Package: sparsenet 1.7
Trevor Hastie
sparsenet: Fit Sparse Linear Regression Models via Nonconvex Optimization
Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <doi:10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
Authors:
sparsenet_1.7.tar.gz
sparsenet_1.7.tar.gz(r-4.5-noble)sparsenet_1.7.tar.gz(r-4.4-noble)
sparsenet_1.7.tgz(r-4.4-emscripten)sparsenet_1.7.tgz(r-4.3-emscripten)
sparsenet.pdf |sparsenet.html✨
sparsenet/json (API)
| # Install 'sparsenet' in R: |
| install.packages('sparsenet', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 12 days agofrom:ea4cba4da1. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 25 2024 |
| R-4.5-linux-x86_64 | OK | Nov 25 2024 |
Exports:coef.cv.sparsenetcoef.sparsenetcv.sparsenetgendatagetcoef_listplot.cv.sparsenetplot.sparsenetpredict.cv.sparsenetpredict.sparsenetprint.cv.sparsenetprint.sparsenetsparsenetsparsepredictsummary.sparsenet
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fit a linear model regularized by the nonconvex MC+ sparsity penalty | sparsenet-package |
| Cross-validation for sparsenet | cv.sparsenet |
| Generate data for testing sparse model selection | gendata |
| plot the cross-validation curves produced by cv.sparsenet | plot.cv.sparsenet |
| plot coefficients from a "sparsenet" object | plot.sparsenet |
| make predictions from a "cv.sparsenet" object. | coef.cv.sparsenet predict.cv.sparsenet |
| make predictions from a "sparsenet" object. | coef.sparsenet predict.sparsenet |
| Fit a linear model regularized by the nonconvex MC+ sparsity penalty | sparsenet |
