R help - Apr 2019 - Problem to find the Maximum likelihood estimates of generalized normal distribution in R

First I estimated the parameters of exponentiated generalized normal
distribution using the dataset (data1: generated from normal distribution). Then
I used real dataset (data2) and tried to find the maximum likelihood estimates
(MLE) using the AdequacyModel packages. It gives the error message (mentioned
right below the code).
Why does the same code estimate for first dataset (data1), but it doesn't
estimate for the second dataset (data2)?
Is my way of plugging the baseline distribution (normal) in generalized class of
distributions F(x)= [1-(1-G(x))^beta]^gamma introduced by Cordeiro et al.(2013)
in R environment right? Am I defining the cdf and pdf of an generalized normal
distribution in R in right way?

# First Problem

library(AdequacyModel)

data1 <- rnorm(100)

pdf_exps <- function(par,x){
beta = par[1]
gamma= par[2]
mean = par[3]
sd = par[4]
             ( beta*gamma* ((1-(1-(pnorm(x,mean,sd)))^beta)^(gamma-1)) *
((1-(pnorm(x,mean,sd)))^(beta-1)) ) * (dnorm(x,mean,sd))
}


cdf_exps <- function(par,x){
beta = par[1]
gamma= par[2]
mean = par[3]
sd = par[4]
( 1-(1-(pnorm(x,mean,sd)))^beta)^gamma 
}


set.seed(1)
result_1 = goodness.fit(pdf = pdf_exps, cdf = cdf_exps,
starts = c(1,1,1,1),data = data1  , method = "BFGS",
domain = c(-Inf,Inf), lim_inf = c(0,0,0,0),
lim_sup = c(2,2,2,2), S = 250, prop=0.1, N=50)
result_1$mle

 5.688120 4.413153 1.115777 1.996108

#----------------------------------------

data2 <-c( 20.56, 20.67, 21.86, 21.88, 18.96, 21.04, 21.69, 20.62, 22.64,
19.44, 25.75, 21.20,
  22.03, 25.44, 22.63, 21.86, 22.27, 21.27, 23.47, 23.19, 23.17, 24.54, 22.96,
19.76,
  23.36, 22.67, 24.24, 24.21, 20.46, 20.81, 20.17, 23.06, 24.40, 23.97, 22.62,
19.16,
  21.15, 21.40, 21.03, 21.77, 21.38, 21.47, 24.45, 22.63, 22.80, 23.58, 20.06,
23.01,
  24.64, 18.26, 24.47, 23.99, 26.24, 20.04, 25.72, 25.64, 19.87, 23.35, 22.42,
20.42,
  22.13, 25.17, 23.72, 21.28, 20.87, 19.00, 22.04, 20.12, 21.35, 28.57, 26.95,
28.13,
  26.85, 25.27, 31.93, 16.75, 19.54, 20.42, 22.76, 20.12, 22.35, 19.16, 20.77,
19.37,
  22.37, 17.54, 19.06, 20.30, 20.15, 25.36, 22.12, 21.25, 20.53, 17.06, 18.29,
18.37,
  18.93, 17.79, 17.05, 20.31, 22.46, 23.88, 23.68, 23.15, 22.32, 24.02, 23.29,
25.11,
 22.81, 26.25, 21.38, 22.52, 26.73, 23.57, 25.84, 24.06, 23.85, 25.09, 23.84,
25.31,
 19.69, 26.07, 25.50, 23.69, 26.79, 25.61, 25.06, 24.93, 22.96, 20.69, 23.97,
24.64,
 25.93, 23.69, 25.38, 22.68, 23.36, 22.44, 22.57, 19.81, 21.19, 20.39, 21.12,
21.89,
 29.97, 27.39, 23.11, 21.75, 20.89, 22.83, 22.02, 20.07, 20.15, 21.24, 19.63,
23.58,
 21.65, 25.17, 23.25, 32.52, 22.59, 30.18, 34.42, 21.86, 23.99, 24.81, 21.68,
21.04,
 23.12, 20.76, 23.13, 22.35, 22.28, 23.55, 19.85, 26.51, 24.78, 33.73, 30.18,
23.31,
 24.51, 25.37, 23.67, 24.28, 25.82, 21.93, 23.38, 23.07, 25.21, 23.25, 22.93,
26.86,
 21.26, 25.43, 24.54, 27.79, 23.58, 27.56, 23.76, 22.01, 22.34, 21.07)


set.seed(1)
result_2 = goodness.fit(pdf = pdf_exps, cdf = cdf_exps,
starts = c(1,1,1,1),data = data2  , method = "BFGS",
domain = c(-Inf,Inf), lim_inf = c(0,0,0,0),
lim_sup = c(2,2,2,2), S = 250, prop=0.1, N=50)
result_2$mle

Error in optim(par = starts, fn = likelihood, x = data, method =
"BFGS",  :
  non-finite finite-difference value [1]


JAWAD HUSSAIN ASHRAF