Uses of Class
dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction
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Uses of AbstractBivariateRealFunction in dev.nm.analysis.differentiation.univariate
Subclasses of AbstractBivariateRealFunction in dev.nm.analysis.differentiation.univariate Modifier and Type Class Description class
DBeta
This is the first order derivative function of theBeta
function w.r.t x, \({\partial \over \partial x} \mathrm{B}(x, y)\). -
Uses of AbstractBivariateRealFunction in dev.nm.analysis.function.special.beta
Subclasses of AbstractBivariateRealFunction in dev.nm.analysis.function.special.beta Modifier and Type Class Description class
Beta
The beta function defined as: \[ B(x,y) = \frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}= \int_0^1t^{x-1}(1-t)^{y-1}\,dt, x > 0, y > 0 \]class
LogBeta
This class represents the log of Beta functionlog(B(x, y))
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Uses of AbstractBivariateRealFunction in dev.nm.analysis.function.special.gamma
Subclasses of AbstractBivariateRealFunction in dev.nm.analysis.function.special.gamma Modifier and Type Class Description class
GammaLowerIncomplete
The Lower Incomplete Gamma function is defined as: \[ \gamma(s,x) = \int_0^x t^{s-1}\,e^{-t}\,{\rm d}t = P(s,x)\Gamma(s) \] P(s,x) is the Regularized Incomplete Gamma P function.class
GammaRegularizedP
The Regularized Incomplete Gamma P function is defined as: \[ P(s,x) = \frac{\gamma(s,x)}{\Gamma(s)} = 1 - Q(s,x), s \geq 0, x \geq 0 \]class
GammaRegularizedPInverse
The inverse of the Regularized Incomplete Gamma P function is defined as: \[ x = P^{-1}(s,u), 0 \geq u \geq 1 \] Whens > 1
, we use the asymptotic inversion method. Whens <= 1
, we use an approximation of P(s,x) together with a higher-order Newton like method. In both cases, the estimated value is then improved using Halley's method, c.f.,HalleyRoot
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GammaRegularizedQ
The Regularized Incomplete Gamma Q function is defined as: \[ Q(s,x)=\frac{\Gamma(s,x)}{\Gamma(s)}=1-P(s,x), s \geq 0, x \geq 0 \] The algorithm used for computing the regularized incomplete Gamma Q function depends on the values of s and x.class
GammaUpperIncomplete
The Upper Incomplete Gamma function is defined as: \[ \Gamma(s,x) = \int_x^{\infty} t^{s-1}\,e^{-t}\,{\rm d}t = Q(s,x) \times \Gamma(s) \] The integrand has the same form as the Gamma function, but the lower limit of the integration is a variable. -
Uses of AbstractBivariateRealFunction in dev.nm.stat.timeseries.linear.univariate
Subclasses of AbstractBivariateRealFunction in dev.nm.stat.timeseries.linear.univariate Modifier and Type Class Description class
AutoCorrelationFunction
This is the auto-correlation function of a univariate time series {xt}.class
AutoCovarianceFunction
This is the auto-covariance function of a univariate time series {xt}. -
Uses of AbstractBivariateRealFunction in dev.nm.stat.timeseries.linear.univariate.sample
Subclasses of AbstractBivariateRealFunction in dev.nm.stat.timeseries.linear.univariate.sample Modifier and Type Class Description class
SampleAutoCorrelation
This is the sample Auto-Correlation Function (ACF) for a univariate data set.class
SampleAutoCovariance
This is the sample Auto-Covariance Function (ACVF) for a univariate data set.class
SamplePartialAutoCorrelation
This is the sample partial Auto-Correlation Function (PACF) for a univariate data set. -
Uses of AbstractBivariateRealFunction in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
Subclasses of AbstractBivariateRealFunction in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma Modifier and Type Class Description class
AutoCorrelation
Compute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.class
AutoCovariance
Computes the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model by recursion.
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