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Ordered lasso and time-lag sparse regression. Ordered Lasso fits a linear model and imposes an order constraint on the coefficients. It writes the coefficients as positive and negative parts, and requires positive parts and negative parts are non-increasing and positive. Time-Lag Lasso generalizes the ordered Lasso to a general data matrix with multiple predictors. For more details, see Suo, X.,Tibshirani, R., (2014) 'An Ordered Lasso and Sparse Time-lagged Regression'.
Package details |
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Author | Jerome Friedman, Xiaotong Suo and Robert Tibshirani |
Maintainer | Xiaotong Suo <xiaotong@stanford.edu> |
License | GPL-2 |
Version | 1.7.1 |
Package repository | View on CRAN |
Installation |
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