Very simple example of using nimble, ggdist, and tidybayes.

tidybayes_nimble_example.R

# nimble and tidybayes example

# load packages
librarian::shelf(tidybayes, nimble, tidyverse, bayesplot, ggdist, nlist)

# run example from runMCMC

code <- nimbleCode({
  mu ~ dnorm(0, sd = 1000)
  sigma ~ dunif(0, 1000)
  for(i in 1:10) {
    x[i] ~ dnorm(mu, sd = sigma)
  }
})
Rmodel <- nimbleModel(code)
Rmodel$setData(list(x = c(2, 5, 3, 4, 1, 0, 1, 3, 5, 3)))
Rmcmc <- buildMCMC(Rmodel)
Cmodel <- compileNimble(Rmodel)
Cmcmc <- compileNimble(Rmcmc, project = Rmodel)
inits <- function() list(mu = rnorm(1,0,1), sigma = runif(1,0,10))
samplesList <- runMCMC(Cmcmc, niter = 10000, nchains = 3, inits = inits, samplesAsCodaMCMC = TRUE)

samplesList

# check model parameters
nlist::pars(samplesList)

# check caterpillars
mcmc_trace(samplesList)

# grab draws - uses tidybayes
d <- gather_draws(samplesList, mu, sigma)

head(d)

# make plot of distribution of parameter
ggplot(d, aes(.value, group = .chain)) +
  geom_density() +
  facet_wrap(~.variable, scales = 'free')

# use ggdist to make nice plot
ggplot(d, aes(y = .variable, x = .value)) +
  stat_halfeye()

ggplot(d, aes(y = .variable, x = .value)) +
  stat_pointinterval()

# get summary of data
d_summary <- group_by(d, .variable) %>%
  median_qi()
d_summary