NEWS
automatedRecLin 1.1.2
- Added
join_records() to join files after the MEC
record linkage procedure.
automatedRecLin 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.
automatedRecLin 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.
automatedRecLin 1.0.1 (2025-12-13)
automatedRecLin 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.