Co-clustering of the rows and columns of a contingency or binary matrix, or double binary matrices and model selection for the number of row and column clusters. Three models are considered: the Poisson latent block model for contingency matrix, the binary latent block model for binary matrix and a new model we develop: the multiple latent block model for double binary matrices. A new procedure named bikm1 is implemented to investigate more efficiently the grid of numbers of clusters. Then, the studied model selection criteria are the integrated completed likelihood (ICL) and the Bayesian integrated likelihood (BIC). Finally, the co-clustering adjusted Rand index (CARI) to measure agreement between co-clustering partitions is implemented. Robert Valerie, Vasseur Yann, Brault Vincent (2021) <doi:10.1007/s00357-020-09379-w>.
Version: 1.1.0 Imports: gtools, stats, graphics, grDevices, methods, parallel, ade4, pracma, ggplot2, reshape2, grid, lpSolve Published: 2021-07-16 DOI: 10.32614/CRAN.package.bikm1 Author: Valerie Robert [aut, cre] Maintainer: Valerie Robert <valerie.robert.math at gmail.com> License: GPL-2 NeedsCompilation: no Materials: NEWS CRAN checks: bikm1 results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=bikm1 to link to this page.
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