Problem with noncentral confidence interval estimation

[mtorchiano]

From email on March 19, 2017

I have a question regarding cohen.d function.
I am trying to use this function to calculate confidence intervals for effect size using non-central t distribution for 500 samples. However, the function does not run inside a for loop if non-central=TRUE, even for small loops such as a loop of 2.
Here is the code that I have used:

# I generate two independent normal random samples size 10 with 500 repetition

x1 <-matrix(0,10,500)
x2 <-matrix(0,10,500)

for (i in 1:500) {	
 x1[,i] <- rnorm(10,9,2)
 x2[,i] <- rnorm(10,8,2)

      }

####Then I calculate confidence intervals for effect size using non-central t distribution

lb<-c(0)
ub<-c(0)
for (i in 1:500) {
 
 cohen.dres<-cohen.d(x1[,i],x2[,i],hedges.correction = FALSE, noncentral =TRUE)
 lb[i]<-cohen.dres$conf.int[1]  ##saving the lower limit in a vector
 ub[i]<-cohen.dres$conf.int[2]  ##saving the upper limit in a vector
 }

However, as I mentioned function cohen.d in the above for loop does not work.

[mtorchiano]

After looking into details, I found a bug in the code that identifies the range for the ncp estimation.

The bug makes a loop non-converging, in practice a never ending loop is entered.

MWE

a = c(9.81605624621576, 8.93891560898168, 9.05436620537713, 6.01771305071382, 
8.98575772172126, 7.2595761413452, 13.7537304479165, 9.89861975363729, 
7.37246838418731, 8.33888742066376)
b = c(8.62798051757027, 5.25390514295654, 12.0869496333763, 7.20553319908951, 
6.99017188584104, 8.22363490828756, 8.05485287987422, 6.50534569228974, 
8.68871033652186, 4.62897740315003)
cohen.d(a,b,hedges.correction = FALSE, noncentral =TRUE)