Version 1.1.2

• 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

• 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

• 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

• Fixed CRAN errors.

Version 1.0.0

• 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.