R Under development (unstable) (2023-08-27 r85021 ucrt) -- "Unsuffered Consequences"
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> library(mbbefd)
Loading required package: fitdistrplus
Loading required package: MASS
Loading required package: survival
Loading required package: alabama
Loading required package: numDeriv
Loading required package: Rcpp
Package:  mbbefd
Version:  0.8.11
Date:     
BugReport: https://github.com/spedygiorgio/mbbefd/issues


> library(fitdistrplus)
> 
> 
> #oibeta
> n <- 1e3
> nboot <- 1000
> nboot <- 10
> set.seed(12345)
> x <- roibeta(n, 3, 2, 1/6)
> 
> f1 <- fitDR(x, "oibeta", method="mle")
> summary(f1)
Fitting of the distribution ' oibeta ' by maximum likelihood 
Parameters : 
       estimate Std. Error
shape1 2.775297 0.13277006
shape2 1.821438 0.08325969
p1     0.163000 0.36936567
Loglikelihood:  -268.8183   AIC:  543.6365   BIC:  558.3598 
Correlation matrix:
          shape1    shape2  
shape1 1.0000000 0.8019798 0
shape2 0.8019798 1.0000000 0
       0.0000000 0.0000000 1

> 
> b1 <- bootDR(f1, niter=nboot)
> summary(b1)
Parametric bootstrap medians and 95% percentile CI 
         Median     2.5%    97.5%
shape1 2.821540 2.544975 3.048734
shape2 1.843019 1.703373 2.055265
p1     0.167000 0.154450 0.177775
> 
> plot(b1, enhance=TRUE, trueval=c(3, 2, 1/6))
> 
> hist(b1$estim[,1])
> hist(b1$estim[,2])
> hist(b1$estim[,3])
> 
> 
> f2 <- fitDR(x, "oigbeta", method="tlmme")
> summary(f2)
Parameters : 
        estimate
shape0 0.9166412
shape1 3.0162083
shape2 1.7998662
p1     0.1630052
Loglikelihood:  -268.8418   AIC:  545.6836   BIC:  565.3147 
> 
> gofstat(list(f1, f2))
Goodness-of-fit statistics
                             1-mle-oibeta 2-tlmme-oigbeta
Kolmogorov-Smirnov statistic     0.163000        0.163000
Cramer-von Mises statistic       1.463433        1.460025
Anderson-Darling statistic            Inf             Inf

Goodness-of-fit criteria
                               1-mle-oibeta 2-tlmme-oigbeta
Akaike's Information Criterion     543.6365        545.6836
Bayesian Information Criterion     558.3598        565.3147
> cdfcomp(list(f1, f2), do.points=FALSE)
> ppcomp(list(f1, f2))
> 
> proc.time()
   user  system elapsed 
   1.57    0.10    1.65