KSLCore/ksl.utilities.distributions/NegativeBinomial/Companion

Companion

object Companion

Functions

canMatchMoments

fun canMatchMoments(vararg moments: Double): Boolean

createFromMoments

fun createFromMoments(vararg moments: Double): NegativeBinomial

invCDFViaNormalApprox

fun invCDFViaNormalApprox(x: Double, r: Double, p: Double): Int

negBinomialCCDF

fun negBinomialCCDF(j: Int, r: Double, p: Double, recursive: Boolean = true): Double

Allows static computation of complementary cdf function assumes that distribution's range is {0,1, ...}

fun negBinomialCDF(j: Int, r: Double, p: Double, recursive: Boolean = true): Double

Allows static computation of the CDF assumes that distribution's range is {0,1, ...}

negBinomialInvCDF

fun negBinomialInvCDF(x: Double, r: Double, p: Double, recursive: Boolean = true): Int

Returns the quantile associated with the supplied probability, x assumes that distribution's range is {0,1, ...}

fun negBinomialLF1(j: Int, r: Double, p: Double, recursive: Boolean = true): Double

Allows static computation of 1st order loss function assumes that distribution's range is {0,1, ...}

fun negBinomialLF2(j: Int, r: Double, p: Double, recursive: Boolean = true): Double

Allows static computation of 2nd order loss function assumes that distribution's range is {0,1, ...}

fun negBinomialPMF(j: Int, r: Double, p: Double, recursive: Boolean = true): Double

Allows static computation of prob mass function assumes that distribution's range is {0,1, ...}

parametersFromMoments

fun parametersFromMoments(vararg moments: Double): DoubleArray

fun recursiveCDF(j: Int, r: Double, p: Double): Double

Computes the cdf at j using a recursive (iterative) algorithm using logarithms

fun recursivePMF(j: Int, r: Double, p: Double): Double

Computes the probability mass function at j using a recursive (iterative) algorithm using logarithms