Changes in version 1.1.2                        

  - Added join_records() to join files after the MEC record linkage
    procedure.

                 Changes in version 1.1.1 (2026-05-21)                  

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

                 Changes in version 1.1.0 (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.

                 Changes in version 1.0.1 (2025-12-13)                  

  - Fixed CRAN errors.

                 Changes in version 1.0.0 (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.