Module correlation

fn is_significant(&self, alpha: f64) -> bool

Source: correlation.joule:18

fn pearson(x: &[f64], y: &[f64]) -> Option<CorrelationResult>

Source: correlation.joule:25

fn spearman(x: &[f64], y: &[f64]) -> Option<CorrelationResult>

Source: correlation.joule:68

fn kendall(x: &[f64], y: &[f64]) -> Option<CorrelationResult>

Source: correlation.joule:85

fn point_biserial(continuous: &[f64], binary: &[f64]) -> Option<CorrelationResult>

Source: correlation.joule:149

fn partial(x: &[f64], y: &[f64], z: &[f64]) -> Option<CorrelationResult>

Source: correlation.joule:167

fn semi_partial(x: &[f64], y: &[f64], z: &[f64]) -> Option<CorrelationResult>

Source: correlation.joule:201

fn distance_correlation(x: &[f64], y: &[f64]) -> Option<f64>

Source: correlation.joule:229

fn correlation_matrix(variables: &[&[f64]]) -> Option<Vec<Vec<f64>>>

Source: correlation.joule:278

fn autocorrelation(x: &[f64], max_lag: usize) -> Vec<f64>

Source: correlation.joule:309

fn partial_autocorrelation(x: &[f64], max_lag: usize) -> Vec<f64>

Source: correlation.joule:339

fn cross_correlation(x: &[f64], y: &[f64], max_lag: usize) -> Vec<(i64, f64)>

Source: correlation.joule:383

fn rank_data(data: &[f64]) -> Vec<f64>

Convert data to ranks (average rank for ties)

Source: correlation.joule:435

fn double_center(d: &[Vec<f64>]) -> Vec<Vec<f64>>

Double center a distance matrix

Source: correlation.joule:468