The R Journal: article published in 2019, volume 11:2

biclustermd: An R Package for Biclustering with Missing Values PDF download
John Reisner, Hieu Pham, Sigurdur Olafsson, Stephen Vardeman and Jing Li , The R Journal (2019) 11:2, pages 69-84.

Abstract 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.

Received: ; online 2019-12-27, supplementary material, (3.1 Kb)
CRAN packages: biclust, superbiclust, s4vd, BiBitR, biclustermd, clues, nycflights13, tidyverse, ggplot2
CRAN Task Views implied by cited CRAN packages: Graphics, Cluster, Phylogenetics, TeachingStatistics
Bioconductor packages: iBBiG, QUBIC


CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2019-045,
  author = {John Reisner and Hieu Pham and Sigurdur Olafsson and Stephen
          Vardeman and Jing Li},
  title = {{biclustermd: An R Package for Biclustering with Missing
          Values}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-045},
  url = {https://doi.org/10.32614/RJ-2019-045},
  pages = {69--84},
  volume = {11},
  number = {2}
}