are.parkmu.valid

A distribution parameter list returned by <code>parkmu</code>.

para

A logical switch on warning surpression. If <code>TRUE</code> then <code>options(warn=-1)</code> is made and restored on return. This switch is to permit calls in which warnings are not desired as the user knows how to handle the returned value---say in a

nowarn

The distribution parameter object returned by functions of this package such as by <code>parkmu</code> are consistent with the corresponding distribution, otherwise a list would not have been returned.  However, other functions (<code><a href="/link/pdfkmu?package=lmomco&version=2.0.1" rd-options="" data-mini-rdoc="lmomco::pdfkmu">pdfkmu</a></code>, <code><a href="/link/cdfkmu?package=lmomco&version=2.0.1" rd-options="" data-mini-rdoc="lmomco::cdfkmu">cdfkmu</a></code>, <code><a href="/link/quakmu?package=lmomco&version=2.0.1" rd-options="" data-mini-rdoc="lmomco::quakmu">quakmu</a></code>, and <code><a href="/link/lmomkmu?package=lmomco&version=2.0.1" rd-options="" data-mini-rdoc="lmomco::lmomkmu">lmomkmu</a></code> require consistent parameters to return the cumulative probability (nonexceedance), quantile, and L-moments of the distribution, respectively.  These functions internally use the <code>are.parkmu.valid</code> function. The documentation <code><a href="/link/pdfkmu?package=lmomco&version=2.0.1" rd-options="" data-mini-rdoc="lmomco::pdfkmu">pdfkmu</a></code> provides the conditions on valid parameters and their interrelation.

utility (distribution)

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).

are.parkmu.valid: Are the Distribution Parameters Consistent with the Kappa-Mu Distribution

Description

Usage

Arguments

Value

See Also

Examples