R Under development (unstable) (2024-07-10 r86888 ucrt) -- "Unsuffered Consequences"
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Platform: x86_64-w64-mingw32/x64

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> visualize <- FALSE # TRUE for manual tests with visualization of results
> require(fitdistrplus)
Loading required package: fitdistrplus
Loading required package: MASS
Loading required package: survival
> 
> myoptimize <- function(fn,par,ui,ci,...){
+   res <- constrOptim(f=fn,theta=par,method="Nelder-Mead",ui=ui,ci=ci, ...)
+   standardres <- c(res,convergence=0,value=res$objective,par=res$minimum,hessian=NA)
+   return(standardres)
+ }
> 
> 
> #one parameter example
> x <- rexp(100)
> 
> #binding example 
> fitdist(x, "exp", custom.optim=myoptimize, ui=1, ci=2, start=list(rate=10))
Fitting of the distribution ' exp ' by maximum likelihood 
Parameters:
     estimate
rate        2
> fitdist(x, "exp", lower= 2, optim.method="L-BFGS-B")
Fitting of the distribution ' exp ' by maximum likelihood 
Parameters:
     estimate Std. Error
rate        2  0.1999999
> 
> #two parameter example
> 
> if(visualize) {  # check ERROR on aarch64-apple-darwin20.4.0 (64-bit) (2021/05/12)
+   x <- rbeta(100, pi, 1/pi)
+ 
+     set.seed(1234)
+   fitdist(x, "beta")
+   
+   #binding example 
+   fitdist(x, "beta", custom.optim=myoptimize, ui=rbind(1,1), ci=c(1/2,1/2), start=list(shape1=5, shape2=5))
+   fitdist(x, "beta", lower= c(1/2,1/2), optim.method="L-BFGS-B")
+ }
> 
> 
> #true example
> library(GeneralizedHyperbolic)
> args(dnig)
function (x, mu = 0, delta = 1, alpha = 1, beta = 0, param = c(mu, 
    delta, alpha, beta)) 
NULL
> 
> 
> x <- rnig(100, 3, 1/2, 1/2, 1/4)
> ui<-rbind(c(0,1,0,0),c(0,0,1,0),c(0,0,1,-1),c(0,0,1,1))
> ci<-c(0,0,0,0)
> fitdist(x, "nig", custom.optim=myoptimize, ui=ui, ci=ci, start=list(mu = 0, delta = 1, alpha = 1, beta = 0))
Fitting of the distribution ' nig ' by maximum likelihood 
Parameters:
       estimate
mu    2.9008141
delta 0.7821121
alpha 1.0770687
beta  0.3289673
Warning messages:
1: In fitdist(x, "nig", custom.optim = myoptimize, ui = ui, ci = ci,  :
  The dnig function should return a vector of with NaN values when input has inconsistent parameters and not raise an error
2: In fitdist(x, "nig", custom.optim = myoptimize, ui = ui, ci = ci,  :
  The pnig function should return a vector of with NaN values when input has inconsistent values and not raise an error
> 
> 
> proc.time()
   user  system elapsed 
   1.67    0.25    1.90