kstats-correlation

Correlation coefficients, covariance/correlation matrices, and simple linear regression.

• Pearson, Spearman, Kendall (O(n log n)), point-biserial, and partial correlation — each returns a CorrelationResult with coefficient, p-value, and sample size.

• covarianceMatrix() and correlationMatrix() for multivariate analysis.

• simpleLinearRegression() fits y = intercept + slope * x and returns R², standard errors, residuals, and a predict() function.

Getting started

val x = doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0)
val y = doubleArrayOf(2.1, 3.9, 6.2, 7.8, 10.1)

val r = pearsonCorrelation(x, y)
r.coefficient // 0.999...
r.pValue      // < 0.001

val reg = simpleLinearRegression(x, y)
reg.slope        // ~2.0
reg.intercept    // ~0.04
reg.rSquared     // 0.999...
reg.predict(6.0) // predicted y for x = 6

Correlation functions

• pearsonCorrelation(x, y) — Pearson product-moment correlation

• spearmanCorrelation(x, y) — Spearman rank correlation

• kendallTau(x, y) — Kendall rank correlation (O(n log n) merge-sort algorithm)

• pointBiserialCorrelation(x, y) — point-biserial correlation (overloads for DoubleArray, BooleanArray, IntArray)

• partialCorrelation(target, control) — partial correlation controlling for a third variable

Matrices

• correlationMatrix(vararg variables) — Pearson correlation matrix

• covarianceMatrix(vararg variables, kind) — covariance matrix (sample or population)

• covariance(x, y, kind) — pairwise covariance

Regression

• simpleLinearRegression(x, y) — returns SimpleLinearRegressionResult with intercept, slope, rSquared, residuals, standardErrors, residualStandardError, and predict(x).