Package: nnlasso 0.3

nnlasso: Non-Negative Lasso and Elastic Net Penalized Generalized Linear Models

Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.

Authors:Baidya Nath Mandal <[email protected]> and Jun Ma <[email protected]>

nnlasso_0.3.tar.gz
nnlasso_0.3.zip(r-4.7)nnlasso_0.3.zip(r-4.6)nnlasso_0.3.zip(r-4.5)
nnlasso_0.3.tgz(r-4.6-any)nnlasso_0.3.tgz(r-4.5-any)
nnlasso_0.3.tar.gz(r-4.7-any)nnlasso_0.3.tar.gz(r-4.6-any)
nnlasso_0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
nnlasso/json (API)

# Install 'nnlasso' in R:
install.packages('nnlasso', repos = c('https://doer0.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • car - The car data

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.48 score 1 stars 1 packages 8 scripts 207 downloads 20 exports 0 dependencies

Last updated from:601e3cf0cc. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK95
source / vignettesOK150
linux-release-x86_64OK95
macos-release-arm64OK67
macos-oldrel-arm64OK86
windows-develOK73
windows-releaseOK70
windows-oldrelOK64
wasm-releaseOK83

Exports:barscoef.nnlassocv.nnlassocv.nnlasso.binomialcv.nnlasso.normalcv.nnlasso.poissonfoldkfoldmsefun.binomialmsefun.normalmsefun.poissonnnlassonnlasso.binomialnnlasso.binomial.lambdannlasso.normalnnlasso.normal.lambdannlasso.poissonnnlasso.poisson.lambdaplot.nnlassopredict.nnlasso

Dependencies: