R Under development (unstable) (2024-07-10 r86888 ucrt) -- "Unsuffered Consequences"
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> library(fitdistrplus)
Loading required package: MASS
Loading required package: survival
> library(actuar)

Attaching package: 'actuar'

The following objects are masked from 'package:stats':

    sd, var

The following object is masked from 'package:grDevices':

    cm

> 
> data(groundbeef)
> obs <- groundbeef$serving
> 
> gammastart <- fitdistrplus:::startargdefault(obs, "gamma")
> ll <- fitdistrplus:::manageparam(gammastart, list(rate = 0.5), obs, "gamma")
> 
> fitdist(obs, "gamma", fix.arg = list(rate = 0.5))
Fitting of the distribution ' gamma ' by maximum likelihood 
Parameters:
      estimate Std. Error
shape 32.83605  0.3568157
Fixed parameters:
     value
rate   0.5
> fitdist(obs, "gamma", fix.arg = list(scale = 2))
Fitting of the distribution ' gamma ' by maximum likelihood 
Parameters:
      estimate Std. Error
shape 32.83605  0.3568157
Fixed parameters:
      value
scale     2
> 
> 
> pareto2start <- fitdistrplus:::startargdefault(obs, "pareto2")
> ll <- fitdistrplus:::manageparam(pareto2start, list(rate = 0.5), obs, "pareto2")
> 
> fitdist(obs, "pareto2", fix.arg = list(rate = 0.5), lower=0)
Fitting of the distribution ' pareto2 ' by maximum likelihood 
Parameters:
        estimate Std. Error
min    9.9999999        NaN
shape  0.3019848 0.07920733
Fixed parameters:
     value
rate   0.5
Warning messages:
1: In cov2cor(varcovar) :
  diag(V) had non-positive or NA entries; the non-finite result may be dubious
2: In sqrt(diag(varcovar)) : NaNs produced
> fitdist(obs, "pareto2", fix.arg = list(scale = 2), lower=0)
Fitting of the distribution ' pareto2 ' by maximum likelihood 
Parameters:
        estimate Std. Error
min    9.9999999        NaN
shape  0.3019848 0.07920733
Fixed parameters:
      value
scale     2
Warning messages:
1: In cov2cor(varcovar) :
  diag(V) had non-positive or NA entries; the non-finite result may be dubious
2: In sqrt(diag(varcovar)) : NaNs produced
> 
> proc.time()
   user  system elapsed 
   1.62    0.34    1.95