This is a collection of custom brms families initially written for our simulation study and bayesim, our simulation framework.
They can be used like any other brms family. The only thing to remember is to explicitly hand brm the stanvar
value that is part of the custom family object.
library(brms)
library(bayesfam)
data <- list(y = rbetaprime(1000, mu = 10, phi = 2))
brm(
y ~ 1 ,
data = data,
family = betaprime(),
stanvars = betaprime()$stanvars, # This is the important part to remember
)
Family: betaprime
Links: mu = log; phi = identity
Formula: y ~ 1
Data: data (Number of observations: 1000)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 2.29 0.02 2.25 2.33 1.00 1329 2061
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
phi 2.12 0.18 1.78 2.48 1.00 1275 1828
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
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