Searching on "lasso penalty with deviance" on rseek.org brought up many packages.
-- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Sat, Oct 19, 2019 at 7:54 AM Davor Josipovic <[email protected]> wrote: > 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. > [[alternative HTML version deleted]] ______________________________________________ [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.
