mimi: 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) <arXiv:1806.09734>.

Version: 0.1.0
Depends: R (≥ 2.10)
Imports: glmnet, softImpute, stats, ade4, FactoMineR, parallel, doParallel, foreach, data.table
Suggests: knitr, rmarkdown
Published: 2019-01-11
Author: Geneviève Robin
Maintainer: Genevieve Robin <genevieve.robin at polytechnique.edu>
License: GPL-3
NeedsCompilation: no
In views: MissingData
CRAN checks: mimi results


Reference manual: mimi.pdf
Package source: mimi_0.1.0.tar.gz
Windows binaries: r-devel: mimi_0.1.0.zip, r-release: mimi_0.1.0.zip, r-oldrel: mimi_0.1.0.zip
OS X binaries: r-release: mimi_0.1.0.tgz, r-oldrel: mimi_0.1.0.tgz


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