Let my.ncp be greater 0. Then, for any my.p in [0, 1] qt(my.p, Inf, my.ncp) results in NaN (including a warning message). Based on the line if (!R_FINITE(df)) ML_ERR_return_NAN; in qnt.c it appears that this behavior is intended. But according to the manual df = Inf should be allowed. Because for increasing degrees of freedom the non-central t distribution approaches the normal distribution with mean = my.ncp and sd = 1, I would have expected to get qnorm(my.p, mean = my.ncp, sd = 1) for the limiting case df = Inf. My question therefore is: Wouldn’t it make sense to generally return the result from qnorm in case df exceeds some large value, say 10^10? Thank you, Benjamin

Thank you, Benjamin, your suggestion makes sense ('cum grano salis' in any case). Instead of an arbitrary cutoff (which should depend on "my.p" and "my.ncp") it seems much easier -- and to work correctly -- I'll have to check more thoroughly -- to just replace 'Inf' by something like 1e300. Thank you for the good observation and suggestion! Martin Maechler, ETH Zurich

Fix --- only for the case df = Inf --- now commited to R-devel and R-patched, i.e., will be in R 3.2.2, due in mid August. There are many numerical problems if you look in the hairy details of the non-central t distribution and hence also quantile function. For finite df, the distrib and inverse (i.e. the quantile) depend on three parameters, and cutoffs should depend on all three parameters.

I agree; Thank you for taking this up!