Description Usage Arguments Value Author(s) References Examples
Computes the pdf, cdf, quantile and random numbers of the exponentiated generalized G distribution due to Cordeiro et al. (2013) specified by the pdf
f (x) = a b g (x) ≤ft[ 1 - G (x) \right]^{a - 1} ≤ft\{ 1 - ≤ft[ 1 - G (x) \right]^a \right\}^{b - 1}
for G any valid cdf, g the corresponding pdf, a > 0, the first shape parameter, and b > 0, the second shape parameter. Also computes the Cramer-von Misses statistic, Anderson Darling statistic, Kolmogorov Smirnov test statistic and p-value, maximum likelihood estimates, Akaike Information Criterion, Consistent Akaikes Information Criterion, Bayesian Information Criterion, Hannan-Quinn information criterion, standard errors of the maximum likelihood estimates, minimum value of the negative log-likelihood function and convergence status when the distribution is fitted to some data
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x | 
 scaler or vector of values at which the pdf or cdf needs to be computed  | 
p | 
 scaler or vector of probabilities at which the quantile needs to be computed  | 
n | 
 number of random numbers to be generated  | 
a | 
 the value of the first shape parameter, must be positive, the default is 1  | 
b | 
 the value of the second shape parameter, must be positive, the default is 1  | 
spec | 
 a character string specifying the distribution of G and g (for example, "norm" if G and g correspond to the standard normal).  | 
log | 
 if TRUE then log(pdf) are returned  | 
log.p | 
 if TRUE then log(cdf) are returned and quantiles are computed for exp(p)  | 
lower.tail | 
 if FALSE then 1-cdf are returned and quantiles are computed for 1-p  | 
... | 
 other parameters  | 
g | 
 same as spec but must be one of chisquare ("chisq"), exponential ("exp"), F ("f"), gamma ("gamma"), lognormal ("lognormal"), Weibull ("weibull"), Burr XII ("burrxii"), Chen ("chen"), Frechet ("frechet"), Gompertz ("gompertz"), linear failure rate ("lfr"), log-logistic ("log-logistic"), Lomax ("lomax") and Rayleigh ("rayleigh").  Each of these distributions has one parameter (  | 
data | 
 a vector of data values for which the distribution is to be fitted  | 
starts | 
 initial values of   | 
method | 
 the method for optimizing the log likelihood function.  It can be one of   | 
An object of the same length as x, giving the pdf or cdf values computed at x or an object of the same length as p, giving the quantile values computed at p or an object of the same length as n, giving the random numbers generated or an object giving the values of Cramer-von Misses statistic, Anderson Darling statistic, Kolmogorov Smirnov test statistic and p-value, maximum likelihood estimates, Akaike Information Criterion, Consistent Akaikes Information Criterion, Bayesian Information Criterion, Hannan-Quinn information criterion, standard errors of the maximum likelihood estimates, minimum value of the negative log-likelihood function and convergence status.
Saralees Nadarajah, Ricardo Rocha
S. Nadarajah and R. Rocha, Newdistns: An R Package for New Families of Distributions, Journal of Statistical Software, 69(10), 1-32, doi:10.18637/jss.v069.i10
G. M. Cordeiro, E. M. M. Ortega, D. C. C. da Cunha, The exponentiated generalized class of distributions, Journal of Data Science 11 (2013) 1-27
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