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'.
| Version: | 1.7 |
| Depends: | R (≥ 3.0.0), Matrix |
| Imports: | Iso, quadprog, ggplot2, reshape2 |
| Published: | 2014-11-27 |
| Author: | Jerome Friedman, Xiaotong Suo and Robert Tibshirani |
| Maintainer: | Xiaotong Suo <xiaotong at stanford.edu> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| In views: | TimeSeries |
| CRAN checks: | orderedLasso results |
| Reference manual: | orderedLasso.pdf |
| Package source: | orderedLasso_1.7.tar.gz |
| Windows binaries: | r-devel: orderedLasso_1.7.zip, r-release: orderedLasso_1.7.zip, r-oldrel: orderedLasso_1.7.zip |
| OS X binaries: | r-release: orderedLasso_1.7.tgz, r-oldrel: orderedLasso_1.7.tgz |
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