IsingFit
Fitting Ising Models Using the ELasso Method
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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.4 |
rolling linux/jammy R-4.5 | IsingFit_0.4.tar.gz |
37.2 KiB |
0.4 |
rolling linux/noble R-4.5 | IsingFit_0.4.tar.gz |
37.1 KiB |
0.4 |
rolling source/ R- | IsingFit_0.4.tar.gz |
13.0 KiB |
0.4 |
latest linux/jammy R-4.5 | IsingFit_0.4.tar.gz |
37.2 KiB |
0.4 |
latest linux/noble R-4.5 | IsingFit_0.4.tar.gz |
37.1 KiB |
0.4 |
latest source/ R- | IsingFit_0.4.tar.gz |
13.0 KiB |
0.4 |
2026-04-26 source/ R- | IsingFit_0.4.tar.gz |
13.0 KiB |
0.4 |
2026-04-23 source/ R- | IsingFit_0.4.tar.gz |
13.0 KiB |
0.4 |
2026-04-09 windows/windows R-4.5 | IsingFit_0.4.zip |
39.4 KiB |
0.4 |
2025-04-20 source/ R- | IsingFit_0.4.tar.gz |
13.0 KiB |