---
title: "History of the hmetad package"
output: rmarkdown::html_vignette
bibliography: citations.bib
csl: apa.csl
link-citations: true
vignette: >
  %\VignetteIndexEntry{History of the hmetad package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

The `hmetad` package is the most recent implementation of the meta-d' model, developed by Brian Maniscalco and Hakwan Lau [@maniscalco2012]. This model has had several implementations since its creation. 

The first implementation of the model used maximum likelihood estimation for 
single participant data [@maniscalco2012], and is still available for download 
[here](https://www.columbia.edu/~bsm2105/type2sdt/). The original code is 
written in MATLAB and a version is also available in Python.

The model was later implemented by @fleming2017 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](https://github.com/metacoglab/HMeta-d), 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.



## References