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The hmetad package is the most recent implementation of
the meta-d’ model, developed by Brian Maniscalco and Hakwan Lau (Maniscalco & Lau,
2012). This model has had several implementations since its
creation.
The first implementation of the model used maximum likelihood estimation for single participant data (Maniscalco & Lau, 2012), and is still available for download here. The original code is written in MATLAB and a 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, retaining
the hierarchical Bayesian approach of the Hmeta-d toolbox while also
allowing for flexible estimation of parameters within arbitrarily
complex regression designs. Additionally, brms uses the
probabilistic programming language Stan, which permits much more
efficient sampling with more reliable model convergence warnings and
extensive diagnostics. Because of its increased efficiency and
flexibility, the hmetad package is our recommended approach
to fitting the meta-d’ model.
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.