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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.

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conda 44.9 kB | noarch/r-isingfit-0.4-r43hc72bb7e_1.conda  7 months and 13 days ago 609 main
conda 44.8 kB | noarch/r-isingfit-0.4-r44hc72bb7e_1.conda  7 months and 13 days ago 578 main
conda 44.5 kB | noarch/r-isingfit-0.4-r42hc72bb7e_0.conda  1 year and 5 months ago 914 main
conda 45.0 kB | noarch/r-isingfit-0.4-r43hc72bb7e_0.conda  1 year and 5 months ago 945 main
conda 44.2 kB | noarch/r-isingfit-0.3.1-r43hc72bb7e_2.conda  1 year and 9 months ago 1225 main
conda 44.1 kB | noarch/r-isingfit-0.3.1-r42hc72bb7e_2.conda  1 year and 9 months ago 1145 main
conda 45.7 kB | noarch/r-isingfit-0.3.1-r42hc72bb7e_1.tar.bz2  2 years and 5 months ago 1560 main
conda 45.6 kB | noarch/r-isingfit-0.3.1-r41hc72bb7e_1.tar.bz2  2 years and 5 months ago 1482 main
conda 44.6 kB | noarch/r-isingfit-0.3.1-r40hc72bb7e_0.tar.bz2  3 years and 8 months ago 2033 main
conda 44.6 kB | noarch/r-isingfit-0.3.1-r41hc72bb7e_0.tar.bz2  3 years and 8 months ago 2187 main

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