HRDAG Offers New R Package – dga
Much of the work we do at HRDAG involves estimating the number of undocumented deaths using a statistical technique called multiple systems estimation (MSE, described in more detail here). One of our goals is to make this class of methods more broadly available to human rights researchers. In particular, we are finding that Bayesian approaches are extremely valuable for MSE. Accordingly, we are pleased to offer a new R package called dga (“decomposable graphs approach”) that performs Bayesian model averaging for MSE.
The main function in this package implements a model created by David Madigan and Jeremy York. This model was designed to overcome two of the principal challenges we often face in multiple systems estimation: (1) adequately incorporating statistical uncertainty about the structure of dependence between the lists into the estimates; and, (2) obtaining estimates in the presence of data sparsity that causes maximum likelihood estimation to be highly unstable. In our research, we find these benefits to result from Bayesian approaches to MSE generally. This Bayesian model offers a principled way to account for list dependence uncertainty, allow estimation with sparse data, and, where appropriate, to incorporate expert input about the likely number of undocumented deaths.
The dga package includes some additional functions to process, visualize, and run diagnostics on data. It also includes functions to visualize the results. (The package does not perform record linkage.) Currently, dga can produce estimates for three-, four-, or five-list systems. In the future, we plan to expand this to up to seven lists. A limitation of the method is that it becomes computationally intractable to enumerate all of the possible list dependence structures for more than about seven lists. Fortunately, large numbers of lists are rarely encountered in human rights applications. In later versions of the package, we hope to incorporate a stochastic search feature to allow scaling to higher numbers of lists.
Let us know what you think about our package!