Are these transformations of the $\beta^\prime$ distribution from $\beta$ and to $F$ correct? Motivation
I have a prior on a random variable $X\sim \beta(\alpha,\beta)$ but I need to transform the variable to $Y=\frac{X}{1-X}$, for use in an analysis and I would like to know the distribution of $Y$.
Wikipedia states:

if $X\sim\beta(\alpha,\beta)$ then $\frac{X}{1-X} \sim\beta^\prime(\alpha,\beta)$

Thus, the distribution is $Y\sim\beta^\prime(\alpha,\beta)$. The software that I am using, JAGS, does not support the $\beta^\prime$ distribution. So I would like to find an equivalent of a distribution that is supported by JAGS, such as the $F$ or $\beta$.
In addition to the above relationship between the $\beta$ and $\beta^\prime$,
Wikipedia states:

if $X\sim\beta^\prime(\alpha,\beta)$ then $\frac{X\beta}{\alpha}\sim F(2\alpha, 2\beta)$

Unfortunately, neither of these statements are referenced. 
Questions


*

*1) Can I find $c$, $d$ for $Y\sim\beta^\prime(\alpha,\beta)$ where $Y\sim\beta(c,d)$

*2) Are these transformations correct? If so, are there limitations to using them, or a reason to use one versus the other (I presume $\beta$ is a more direct transformation, but why)? 

*3) Where can I find such a proof or how would one demonstrate the validity of these relatively simple transformations?


Answer to 1:
There is no $c$, $d$ for $Y~\beta^\prime(\alpha,\beta)$ where $Y~\beta(c,d)$
This is because $Y>0$ but $X\in(0; 1)$
Use the $F$ distribution which has support for $Y$ which is defined for positive real numbers.
Answer to 2 (simulation approach):
although this is not a mathematical proof, it provides an example of how to sufficiently support the postulate that $X\frac{\beta}{\alpha}\sim F(2*\alpha,2*\beta)$. Feedback appreciated.
Choose arbitrary parameter values
alpha <- 2
beta  <- 4

Create a vector of a $\beta^\prime(\alpha, \beta)$ variate 
X <- rbeta(1000000,alpha, beta)
Y1 <- X/(1-X)

Create a vector of an $F(2*\alpha, 2*\beta)$ variate and divide by $\frac{\beta}{\alpha}$
Y2 <- rf(1000000, 2*alpha, 2*beta) / (beta / alpha)

Compare the variables at decile intervals across their range.
testY1 <- quantile(Y1, seq(0.1,0.9,0.1))
testY2 <- quantile(Y2, seq(0.1,0.9,0.1))

signif(testY1,2) == signif(testY2,2)

Answer to 3:
Leemis and McQuestion (2008) is an excellent peer-reviewed reference detailing the inter-relationships among distributions 
Thanks Mike for providing the proper answer to 2 and 3.
Leemis, L.M. and J.T. McQuestion. 2008 Univariate Distribution Relationships. The American Statistician 62(1) 45:53
