quagov

Nonexceedance probability ($0 \le F \le 1$).

The parameters from <code><a href="/link/pargov?package=lmomco&version=2.0.1" rd-options="" data-mini-rdoc="lmomco::pargov">pargov</a></code> or similar.

para

A logical controlling whether the parameters and checked for validity. Overriding of this check might be extremely important and needed for use of the distribution quantile function in the context of TL-moments with nonzero trimming.

paracheck

This function computes the quantiles of the Govindarajulu distribution given
parameters ($\xi$, $\alpha$, and $\beta$) of the distribution computed by
<code><a href="/link/pargov?package=lmomco&version=2.0.1" rd-options="" data-mini-rdoc="lmomco::pargov">pargov</a></code>. The quantile function of the distribution is$$x(F) = \xi + \alpha[(\beta+1)F^\beta - \beta F^{\beta+1}] \mbox{,}$$where $x(F)$ is the quantile for nonexceedance probability $F$, $\xi$ is a location parameter, $\alpha$ is a scale parameter, and $\beta$ is a shape parameter.

distribution

quantile function

The package is a comparatively comprehensive implementation of the theory of L-moments. L-moment, probability-weighted moment (PWM), and parameter estimation for numerous familiar and not-so-familiar distributions are provided. L-moment estimate for the same distributions are provided. L-moments are analogous to product moments; however, L-moments have many advantages including unbiasedness, robustness, and consistency. L-moments can outperform maximum likelihood for small to moderate samples. The package originally was originally oriented around circa 1996-FORTRAN algorithms by Hosking, and the nomenclature parallels that of Hosking. The Hosking algorithms later became available in the lmom package. However, vast extensions, components, concepts, more distributions, L-moment curiosities, and research topics are added. Such extensions the Sen weighted mean, Gini mean difference, plotting positions, and conditional probability adjustments. L-moment ratio diagrams are supported. L-moment support for right-tail and left-tail censoring by known or unknown censoring threshold and also by indicator variable. Asymmetric trimmed L-moments are supported as are numerical integration to dynamically compute trajectories of select TL-moment ratios for the construction of TL-moment ratio diagrams. L-moments have multi-variate analogs; the sample L-comoments are implemented and might have considerable application with copulas because L-comoments measure asymmetric association and higher comoments or comovements of variables. Support exists for exact analytical boot-strap estimates of order statistics, L-moments, and variances-covariances of L-moments. The Harri-Coble Tau34-squared Test for Normality using sample L-skew and L-kurtosis is available. Support for the following distributions, with moment type shown as "L" (L-moments) or "TL" (trimmed L-moments) and additional support for right-tail censoring ([RC]) include, is available: Asymmetric Exponential Power (L), Cauchy (TL), Eta-Mu (L), Exponential (L), Gamma (L), Generalized Extreme Value (L), Generalized Lambda (L & TL), Generalized Logistic (L), Generalized Normal (L), Generalized Pareto (L[RC] & TL), Govindarajulu (L), Gumbel (L), Kappa (L), Kappa-Mu (L), Kumaraswamy (L), Laplace (L), Normal (L), 3-parameter log-Normal (L), Pearson Type III (L), Rayleigh (L), Reverse Gumbel (L[RC]), Rice/Rician (L), Slash (TL), 3-parameter Student T (L), Truncated Exponential (L), Wakeby (L), and Weibull (L).

quagov: Quantile Function of the Govindarajulu Distribution

Description

Usage

Arguments

Value

References

See Also

Examples