isingfit-package: Network estimation using the eLasso method
In cvborkulo/IsingFit: Fitting Ising Models Using the ELasso Method
IsingFit-package R Documentation
Network estimation using the eLasso method
Description
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.
Details
Package: IsingFit
Type: Package
Version: 0.3.2
Date: 2018-6-1
License: What license is it under?
Author(s)
Claudia D. van Borkulo, Sacha Epskamp; with contributions from Alexander Robitzsch and Mihai Alexandru Constantin
Maintainer: Claudia D. van Borkulo <cvborkulo@gmail.com>
References
Chen, J., & Chen, Z. (2008). Extended bayesian information criteria for model selection with large model spaces. Biometrika, 95(3), 759-771.
Foygel, R., & Drton, M. (2011). Bayesian model choice and information criteria in sparse generalized linear models. arXiv preprint arXiv:1112.5635.
Ravikumar, P., Wainwright, M. J., & Lafferty, J. D. (2010). High-dimensional Ising model selection using l1-regularized logistic regression. The Annals of Statistics, 38, 1287 - 1319.
van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific Reports 4, 5918; DOI:10.1038/srep05918.
cvborkulo/IsingFit documentation built on Oct. 14, 2023, 3:13 p.m.
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| IsingFit-package | R Documentation |
Network estimation using the eLasso method
Description
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.
Details
| Package: | IsingFit |
| Type: | Package |
| Version: | 0.3.2 |
| Date: | 2018-6-1 |
| License: | What license is it under? |
Author(s)
Claudia D. van Borkulo, Sacha Epskamp; with contributions from Alexander Robitzsch and Mihai Alexandru Constantin
Maintainer: Claudia D. van Borkulo <cvborkulo@gmail.com>
References
Chen, J., & Chen, Z. (2008). Extended bayesian information criteria for model selection with large model spaces. Biometrika, 95(3), 759-771.
Foygel, R., & Drton, M. (2011). Bayesian model choice and information criteria in sparse generalized linear models. arXiv preprint arXiv:1112.5635.
Ravikumar, P., Wainwright, M. J., & Lafferty, J. D. (2010). High-dimensional Ising model selection using l1-regularized logistic regression. The Annals of Statistics, 38, 1287 - 1319.
van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific Reports 4, 5918; DOI:10.1038/srep05918.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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