NIMAA: Nominal Data Mining Analysis

Functions for nominal data mining based on bipartite graphs, which build a pipeline for analysis and missing values imputation. Methods are mainly from the paper: Jafari, Mohieddin, et al. (2021) <doi:10.1101/2021.03.18.436040>, some new ones are also included.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: plotly, tidyr, bipartite, crayon, dplyr, ggplot2, igraph, purrr, skimr, bnstruct, RColorBrewer, fpc, mice, missMDA, networkD3, scales, softImpute, tibble, tidytext, visNetwork, stats
Suggests: knitr, utils, rmarkdown, htmltools, testthat (≥ 3.0.0)
Published: 2021-09-13
Author: Mohieddin Jafari [aut], Cheng Chen [aut, cre]
Maintainer: Cheng Chen <cheng.chen at helsinki.fi>
BugReports: https://github.com/jafarilab/NIMAA/issues
License: GPL (≥ 3)
URL: https://github.com/jafarilab/NIMAA
NeedsCompilation: no
Materials: README
CRAN checks: NIMAA results

Downloads:

Reference manual: NIMAA.pdf
Vignettes: NIMAA-vignette
Package source: NIMAA_0.1.0.tar.gz
Windows binaries: r-devel: NIMAA_0.1.0.zip, r-release: NIMAA_0.1.0.zip, r-oldrel: NIMAA_0.1.0.zip
macOS binaries: r-release (arm64): NIMAA_0.1.0.tgz, r-release (x86_64): NIMAA_0.1.0.tgz, r-oldrel: NIMAA_0.1.0.tgz

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