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	ARMA model fitting and forecasting from an ordered observation history.

Uses of AbstractBivariateRealFunction in dev.nm.analysis.differentiation.univariate

Subclasses of AbstractBivariateRealFunction in dev.nm.analysis.differentiation.univariate

Modifier and Type	Class and Description
class	DBeta This is the first order derivative function of the Beta function w.r.t x, \({\partial \over \partial x} \mathrm{B}(x, y)\). \[ {\partial \over \partial x} \mathrm{B}(x, y) = \mathrm{B}(x, y) (\psi(x) - \psi(x + y)) \] where \(x > 0, y > 0\), \(\psi\) is the Digamma function.

Uses of AbstractBivariateRealFunction in dev.nm.analysis.function.special.beta

Subclasses of AbstractBivariateRealFunction in dev.nm.analysis.function.special.beta

Modifier and Type	Class and Description
class	Beta The beta function defined as: \[ B(x,y) = \frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}= \int_0^1t^{x-1}(1-t)^{y-1}\,dt, x > 0, y > 0 \] The R equivalent function is beta.
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 and 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: \[ P(s,x) = \frac{\gamma(s,x)}{\Gamma(s)} = 1 - Q(s,x), s \geq 0, x \geq 0 \] The R equivalent function is pgamma.
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.
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 and Description
class	AutoCorrelationFunction This is the auto-correlation function of a univariate time series \(\{x_{t}\}\).
class	AutoCovarianceFunction This is the auto-covariance function of a univariate time series \(\{x_{t}\}\).

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 and 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 and Description
class	AutoCorrelation Compute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that \(EX_{t} = 0\).
class	AutoCovariance Computes the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model by recursion.