camel: Calibrated Machine Learning. The package ”camel” provides the implementation of a family of high-dimensional calibrated machine learning tools, including (1) LAD, SQRT Lasso and Calibrated Dantzig Selector for estimating sparse linear models; (2) Calibrated Multivariate Regression for estimating sparse multivariate linear models; (3) Tiger, Calibrated Clime for estimating sparse Gaussian graphical models. We adopt the combination of the dual smoothing and monotone fast iterative soft-thresholding algorithm (MFISTA). The computation is memory-optimized using the sparse matrix output, and accelerated by the path following and active set tricks.
Keywords for this software
References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Liu, Han; Wang, Lie: TIGER: A tuning-insensitive approach for optimally estimating Gaussian graphical models (2017)
- Zhang, Haixiang; Zheng, Yinan; Yoon, Grace; Zhang, Zhou; Gao, Tao; Joyce, Brian; Zhang, Wei; Schwartz, Joel; Vokonas, Pantel; Colicino, Elena; Baccarelli, Andrea; Hou, Lifang; Liu, Lei: Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study (2017)
- Liu, Han; Wang, Lie; Zhao, Tuo: Calibrated multivariate regression with application to neural semantic basis discovery (2015)
- Pircalabelu, Eugen; Claeskens, Gerda; Jahfari, Sara; Waldorp, Lourens J.: A focused information criterion for graphical models in fMRI connectivity with high-dimensional data (2015)