sim() from model with missing value imputation breaks with error "function merge_missing not found"

[andins]

I executed the code for model 15.5 from the book (for doing missing data imputation with primate milk data.
The model runs fine but when I try to sample from posterior distribution using sim() function an exception is raised with error

Error in merge_missing(B_missidx, to_vector(B), B_impute) : 
  could not find function "merge_missing"

Any idea how to fix it @rmcelreath ?

Full code to reproduce the bug:

library(rethinking)
data(milk)
d <- milk
d$neocortex.prop <- d$neocortex.perc / 100
d$logmass <- log(d$mass)
dat_list <- list(
K = standardize( d$kcal.per.g ),
B = standardize( d$neocortex.prop ),
M = standardize( d$logmass ) )
m15.5 <- ulam(
alist(
K ~ dnorm( mu , sigma ),
mu <- a + bB*B + bM*M,
B ~ dnorm( nu , sigma_B ),
c(a,nu) ~ dnorm( 0 , 0.5 ),
c(bB,bM) ~ dnorm( 0, 0.5 ),
sigma_B ~ dexp( 1 ),
sigma ~ dexp( 1 )
) , data=dat_list , chains=4 , cores=4 )
sim(m15.5)

[rmcelreath]

I don't think this has ever been supported, but maybe it wouldn't be hard to add it. I will make a note of it.

[jebyrnes]

Second! HA! Trying to write an exercise to look at missing data and how it compares to observed, and indeed, hit this roadblock as well.