final def !=(arg0: Any): Boolean

Definition Classes: AnyRef → Any

final def ##(): Int

Definition Classes: AnyRef → Any

final def ==(arg0: Any): Boolean

Definition Classes: AnyRef → Any

def _normalCDF(x: Double, pr: Parameters = null): Double

Compute the Cumulative Distribution Function (CDF) for the Normal distribution.

x: the x coordinate, argument to F(x)
pr: parameters for the mean and standard deviation

def _normalCDF(x: Double): Double

Compute the Cumulative Distribution Function (CDF) 'F(x)' for the Standard Normal distribution using the Hart function.

Compute the Cumulative Distribution Function (CDF) 'F(x)' for the Standard Normal distribution using the Hart function. Recoded in Scala from C code

x: the x coordinate, argument to F(x)

See also: www.codeplanet.eu/files/download/accuratecumnorm.pdf
stackoverflow.com/questions/2328258/cumulative-normal-distribution-function-in-c-c which was recoded from VB code.

final def asInstanceOf[T0]: T0

Definition Classes: Any

def buildEmpiricalCDF(x: VectorD): (VectorD, VectorD)

Build an empirical CDF from input data vector 'x'.

Build an empirical CDF from input data vector 'x'. Ex: x = (2, 1, 2, 3, 2) -> cdf = ((1, .2), (2, .8), (3, 1.))

x: the input data vector

def chiSquareCDF(x: Double, pr: Parameters = null): Double

Compute the Cumulative Distribution Function (CDF) for the ChiSquare distribution.

x: the x coordinate, argument to F(x)

def chiSquareCDF(x: Double, df: Int): Double

Compute the Cumulative Distribution Function (CDF) for the ChiSquare distribution by numerically integrating the ChiSquare probability density function (pdf).

Compute the Cumulative Distribution Function (CDF) for the ChiSquare distribution by numerically integrating the ChiSquare probability density function (pdf). See Variate.scala.

x: the x coordinate, argument to F(x)
df: the degrees of freedom

def clone(): AnyRef

Attributes: protected[java.lang]
Definition Classes: AnyRef
Annotations: @native() @throws( ... )

def empiricalCDF(x: Double, eCDF: (VectorD, VectorD)): Double

Compute the Cumulative Distribution Function 'CDF' 'F(x)' for the Empirical distribution 'eCDF'.

x: the x coordinate, argument to F(x)
eCDF: the Empirical CDF

final def eq(arg0: AnyRef): Boolean

Definition Classes: AnyRef

def equals(arg0: Any): Boolean

Definition Classes: AnyRef → Any

def exponentialCDF(x: Double, pr: Parameters = null): Double

Compute the Cumulative Distribution Function 'CDF' 'F(x)' for the Exponential distribution.

x: the x coordinate, argument to F(x)

def exponentialCDF(x: Double, λ: Double): Double

Compute the Cumulative Distribution Function 'CDF' 'F(x)' for the Exponential distribution.

x: the x coordinate, argument to F(x)
λ: the rate parameter

def finalize(): Unit

Attributes: protected[java.lang]
Definition Classes: AnyRef
Annotations: @throws( classOf[java.lang.Throwable] )

def fisherCDF(x: Double, pr: Parameters = null): Double

Compute the Cumulative Distribution Function (CDF) for the Fisher (F) distribution using beta functions.

x: the x coordinate, argument to F(x)

def fisherCDF(x: Double, df: (Int, Int)): Double

Compute the Cumulative Distribution Function (CDF) for the Fisher (F) distribution using beta functions.

x: the x coordinate, argument to F(x)
df: the pair of degrees of freedom ('df1', 'df2')

def fisherCDF(x: Double, df1: Int, df2: Int): Double

Compute the Cumulative Distribution Function (CDF) for the Fisher (F) distribution using beta functions.

x: the x coordinate, argument to F(x)
df1: the degrees of freedom 1 (numerator)
df2: the degrees of freedom 2 (denominator)

final def flaw(method: String, message: String): Unit

Show the flaw by printing the error message.

method: the method where the error occurred
message: the error message

Definition Classes: Error

final def getClass(): Class[_]

Definition Classes: AnyRef → Any
Annotations: @native()

def hashCode(): Int

Definition Classes: AnyRef → Any
Annotations: @native()

final def isInstanceOf[T0]: Boolean

Definition Classes: Any

final def ne(arg0: AnyRef): Boolean

Definition Classes: AnyRef

def noncentralTCDF(x: Double, mu: Double, df: Double): Double

Compute the Cumulative Distribution Function (CDF) for "Noncentral t" distribution.

x: the x coordinate, argument to F(x)
mu: the noncentrality parameter (or mean)
df: the degrees of freedom (must be > 0.0)

See also: https://en.wikipedia.org/wiki/Noncentral_t-distribution

def normalCDF(x: Double, pr: Parameters = null): Double

Compute the Cumulative Distribution Function (CDF) for the Normal distribution.

x: the x coordinate, argument to F(x)
pr: parameters for the mean and standard deviation

def normalCDF(x: Double): Double

Compute the Cumulative Distribution Function 'CDF' 'F(x)' for the Standard Normal distribution using the Hart function.

Compute the Cumulative Distribution Function 'CDF' 'F(x)' for the Standard Normal distribution using the Hart function. Recoded in Scala from Java code. Apache license given above.

x: the x coordinate, argument to F(x)

See also: mail-archives.apache.org/mod_mbox/commons-dev/200401.mbox/%3C20040126030431.92035.qmail@minotaur.apache.org%3E

final def notify(): Unit

Definition Classes: AnyRef
Annotations: @native()

final def notifyAll(): Unit

Definition Classes: AnyRef
Annotations: @native()

def studentTCDF(x: Double, pr: Parameters = null): Double

Compute the Cumulative Distribution Function (CDF) for "Student's t" distribution.

x: the x coordinate, argument to F(x)
pr: parameter for the degrees of freedom

def studentTCDF(x: Double, df: Double): Double

Compute the Cumulative Distribution Function (CDF) for "Student's t" distribution.

x: the x coordinate, argument to F(x)
df: the degrees of freedom (must be > 0.0)

See also: [JKB 1995] Johnson, Kotz & Balakrishnan "Continuous Univariate Distributions" (Volume 2) (2nd Edition) (Chapter 28) (1995)

final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes: AnyRef

def toString(): String

Definition Classes: AnyRef → Any

def uniformCDF(x: Double, pr: Parameters = null): Double

Compute the Cumulative Distribution Function 'CDF' 'F(x)' for the Uniform distribution.

x: the x coordinate, argument to F(x)
pr: parameters giving the end-points of the uniform distribution

def uniformCDF(x: Double, a: Double, b: Double): Double

Compute the Cumulative Distribution Function 'CDF' 'F(x)' for the Uniform distribution.

x: the x coordinate, argument to F(x)
a: the lower end-point of the uniform distribution
b: the upper end-point of the uniform distribution

final def wait(): Unit

Definition Classes: AnyRef
Annotations: @throws( ... )

final def wait(arg0: Long, arg1: Int): Unit

Definition Classes: AnyRef
Annotations: @throws( ... )

final def wait(arg0: Long): Unit

Definition Classes: AnyRef
Annotations: @native() @throws( ... )

def weibullCDF(x: Double, pr: Parameters = null): Double

Compute the Cumulative Distribution Function 'CDF' 'F(x)' for the Weibull distribution.

x: the x coordinate, argument to F(x)

def weibullCDF(x: Double, α: Double, β: Double): Double

Compute the Cumulative Distribution Function 'CDF' 'F(x)' for the Weibull distribution.

x: the x coordinate, argument to F(x)
α: the shape parameter
β: the scale parameter

Packages

CDF

object CDF extends Error

Value Members

Inherited from Error

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Packages

CDF 

object CDF extends Error

Value Members

Inherited from Error

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CDF