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README.md
In PUlasso: High-Dimensional Variable Selection with Presence-Only Data
PUlasso
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.
Also available on CRAN: https://cran.r-project.org/web/packages/PUlasso/index.html
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PUlasso documentation built on Jan. 17, 2021, 9:05 a.m.
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PUlasso
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.
Also available on CRAN: https://cran.r-project.org/web/packages/PUlasso/index.html
Try the PUlasso package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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