R Under development (unstable) (2026-01-19 r89308 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:  0.8.14
Date:     
BugReport: https://github.com/spedygiorgio/mbbefd/issues


> library(fitdistrplus)
> 
> 
> #oigbeta
> n <- 1e3
> nboot <- 1000
> nboot <- 10
> set.seed(12345)
> x <- roigbeta(n, 3, 2, 5/2, 1/6)
> 
> 
> f1 <- fitDR(x, "oigbeta", method="mle", control=list(trace=1, REPORT=1, maxit=500)) #
initial  value -0.646713 
iter   1 value -0.646713
final  value -0.646713 
converged
> summary(f1)
Fitting of the distribution ' oigbeta ' by maximum likelihood 
Parameters : 
        estimate
shape0 5.8587369
shape1 0.9400734
shape2 2.8596635
p1     0.1690000
Loglikelihood:  83.12166   AIC:  -158.2433   BIC:  -138.6123 
> 
> b1 <- bootDR(f1, niter=nboot, silent=TRUE)
> summary(b1)
Parametric bootstrap medians and 95% percentile CI 
         Median     2.5%    97.5%
shape0 5.875740 3.525142 8.216636
shape1 1.004075 0.632711 1.884067
shape2 2.988703 2.777757 3.288232
p1     0.164500 0.155575 0.174100
> 
> plot(b1, enhance=TRUE, trueval=c(3, 2, 5/2, 1/6))
> 
> f2 <- fitDR(x, "oigbeta", method="tlmme")
> summary(f2)
Parameters : 
        estimate
shape0 0.9904635
shape1 7.0770854
shape2 2.4575438
p1     0.1690003
Loglikelihood:  81.77526   AIC:  -155.5505   BIC:  -135.9195 
> 
> gofstat(list(f1, f2))
Goodness-of-fit statistics
                             1-mle-oigbeta 2-tlmme-oigbeta
Kolmogorov-Smirnov statistic      0.169000        0.169000
Cramer-von Mises statistic        1.647926        1.690731
Anderson-Darling statistic             Inf             Inf

Goodness-of-fit criteria
                               1-mle-oigbeta 2-tlmme-oigbeta
Akaike's Information Criterion     -158.2433       -155.5505
Bayesian Information Criterion     -138.6123       -135.9195
> cdfcomp(list(f1, f2), do.points=FALSE, ylogscale = TRUE)
> ppcomp(list(f1, f2))
> 
> 
> proc.time()
   user  system elapsed 
   6.96    0.65    7.60