Daphne Koller (2009) describes L1 regularization (Chapter 20) as an efficient way for Markov network (i.e. undirected graphical model) structure learning and feature parameter estimation.
Her focus, and mine, are log-linear models for high-dimensional contingency tables (i.e. categorical data). I wonder whether there are any good implementations of this? I have looked here (https://cran.r-project.org/web/views/gR.html) and found only implementations for continuous data: * parcor: Regularized estimation of partial correlation matrices * glasso: Graphical Lasso: Estimation of Gaussian Graphical Models Both are for continuous (Gaussian) data, not categorical. Any suggestions? ______________________________________________ [email protected] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
