Identifying important predictor variables in datasets with thousands of features (i.e. genes) but many fewer samples is a common challange in genomics. mvIC extends AIC and BIC to the context of multivariate regression. Regression models are fit across each response variable and the information criterion explicitly considers correlation between reponses. mvIC is appiciable to linear and linear mixed models. Forward stepwise regression with the mvIC criterion enables automated variable selection for high dimensional genomics datasets.
Package details 


Bioconductor views  BatchEffect Normalization Preprocessing QualityControl Regression Software 
Maintainer  
License  GPL (>= 2) 
Version  1.6.3 
URL  https://github.com/GabrielHoffman/mvIC 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:

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