Biclustering with Missing Data

Biclustering is a statistical learning technique that simultaneously partitions and clusters rows and columns of a data matrix. Since the solution space of biclustering is in infeasible to completely search with current computational mechanisms, this package uses a greedy heuristic. The algorithm featured in this package is, to the best our knowledge, the first biclustering algorithm to work on data with missing values. Li, J., Reisner, J., Pham, H., Olafsson, S., and Vardeman, S. (2020) Biclustering with Missing Data. Information Sciences, 510, 304–316.


Reference manual

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0.2.3 by John Reisner, 4 months ago

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Browse source code at

Authors: John Reisner [cre, aut, cph] , Hieu Pham [ctb, cph] , Jing Li [ctb, cph]

Documentation:   PDF Manual  

Task views: Missing Data

MIT + file LICENSE license

Imports biclust, doParallel, dplyr, foreach, magrittr, nycflights13, phyclust

Depends on ggplot2, tidyr

Suggests knitr, rmarkdown, testthat

See at CRAN