Uses of Class
dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction

Packages that use AbstractBivariateRealFunction

Package	Description
dev.nm.analysis.differentiation.univariate
dev.nm.analysis.function.special.beta
dev.nm.analysis.function.special.gamma
dev.nm.stat.timeseries.linear.univariate
dev.nm.stat.timeseries.linear.univariate.sample
dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma

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 the Beta 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 function log(B(x, y)).

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: \[ x = P^{-1}(s,u), 0 \geq u \geq 1 \] When s > 1, we use the asymptotic inversion method. When s <= 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.
class	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.