kstats-core/org.oremif.kstats.descriptive

Package-level declarations

Types

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data class ControlChartLimits(val centerLine: Double, val ucl: Double, val lcl: Double)

Control limits for a single Shewhart control chart.

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data class CusumResult(val sPlus: DoubleArray, val sMinus: DoubleArray, val alarmIndex: Int)

Results of a two-sided tabular CUSUM control chart analysis.

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data class DescriptiveStatistics(val count: Long, val mean: Double, val standardDeviation: Double, val min: Double, val q1: Double, val median: Double, val q3: Double, val max: Double, val variance: Double, val skewness: Double, val kurtosis: Double, val sum: Double, val range: Double, val interquartileRange: Double, val standardError: Double)

A snapshot of common descriptive statistics for a dataset.

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data class EwmaResult(val smoothedValues: DoubleArray, val ucl: DoubleArray, val lcl: DoubleArray, val outOfControl: IntArray)

Results of an EWMA (Exponentially Weighted Moving Average) control chart analysis.

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class Frequency<T : Comparable<T>>

A mutable accumulator that counts the frequency of discrete values.

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data class FrequencyEntry<T>(val value: T, val count: Long)

A data holder representing a single value and its observed frequency.

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class OnlineStatistics

A streaming one-pass accumulator for descriptive statistics.

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enum PopulationKind : Enum<PopulationKind>

Selects between sample and population formulas for variance and standard deviation.

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data class ProcessCapabilityResult(val cp: Double, val cpk: Double, val pp: Double, val ppk: Double)

Results of a process capability analysis.

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enum QuantileInterpolation : Enum<QuantileInterpolation>

Controls how values between two data points are interpolated when computing quantiles and percentiles.

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enum QuantileMethod : Enum<QuantileMethod>

Selects the algorithm used to compute quantiles and percentiles from sample data.

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enum SemiVarianceDirection : Enum<SemiVarianceDirection>

Selects which side of the threshold to measure in a semi-variance calculation.

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data class SpcConstants(val a2: Double, val a3: Double, val d3: Double, val d4: Double, val b3: Double, val b4: Double, val c4: Double)

Standard Statistical Process Control (SPC) constants for a given subgroup size.

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data class WesternElectricRulesResult(val rule1: IntArray, val rule2: IntArray, val rule3: IntArray, val rule4: IntArray)

Indices of observations that violate each of the four Western Electric Rules.

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data class XBarRChartResult(val centerLine: Double, val ucl: Double, val lcl: Double, val rChart: ControlChartLimits)

Results of an x-bar and R (range) control chart analysis.

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data class XBarSChartResult(val centerLine: Double, val ucl: Double, val lcl: Double, val sChart: ControlChartLimits)

Results of an x-bar and S (standard deviation) control chart analysis.

Functions

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fun DoubleArray.centralMoment(order: Int): Double
fun Iterable<Double>.centralMoment(order: Int): Double

Computes the n-th central moment of the values.

fun Sequence<Double>.centralMoment(order: Int): Double

Computes the n-th central moment of the values in this sequence.

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fun DoubleArray.coefficientOfVariation(kind: PopulationKind = SAMPLE): Double

Computes the coefficient of variation (CV) of the values in this array.

fun Iterable<Double>.coefficientOfVariation(kind: PopulationKind = SAMPLE): Double

Computes the coefficient of variation (CV) of the values in this iterable.

fun Sequence<Double>.coefficientOfVariation(kind: PopulationKind = SAMPLE): Double

Computes the coefficient of variation (CV) of the values in this sequence.

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fun cusum(observations: DoubleArray, target: Double, k: Double, h: Double): CusumResult

Computes a two-sided tabular CUSUM control chart for the given observations.

fun cusum(observations: Iterable<Double>, target: Double, k: Double, h: Double): CusumResult

Computes a two-sided tabular CUSUM control chart for an Iterable of observations.

fun cusum(observations: Sequence<Double>, target: Double, k: Double, h: Double): CusumResult

Computes a two-sided tabular CUSUM control chart for a Sequence of observations.

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fun DoubleArray.describe(): DescriptiveStatistics

Computes a comprehensive descriptive statistics summary of the values in this array.

fun Iterable<Double>.describe(): DescriptiveStatistics

Computes a comprehensive descriptive statistics summary of the values in this iterable.

@JvmName(name = "describeOfInt")
fun Iterable<Int>.describe(): DescriptiveStatistics

Computes a descriptive statistics summary of the Int values.

@JvmName(name = "describeOfLong")
fun Iterable<Long>.describe(): DescriptiveStatistics

Computes a descriptive statistics summary of the Long values.

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fun ewma(observations: DoubleArray, target: Double, sigma: Double, lambda: Double, controlLimitWidth: Double): EwmaResult

Computes an EWMA (Exponentially Weighted Moving Average) control chart for the given observations.

fun ewma(observations: Iterable<Double>, target: Double, sigma: Double, lambda: Double, controlLimitWidth: Double): EwmaResult

Computes an EWMA control chart for an Iterable of observations.

fun ewma(observations: Sequence<Double>, target: Double, sigma: Double, lambda: Double, controlLimitWidth: Double): EwmaResult

Computes an EWMA control chart for a Sequence of observations.

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fun DoubleArray.geometricMean(): Double

Computes the geometric mean of the values in this array.

fun Iterable<Double>.geometricMean(): Double

Computes the geometric mean of the values in this iterable.

fun Sequence<Double>.geometricMean(): Double

Computes the geometric mean of the values in this sequence.

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fun DoubleArray.harmonicMean(): Double

Computes the harmonic mean of the values in this array.

fun Iterable<Double>.harmonicMean(): Double

Computes the harmonic mean of the values in this iterable.

fun Sequence<Double>.harmonicMean(): Double

Computes the harmonic mean of the values in this sequence.

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fun DoubleArray.interquartileRange(method: QuantileMethod = QuantileMethod.LINEAR): Double

Computes the interquartile range (IQR) of the values in this array.

fun Iterable<Double>.interquartileRange(method: QuantileMethod = QuantileMethod.LINEAR): Double

Computes the interquartile range (IQR) of the values in this iterable.

fun Sequence<Double>.interquartileRange(method: QuantileMethod = QuantileMethod.LINEAR): Double

Computes the interquartile range (IQR) of the values in this sequence.

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fun DoubleArray.kStatistic(order: Int): Double
fun Iterable<Double>.kStatistic(order: Int): Double
fun Sequence<Double>.kStatistic(order: Int): Double

Computes the k-statistic of the given order, the unique symmetric unbiased estimator of the corresponding cumulant.

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fun DoubleArray.kurtosis(kind: PopulationKind = SAMPLE, excess: Boolean = true): Double

Computes the kurtosis of the values in this array.

fun Iterable<Double>.kurtosis(kind: PopulationKind = SAMPLE, excess: Boolean = true): Double

Computes the kurtosis of the values in this iterable.

fun Sequence<Double>.kurtosis(kind: PopulationKind = SAMPLE, excess: Boolean = true): Double

Computes the kurtosis of the values in this sequence.

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fun DoubleArray.mean(): Double

Computes the arithmetic mean of the values in this array.

fun Iterable<Double>.mean(): Double

Computes the arithmetic mean of the values in this iterable.

@JvmName(name = "meanOfInt")
fun Iterable<Int>.mean(): Double

Computes the arithmetic mean of the Int values.

@JvmName(name = "meanOfLong")
fun Iterable<Long>.mean(): Double

Computes the arithmetic mean of the Long values.

fun Sequence<Double>.mean(): Double

Computes the arithmetic mean of the values in this sequence.

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fun DoubleArray.meanAbsoluteDeviation(): Double

Computes the mean absolute deviation (MAD) of the values in this array.

fun Iterable<Double>.meanAbsoluteDeviation(): Double

Computes the mean absolute deviation (MAD) of the values in this iterable.

fun Sequence<Double>.meanAbsoluteDeviation(): Double

Computes the mean absolute deviation (MAD) of the values in this sequence.

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fun DoubleArray.median(): Double

Computes the median of the values in this array.

fun Iterable<Double>.median(): Double

Computes the median of the values in this iterable.

@JvmName(name = "medianOfInt")
fun Iterable<Int>.median(): Double

Computes the median of the Int values.

@JvmName(name = "medianOfLong")
fun Iterable<Long>.median(): Double

Computes the median of the Long values.

fun Sequence<Double>.median(): Double

Computes the median of the values in this sequence.

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fun DoubleArray.medianAbsoluteDeviation(): Double

Computes the median absolute deviation (median AD) of the values in this array.

fun Iterable<Double>.medianAbsoluteDeviation(): Double

Computes the median absolute deviation (median AD) of the values in this iterable.

fun Sequence<Double>.medianAbsoluteDeviation(): Double

Computes the median absolute deviation (median AD) of the values in this sequence.

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fun <T> Iterable<T>.mode(): Set<T>

Returns the mode (most frequently occurring values) of this iterable.

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fun DoubleArray.percentile(p: Double, interpolation: QuantileInterpolation): Double
fun DoubleArray.percentile(p: Double, method: QuantileMethod = QuantileMethod.LINEAR): Double

Computes the p-th percentile of the values in this array.

fun Iterable<Double>.percentile(p: Double, interpolation: QuantileInterpolation): Double
fun Iterable<Double>.percentile(p: Double, method: QuantileMethod = QuantileMethod.LINEAR): Double

Computes the p-th percentile of the values in this iterable.

@JvmName(name = "percentileOfIntDeprecated")
fun Iterable<Int>.percentile(p: Double, interpolation: QuantileInterpolation): Double
@JvmName(name = "percentileOfInt")
fun Iterable<Int>.percentile(p: Double, method: QuantileMethod = QuantileMethod.LINEAR): Double

Computes the p-th percentile of the Int values.

@JvmName(name = "percentileOfLongDeprecated")
fun Iterable<Long>.percentile(p: Double, interpolation: QuantileInterpolation): Double
@JvmName(name = "percentileOfLong")
fun Iterable<Long>.percentile(p: Double, method: QuantileMethod = QuantileMethod.LINEAR): Double

Computes the p-th percentile of the Long values.

fun Sequence<Double>.percentile(p: Double, interpolation: QuantileInterpolation): Double
fun Sequence<Double>.percentile(p: Double, method: QuantileMethod = QuantileMethod.LINEAR): Double

Computes the p-th percentile of the values in this sequence.

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fun <T : Comparable<T>> List<T>.percentileSelect(p: Double, method: QuantileMethod = QuantileMethod.NEAREST): T

Selects the element at the p-th percentile position from this sorted list.

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fun DoubleArray.processCapability(lsl: Double, usl: Double): ProcessCapabilityResult

Computes process capability indices (Cp, Cpk, Pp, Ppk) for the values in this array.

fun Iterable<Double>.processCapability(lsl: Double, usl: Double): ProcessCapabilityResult

Computes process capability indices (Cp, Cpk, Pp, Ppk) for the values in this iterable.

@JvmName(name = "processCapabilityOfInt")
fun Iterable<Int>.processCapability(lsl: Double, usl: Double): ProcessCapabilityResult

Computes process capability indices (Cp, Cpk, Pp, Ppk) for the Int values.

@JvmName(name = "processCapabilityOfLong")
fun Iterable<Long>.processCapability(lsl: Double, usl: Double): ProcessCapabilityResult

Computes process capability indices (Cp, Cpk, Pp, Ppk) for the Long values.

fun Sequence<Double>.processCapability(lsl: Double, usl: Double): ProcessCapabilityResult

Computes process capability indices (Cp, Cpk, Pp, Ppk) for the values in this sequence.

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fun DoubleArray.quantile(q: Double, interpolation: QuantileInterpolation): Double
fun DoubleArray.quantile(q: Double, method: QuantileMethod = QuantileMethod.LINEAR): Double

Computes the q-th quantile of the values in this array.

fun Iterable<Double>.quantile(q: Double, interpolation: QuantileInterpolation): Double
fun Iterable<Double>.quantile(q: Double, method: QuantileMethod = QuantileMethod.LINEAR): Double

Computes the q-th quantile of the values in this iterable.

fun Sequence<Double>.quantile(q: Double, interpolation: QuantileInterpolation): Double
fun Sequence<Double>.quantile(q: Double, method: QuantileMethod = QuantileMethod.LINEAR): Double

Computes the q-th quantile of the values in this sequence.

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fun <T : Comparable<T>> List<T>.quantileSelect(q: Double, method: QuantileMethod = QuantileMethod.NEAREST): T

Selects the element at the q-th quantile position from this list.

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fun DoubleArray.quartiles(method: QuantileMethod = QuantileMethod.LINEAR): Triple<Double, Double, Double>

Computes the three quartiles (Q1, Q2, Q3) of the values in this array.

fun Iterable<Double>.quartiles(method: QuantileMethod = QuantileMethod.LINEAR): Triple<Double, Double, Double>

Computes the three quartiles (Q1, Q2, Q3) of the values in this iterable.

fun Sequence<Double>.quartiles(method: QuantileMethod = QuantileMethod.LINEAR): Triple<Double, Double, Double>

Computes the three quartiles (Q1, Q2, Q3) of the values in this sequence.

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fun DoubleArray.range(): Double

Computes the range of the values in this array.

fun Iterable<Double>.range(): Double

Computes the range of the values in this iterable.

fun Sequence<Double>.range(): Double

Computes the range of the values in this sequence.

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fun Iterable<Double>.semiVariance(direction: SemiVarianceDirection = SemiVarianceDirection.DOWNSIDE, kind: PopulationKind = SAMPLE): Double

Computes the semi-variance using the mean as the threshold.

fun Sequence<Double>.semiVariance(direction: SemiVarianceDirection = SemiVarianceDirection.DOWNSIDE, kind: PopulationKind = SAMPLE): Double
fun DoubleArray.semiVariance(threshold: Double = mean(), direction: SemiVarianceDirection = SemiVarianceDirection.DOWNSIDE, kind: PopulationKind = SAMPLE): Double
fun Iterable<Double>.semiVariance(threshold: Double, direction: SemiVarianceDirection = SemiVarianceDirection.DOWNSIDE, kind: PopulationKind = SAMPLE): Double
fun Sequence<Double>.semiVariance(threshold: Double, direction: SemiVarianceDirection = SemiVarianceDirection.DOWNSIDE, kind: PopulationKind = SAMPLE): Double

Computes the semi-variance of the values on one side of a threshold.

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fun DoubleArray.skewness(kind: PopulationKind = SAMPLE): Double

Computes the skewness of the values in this array.

fun Iterable<Double>.skewness(kind: PopulationKind = SAMPLE): Double

Computes the skewness of the values in this iterable.

fun Sequence<Double>.skewness(kind: PopulationKind = SAMPLE): Double

Computes the skewness of the values in this sequence.

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fun spcConstants(subgroupSize: Int): SpcConstants

Returns the standard SPC constants for the given subgroupSize.

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fun DoubleArray.standardDeviation(kind: PopulationKind = SAMPLE): Double

Computes the standard deviation of the values in this array.

fun Iterable<Double>.standardDeviation(kind: PopulationKind = SAMPLE): Double

Computes the standard deviation of the values in this iterable.

@JvmName(name = "standardDeviationOfInt")
fun Iterable<Int>.standardDeviation(kind: PopulationKind = PopulationKind.SAMPLE): Double

Computes the standard deviation of the Int values.

@JvmName(name = "standardDeviationOfLong")
fun Iterable<Long>.standardDeviation(kind: PopulationKind = PopulationKind.SAMPLE): Double

Computes the standard deviation of the Long values.

fun Sequence<Double>.standardDeviation(kind: PopulationKind = SAMPLE): Double

Computes the standard deviation of the values in this sequence.

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fun DoubleArray.standardError(): Double

Computes the standard error of the mean for the values in this array.

fun Iterable<Double>.standardError(): Double

Computes the standard error of the mean for the values in this iterable.

fun Sequence<Double>.standardError(): Double

Computes the standard error of the mean for the values in this sequence.

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fun <T : Comparable<T>> Iterable<T>.toFrequency(): Frequency<T>

Creates a Frequency accumulator from the elements of this iterable.

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fun DoubleArray.trimmedMean(proportion: Double): Double
fun Iterable<Double>.trimmedMean(proportion: Double): Double
fun Sequence<Double>.trimmedMean(proportion: Double): Double

Computes the trimmed (truncated) mean by removing a fraction of values from each tail.

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fun DoubleArray.trimmedStd(proportion: Double, kind: PopulationKind = SAMPLE): Double
fun Iterable<Double>.trimmedStd(proportion: Double, kind: PopulationKind = SAMPLE): Double
fun Sequence<Double>.trimmedStd(proportion: Double, kind: PopulationKind = SAMPLE): Double

Computes the standard deviation of the values after removing a fraction from each tail.

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fun DoubleArray.trimmedVariance(proportion: Double, kind: PopulationKind = SAMPLE): Double
fun Iterable<Double>.trimmedVariance(proportion: Double, kind: PopulationKind = SAMPLE): Double
fun Sequence<Double>.trimmedVariance(proportion: Double, kind: PopulationKind = SAMPLE): Double

Computes the variance of the values after removing a fraction from each tail.

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fun DoubleArray.variance(kind: PopulationKind = SAMPLE): Double

Computes the variance of the values in this array.

fun Iterable<Double>.variance(kind: PopulationKind = SAMPLE): Double

Computes the variance of the values in this iterable.

@JvmName(name = "varianceOfInt")
fun Iterable<Int>.variance(kind: PopulationKind = PopulationKind.SAMPLE): Double

Computes the variance of the Int values.

@JvmName(name = "varianceOfLong")
fun Iterable<Long>.variance(kind: PopulationKind = PopulationKind.SAMPLE): Double

Computes the variance of the Long values.

fun Sequence<Double>.variance(kind: PopulationKind = SAMPLE): Double

Computes the variance of the values in this sequence.

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fun DoubleArray.weightedMean(weights: DoubleArray): Double
fun Iterable<Double>.weightedMean(weights: Iterable<Double>): Double

Computes the weighted arithmetic mean of the values using the given weights.

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fun westernElectricRules(observations: DoubleArray, center: Double, sigma: Double): WesternElectricRulesResult

Applies the Western Electric Rules to the given observations to detect non-random patterns on a control chart.

fun westernElectricRules(observations: Iterable<Double>, center: Double, sigma: Double): WesternElectricRulesResult

Applies the Western Electric Rules to an Iterable of observations to detect non-random patterns on a control chart.

fun westernElectricRules(observations: Sequence<Double>, center: Double, sigma: Double): WesternElectricRulesResult

Applies the Western Electric Rules to a Sequence of observations to detect non-random patterns on a control chart.

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fun xBarRChart(subgroups: List<DoubleArray>): XBarRChartResult

Computes x-bar and R control chart limits for the given subgroups.

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fun xBarSChart(subgroups: List<DoubleArray>): XBarSChartResult

Computes x-bar and S control chart limits for the given subgroups.

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