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An implementation of the model in Steorts (2015) <doi:10.1214/15-BA965SI>, which performs Bayesian entity resolution for categorical and text data, for any distance function defined by the user. In addition, the precision and recall are in the package to allow one to compare to any other comparable method such as logistic regression, Bayesian additive regression trees (BART), or random forests. The experiments are reproducible and illustrated using a simple vignette. LICENSE: GPL-3 + file license.
| Version: | 1.1.0 |
| Depends: | R (≥ 3.0.2), stringdist, plyr |
| Imports: | stats, utils |
| Suggests: | knitr, rmarkdown |
| Published: | 2020-10-06 |
| DOI: | 10.32614/CRAN.package.blink |
| Author: | Rebecca Steorts [aut, cre] |
| Maintainer: | Rebecca Steorts <beka at stat.duke.edu> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | blink results |
| Reference manual: | blink.pdf |
| Vignettes: |
Partitions |
| Package source: | blink_1.1.0.tar.gz |
| Windows binaries: | r-devel: blink_1.1.0.zip, r-release: blink_1.1.0.zip, r-oldrel: blink_1.1.0.zip |
| macOS binaries: | r-release (arm64): blink_1.1.0.tgz, r-oldrel (arm64): blink_1.1.0.tgz, r-release (x86_64): blink_1.1.0.tgz, r-oldrel (x86_64): blink_1.1.0.tgz |
| Old sources: | blink archive |
| Reverse depends: | klsh |
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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.