Biclustering is a statistical learning technique that attempts to find homogeneous partitions of rows and columns of a data matrix. For example, movie ratings might be biclustered to group both raters and movies. biclust is a current R package allowing users to implement a variety of biclustering algorithms. However, its algorithms do not allow the data matrix to have missing values. We provide a new R package, biclustermd, which allows users to perform biclustering on numeric data even in the presence of missing values.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-045.zip
biclust, superbiclust, s4vd, BiBitR, biclustermd, clues, nycflights13, tidyverse, ggplot2
Graphics, Cluster, Phylogenetics, TeachingStatistics
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For attribution, please cite this work as
Reisner, et al., "The R Journal: biclustermd: An R Package for Biclustering with Missing Values", The R Journal, 2019
BibTeX citation
@article{RJ-2019-045,
author = {Reisner, John and Pham, Hieu and Olafsson, Sigurdur and Vardeman, Stephen and Li, Jing},
title = {The R Journal: biclustermd: An R Package for Biclustering with Missing Values},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-045},
doi = {10.32614/RJ-2019-045},
volume = {11},
issue = {2},
issn = {2073-4859},
pages = {69-84}
}