Implements a Fellegi-Sunter probabilistic record linkage model that allows for missing data and the inclusion of auxiliary information. This includes functionalities to conduct a merge of two datasets under the Fellegi-Sunter model using the Expectation-Maximization algorithm. In addition, tools for preparing, adjusting, and summarizing data merges are included. The package implements methods described in Enamorado, Fifield, and Imai (2019) ''Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records'', American Political Science Review and is available at <http://imai.fas.harvard.edu/research/linkage.html>.
|Author||Ted Enamorado [aut, cre], Ben Fifield [aut], Kosuke Imai [aut]|
|Maintainer||Ted Enamorado <firstname.lastname@example.org>|
|License||GPL (>= 3)|
|Package repository||View on CRAN|
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