Abstract
In this paper, we introduce the R package gendist that computes the probability density function, the cumulative distribution function, the quantile function and generates random values for several generated probability distribution models including the mixture model, the composite model, the folded model, the skewed symmetric model and the arc tan model. These models are extensively used in the literature and the R functions provided here are flexible enough to accommodate various univariate distributions found in other R packages. We also show its applications in graphing, estimation, simulation and risk measurements.
Highlights
Various probability distribution models have been proposed in the past and the number increases with time
We introduce the R package gendist that computes the probability density function, the cumulative distribution function, the quantile function and generates random values for several generated probability distribution models including the mixture model, the composite model, the folded model, the skewed symmetric model and the arc tan model
To name a few: a logistic mixture distribution model has been applied on polychotomous item responses [4]; mixture of logistic has been proposed to fit long tail distributions in analyzing network performance [5]; Rayleigh mixture model has been studied for plaque characterization in intravascular ultrasound [6]; a finite mixture of two Weibull distributions has been suggested to model the diameter distributions of rotatedsigmoid, uneven-aged stands [7]; two-component mixture Weibull statistics has been used to PLOS ONE | DOI:10.1371/journal.pone
Summary
Various probability distribution models have been proposed in the past and the number increases with time. Distribution models provided in the R package gendist include the mixture, the composite, the folded, the skewed symmetric and the arc tan models. Computation functions of these models are given for probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and random generated values (rg). The gendist package follows similar rule to define all the functions related to the generated probability distribution models. The following commands describe the plotting of CTE with respect to α: R> curve((1/(x))Ãintegrate(function(x)qfolded(1-x/2, spec = “norm”, arg = c(mean = 0.8, sd = 0.9), interval = c(0,100)), x, 1)\$value, xlim = c(0,1), ylim = c(0,20), xlab = expression(alpha), ylab = “CTE”).
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