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

[Bert Gunter]

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 <davorj using live.com> 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?
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