For which distributions are the parameterizations in BUGS and R different? I have found some distributions for which BUGS and R have different parameterizations: Normal, log-Normal, and Weibull.
For each of these, I gather that the second parameter used by R needs to be inverse transformed (1/parameter) before being used in BUGS (or JAGS in my case). 
Does anyone know of a comprehensive list of these transformations that currently exists?
The closest I can find would be comparing the distributions in table 7 of the JAGS 2.2.0 user manual with the results of ?rnorm etc. and perhaps a few probability texts. This approach appears to require that the transformations will need to be deduced from the pdfs separately. 
I would prefer to avoid this task (and possible errors) if it has already been done, or else start the list here.
Update
Based on Ben's suggestions, I have written the following function to transform a dataframe of parameters from R to BUGS parameterizations.
##' convert R parameterizations to BUGS paramaterizations
##' 
##' R and BUGS have different parameterizations for some distributions. 
##' This function transforms the distributions from R defaults to BUGS 
##' defaults. BUGS is an implementation of the BUGS language, and these 
##' transformations are expected to work for bugs.
##' @param priors data.frame with colnames c('distn', 'parama', 'paramb')
##' @return priors with jags parameterizations
##' @author David LeBauer

r2bugs.distributions <- function(priors) {

  norm   <- priors$distn %in% 'norm'
  lnorm  <- priors$distn %in% 'lnorm'
  weib   <- priors$distn %in% 'weibull'
  bin    <- priors$distn %in% 'binom'

  ## Convert sd to precision for norm & lnorm
  priors$paramb[norm | lnorm] <-  1/priors$paramb[norm | lnorm]^2
  ## Convert R parameter b to JAGS parameter lambda by l = (1/b)^a
  priors$paramb[weib] <-   1 / priors$paramb[weib]^priors$parama[weib]
  ## Reverse parameter order for binomial
  priors[bin, c('parama', 'paramb')] <-  priors[bin, c('parama', 'paramb')]

  ## Translate distribution names
  priors$distn <- gsub('weibull', 'weib',
                       gsub('binom', 'bin',
                            gsub('chisq', 'chisqr',
                                 gsub('nbinom', 'negbin',
                                      as.vector(priors$distn)))))
  return(priors)
}

##' @examples
##' priors <- data.frame(distn = c('weibull', 'lnorm', 'norm', 'gamma'),
##'                     parama = c(1, 1, 1, 1),
##'                     paramb = c(2, 2, 2, 2))
##' r2bugs.distributions(priors)

 A: I don't know of a canned list. 
update: this list (plus additional information) is now published as Translating Probability Density Functions: From R to BUGS and Back Again (2013), DS LeBauer, MC Dietze, BM Bolker R Journal 5 (1), 207-209.
Here is my list (edits provided by original questioner):
Normal and log-normal  are parameterized in terms of $\tau$ (precision) rather
than $\sigma$ or $\sigma^2$ (std. dev. or variance); $\tau = 1/\sigma^2 =
1/\mbox{var}$
Beta, Poisson, Exponential, Uniform are all the same
Negative binomial in BUGS has only the discrete parameterization
(size,prob), not the "ecological" (size,mu, where size can be
non-integer) parameterization.
edit: 
Weibull in BUGS is ($\nu$=shape,$\lambda$=lambda), in R is ($a$=shape,$b$=scale) [the math notation is consistent with the notation used in the respective documentation] As pointed out at How do I parameterize a Weibull distribution in JAGS / BUGS? , $\lambda= (1/b)^a$
Gamma in BUGS is (shape,rate). This is the default in R, but R
also allows (shape,scale) [if the scale argument is named]; rate =
1/scale
Order matters, especially in BUGS (which doesn't have named
arguments), e.g. R dbinom(x,size,prob) vs BUGS dbin(p,n) [same
parameters, opposite order].
Name differences: 


*

*Binomial: R=dbinom,BUGS=dbin

*Chi-squared: R=dchisq,BUGS=dchisqr

*Weibull: R=dweibull,BUGS=dweib

*Negative binomial: R=dnbinom, BUGS=dnegbin
edit: for truncated distributions BUGS uses I(), JAGS uses dinterval() [it's worth looking in the JAGS documentation if you're going to use this, there may be other subtle differences]
