exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions,

Description: Set of tools to generate samples of order statistics and others quantities of interest from new families of distributions.
The main references for this package are: Gentle, J. (2009), Computational Statistics, Springer-Verlag;
Naradajah, S. and Rocha, R. (2016), <DOI:10.18637/jss.v069.i10> and
and exponentiated exponential Poisson G distributions, Poisson-inverse gaussian distribution, Skew normal type 1 distributions, Skew student t distribution, Sinh-Arcsinh distribution, Sichel distribution and Zero inflated Poisson.

Title: Sampling from k-th Order Statistics of New Families of

Description: Set of tools to generate samples of k-th order statistics and others quantities of interest from new families of distributions.
The main references for this package are: C. Kleiber and S. Kotz (2003) Statistical size distributions in economics and actuarial sciences; Gentle, J. (2009), Computational Statistics, Springer-Verlag;
The families of distributions are: Burr distributions, Feller-Pareto distributions, Generalized Pareto Distributions, The Inverse Paralogistic Distributions, Marshall-Olkin G distributions, exponentiated G distributions, beta G distributions,
gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions,
exponentiated exponential Poisson G distributions, Poisson-inverse gaussian distribution, Skew normal type 1 distributions, Skew student t distribution, Sinh-Arcsinh distribution, Sichel distribution, Zero inflated Poisson.

Title: Efficiently Generates Random Order Statistic Variables
Description: All the methods in this package generate a vector of uniform order statistics using a beta distribution and use an inverse cumulative distribution function for some distribution to give a vector of random order statistic variables for some distribution. This is much more efficient than using a loop since it is directly sampling from the order statistic distribution.
Packaged: 2017-06-11 20:40:27 UTC; optionsgambler