Package: NNPreg 0.1.0
NNPreg: Nuclear Norm Penalized Regression
Fits nuclear norm penalized multivariate linear regression models using the fast iterative shrinkage-thresholding algorithm (FISTA) of Beck and Teboulle (2009) <doi:10.1137/080716542>. Provides fixed-penalty estimation, regularization paths, K-fold cross-validation, prediction, coefficient extraction, and diagnostic plots.
Authors:
NNPreg_0.1.0.tar.gz
NNPreg_0.1.0.zip(r-4.7-any)NNPreg_0.1.0.zip(r-4.6-any)NNPreg_0.1.0.zip(r-4.5-any)
NNPreg_0.1.0.tgz(r-4.6-any)NNPreg_0.1.0.tgz(r-4.5-any)
NNPreg_0.1.0.tar.gz(r-4.7-any)NNPreg_0.1.0.tar.gz(r-4.6-any)
NNPreg_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
NNPreg/json (API)
| # Install 'NNPreg' in R: |
| install.packages('NNPreg', repos = c('https://mayukhc13.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mayukhc13/nnpreg/issues
Last updated from:7ba57c8a88. Checks:7 ERROR, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | ERROR | 103 | ||
| source / vignettes | OK | 144 | ||
| linux-release | ERROR | 91 | ||
| macos-release | ERROR | 147 | ||
| macos-oldrel | ERROR | 147 | ||
| windows-devel | ERROR | 56 | ||
| windows-release | ERROR | 61 | ||
| windows-oldrel | ERROR | 43 | ||
| wasm-release | OK | 76 |
Exports:nnp
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Extract coefficients from an NNP model | coef.nnp |
| Nuclear norm penalized regression (glmnet-style interface) | nnp |
| Cross-validated nuclear norm penalized regression | nnp_cv |
| Generate a lambda sequence for nuclear norm penalized regression | nnp_lambda_seq |
| FISTA solver for nuclear norm penalized regression | nnp_solver |
| Nuclear norm solution path | nnp_solver_path |
| Plot method for NNP objects | plot.nnp |
| Predict method for NNP objects | predict.nnp |
| Print method for NNP objects | print.nnp |
| Print method for NNP solution paths | print.nnp.path |
| Summary method for NNP objects | summary.nnp |
