Nothing

```
qtruncdist <- function(p, ..., dist='norm', truncmin=-Inf,
truncmax=Inf){
##
## 1. dist
##
pdist <- paste0('p', dist)
plist <- as.list(args(pdist))
qdist <- paste0('q', dist)
qlist <- as.list(args(qdist))
##
## 2. dots
##
dots <- list(...)
pots <- dots
pots$log <- NULL
if(('log.p' %in% names(dots)) && dots$log.p){
oops <- (p>0)
if(any(oops)){
warning('log(p)>0 for ', sum(oops),
' observations, the first of which is ',
which(oops)[1], '; will set to NaN')
}
# unlog p to simplify the algorithm
# Otherwise, the algorithm becomes so complicated
# it's not worth the effort -- at least at the moment.
# If we find an example where that complexity is needed,
# we can reconsider.
p <- exp(p) # unlog
dots$log.p <- FALSE
} else {
oops <- ((p<0) | (1<p))
if(any(oops)){
warning('probabilities outside [0, 1] for ', sum(oops),
' observations, the first of which is ',
which(oops)[1], '; will set to NaN')
}
}
##
## 3. pdist(truncmin < X <= truncmax)
##
if('log.p' %in% names(plist)){
pots$log.p <- FALSE
}
pots$q <- truncmax
p.max <- do.call(pdist, pots)
pots$q <- truncmin
p.min <- do.call(pdist, pots)
px_n <- (p.max-p.min)
##
## 4. qdist(p.min+px_n*p, ...)
##
dots$p <- (p.min+px_n*p)
out <- do.call(qdist, dots)
out[oops] <- 0/0
##
## 5. done
##
out
}
```

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