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Changelog

Source: NEWS.md

Version 1.1.2

CRAN release: 2026-06-29

  • Renamed the mec_blocking() trade-off argument from alpha to rho to avoid confusion with the hurdle Gamma shape parameter. The old alpha argument remains accepted as an undocumented compatibility alias.
  • Added join_records() to join files after the MEC record linkage procedure.

Version 1.1.1

CRAN release: 2026-05-21

  • Improved mec_blocking() by using inverted unsupervised MEC.
  • Added the trade-off argument in mec_blocking() for controlling the FLR-MMR trade-off.

Version 1.1.0

CRAN release: 2026-05-08

  • Added mec_blocking() for blocked unsupervised MEC with pooled training and blockwise prediction using the blocking package.
  • Added support for creating comparison vectors on a supplied table of record pairs through the pairs argument in comparison_vectors().
  • Added census and cis example datasets for larger record linkage examples.
  • Added a vignette showing MEC with blocking on the cis and census datasets.
  • Added optional progress messages via the verbose argument in mec(), train_rec_lin(), predict.rec_lin_model(), and mec_blocking().
  • Improved validation of supplied match and pair tables, including clearer checks for row indices, duplicate pairs, missing values, and non-finite comparison values.
  • Improved print methods for linkage results, including consistent percentage formatting for error rates.

Version 1.0.1

CRAN release: 2025-12-13

  • Fixed CRAN errors.

Version 1.0.0

CRAN release: 2025-11-18

  • Implemented comparison functions abs_distance() and jarowinkler_complement().
  • Added support for comparing two datasets using comparison functions.
  • Added support for training a supervised record linkage model using probability or density ratio estimation, based on the following methods: "binary", "continuous_parametric", and "continuous_nonparametric".
  • Added support for creating a supervised record linkage model using a custom machine learning (ML) classifier.
  • Added support for predicting matches based on a record linkage model.
  • Added the unsupervised maximum entropy classification (MEC) algorithm for record linkage. Supported methods are: "binary", "continuous_parametric", "continuous_nonparametric", and "hit_miss".
  • Added support for creating the predicted set of matches based on: its estimated size, a target false link rate (FLR) or a target missing match rate (MMR).
  • Implemented S3 methods for printing.
  • Added support for evaluation when true matches are known.
  • Added documentation and examples.