kstats-core

Descriptive statistics, special math functions, and shared foundations for every other kstats module.

  • Extension functions on DoubleArray and Iterable<Double> for mean, median, variance, standard deviation, skewness, kurtosis, quantiles, and frequency tables.

  • describe() produces a full DescriptiveStatistics summary in a single call.

  • OnlineStatistics accumulates data in a single streaming pass (Welford's algorithm with Terriberry extension for skewness and kurtosis).

  • Special functions: gamma, beta, erf/erfInv, digamma, trigamma, combinatorics.

  • Typed exception hierarchy (InsufficientDataException, InvalidParameterException, ConvergenceException, DegenerateDataException) and reusable validation helpers.

Getting started

val data = doubleArrayOf(2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0)

val stats = data.describe()
stats.mean              // 5.0
stats.standardDeviation // 2.0
stats.median            // 4.5
stats.skewness          // 0.656...

// Streaming computation — no need to hold all data in memory
val online = OnlineStatistics()
online.addAll(data)
online.mean       // 5.0
online.variance() // 4.571...
Content copied to clipboard

Descriptive statistics

Central tendency, dispersion, and shape — all available as extension functions on DoubleArray, Iterable<Double>, and Sequence<Double>.

Central tendency: mean(), geometricMean(), harmonicMean(), median(), mode(), trimmedMean(proportion), weightedMean(weights).

Dispersion: variance(kind), standardDeviation(kind), range(), interquartileRange(), meanAbsoluteDeviation(), medianAbsoluteDeviation(), standardError(), coefficientOfVariation(kind), semiVariance(direction, kind).

Shape: skewness(kind), kurtosis(kind, excess), centralMoment(order), kStatistic(order).

Quantiles: percentile(p), quantile(p), quartiles().

Summary: describe() returns a DescriptiveStatistics snapshot with all of the above.

Use PopulationKind.SAMPLE (default) or PopulationKind.POPULATION to switch between sample and population formulas.

Special functions

Pure-Kotlin implementations of mathematical special functions used internally by distributions and hypothesis tests, and available for direct use:

  • lnGamma(x), gamma(x) — log-gamma and gamma function

  • lnBeta(a, b), beta(a, b) — log-beta and beta function

  • regularizedBeta(x, a, b) — regularized incomplete beta

  • regularizedGammaP(a, x), regularizedGammaQ(a, x) — regularized incomplete gamma

  • erf(x), erfc(x), erfInv(x), erfcInv(x) — error functions and inverses

  • digamma(x), trigamma(x) — polygamma functions

  • lnFactorial(n), lnCombination(n, k), lnPermutation(n, k) — combinatorics

  • generalizedHarmonic(n, s) — generalized harmonic numbers

Descriptive statistics, special math functions, and shared foundations for every other kstats module.

  • Extension functions on DoubleArray and Iterable<Double> for mean, median, variance, standard deviation, skewness, kurtosis, quantiles, and frequency tables.

  • describe() produces a full DescriptiveStatistics summary in a single call.

  • OnlineStatistics accumulates data in a single streaming pass (Welford's algorithm with Terriberry extension for skewness and kurtosis).

  • Special functions: gamma, beta, erf/erfInv, digamma, trigamma, combinatorics.

  • Typed exception hierarchy (InsufficientDataException, InvalidParameterException, ConvergenceException, DegenerateDataException) and reusable validation helpers.

Getting started

val data = doubleArrayOf(2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0)

val stats = data.describe()
stats.mean              // 5.0
stats.standardDeviation // 2.0
stats.median            // 4.5
stats.skewness          // 0.656...

// Streaming computation — no need to hold all data in memory
val online = OnlineStatistics()
online.addAll(data)
online.mean       // 5.0
online.variance() // 4.571...
Content copied to clipboard

Descriptive statistics

Central tendency, dispersion, and shape — all available as extension functions on DoubleArray, Iterable<Double>, and Sequence<Double>.

Central tendency: mean(), geometricMean(), harmonicMean(), median(), mode(), trimmedMean(proportion), weightedMean(weights).

Dispersion: variance(kind), standardDeviation(kind), range(), interquartileRange(), meanAbsoluteDeviation(), medianAbsoluteDeviation(), standardError(), coefficientOfVariation(kind), semiVariance(direction, kind).

Shape: skewness(kind), kurtosis(kind, excess), centralMoment(order), kStatistic(order).

Quantiles: percentile(p), quantile(p), quartiles().

Summary: describe() returns a DescriptiveStatistics snapshot with all of the above.

Use PopulationKind.SAMPLE (default) or PopulationKind.POPULATION to switch between sample and population formulas.

Special functions

Pure-Kotlin implementations of mathematical special functions used internally by distributions and hypothesis tests, and available for direct use:

  • lnGamma(x), gamma(x) — log-gamma and gamma function

  • lnBeta(a, b), beta(a, b) — log-beta and beta function

  • regularizedBeta(x, a, b) — regularized incomplete beta

  • regularizedGammaP(a, x), regularizedGammaQ(a, x) — regularized incomplete gamma

  • erf(x), erfc(x), erfInv(x), erfcInv(x) — error functions and inverses

  • digamma(x), trigamma(x) — polygamma functions

  • lnFactorial(n), lnCombination(n, k), lnPermutation(n, k) — combinatorics

  • generalizedHarmonic(n, s) — generalized harmonic numbers

Descriptive statistics, special math functions, and shared foundations for every other kstats module.

  • Extension functions on DoubleArray and Iterable<Double> for mean, median, variance, standard deviation, skewness, kurtosis, quantiles, and frequency tables.

  • describe() produces a full DescriptiveStatistics summary in a single call.

  • OnlineStatistics accumulates data in a single streaming pass (Welford's algorithm with Terriberry extension for skewness and kurtosis).

  • Special functions: gamma, beta, erf/erfInv, digamma, trigamma, combinatorics.

  • Typed exception hierarchy (InsufficientDataException, InvalidParameterException, ConvergenceException, DegenerateDataException) and reusable validation helpers.

Getting started

val data = doubleArrayOf(2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0)

val stats = data.describe()
stats.mean              // 5.0
stats.standardDeviation // 2.0
stats.median            // 4.5
stats.skewness          // 0.656...

// Streaming computation — no need to hold all data in memory
val online = OnlineStatistics()
online.addAll(data)
online.mean       // 5.0
online.variance() // 4.571...
Content copied to clipboard

Descriptive statistics

Central tendency, dispersion, and shape — all available as extension functions on DoubleArray, Iterable<Double>, and Sequence<Double>.

Central tendency: mean(), geometricMean(), harmonicMean(), median(), mode(), trimmedMean(proportion), weightedMean(weights).

Dispersion: variance(kind), standardDeviation(kind), range(), interquartileRange(), meanAbsoluteDeviation(), medianAbsoluteDeviation(), standardError(), coefficientOfVariation(kind), semiVariance(direction, kind).

Shape: skewness(kind), kurtosis(kind, excess), centralMoment(order), kStatistic(order).

Quantiles: percentile(p), quantile(p), quartiles().

Summary: describe() returns a DescriptiveStatistics snapshot with all of the above.

Use PopulationKind.SAMPLE (default) or PopulationKind.POPULATION to switch between sample and population formulas.

Special functions

Pure-Kotlin implementations of mathematical special functions used internally by distributions and hypothesis tests, and available for direct use:

  • lnGamma(x), gamma(x) — log-gamma and gamma function

  • lnBeta(a, b), beta(a, b) — log-beta and beta function

  • regularizedBeta(x, a, b) — regularized incomplete beta

  • regularizedGammaP(a, x), regularizedGammaQ(a, x) — regularized incomplete gamma

  • erf(x), erfc(x), erfInv(x), erfcInv(x) — error functions and inverses

  • digamma(x), trigamma(x) — polygamma functions

  • lnFactorial(n), lnCombination(n, k), lnPermutation(n, k) — combinatorics

  • generalizedHarmonic(n, s) — generalized harmonic numbers

Descriptive statistics, special math functions, and shared foundations for every other kstats module.

  • Extension functions on DoubleArray and Iterable<Double> for mean, median, variance, standard deviation, skewness, kurtosis, quantiles, and frequency tables.

  • describe() produces a full DescriptiveStatistics summary in a single call.

  • OnlineStatistics accumulates data in a single streaming pass (Welford's algorithm with Terriberry extension for skewness and kurtosis).

  • Special functions: gamma, beta, erf/erfInv, digamma, trigamma, combinatorics.

  • Typed exception hierarchy (InsufficientDataException, InvalidParameterException, ConvergenceException, DegenerateDataException) and reusable validation helpers.

Getting started

val data = doubleArrayOf(2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0)

val stats = data.describe()
stats.mean              // 5.0
stats.standardDeviation // 2.0
stats.median            // 4.5
stats.skewness          // 0.656...

// Streaming computation — no need to hold all data in memory
val online = OnlineStatistics()
online.addAll(data)
online.mean       // 5.0
online.variance() // 4.571...
Content copied to clipboard

Descriptive statistics

Central tendency, dispersion, and shape — all available as extension functions on DoubleArray, Iterable<Double>, and Sequence<Double>.

Central tendency: mean(), geometricMean(), harmonicMean(), median(), mode(), trimmedMean(proportion), weightedMean(weights).

Dispersion: variance(kind), standardDeviation(kind), range(), interquartileRange(), meanAbsoluteDeviation(), medianAbsoluteDeviation(), standardError(), coefficientOfVariation(kind), semiVariance(direction, kind).

Shape: skewness(kind), kurtosis(kind, excess), centralMoment(order), kStatistic(order).

Quantiles: percentile(p), quantile(p), quartiles().

Summary: describe() returns a DescriptiveStatistics snapshot with all of the above.

Use PopulationKind.SAMPLE (default) or PopulationKind.POPULATION to switch between sample and population formulas.

Special functions

Pure-Kotlin implementations of mathematical special functions used internally by distributions and hypothesis tests, and available for direct use:

  • lnGamma(x), gamma(x) — log-gamma and gamma function

  • lnBeta(a, b), beta(a, b) — log-beta and beta function

  • regularizedBeta(x, a, b) — regularized incomplete beta

  • regularizedGammaP(a, x), regularizedGammaQ(a, x) — regularized incomplete gamma

  • erf(x), erfc(x), erfInv(x), erfcInv(x) — error functions and inverses

  • digamma(x), trigamma(x) — polygamma functions

  • lnFactorial(n), lnCombination(n, k), lnPermutation(n, k) — combinatorics

  • generalizedHarmonic(n, s) — generalized harmonic numbers

Descriptive statistics, special math functions, and shared foundations for every other kstats module.

  • Extension functions on DoubleArray and Iterable<Double> for mean, median, variance, standard deviation, skewness, kurtosis, quantiles, and frequency tables.

  • describe() produces a full DescriptiveStatistics summary in a single call.

  • OnlineStatistics accumulates data in a single streaming pass (Welford's algorithm with Terriberry extension for skewness and kurtosis).

  • Special functions: gamma, beta, erf/erfInv, digamma, trigamma, combinatorics.

  • Typed exception hierarchy (InsufficientDataException, InvalidParameterException, ConvergenceException, DegenerateDataException) and reusable validation helpers.

Getting started

val data = doubleArrayOf(2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0)

val stats = data.describe()
stats.mean              // 5.0
stats.standardDeviation // 2.0
stats.median            // 4.5
stats.skewness          // 0.656...

// Streaming computation — no need to hold all data in memory
val online = OnlineStatistics()
online.addAll(data)
online.mean       // 5.0
online.variance() // 4.571...
Content copied to clipboard

Descriptive statistics

Central tendency, dispersion, and shape — all available as extension functions on DoubleArray, Iterable<Double>, and Sequence<Double>.

Central tendency: mean(), geometricMean(), harmonicMean(), median(), mode(), trimmedMean(proportion), weightedMean(weights).

Dispersion: variance(kind), standardDeviation(kind), range(), interquartileRange(), meanAbsoluteDeviation(), medianAbsoluteDeviation(), standardError(), coefficientOfVariation(kind), semiVariance(direction, kind).

Shape: skewness(kind), kurtosis(kind, excess), centralMoment(order), kStatistic(order).

Quantiles: percentile(p), quantile(p), quartiles().

Summary: describe() returns a DescriptiveStatistics snapshot with all of the above.

Use PopulationKind.SAMPLE (default) or PopulationKind.POPULATION to switch between sample and population formulas.

Special functions

Pure-Kotlin implementations of mathematical special functions used internally by distributions and hypothesis tests, and available for direct use:

  • lnGamma(x), gamma(x) — log-gamma and gamma function

  • lnBeta(a, b), beta(a, b) — log-beta and beta function

  • regularizedBeta(x, a, b) — regularized incomplete beta

  • regularizedGammaP(a, x), regularizedGammaQ(a, x) — regularized incomplete gamma

  • erf(x), erfc(x), erfInv(x), erfcInv(x) — error functions and inverses

  • digamma(x), trigamma(x) — polygamma functions

  • lnFactorial(n), lnCombination(n, k), lnPermutation(n, k) — combinatorics

  • generalizedHarmonic(n, s) — generalized harmonic numbers

Descriptive statistics, special math functions, and shared foundations for every other kstats module.

  • Extension functions on DoubleArray and Iterable<Double> for mean, median, variance, standard deviation, skewness, kurtosis, quantiles, and frequency tables.

  • describe() produces a full DescriptiveStatistics summary in a single call.

  • OnlineStatistics accumulates data in a single streaming pass (Welford's algorithm with Terriberry extension for skewness and kurtosis).

  • Special functions: gamma, beta, erf/erfInv, digamma, trigamma, combinatorics.

  • Typed exception hierarchy (InsufficientDataException, InvalidParameterException, ConvergenceException, DegenerateDataException) and reusable validation helpers.

Getting started

val data = doubleArrayOf(2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0)

val stats = data.describe()
stats.mean              // 5.0
stats.standardDeviation // 2.0
stats.median            // 4.5
stats.skewness          // 0.656...

// Streaming computation — no need to hold all data in memory
val online = OnlineStatistics()
online.addAll(data)
online.mean       // 5.0
online.variance() // 4.571...
Content copied to clipboard

Descriptive statistics

Central tendency, dispersion, and shape — all available as extension functions on DoubleArray, Iterable<Double>, and Sequence<Double>.

Central tendency: mean(), geometricMean(), harmonicMean(), median(), mode(), trimmedMean(proportion), weightedMean(weights).

Dispersion: variance(kind), standardDeviation(kind), range(), interquartileRange(), meanAbsoluteDeviation(), medianAbsoluteDeviation(), standardError(), coefficientOfVariation(kind), semiVariance(direction, kind).

Shape: skewness(kind), kurtosis(kind, excess), centralMoment(order), kStatistic(order).

Quantiles: percentile(p), quantile(p), quartiles().

Summary: describe() returns a DescriptiveStatistics snapshot with all of the above.

Use PopulationKind.SAMPLE (default) or PopulationKind.POPULATION to switch between sample and population formulas.

Special functions

Pure-Kotlin implementations of mathematical special functions used internally by distributions and hypothesis tests, and available for direct use:

  • lnGamma(x), gamma(x) — log-gamma and gamma function

  • lnBeta(a, b), beta(a, b) — log-beta and beta function

  • regularizedBeta(x, a, b) — regularized incomplete beta

  • regularizedGammaP(a, x), regularizedGammaQ(a, x) — regularized incomplete gamma

  • erf(x), erfc(x), erfInv(x), erfcInv(x) — error functions and inverses

  • digamma(x), trigamma(x) — polygamma functions

  • lnFactorial(n), lnCombination(n, k), lnPermutation(n, k) — combinatorics

  • generalizedHarmonic(n, s) — generalized harmonic numbers

Descriptive statistics, special math functions, and shared foundations for every other kstats module.

  • Extension functions on DoubleArray and Iterable<Double> for mean, median, variance, standard deviation, skewness, kurtosis, quantiles, and frequency tables.

  • describe() produces a full DescriptiveStatistics summary in a single call.

  • OnlineStatistics accumulates data in a single streaming pass (Welford's algorithm with Terriberry extension for skewness and kurtosis).

  • Special functions: gamma, beta, erf/erfInv, digamma, trigamma, combinatorics.

  • Typed exception hierarchy (InsufficientDataException, InvalidParameterException, ConvergenceException, DegenerateDataException) and reusable validation helpers.

Getting started

val data = doubleArrayOf(2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0)

val stats = data.describe()
stats.mean              // 5.0
stats.standardDeviation // 2.0
stats.median            // 4.5
stats.skewness          // 0.656...

// Streaming computation — no need to hold all data in memory
val online = OnlineStatistics()
online.addAll(data)
online.mean       // 5.0
online.variance() // 4.571...
Content copied to clipboard

Descriptive statistics

Central tendency, dispersion, and shape — all available as extension functions on DoubleArray, Iterable<Double>, and Sequence<Double>.

Central tendency: mean(), geometricMean(), harmonicMean(), median(), mode(), trimmedMean(proportion), weightedMean(weights).

Dispersion: variance(kind), standardDeviation(kind), range(), interquartileRange(), meanAbsoluteDeviation(), medianAbsoluteDeviation(), standardError(), coefficientOfVariation(kind), semiVariance(direction, kind).

Shape: skewness(kind), kurtosis(kind, excess), centralMoment(order), kStatistic(order).

Quantiles: percentile(p), quantile(p), quartiles().

Summary: describe() returns a DescriptiveStatistics snapshot with all of the above.

Use PopulationKind.SAMPLE (default) or PopulationKind.POPULATION to switch between sample and population formulas.

Special functions

Pure-Kotlin implementations of mathematical special functions used internally by distributions and hypothesis tests, and available for direct use:

  • lnGamma(x), gamma(x) — log-gamma and gamma function

  • lnBeta(a, b), beta(a, b) — log-beta and beta function

  • regularizedBeta(x, a, b) — regularized incomplete beta

  • regularizedGammaP(a, x), regularizedGammaQ(a, x) — regularized incomplete gamma

  • erf(x), erfc(x), erfInv(x), erfcInv(x) — error functions and inverses

  • digamma(x), trigamma(x) — polygamma functions

  • lnFactorial(n), lnCombination(n, k), lnPermutation(n, k) — combinatorics

  • generalizedHarmonic(n, s) — generalized harmonic numbers

Descriptive statistics, special math functions, and shared foundations for every other kstats module.

  • Extension functions on DoubleArray and Iterable<Double> for mean, median, variance, standard deviation, skewness, kurtosis, quantiles, and frequency tables.

  • describe() produces a full DescriptiveStatistics summary in a single call.

  • OnlineStatistics accumulates data in a single streaming pass (Welford's algorithm with Terriberry extension for skewness and kurtosis).

  • Special functions: gamma, beta, erf/erfInv, digamma, trigamma, combinatorics.

  • Typed exception hierarchy (InsufficientDataException, InvalidParameterException, ConvergenceException, DegenerateDataException) and reusable validation helpers.

Getting started

val data = doubleArrayOf(2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0)

val stats = data.describe()
stats.mean              // 5.0
stats.standardDeviation // 2.0
stats.median            // 4.5
stats.skewness          // 0.656...

// Streaming computation — no need to hold all data in memory
val online = OnlineStatistics()
online.addAll(data)
online.mean       // 5.0
online.variance() // 4.571...
Content copied to clipboard

Descriptive statistics

Central tendency, dispersion, and shape — all available as extension functions on DoubleArray, Iterable<Double>, and Sequence<Double>.

Central tendency: mean(), geometricMean(), harmonicMean(), median(), mode(), trimmedMean(proportion), weightedMean(weights).

Dispersion: variance(kind), standardDeviation(kind), range(), interquartileRange(), meanAbsoluteDeviation(), medianAbsoluteDeviation(), standardError(), coefficientOfVariation(kind), semiVariance(direction, kind).

Shape: skewness(kind), kurtosis(kind, excess), centralMoment(order), kStatistic(order).

Quantiles: percentile(p), quantile(p), quartiles().

Summary: describe() returns a DescriptiveStatistics snapshot with all of the above.

Use PopulationKind.SAMPLE (default) or PopulationKind.POPULATION to switch between sample and population formulas.

Special functions

Pure-Kotlin implementations of mathematical special functions used internally by distributions and hypothesis tests, and available for direct use:

  • lnGamma(x), gamma(x) — log-gamma and gamma function

  • lnBeta(a, b), beta(a, b) — log-beta and beta function

  • regularizedBeta(x, a, b) — regularized incomplete beta

  • regularizedGammaP(a, x), regularizedGammaQ(a, x) — regularized incomplete gamma

  • erf(x), erfc(x), erfInv(x), erfcInv(x) — error functions and inverses

  • digamma(x), trigamma(x) — polygamma functions

  • lnFactorial(n), lnCombination(n, k), lnPermutation(n, k) — combinatorics

  • generalizedHarmonic(n, s) — generalized harmonic numbers

Descriptive statistics, special math functions, and shared foundations for every other kstats module.

  • Extension functions on DoubleArray and Iterable<Double> for mean, median, variance, standard deviation, skewness, kurtosis, quantiles, and frequency tables.

  • describe() produces a full DescriptiveStatistics summary in a single call.

  • OnlineStatistics accumulates data in a single streaming pass (Welford's algorithm with Terriberry extension for skewness and kurtosis).

  • Special functions: gamma, beta, erf/erfInv, digamma, trigamma, combinatorics.

  • Typed exception hierarchy (InsufficientDataException, InvalidParameterException, ConvergenceException, DegenerateDataException) and reusable validation helpers.

Getting started

val data = doubleArrayOf(2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0)

val stats = data.describe()
stats.mean              // 5.0
stats.standardDeviation // 2.0
stats.median            // 4.5
stats.skewness          // 0.656...

// Streaming computation — no need to hold all data in memory
val online = OnlineStatistics()
online.addAll(data)
online.mean       // 5.0
online.variance() // 4.571...
Content copied to clipboard

Descriptive statistics

Central tendency, dispersion, and shape — all available as extension functions on DoubleArray, Iterable<Double>, and Sequence<Double>.

Central tendency: mean(), geometricMean(), harmonicMean(), median(), mode(), trimmedMean(proportion), weightedMean(weights).

Dispersion: variance(kind), standardDeviation(kind), range(), interquartileRange(), meanAbsoluteDeviation(), medianAbsoluteDeviation(), standardError(), coefficientOfVariation(kind), semiVariance(direction, kind).

Shape: skewness(kind), kurtosis(kind, excess), centralMoment(order), kStatistic(order).

Quantiles: percentile(p), quantile(p), quartiles().

Summary: describe() returns a DescriptiveStatistics snapshot with all of the above.

Use PopulationKind.SAMPLE (default) or PopulationKind.POPULATION to switch between sample and population formulas.

Special functions

Pure-Kotlin implementations of mathematical special functions used internally by distributions and hypothesis tests, and available for direct use:

  • lnGamma(x), gamma(x) — log-gamma and gamma function

  • lnBeta(a, b), beta(a, b) — log-beta and beta function

  • regularizedBeta(x, a, b) — regularized incomplete beta

  • regularizedGammaP(a, x), regularizedGammaQ(a, x) — regularized incomplete gamma

  • erf(x), erfc(x), erfInv(x), erfcInv(x) — error functions and inverses

  • digamma(x), trigamma(x) — polygamma functions

  • lnFactorial(n), lnCombination(n, k), lnPermutation(n, k) — combinatorics

  • generalizedHarmonic(n, s) — generalized harmonic numbers

Descriptive statistics, special math functions, and shared foundations for every other kstats module.

  • Extension functions on DoubleArray and Iterable<Double> for mean, median, variance, standard deviation, skewness, kurtosis, quantiles, and frequency tables.

  • describe() produces a full DescriptiveStatistics summary in a single call.

  • OnlineStatistics accumulates data in a single streaming pass (Welford's algorithm with Terriberry extension for skewness and kurtosis).

  • Special functions: gamma, beta, erf/erfInv, digamma, trigamma, combinatorics.

  • Typed exception hierarchy (InsufficientDataException, InvalidParameterException, ConvergenceException, DegenerateDataException) and reusable validation helpers.

Getting started

val data = doubleArrayOf(2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0)

val stats = data.describe()
stats.mean              // 5.0
stats.standardDeviation // 2.0
stats.median            // 4.5
stats.skewness          // 0.656...

// Streaming computation — no need to hold all data in memory
val online = OnlineStatistics()
online.addAll(data)
online.mean       // 5.0
online.variance() // 4.571...
Content copied to clipboard

Descriptive statistics

Central tendency, dispersion, and shape — all available as extension functions on DoubleArray, Iterable<Double>, and Sequence<Double>.

Central tendency: mean(), geometricMean(), harmonicMean(), median(), mode(), trimmedMean(proportion), weightedMean(weights).

Dispersion: variance(kind), standardDeviation(kind), range(), interquartileRange(), meanAbsoluteDeviation(), medianAbsoluteDeviation(), standardError(), coefficientOfVariation(kind), semiVariance(direction, kind).

Shape: skewness(kind), kurtosis(kind, excess), centralMoment(order), kStatistic(order).

Quantiles: percentile(p), quantile(p), quartiles().

Summary: describe() returns a DescriptiveStatistics snapshot with all of the above.

Use PopulationKind.SAMPLE (default) or PopulationKind.POPULATION to switch between sample and population formulas.

Special functions

Pure-Kotlin implementations of mathematical special functions used internally by distributions and hypothesis tests, and available for direct use:

  • lnGamma(x), gamma(x) — log-gamma and gamma function

  • lnBeta(a, b), beta(a, b) — log-beta and beta function

  • regularizedBeta(x, a, b) — regularized incomplete beta

  • regularizedGammaP(a, x), regularizedGammaQ(a, x) — regularized incomplete gamma

  • erf(x), erfc(x), erfInv(x), erfcInv(x) — error functions and inverses

  • digamma(x), trigamma(x) — polygamma functions

  • lnFactorial(n), lnCombination(n, k), lnPermutation(n, k) — combinatorics

  • generalizedHarmonic(n, s) — generalized harmonic numbers

Packages

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androidNative
apple
common
js
linux
mingw
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