[R] L1 (lasso) regularized log-linear model selection procedure

Davor Josipovic d@vorj @end|ng |rom ||ve@com
Fri Oct 18 19:28:12 CEST 2019

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?

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