The hmetad package is the most recent implementation of
the meta-d’ model. However, this model has had several implementations
since its creation.
The first implementation of the model used maximum likelihood estimation for single participant data (Maniscalco and Lau 2012), and is still available for download here. The original code is written in MATLAB, however an updated version is also available in Python.
The model was later implemented by (Fleming 2017) in a hierarchical Bayesian framework, which has been shown to provide much more reliable estimates in the relatively small sample sizes commonly used in psychological experiments. This version, known as the Hmeta-d toolbox, was implemented in the probabilistic programming language JAGS, which in turn has interfaces in both MATLAB and in R.
The hmetad package builds on these previous versions
through implementation in the brms package in R, allowing
for estimation with complex regression designs. Additionally,
brms uses the probabilistic programming language Stan,
which permits much more efficient sampling with more reliable model
convergence warnings. Because of its increased efficiency and
flexibility, the hmetad package is now the recommended
approach to fitting the meta-d’ model.