PUlasso-package: PUlasso : An efficient algorithm to solve Positive and...

In PUlasso: High-Dimensional Variable Selection with Presence-Only Data

Description

The package efficiently solves PU problem in low or high dimensional setting using Maximization-Minorization and (block) coordinate descent. It allows simultaneous feature selection and parameter estimation for classification. Sparse calculation and parallel computing are supported for the further computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) <https://arxiv.org/abs/1711.08129>.

Details

Main functions: grpPUlasso, cv.grpPUlasso, coef, predict

Author(s)

Hyebin Song, [e-mail], Garvesh Raskutti, [e-mail].

See Also

Useful links:

• https://arxiv.org/abs/1711.08129

• Report bugs at https://github.com/hsong1/PUlasso/issues

Examples

data("simulPU")
fit<-grpPUlasso(X=simulPU$X,z=simulPU$z,py1=simulPU$truePY1)
## Not run: 
cvfit<-cv.grpPUlasso(X=simulPU$X,z=simulPU$z,py1=simulPU$truePY1)

## End(Not run)
coef(fit,lambda=fit$lambda[10])
predict(fit,newdata = head(simulPU$X), lambda= fit$lambda[10],type = "response")

PUlasso documentation built on Jan. 17, 2021, 9:05 a.m.