biclustermd: An R Package for Biclustering with Missing Values

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.

John Reisner , Hieu Pham , Sigurdur Olafsson , Stephen Vardeman , Jing Li

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at

CRAN packages used

biclust, superbiclust, s4vd, BiBitR, biclustermd, clues, nycflights13, tidyverse, ggplot2

CRAN Task Views implied by cited packages

Graphics, Cluster, Phylogenetics, TeachingStatistics

Bioconductor packages used



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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

  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 = {},
  doi = {10.32614/RJ-2019-045},
  volume = {11},
  issue = {2},
  issn = {2073-4859},
  pages = {69-84}