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This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.

copied from cf-post-staging / r-isingfit
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conda 44.2 kB | noarch/r-isingfit-0.3.1-r43hc72bb7e_2.conda  2 years and 3 months ago 1552 main
conda 44.1 kB | noarch/r-isingfit-0.3.1-r42hc72bb7e_2.conda  2 years and 3 months ago 1490 main
conda 45.7 kB | noarch/r-isingfit-0.3.1-r42hc72bb7e_1.tar.bz2  2 years and 11 months ago 1930 main
conda 45.6 kB | noarch/r-isingfit-0.3.1-r41hc72bb7e_1.tar.bz2  2 years and 11 months ago 1816 main
conda 44.6 kB | noarch/r-isingfit-0.3.1-r40hc72bb7e_0.tar.bz2  4 years and 2 months ago 2373 main
conda 44.6 kB | noarch/r-isingfit-0.3.1-r41hc72bb7e_0.tar.bz2  4 years and 2 months ago 2554 main

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