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Ordered lasso and timelag 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 nonincreasing and positive. TimeLag 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 Timelagged Regression'.
Package details 


Author  Jerome Friedman, Xiaotong Suo and Robert Tibshirani 
Maintainer  Xiaotong Suo <xiaotong@stanford.edu> 
License  GPL2 
Version  1.7.1 
Package repository  View on CRAN 
Installation 
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