Modelling mean of gaussian component in brms version 2.3.0

Roberto_Petrosino · July 20, 2025, 8:10am

As a follow up on this post, I have been trying to set up a custom_family function that would provide the effects on the mean of the Gaussian component xi (instead of the default mean of the ex-Gaussian component). Here’s my attempt:

exgaussian_mg <- custom_family(
  "exgaussian_mg",
  dpars = c("mu", "sigma", "beta"),
  links = c("identity", "log", "log"),
  lb = c(NA, 0, 0),
  type = "real"
)

stan_funs <- "
real exgaussian_mg_lpdf(real y, real mu, real sigma, real beta) {
    // This is the log-probability density function (lpdf)
    real lambda = 1.0 / beta;
    real val = lambda * (y - mu);
    return log(lambda) + // this is the rate of the function 
            normal_lpdf(y | mu, sigma) + // this is the gaussian lpdf (built-in)
            log_diff_exp(0, normal_lcdf(0 | val, lambda * sigma)); // exponential component
}"

stanvars <- stanvar(scode = stan_funs, block = "functions")

This does seem to work in the following call:

formulas <- bf(RT ~ condition*primetype*experiment + (1||target) + (1||subj),
               beta ~ condition*primetype*experiment + (1||target) + (1||subj))

# modelling
bay_fit <- brm(
  formula = formulas,
  data = data_sample,
  family = exgaussian_mg, 
  prior = c(
     set_prior('normal(0, 100)', class = "b") # for fixed effects only
  ),
  stanvar = stanvars,
  cores = getOption("mc.cores", 4),
  threads = threading(2), #within-chain parallelization
  chains = 4, # Number of MCMC chains to run at the same time
  iter = 2000, # Total iterations per chain (including warmup)
  warmup = 1000, # Number of warmup iterations (discarded for posterior inference)
  seed = 42,   # Random seed for reproducibility of MCMC sampling
  init = 0, # set the initial values for the sampler; the inits_list won't work for some reason
  control = list(adapt_delta = 0.99, max_treedepth = 15), # Increased adapt_delta and max_treedepth to prevent divergences 
  )

However, the chains seem to loop indefinitely even after reach the number of iterations:

Compiling Stan program...
Start sampling
starting worker pid=19488 on localhost:11205 at 16:57:39.837
starting worker pid=19501 on localhost:11205 at 16:57:40.097
starting worker pid=19514 on localhost:11205 at 16:57:40.350
starting worker pid=19527 on localhost:11205 at 16:57:40.591

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 0.020153 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 201.53 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 0.020182 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 201.82 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2: 
Chain 2: 
Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 0.014891 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 148.91 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3: 
Chain 3: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
Chain 4: 
Chain 4: Gradient evaluation took 0.018561 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 185.61 seconds.
Chain 4: Adjust your expectations accordingly!
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# manually stopped here after ~3 days

So I wanted to ask for advice here:

Is the custom family definition correct? This was my first attempt, and I am not a mathematician, so the formula might just be mistaken.
Is it normal that the sampling process would loop over chains that have previously completed?

Thanks!

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Modelling mean of gaussian component in brms version 2.3.0

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