Nothing
PUlasso: High-Dimensional Variable Selection with Presence-Only Data
Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) <arXiv:1711.08129>.
Getting started
Browse package contents
Package details |
|
|---|---|
| Author | Hyebin Song [aut, cre], Garvesh Raskutti [aut] |
| Maintainer | Hyebin Song <hps5320@psu.edu> |
| License | GPL-2 |
| Version | 3.2.5 |
| URL | https://arxiv.org/abs/1711.08129 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the PUlasso package in your browser
Any scripts or data that you put into this service are public.
