As I wanted to simulate truncated normals in a hurry, I coded the inverse cdf approach:

truncnorm=function(a,b,mu,sigma){
  u=runif(1)
  u=qnorm(pnorm((a-mu)/sigma)*(1-u)+u*pnorm((b-mu)/sigma))
  return(mu+sigma*u)
  }

instead of using my own accept-reject algorithm. Poor shortcut as the method fails when a and b are too far from μ

> truncnorm(1,2,3,4)
[1] -0.4912926
> truncnorm(1,2,13,1)
[1] Inf

So I introduced a control (and ended up wasting more time than if I had used my optimised accept-reject version!)

truncnorm=function(a,b,mu,sigma){
  u=runif(1)
  if (pnorm((b-mu)/sigma)-pnorm((a-mu)/sigma)>0){
    u=qnorm(pnorm((a-mu)/sigma)*(1-u)+u*pnorm((b-mu)/sigma))
  }else{
    u=-qnorm(pnorm(-(a-mu)/sigma)*(1-u)-u*pnorm(-(b-mu)/sigma))}
  return(mu+sigma*u)
  }

As shown by the above, it works, even when a=1, b=2 and μ=20. However, this eventually collapses as well and I ended up installing the msm R package that includes rtnorm, an R function running my accept-reject version. (This package was written by Chris Jackson from the MRC Unit in Cambridge.)