Data simulation | Estimating $\sigma$ with natural lasso | Computing natural lasso along a path of tuning parameters | Selecting the tuning parameter of natural lasso using cross-validation | Computing natural lasso with glmnet output | Estimating $\sigma$ with organic lasso
Package: natural 0.9.0
natural: Estimating the Error Variance in a High-Dimensional Linear Model
Implementation of the two error variance estimation methods in high-dimensional linear models of Yu, Bien (2017) <arxiv:1712.02412>.
Authors:
natural_0.9.0.tar.gz
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natural_0.9.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
natural/json (API)
| # Install 'natural' in R: |
| install.packages('natural', repos = c('https://hugogogo.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/hugogogo/natural/issues
Last updated from:745ee2468e. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 143 | ||
| linux-devel-x86_64 | OK | 134 | ||
| source / vignettes | OK | 234 | ||
| linux-release-arm64 | OK | 129 | ||
| linux-release-x86_64 | OK | 133 | ||
| macos-release-arm64 | OK | 104 | ||
| macos-release-x86_64 | OK | 315 | ||
| macos-oldrel-arm64 | OK | 116 | ||
| macos-oldrel-x86_64 | OK | 200 | ||
| windows-devel | OK | 96 | ||
| windows-release | OK | 112 | ||
| windows-oldrel | OK | 91 | ||
| wasm-release | OK | 136 |
Exports:make_sparse_modelnlasso_cvnlasso_patholassoolasso_cvolasso_path
Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixRcppRcppEigenshapesurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Get the two (theoretical) values of lambdas used in the organic lasso | getLam_olasso |
| Get the two (theoretical) values of lambdas used in scaled lasso | getLam_slasso |
| Generate sparse linear model and random samples | make_sparse_model |
| natural: Natural and Organic lasso estimates of error variance in high-dimensional linear models | natural-package natural |
| Cross-validation for natural lasso | nlasso_cv |
| Fit a linear model with natural lasso | nlasso_path |
| Error standard deviation estimation using organic lasso | olasso |
| Cross-validation for organic lasso | olasso_cv |
| Fit a linear model with organic lasso | olasso_path |
| Solve organic lasso problem with a single value of lambda The lambda values are for slow rates, which could give less satisfying results | olasso_slow |
| plot a natural.cv object | plot.natural.cv |
| plot a natural.path object | plot.natural.path |
| print a natural.path object | print.natural.path |
| Standardize the n -by- p design matrix X to have column means zero and ||X_j||_2^2 = n for all j | standardize |
