microclustr: Entity Resolution with Random Partition Priors for Microclustering

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

Downloads:

Reference manual: microclustr.pdf
Vignettes: Partitions
Package source: microclustr_0.1.0.tar.gz
Windows binaries: r-devel: microclustr_0.1.0.zip, r-release: microclustr_0.1.0.zip, r-oldrel: microclustr_0.1.0.zip
macOS binaries: r-release: microclustr_0.1.0.tgz, r-oldrel: microclustr_0.1.0.tgz

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