Fits low-rank matrix and tensor mean models using non-parametric unimodal priors on the components. For fitting a rank-1 matrix or tensor mean model, flashr will run a variational expectation-maximization (VEM) algorithm where the componenets and variances are assumed to be separable. For higher-ranks, a greedy algorithm is available where the VEM algorithm is iteratively run on the residuals of the previous iteration. A backfitting procedure is available for refined estimation.
|Bioconductor views||FactorAnalysis GeneExpression RNASeq Shrinkage Software Visualisation|
|Maintainer||Wei Wang <firstname.lastname@example.org>|
|Package repository||View on GitHub|
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