| Name | Description |
betaCDF(x, alpha, beta)
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betaCDFR(x, alpha, beta)
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betaPDF(x, alpha, beta)
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binomialCDF(k, n, p)
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P(K <= k) where K is random variable.
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binomialCDFR(k, n, p)
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P(K >= k) where K is random variable.
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binomialPMF(k, n, p)
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cauchyCDF(X, X0, gamma)
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cauchyCDFR(X, X0, gamma)
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cauchyPDF(X, X0, gamma)
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chiSquareCDF(x, v)
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χ,2 distribution function and its complement.
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chiSquareCDFR(x, v)
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chiSquarePDF(x, v)
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dirichletPDF(x, alpha)
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The Dirichlet probability density.
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exponentialCDF(x, lambda)
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exponentialCDFR(x, lambda)
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exponentialPDF(x, lambda)
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fisherCDF(x, df1, df2)
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The Fisher distribution, its complement, and inverse.
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fisherCDFR(x, df1, df2)
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The Fisher distribution, its complement, and inverse.
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gammaCDF(x, rate, shape)
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gammaCDFR(x, rate, shape)
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gammaPDF(x, rate, shape)
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hyperExact(x, n1, n2, n, startAt)
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hypergeometricCDF(x, n1, n2, n)
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P(X <= x), where X is random variable. Uses either direct summation,
normal or binomial approximation depending on parameters.
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hypergeometricCDFR(x, n1, n2, n)
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P(X >= x), where X is random variable.
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hypergeometricPMF(x, n1, n2, n)
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invBetaCDF(p, alpha, beta)
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invBinomialCDF(pVal, n, p)
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Returns the value of k for the given p-value, n and p. If p-value does
not exactly map to a value of k, the value for which binomialCDF(k, n, p)
is closest to pVal is used.
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invCauchyCDF(p, X0, gamma)
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invChiSquareCDFR(v, p)
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Inverse of complemented χ, 2 distribution
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invExponentialCDF(p, lambda)
|
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invFisherCDFR(df1, df2, p)
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Inverse of complemented Fisher distribution
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invGammaCDF(p, rate, shape)
|
This just calls invGammaCDFR w/ 1 - p b/c invGammaCDFR is more accurate,
but this function is necessary for consistency.
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invGammaCDFR(p, rate, shape)
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invLaplaceCDF(p, mu, b)
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invNegBinomCDF(pVal, n, p)
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invNormalCDF(p, mean, sd)
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Inverse of Normal distribution function
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invPoissonCDF(pVal, lambda)
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Returns the value of k for the given p-value and lambda. If p-val
doesn't exactly map to a value of k, the k for which poissonCDF(k, lambda)
is closest to pVal is used.
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invStudentsTCDF(p, df)
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Inverse of Student's t distribution
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kolmDist(x)
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kolmogorovDistrib(x)
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Kolmogorov distribution. Used in Kolmogorov-Smirnov testing.
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laplaceCDF(X, mu, b)
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laplaceCDFR(X, mu, b)
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laplacePDF(x, mu, b)
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logisticCDF(x, loc, shape)
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logNormalCDF(x, mu, sigma)
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logNormalCDFR(x, mu, sigma)
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logNormalPDF(x, mu, sigma)
|
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negBinomCDF(k, n, p)
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Negative binomial distribution.
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negBinomCDFR(k, n, p)
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Probability that k or more failures precede the nth success.
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negBinomPMF(k, n, p)
|
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normalCDF(x, mean, stdev)
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P(X < x) for normal distribution where X is random var.
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normalCDFR(x, mean, stdev)
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P(X > x) for normal distribution where X is random var.
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normalPDF(x, mean, sd)
|
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normApproxHyper(x, n1, n2, n)
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parametrize(parameters)
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Takes a distribution function (CDF or PDF/PMF) as a template argument, and
parameters as function arguments in the order that they appear in the
function declaration and returns a delegate that binds the supplied
parameters to the distribution function. Assumes the non-parameter
argument is the first argument to the distribution function.
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paramFunctor(parameters)
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Takes a distribution function (CDF or PDF/PMF) as a template argument, and
parameters as function arguments in the order that they appear in the
function declaration and returns a functor that binds the supplied
parameters to the distribution function. Assumes the non-parameter
argument is the first argument to the distribution function.
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poissonCDF(k, lambda)
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P(K <= k) where K is r.v.
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poissonCDFR(k, lambda)
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P(K >= k) where K is r.v.
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poissonPMF(k, lambda)
|
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rayleighCDF(x, mode)
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studentsTCDF(t, df)
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studentsTCDFR(t, df)
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studentsTPDF(t, df)
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uniformCDF(X, lower, upper)
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uniformCDFR(X, lower, upper)
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uniformPDF(X, lower, upper)
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waldCDF(x, mu, lambda)
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weibullCDF(x, shape, scale)
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weibullCDFR(x, shape, scale)
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weibullPDF(x, shape, scale)
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