skew normal computation

Question

I want to compute probabilities assuming data have log skew normal distribution (in R). As I couldn't find any package that directly computes log skew normal (as plnorm does log normal), I am wondering, if I can get log normal with pnorm, for example to compute log normal probability at point 2 with mean=2 and sd=3:

plnorm(2,2,3)
pnorm(log(2),2,3)

can I use psn to compute skew-log-normal probability at point 2 with mean=2, sd=3 and skewness=1:

psn(log(2), xi=2, omega=3, alpha=1, tau=0, dp=NULL)

or I need some other transformations of the mean/sd/skewness before I apply it? In case I need to transform it first, didnt I need some transformations also in plnorm, instead of just putting mean and sd?

Answer 1

Let $X$ be a skew normal random variable (or really, what is used here is only that it is some random variable on the real line.) Of interest is the distribution of $e^X$. Then, as usual, $$ F_{e^X}(Z)= F_X(\log z) \quad \text{for $z>0$.} $$ and by differentiation (assuming $X$ has a density) we find $$ f_{e^X}(z)= f_X(\log z)/z.$$ Assuming you have functions psn, dsn in R available, you can define

library(sn)
plsn <-  function(x, ...) sn::psn(log(x), ...)

And for the density:

dlsn <- function(x, ..., log=FALSE) {
    d <- x;  ind0 <- ifelse(x <= 0,1L,0L) ; ind1 <- 1-ind0
    d[ind0] <- 0
    d[ind1] <- sn::dsn(log(x[ind1]), ..., log=TRUE) - log(x[ind1])
    if (log=FALSE)  d[ind1] <- exp(d[ind1])
    d

}

(not tested)