FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks
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DOI: 10.1371/journal.pcbi.1004755
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References listed on IDEAS
- Yin, Jianxin & Li, Hongzhe, 2013. "Adjusting for high-dimensional covariates in sparse precision matrix estimation by ℓ1-penalization," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 365-381.
- Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
- Ming Yuan & Yi Lin, 2007. "Model selection and estimation in the Gaussian graphical model," Biometrika, Biometrika Trust, vol. 94(1), pages 19-35.
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Cited by:
- Rong Zhang & Zhao Ren & Wei Chen, 2018. "SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-14, August.
- Ning Zhang & Jin Yang, 2023. "Sparse precision matrix estimation with missing observations," Computational Statistics, Springer, vol. 38(3), pages 1337-1355, September.
- Laurenţiu Cătălin Hinoveanu & Fabrizio Leisen & Cristiano Villa, 2020. "A loss‐based prior for Gaussian graphical models," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(4), pages 444-466, December.
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