OHPL (1.3)

0 users

Ordered Homogeneity Pursuit Lasso for Group Variable Selection.


Ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) . The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data.

Maintainer: Nan Xiao
Author(s): You-Wu Lin [aut], Nan Xiao [cre]

License: GPL-3 | file LICENSE

Uses: glmnet, mvtnorm, pls

Released about 1 month ago.

1 previous version



  (0 votes)


  (0 votes)

Log in to vote.


No one has written a review of OHPL yet. Want to be the first? Write one now.

Related packages:(20 best matches, based on common tags.)

Search for OHPL on google, google scholar, r-help, r-devel.

Visit OHPL on R Graphical Manual.