p5;

    // Percentile ratios
    let percentile_ratio_90_10 = if p10 != F::zero() {
        p90 / p10
    } else {
        F::zero()
    };
    let percentile_ratio_95_5 = if p5 != F::zero() { p95 / p5 } else { F::zero() };

    // Shape and distribution measures
    let mean_absolute_deviation = calculate_mean_absolute_deviation(ts, basic_mean)?;
    let median_mean_absolute_deviation = calculate_mean_absolute_deviation(ts, basic_median)?;
    let gini_coefficient = calculate_gini_coefficient(ts)?;
    let index_of_dispersion = if basic_mean != F::zero() {
        basic_std * basic_std / basic_mean
    } else {
        F::zero()
    };
    let quartile_coefficient_dispersion = if basic_q1 + basic_q3 != F::zero() {
        (basic_q3 - basic_q1) / (basic_q3 + basic_q1)
    } else {
        F::zero()
    };
    let relative_mean_deviation = if basic_mean != F::zero() {
        mean_absolute_deviation / basic_mean.abs()
    } else {
        F::zero()
    };

    // Tail statistics
    let lower_tail_ratio = if basic_median != F::zero() {
        p10 / basic_median
    } else {
        F::zero()
    };
    let upper_tail_ratio = if basic_median != F::zero() {
        p90 / basic_median
    } else {
        F::zero()
    };
    let tail_ratio = if basic_median != p10 && p10 != F::zero() {
        (p90 - basic_median) / (basic_median - p10)
    } else {
        F::one()
    };

    let (lower_outlier_count, upper_outlier_count) =
        calculate_outlier_counts(ts, basic_q1, basic_q3)?;
    let outlier_ratio = F::from(lower_outlier_count + upper_outlier_count)
        .expect("Failed to convert to float")
        / n_f;

    // Central tendency variations
    let harmonic_mean = calculate_harmonic_mean(ts)?;
    let geometric_mean = calculate_geometric_mean(ts)?;
    let quadratic_mean = calculate_quadratic_mean(ts)?;
    let cubic_mean = calculate_cubic_mean(ts)?;
    let mode_approximation = calculate_mode_approximation(ts)?;
    let mean_median_distance = (basic_mean - basic_median).abs();

    // Variability measures
    let coefficient_quartile_variation = if midhinge != F::zero() {
        (basic_q3 - basic_q1) / midhinge
    } else {
        F::zero()
    };
    let standard_error_mean = basic_std / n_f.sqrt();
    let coefficient_mean_deviation = if basic_mean != F::zero() {
        mean_absolute_deviation / basic_mean.abs()
    } else {
        F::zero()
    };
    let relative_standard_deviation = if basic_mean != F::zero() {
        (basic_std / basic_mean.abs())
            * F::from(100.0).expect("Failed to convert constant to float")
    } else {
        F::zero()
    };
    let range = basic_max - basic_min;
    let variance_range_ratio = if range != F::zero() {
        (basic_std * basic_std) / range
    } else {
        F::zero()
    };

    // Distribution characteristics (L-moments)
    let (l_scale, l_skewness, l_kurtosis) = calculate_l_moments(ts)?;
    let bowley_skewness = if basic_q3 - basic_q1 != F::zero() {
        (basic_q3 + basic_q1
            - F::from(2.0).expect("Failed to convert constant to float") * basic_median)
            / (basic_q3 - basic_q1)
    } else {
        F::zero()
    };
    let kelly_skewness = if p90 - p10 != F::zero() {
        (p90 + p10 - F::from(2.0).expect("Failed to convert constant to float") * basic_median)
            / (p90 - p10)
    } else {
        F::zero()
    };
    let moors_kurtosis = if p75_minus_p25(&sorted) != F::zero() {
        (p87_5_minus_p12_5(&sorted)) / p75_minus_p25(&sorted)
    } else {
        F::zero()
    };

    // Normality indicators
    let jarque_bera_statistic = calculate_jarque_bera_statistic(ts, basic_mean, basic_std)?;
    let anderson_darling_statistic = calculate_anderson_darling_approximation(ts)?;
    let kolmogorov_smirnov_statistic = calculate_ks_statistic_approximation(ts)?;
    let shapiro_wilk_statistic = calculate_shapiro_wilk_approximation(ts)?;
    let dagostino_statistic = calculate_dagostino_statistic(ts, basic_mean, basic_std)?;
    let normality_score = calculate_normality_composite_score(
        jarque_bera_statistic,
        anderson_darling_statistic,
        kolmogorov_smirnov_statistic,
    );

    // Advanced shape measures
    let biweight_midvariance = calculate_biweight_midvariance(ts, basic_median)?;
    let biweight_midcovariance = biweight_midvariance; // For univariate case
    let qn_estimator = calculate_qn_estimator(ts)?;
    let sn_estimator = calculate_sn_estimator(ts)?;

    // Count-based statistics
    let zero_crossings = calculate_zero_crossings(ts, basic_mean);
    let positive_count = ts.iter().filter(|&&x| x > F::zero()).count();
    let negative_count = ts.iter().filter(|&&x| x < F::zero()).count();
    let (local_maxima_count, local_minima_count) = calculate_local_extrema_counts(ts);
    let above_mean_count = ts.iter().filter(|&&x| x > basic_mean).count();
    let above_mean_proportion =
        F::from(above_mean_count).expect("Failed to convert to float") / n_f;
    let below_mean_proportion = F::one() - above_mean_proportion;

    // Additional descriptive measures
    let energy = ts.iter().fold(F::zero(), |acc, &x| acc + x * x);
    let root_mean_square = (energy / n_f).sqrt();
    let sum_absolute_values = ts.iter().fold(F::zero(), |acc, &x| acc + x.abs());
    let mean_absolute_value = sum_absolute_values / n_f;
    let signal_power = energy / n_f;
    let peak_to_peak = basic_max - basic_min;

    // Concentration measures
    let concentration_coefficient = calculate_concentration_coefficient(ts)?;
    let herfindahl_index = calculate_herfindahl_index(ts)?;
    let shannon_diversity = calculate_shannon_diversity(ts)?;
    let simpson_diversity = calculate_simpson_diversity(ts)?;

    Ok(ExpandedStatisticalFeatures {
        // Higher-order moments
        fifth_moment,
        sixth_moment,
        excess_kurtosis,

        // Robust statistics
        trimmed_mean_10,
        trimmed_mean_20,
        winsorized_mean_5,
        median_absolute_deviation,
        interquartile_mean,
        midhinge,
        trimmed_range,

        // Percentile-based measures
        p5,
        p10,
        p90,
        p95,
        p99,
        percentile_ratio_90_10,
        percentile_ratio_95_5,

        // Shape and distribution measures
        mean_absolute_deviation,
        median_mean_absolute_deviation,
        gini_coefficient,
        index_of_dispersion,
        quartile_coefficient_dispersion,
        relative_mean_deviation,

        // Tail statistics
        lower_tail_ratio,
        upper_tail_ratio,
        tail_ratio,
        lower_outlier_count,
        upper_outlier_count,
        outlier_ratio,

        // Central tendency variations
        harmonic_mean,
        geometric_mean,
        quadratic_mean,
        cubic_mean,
        mode_approximation,
        mean_median_distance,

        // Variability measures
        coefficient_quartile_variation,
        standard_error_mean,
        coefficient_mean_deviation,
        relative_standard_deviation,
        variance_range_ratio,

        // Distribution characteristics
        l_scale,
        l_skewness,
        l_kurtosis,
        bowley_skewness,
        kelly_skewness,
        moors_kurtosis,

        // Normality indicators
        jarque_bera_statistic,
        anderson_darling_statistic,
        kolmogorov_smirnov_statistic,
        shapiro_wilk_statistic,
        dagostino_statistic,
        normality_score,

        // Advanced shape measures
        biweight_midvariance,
        biweight_midcovariance,
        qn_estimator,
        sn_estimator,

        // Count-based statistics
        zero_crossings,
        positive_count,
        negative_count,
        local_maxima_count,
        local_minima_count,
        above_mean_proportion,
        below_mean_proportion,

        // Additional descriptive measures
        energy,
        root_mean_square,
        sum_absolute_values,
        mean_absolute_value,
        signal_power,
        peak_to_peak,

        // Concentration measures
        concentration_coefficient,
        herfindahl_index,
        shannon_diversity,
        simpson_diversity,
    })
}

// =============================================================================
// Helper Functions
// =============================================================================

/// Calculate higher-order moments (5th and 6th)
#[allow(dead_code)]
fn calculate_higher_order_moments<F>(ts: &Array1<F>, mean: F) -> Result<(F, F)>
where
    F: Float + FromPrimitive + Debug,
{
    let n = ts.len();
    let n_f = F::from(n).expect("Failed to convert to float");

    let mut fifth_sum = F::zero();
    let mut sixth_sum = F::zero();

    for &value in ts.iter() {
        let diff = value - mean;
        let diff_2 = diff * diff;
        let diff_3 = diff_2 * diff;
        let diff_5 = diff_3 * diff_2;
        let diff_6 = diff_5 * diff;

        fifth_sum = fifth_sum + diff_5;
        sixth_sum = sixth_sum + diff_6;
    }

    Ok((fifth_sum / n_f, sixth_sum / n_f))
}

/// Calculate excess kurtosis
#[allow(dead_code)]
fn calculate_excess_kurtosis<F>(ts: &Array1<F>, mean: F, std: F) -> Result<F>
where
    F: Float + FromPrimitive + Debug,
{
    let n = ts.len();
    let n_f = F::from(n).expect("Failed to convert to float");

    if std == F::zero() {
        return Ok(F::zero());
    }

    let mut fourth_moment_sum = F::zero();
    for &value in ts.iter() {
        let standardized = (value - mean) / std;
        fourth_moment_sum = fourth_moment_sum + standardized.powi(4);
    }

    let kurtosis = fourth_moment_sum / n_f;
    Ok(kurtosis - F::from(3.0).expect("Failed to convert constant to float")) // Excess kurtosis
}

/// Calculate interquartile mean
#[allow(dead_code)]
fn calculate_interquartile_mean<F>(ts: &Array1<F>, q1: F, q3: F) -> Result<F>
where
    F: Float + FromPrimitive,
{
    let values_in_iqr: Vec<F> = ts
        .iter()
        .filter(|&&x| x >= q1 && x <= q3)
        .cloned()
        .collect();

    if values_in_iqr.is_empty() {
        return Ok(F::zero());
    }

    let sum = values_in_iqr.iter().fold(F::zero(), |acc, &x| acc + x);
    let count = F::from(values_in_iqr.len()).expect("Operation failed");
    Ok(sum / count)
}

/// Calculate mean absolute deviation
#[allow(dead_code)]
fn calculate_mean_absolute_deviation<F>(ts: &Array1<F>, center: F) -> Result<F>
where
    F: Float + FromPrimitive,
{
    let n = ts.len();
    let n_f = F::from(n).expect("Failed to convert to float");

    let sum = ts
        .iter()
        .fold(F::zero(), |acc, &x| acc + (x - center).abs());
    Ok(sum / n_f)
}

/// Calculate Gini coefficient
#[allow(dead_code)]
fn calculate_gini_coefficient<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive + Debug,
{
    let mut sorted = ts.to_vec();
    sorted.sort_by(|a, b| a.partial_cmp(b).expect("Operation failed"));
    let n = sorted.len();
    let n_f = F::from(n).expect("Failed to convert to float");

    let mut numerator = F::zero();
    let mut sum = F::zero();

    for (i, &value) in sorted.iter().enumerate() {
        numerator = numerator + F::from(2 * (i + 1) - n - 1).expect("Operation failed") * value;
        sum = sum + value;
    }

    if sum == F::zero() {
        Ok(F::zero())
    } else {
        Ok(numerator / (n_f * sum))
    }
}

/// Calculate outlier counts using IQR method
#[allow(dead_code)]
fn calculate_outlier_counts<F>(ts: &Array1<F>, q1: F, q3: F) -> Result<(usize, usize)>
where
    F: Float + FromPrimitive,
{
    let iqr = q3 - q1;
    let lower_bound = q1 - F::from(1.5).expect("Failed to convert constant to float") * iqr;
    let upper_bound = q3 + F::from(1.5).expect("Failed to convert constant to float") * iqr;

    let lower_outliers = ts.iter().filter(|&&x| x < lower_bound).count();
    let upper_outliers = ts.iter().filter(|&&x| x > upper_bound).count();

    Ok((lower_outliers, upper_outliers))
}

// Add placeholder implementations for remaining helper functions
// These would need to be fully implemented in a production system

#[allow(dead_code)]
fn calculate_harmonic_mean<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_geometric_mean<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_quadratic_mean<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_cubic_mean<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_mode_approximation<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_l_moments<F>(ts: &Array1<F>) -> Result<(F, F, F)>
where
    F: Float + FromPrimitive,
{
    Ok((F::zero(), F::zero(), F::zero()))
}
#[allow(dead_code)]
fn p75_minus_p25<F>(sorted: &[F]) -> F
where
    F: Float + FromPrimitive,
{
    F::zero()
}
#[allow(dead_code)]
fn p87_5_minus_p12_5<F>(sorted: &[F]) -> F
where
    F: Float + FromPrimitive,
{
    F::zero()
}
#[allow(dead_code)]
fn calculate_jarque_bera_statistic<F>(_ts: &Array1<F>, mean: F, std: F) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_anderson_darling_approximation<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_ks_statistic_approximation<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_shapiro_wilk_approximation<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_dagostino_statistic<F>(_ts: &Array1<F>, mean: F, std: F) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_normality_composite_score<F>(_jb: F, ad: F, ks: F) -> F
where
    F: Float,
{
    F::zero()
}
#[allow(dead_code)]
fn calculate_biweight_midvariance<F>(_ts: &Array1<F>, median: F) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_qn_estimator<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_sn_estimator<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_zero_crossings<F>(_ts: &Array1<F>, mean: F) -> usize
where
    F: Float,
{
    0
}
#[allow(dead_code)]
fn calculate_local_extrema_counts<F>(ts: &Array1<F>) -> (usize, usize)
where
    F: Float,
{
    (0, 0)
}
#[allow(dead_code)]
fn calculate_concentration_coefficient<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_herfindahl_index<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_shannon_diversity<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}
#[allow(dead_code)]
fn calculate_simpson_diversity<F>(ts: &Array1<F>) -> Result<F>
where
    F: Float + FromPrimitive,
{
    Ok(F::zero())
}