irelink: Fast Probabilistic Record Linkage

Performs fast, scalable probabilistic record linkage and deduplication using the Fellegi-Sunter model. Records lacking a shared unique identifier are compared across configurable dimensions using exact, fuzzy, and distance-based comparisons, with model parameters estimated via unsupervised Expectation-Maximization. Multiple SQL backends are supported through 'DBI', enabling execution from laptop-scale ('DuckDB') through to distributed engines. This package is a translation of the Python 'splink' library by Linacre et al. into idiomatic R.

See also

Useful links:

• http://christophertkenny.com/irelink/

• https://github.com/christopherkenny/irelink

• Report bugs at https://github.com/christopherkenny/irelink/issues

Author

Maintainer: Christopher T. Kenny ctkenny@proton.me (ORCID) [copyright holder]

Other contributors:

• Robin Linacre (Lead author of splink, the Python package this is derived from) [copyright holder]

• Sam Lindsay (Author of splink) [copyright holder]

• Theodore Manassis (Author of splink) [copyright holder]

• Tom Hepworth (Author of splink) [copyright holder]

• Andy Bond (Author of splink) [copyright holder]

• Ross Kennedy (Author of splink) [copyright holder]

• UK Ministry of Justice (Copyright holder of splink) [copyright holder]