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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  6 months and 27 days ago 579 main
conda 44.8 kB | noarch/r-isingfit-0.4-r44hc72bb7e_1.conda  6 months and 27 days ago 547 main
conda 44.5 kB | noarch/r-isingfit-0.4-r42hc72bb7e_0.conda  1 year and 4 months ago 888 main
conda 45.0 kB | noarch/r-isingfit-0.4-r43hc72bb7e_0.conda  1 year and 4 months ago 919 main
conda 44.2 kB | noarch/r-isingfit-0.3.1-r43hc72bb7e_2.conda  1 year and 8 months ago 1198 main
conda 44.1 kB | noarch/r-isingfit-0.3.1-r42hc72bb7e_2.conda  1 year and 8 months ago 1118 main
conda 45.7 kB | noarch/r-isingfit-0.3.1-r42hc72bb7e_1.tar.bz2  2 years and 4 months ago 1533 main
conda 45.6 kB | noarch/r-isingfit-0.3.1-r41hc72bb7e_1.tar.bz2  2 years and 4 months ago 1455 main
conda 44.6 kB | noarch/r-isingfit-0.3.1-r40hc72bb7e_0.tar.bz2  3 years and 7 months ago 2005 main
conda 44.6 kB | noarch/r-isingfit-0.3.1-r41hc72bb7e_0.tar.bz2  3 years and 7 months ago 2159 main

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