An implementation of the model in Betancourt, Zanella, Steorts (2020) <arXiv:2004.02008>, which performs microclustering models for categorical data. The package provides a vignette for two proposed methods in the paper as well as two standard Bayesian non-parametric clustering approaches for entity resolution. The experiments are reproducible and illustrated using a simple vignette. LICENSE: GPL-3 + file license.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.2.4) |
| Imports: | Rcpp (≥ 1.0.1), stats |
| LinkingTo: | Rcpp |
| Suggests: | knitr, rmarkdown |
| Published: | 2020-10-01 |
| Author: | Rebecca C Steorts [aut, cre], Brenda Betancourt [aut], Giacomo Zanella [aut] |
| Maintainer: | Rebecca C Steorts <beka at stat.duke.edu> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | microclustr results |
| Reference manual: | microclustr.pdf |
| Vignettes: |
Partitions |
| Package source: | microclustr_0.1.0.tar.gz |
| Windows binaries: | r-devel: microclustr_0.1.0.zip, r-devel-UCRT: microclustr_0.1.0.zip, r-release: microclustr_0.1.0.zip, r-oldrel: microclustr_0.1.0.zip |
| macOS binaries: | r-release (arm64): microclustr_0.1.0.tgz, r-release (x86_64): microclustr_0.1.0.tgz, r-oldrel: microclustr_0.1.0.tgz |
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