Define custom families (i.e. response distribution) for use in
brms models. It allows users to benefit from the modeling
flexibility of brms, while applying their selfdefined likelihood
functions. All of the postprocessing methods for brmsfit
objects can be made compatible with custom families.
See vignette("brms_customfamilies")
for more details.
For a list of builtin families see brmsfamily
.
custom_family( name, dpars = "mu", links = "identity", type = c("real", "int"), lb = NA, ub = NA, vars = NULL, specials = NULL, threshold = "flexible", log_lik = NULL, posterior_predict = NULL, posterior_epred = NULL, predict = NULL, fitted = NULL, env = parent.frame() )
name  Name of the custom family. 

dpars  Names of the distributional parameters of
the family. One parameter must be named 
links  Names of the link functions of the distributional parameters. 
type  Indicates if the response distribution is
continuous ( 
lb  Vector of lower bounds of the distributional
parameters. Defaults to 
ub  Vector of upper bounds of the distributional
parameters. Defaults to 
vars  Names of variables, which are part of the likelihood
function without being distributional parameters. That is,

specials  A character vector of special options to enable for this custom family. Currently for internal use only. 
threshold  Optional threshold type for custom ordinal families. Ignored for nonordinal families. 
log_lik  Optional function to compute loglikelihood values of
the model in R. This is only relevant if one wants to ensure
compatibility with method 
posterior_predict  Optional function to compute posterior prediction of
the model in R. This is only relevant if one wants to ensure compatibility
with method 
posterior_epred  Optional function to compute expected values of the
posterior predictive distribution of the model in R. This is only relevant
if one wants to ensure compatibility with method

predict  Deprecated alias of `posterior_predict`. 
fitted  Deprecated alias of `posterior_epred`. 
env  An 
An object of class customfamily
inheriting
from class brmsfamily
.
The corresponding probability density or mass Stan
functions need to have the same name as the custom family.
That is if a family is called myfamily
, then the
Stan functions should be called myfamily_lpdf
or
myfamily_lpmf
depending on whether it defines a
continuous or discrete distribution.
if (FALSE) { ## demonstrate how to fit a betabinomial model ## generate some fake data phi < 0.7 n < 300 z < rnorm(n, sd = 0.2) ntrials < sample(1:10, n, replace = TRUE) eta < 1 + z mu < exp(eta) / (1 + exp(eta)) a < mu * phi b < (1  mu) * phi p < rbeta(n, a, b) y < rbinom(n, ntrials, p) dat < data.frame(y, z, ntrials) # define a custom family beta_binomial2 < custom_family( "beta_binomial2", dpars = c("mu", "phi"), links = c("logit", "log"), lb = c(NA, 0), type = "int", vars = "trials[n]" ) # define the corresponding Stan density function stan_funs < " real beta_binomial2_lpmf(int y, real mu, real phi, int N) { return beta_binomial_lpmf(y  N, mu * phi, (1  mu) * phi); } " # fit the model fit < brm(y  trials(ntrials) ~ z, data = dat, family = beta_binomial2, stan_funs = stan_funs) summary(fit) }