Gabriel,
That's exactly what I have been doing, and it works fine. However, I just
wanted to *understand* how the random numbers are generated, for no other
reason than to satiate my curiosity.
The one thing that's not very elegant about this way of doing things is
that I have to hard-code specia
Well,
For this particular use case why not just transform the parameters at the R
level and then call the existing function? Is there not a closed form
mapping?
~G
On Jul 1, 2016 2:50 PM, "Joshua Ulrich" wrote:
> On Fri, Jul 1, 2016 at 6:13 AM, Luis Usier
> wrote:
> > Gabriel,
> >
> > Thanks f
On Fri, Jul 1, 2016 at 6:13 AM, Luis Usier
wrote:
> Gabriel,
>
> Thanks for that! I guess I really should have figured that one out sooner,
> huh?
>
> I understand why that wouldn't be CRAN-compliant. But then, what *is* the
> proper way to do it? Is there any way I can call unexported functions f
Gabriel,
Thanks for that! I guess I really should have figured that one out sooner,
huh?
I understand why that wouldn't be CRAN-compliant. But then, what *is* the
proper way to do it? Is there any way I can call unexported functions from
another package and have it accepted by CRAN?
Also, if I i
Luis,
C_rnorm is a symbol but it's not exported. This means that you *can* do
this by using stats:::C_rnorm.
That said, it's not exported, which means that it's not supported to do
this. So your package likely would not be allowed on CRAN, for example.
Best,
~G
On Jun 30, 2016 2:08 PM, "Luis Us
Hi all,
Looking at the body for the function rnorm, I see that the body of the
function is:
.Call(C_rnorm, n, mean, sd)
I want to implement functions that generate normal (and other) random
variables. Now, I understand that I can perfectly well just call the R
wrapper for these functions and
