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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
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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.