SS.Statistics 1.2.0

export record AnovaResult(public int groups, public int observations, public double betweenSumSquares, public double withinSumSquares, public double totalSumSquares, public int betweenDegreesOfFreedom, public int withinDegreesOfFreedom, public double fStatistic, public double pValue, public bool valid, public string diagnostic)

export function binomialPmf(int successes, int trials, double probability) -> double

export function bootstrapMeans(double[] values, int[] starts, int replicates) -> double[]

export record ConfidenceInterval(public double estimate, public double lower, public double upper, public double standardError, public double confidence)

export function correlation(double[] left, double[] right) -> double

export function covariance(double[] left, double[] right, bool sample) -> double

export function exponentialCdf(double value, double rate) -> double

export function exponentialPdf(double value, double rate) -> double

export function fitGeneralizedLinearModel(double[][] predictors, double[] responses, double[] observationWeights, bool includeIntercept, string family, double tolerance, int maximumIterations)->GeneralizedLinearModelFit

export function fitRobustRegression(double[][] predictors, double[] responses, bool includeIntercept, double tuning, double tolerance, int maximumIterations)->RobustRegressionResult

export record GeneralizedLinearModelFit(public string family, public string link, public double[] coefficients, public double[] fitted, public double deviance, public int iterations, public bool converged, public string diagnostic)

export function generalLinearModel(double[][] predictors, double[] responses, double[] weights, bool[] missingMask, bool includeIntercept, double rankTolerance)->GeneralLinearModel

export record GeneralLinearModel(public double[] coefficients, public double[] standardErrors, public double[] fitted, public double[] residuals, public double[] leverage, public int observations, public int rank, public int residualDegreesOfFreedom, public double weightedResidualSumSquares, public double rSquared, public bool valid, public string diagnostic)

export record HypothesisTest(public double statistic, public double pValue, public bool reject, public string diagnostic)

export function linearRegression(double[] predictors, double[] responses) -> LinearRegression

export record LinearRegression(public double intercept, public double slope, public double rSquared, public double residualStandardError, public bool valid, public string diagnostic)

export function mean(double[] values) -> double

export function meanConfidence95(double[] values) -> ConfidenceInterval

export function meanConfidenceStudent(double[] values, double confidence)->ConfidenceInterval

export function median(double[] values) -> double

export function medianAbsoluteDeviation(double[] values, bool normalConsistency) -> double

export function missingDataAnalysis(double[][] values, bool[][] missingMask, string policy)->MissingDataReport

export record MissingDataReport(public int rows, public int columns, public int[] missingByColumn, public int[] completeCaseRows, public double[][] analyzedValues, public double[] columnMeans, public double[] sensitivityShift, public string policy, public string diagnostic)

export record MultipleRegression(public double intercept, public double firstCoefficient, public double secondCoefficient, public double rSquared, public bool valid, public string diagnostic)

export function multipleRegression2(double[] first, double[] second, double[] responses) -> MultipleRegression

export function nextUniform(long state) -> RandomDraw

export function normalCdf(double value, double meanValue, double standardDeviation) -> double

export function normalPdf(double value, double meanValue, double standardDeviation) -> double

export function oneWayAnova(double[][] groups)->AnovaResult

export function poissonPmf(int count, double rate) -> double

export function quantile(double[] values, double probability) -> double

export record RandomDraw(public long state, public double value)

export record RobustRegressionResult(public double[] coefficients, public double[] residuals, public double[] robustWeights, public double scale, public int iterations, public bool converged, public string diagnostic)

export function sampleWithReplacement(double[] values, long seed, int count) -> double[]

export function studentTCdf(double value, double degreesOfFreedom)->double

export function studentTQuantile(double probability, double degreesOfFreedom)->double

export function summarize(double[] values) -> Summary

export record Summary(public int count, public double mean, public double sampleVariance, public double minimum, public double maximum)

export function trimmedMean(double[] values, double fractionEachTail) -> double

export function uniformCdf(double value, double lower, double upper) -> double

export function variance(double[] values, bool sample) -> double

export function weightedAnova(double[][] groups, double[][] weights)->WeightedAnovaResult

export record WeightedAnovaResult(public double[] groupMeans, public double betweenSumSquares, public double withinSumSquares, public int betweenDegreesOfFreedom, public int withinDegreesOfFreedom, public double fStatistic, public double pValue, public string assumptions, public bool valid, public string diagnostic)

export record WelchTest(public double difference, public double statistic, public double degreesOfFreedom, public double pValue, public bool reject, public string diagnostic)

export function welchTTest(double[] first, double[] second, double alpha)->WelchTest

export function zTestDifference(double[] first, double[] second, double firstKnownStandardDeviation, double secondKnownStandardDeviation, double alpha) -> HypothesisTest

export function zTestMean(double[] values, double nullMean, double knownStandardDeviation, double alpha) -> HypothesisTest