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'.
|Author||Jerome Friedman, Xiaotong Suo and Robert Tibshirani|
|Maintainer||Xiaotong Suo <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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