Model-averages posterior distributions based on a list of models, vector of parameters, and a list of indicators the models represent the null or alternative hypothesis for each parameter.

mix_posteriors(
model_list,
parameters,
is_null_list,
conditional = FALSE,
seed = NULL,
n_samples = 10000
)

## Arguments

model_list list of models, each of which contains marginal likelihood estimated with bridge sampling marglik and prior model odds prior_weights vector of parameters names for which inference should be drawn list with entries for each parameter carrying either logical vector of indicators specifying whether the model corresponds to the null or alternative hypothesis (or an integer vector indexing models corresponding to the null hypothesis) whether prior and posterior model probabilities should be returned only for the conditional model. Defaults to FALSE integer specifying seed for sampling posteriors for model averaging. Defaults to 1. number of samples to be drawn for the model-averaged posterior distribution

## Value

mix_posteriors returns a named list of mixed posterior distributions (either a vector of matrix).