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orderedLasso (version 1.7.1)

Ordered Lasso and Time-Lag Sparse Regression

Description

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'.

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Version

Install

install.packages('orderedLasso')

Monthly Downloads

32

Version

1.7.1

License

GPL-2

Maintainer

Last Published

January 7th, 2019

Functions in orderedLasso (1.7.1)

predict.timeLagLasso

make predictions from a fitted "timeLagLasso" object
time_lag_matrix

time_lag_matrix
predict.timeLagLasso.path

make predictions from a fitted "timeLagLasso.path" object
timeLagLasso

Fit a time-lag lasso
timeLagLasso.cv

Cross-validation function for timeLagLasso
timeLagLasso.path

Fit a path of time-lasso models
orderedLasso.path

Fit a path of ordered lasso models
predict.orderedLasso.path

make predictions from a fitted "orderedLasso.path" object
predict.orderedLasso

make predictions from a fitted "orderedLasso" object
orderedLasso

Fit an ordered lasso
orderedLasso.cv

Cross-validation function for the ordered lasso