Continuous (FSharp.Stats)

Continuous Module

Namespace: FSharp.Stats.Distributions
Assembly: FSharp.Stats.dll

Types

Type	Description
Beta	Beta distribution
Chi	Chi distribution.
ChiSquared	ChiSquared distribution.
Exponential	Exponential distribution.
F	F-distribution
Gamma	Gamma distribution Sampling implementation based on: "A Simple Method for Generating Gamma Variables" - Marsaglia & Tsang ACM Transactions on Mathematical Software, Vol. 26, No. 3, September 2000, Pages 363-372.
LogNormal	Log-Normal distribution.
MultivariateNormal	multivariate normal distribution.
Normal	Normal distribution.
StudentT	Student's T-distribution
StudentizedRange	Studentized range (q) distribution. Used in Tukey's HSD post hoc test. method from: QUANTILES FROM THE MAXIMUM STUDENTIZED RANGE DISTRIBUTION, Ferreira, Rev. Mat. Estat., v.25, n.1, p.117-135, 2007 table from: Tables of range and studentized range, Harter, 1960 and Lawal B, Applied Statistical Methods in Agriculture, Health and Life Sciences, DOI 10.1007/978-3-319-05555-8, 2014
Tails
Uniform	Uniform distribution.

Functions and values

Function or value	Description
`beta alpha beta` Full Usage: `beta alpha beta` Parameters: alpha : `float` beta : `float` Returns: `Distribution<float, float>`	Initializes a Beta distribution alpha : `float` beta : `float` Returns: `Distribution<float, float>`
`chi dof` Full Usage: `chi dof` Parameters: dof : `float` Returns: `Distribution<float, float>`	Initializes a Chi distribution dof : `float` Returns: `Distribution<float, float>`
`chiCheckParam dof` Full Usage: `chiCheckParam dof` Parameters: dof : `float`	dof : `float`
`chiSquared dof` Full Usage: `chiSquared dof` Parameters: dof : `float` Returns: `Distribution<float, float>`	Initializes a ChiSquared distribution dof : `float` Returns: `Distribution<float, float>`
`chiSquaredCheckParam dof` Full Usage: `chiSquaredCheckParam dof` Parameters: dof : `float`	dof : `float`
`expCheckParam lambda` Full Usage: `expCheckParam lambda` Parameters: lambda : `float`	lambda : `float`
`exponential lambda` Full Usage: `exponential lambda` Parameters: lambda : `float` Returns: `Distribution<float, float>`	Initializes a Exponential distribution lambda : `float` Returns: `Distribution<float, float>`
`f dof1 dof2` Full Usage: `f dof1 dof2` Parameters: dof1 : `float` dof2 : `float` Returns: `Distribution<float, float>`	Initializes a F-distribution dof1 : `float` dof2 : `float` Returns: `Distribution<float, float>`
`fCheckParam dof1 dof2` Full Usage: `fCheckParam dof1 dof2` Parameters: dof1 : `float` dof2 : `float`	dof1 : `float` dof2 : `float`
`gamma alpha beta` Full Usage: `gamma alpha beta` Parameters: alpha : `float` beta : `float` Returns: `Distribution<float, float>`	Initializes a Gamma distribution alpha : `float` beta : `float` Returns: `Distribution<float, float>`
`gammaCheckParam alpha beta` Full Usage: `gammaCheckParam alpha beta` Parameters: alpha : `float` beta : `float`	alpha : `float` beta : `float`
`getCriticalTValue df significanceLevel tailed` Full Usage: `getCriticalTValue df significanceLevel tailed` Parameters: df : `float` significanceLevel : `float` tailed : `Tails` Returns: `float`	df : `float` significanceLevel : `float` tailed : `Tails` Returns: `float`
`logNormal mu tau` Full Usage: `logNormal mu tau` Parameters: mu : `float` tau : `float` Returns: `Distribution<float, float>`	Initializes a Normal distribution mu : `float` tau : `float` Returns: `Distribution<float, float>`
`logNormalCheckParam mu tau` Full Usage: `logNormalCheckParam mu tau` Parameters: mu : `float` tau : `float`	mu : `float` tau : `float`
`multivariateNormal mu sigma` Full Usage: `multivariateNormal mu sigma` Parameters: mu : `vector` sigma : `matrix` Returns: `Distribution<vector, vector>`	Initializes a multivariate normal distribution with mean mu and covariance matrix sigma mu : `vector` sigma : `matrix` Returns: `Distribution<vector, vector>`
`multivariateNormalCheckParam mu sigma` Full Usage: `multivariateNormalCheckParam mu sigma` Parameters: mu : `vector` sigma : `matrix`	mu : `vector` sigma : `matrix`
`normal mu sigma` Full Usage: `normal mu sigma` Parameters: mu : `float` sigma : `float` Returns: `Distribution<float, float>`	Initializes a Normal distribution mu : `float` sigma : `float` Returns: `Distribution<float, float>`
`normalCheckParam mu sigma` Full Usage: `normalCheckParam mu sigma` Parameters: mu : `float` sigma : `float`	mu : `float` sigma : `float`
`studentT mu tau dof` Full Usage: `studentT mu tau dof` Parameters: mu : `float` tau : `float` dof : `float` Returns: `Distribution<float, float>`	Initializes a Student's T-distribution mu : `float` tau : `float` dof : `float` Returns: `Distribution<float, float>`
`studentTCheckParam mu tau dof` Full Usage: `studentTCheckParam mu tau dof` Parameters: mu : `float` tau : `float` dof : `float`	mu : `float` tau : `float` dof : `float`
`studentizedRange r v c accuracy computeParallel` Full Usage: `studentizedRange r v c accuracy computeParallel` Parameters: r : `float` v : `float` c : `float` accuracy : `int option` computeParallel : `bool` Returns: `Distribution<float, float>`	Initializes a studentized range distribution. Accuracy defines the number of steps within the CDF integration (Recommended: 1k-10k, default: 2k). pValue accuracy is minimum 3 digits for v>3. q:qValue r:number of treatments v:df (n-r) c:1. Integration can be performed in parallel using PSeq r : `float` v : `float` c : `float` accuracy : `int option` computeParallel : `bool` Returns: `Distribution<float, float>`
`studentizedRangeCheckParam q r v` Full Usage: `studentizedRangeCheckParam q r v` Parameters: q : `float` r : `float` v : `float`	Studentized range distribution helper functions. q : `float` r : `float` v : `float`
`uniform min max` Full Usage: `uniform min max` Parameters: min : `float` max : `float` Returns: `Distribution<float, float>`	Initializes a uniform distribution min : `float` max : `float` Returns: `Distribution<float, float>`
`uniformCheckParam min max` Full Usage: `uniformCheckParam min max` Parameters: min : `float` max : `float`	min : `float` max : `float`