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sparsenet (version 1.6)

Fit Sparse Linear Regression Models via Nonconvex Optimization

Description

Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010). Implements the methodology described in Mazumder, Friedman and Hastie (2011) . Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.

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Version

Install

install.packages('sparsenet')

Monthly Downloads

402

Version

1.6

License

GPL-2

Maintainer

Trevor Hastie

Last Published

February 5th, 2024

Functions in sparsenet (1.6)

sparsenet-package

Fit a linear model regularized by the nonconvex MC+ sparsity penalty
sparsenet-internal

Internal sparsenet functions
plot.cv.sparsenet

plot the cross-validation curves produced by cv.sparsenet
predict.cv.sparsenet

make predictions from a "cv.sparsenet" object.
predict.sparsenet

make predictions from a "sparsenet" object.
plot.sparsenet

plot coefficients from a "sparsenet" object
gendata

Generate data for testing sparse model selection
cv.sparsenet

Cross-validation for sparsenet
sparsenet

Fit a linear model regularized by the nonconvex MC+ sparsity penalty