Main Effects and Interactions in Mixed and Incomplete Data

Estimation of main effects and interactions in mixed data sets with missing values. Numeric, binary and count variables are supported. Main effects and interactions are modelled using an exponential family parametric model. Particular examples include the log-linear model for count data and the linear model for numeric data. Estimation is done through a convex program where main effects are assumed sparse and the interactions low-rank. Geneviève Robin, Olga Klopp, Julie Josse, Éric Moulines, Robert Tibshirani (2018) .


Reference manual

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0.1.0 by Genevieve Robin, a month ago

Browse source code at

Authors: Geneviève Robin

Documentation:   PDF Manual  

Task views: Missing Data

GPL-3 license

Imports glmnet, softImpute, stats, ade4, FactoMineR, parallel, doParallel, foreach, data.table

Suggests knitr, rmarkdown

See at CRAN