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metamixr fits Bayesian meta-analytic mixture models: the true effects of the primary studies are assumed to come from a mixture of M normal components, each with its own mean and between-study heterogeneity. The mixture can be combined with a step-function selection model to adjust for publication bias. Models are estimated with Stan and returned as stanfit objects, so models can be inspected via rstan and marginal likelihoods for model comparison can be calculated using bridgesampling. See Maier (2026) for more information.
The package contains Stan models that are compiled when the package is installed, which takes several minutes and requires a C++ toolchain (Rtools on Windows, Xcode command line tools on macOS).
# install.packages("remotes")
remotes::install_github("MaxMaier42/metamix")library(metamixr)
# 100 studies from two components (null and medium effect) with one-sided
# publication bias: non-significant studies are published with probability .2,
# marginally significant ones with probability .5
set.seed(1)
dat <- sim_mix(K = 100, M = 2, mu = c(0, 0.5), tau = c(0.1, 0.1),
steps = c(0.95, 0.975), weights = c(0.2, 0.5, 1), one_sided = TRUE)
# two-component mixture with a selection model
fit <- sel_mix(dat$y, dat$sds, M = 2, steps = c(0.95, 0.975), chains = 4, cores = 4)
print(fit, pars = c("mu", "tau", "theta", "omega"))re_mix(): random-effects mixture model without
publication bias adjustment.sel_mix(): random-effects mixture model with a
step-function selection model.sim_mix(): simulate data from the mixtures.Maier, M. (2026). Addressing heterogeneity with Bayesian meta-analytic mixture modelling. PsyArXiv. https://doi.org/10.31234/osf.io/nkyqm_v2
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.