Fast Probabilistic Record Linkage with Missing Data
aggconfusion
Aggregate EM objects for use in summary.fastLink()
blockData
calcMoversPriors
clusterMatch
Get confusion table for fastLink objects
County-level FIPS Codes
County-level inflow rates by state
County-level outflow rates by state
dedupeMatches
Sample dataset A
Sample dataset B
emlinklog
emlinkMARmov
emlinkRS
Fast Probabilistic Record Linkage with Missing Data
fastLink
gammaCK2par
gammaCKpar
gammaKpar
gammaNUMCK2par
gammaNUMCKpar
getMatches
getPatterns
getPosterior
inspectEM
matchesLink
nameReweight
Plot matching patterns of the EM object by posterior probability of ma...
preprocText
print.inspectEM
State-level FIPS Codes
State-level inflow rates by state
In-state movers rates by state
State-level outflow rates by state
stringSubset
Get summaries of fastLink() objects
tableCounts
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'' <doi:10.1017/S0003055418000783> and is available at <https://imai.fas.harvard.edu/research/linkage.html>.