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.

AuthorBaidya Nath Mandal <mandal.stat@gmail.com> and Jun Ma <jun.ma@mq.edu.au>
Date of publication2016-03-10 08:00:33
MaintainerBaidya Nath Mandal <mandal.stat@gmail.com>
LicenseGPL (>= 2)

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bars Man page
car Man page
coef.nnlasso Man page
cv.nnlasso Man page
cv.nnlasso.binomial Man page
cv.nnlasso.normal Man page
cv.nnlasso.poisson Man page
fold Man page
kfold Man page
msefun.binomial Man page
msefun.normal Man page
msefun.poisson Man page
nnlasso Man page
nnlasso.binomial Man page
nnlasso.binomial.lambda Man page
nnlasso.normal Man page
nnlasso.normal.lambda Man page
nnlasso.poisson Man page
nnlasso.poisson.lambda Man page
plot.nnlasso Man page
predict.nnlasso Man page

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