extlasso: Maximum penalized likelihood estimation with extended lasso penalty

The package estimates coefficients of extended LASSO penalized linear regression and generalized linear models. Currently lasso and elastic net penalized linear regression and generalized linear models are considered. The package currently utilizes an accurate approximation of L1 penalty and then a modified Jacobi algorithm to estimate the coefficients. There is provision for plotting of the solutions and predictions of coefficients at given values of lambda. The package also contains functions for cross validation to select a suitable lambda value given the data. The package also provides a function for estimation in fused lasso penalized linear regression.

AuthorB N Mandal <mandal.stat@gmail.com> and Jun Ma <jun.ma@mq.edu.au>
Date of publication2014-08-19 07:54:21
MaintainerB N Mandal <mandal.stat@gmail.com>
LicenseGPL (>= 2)

View on CRAN


bars Man page
coef.extlasso Man page
cv.binomial Man page
cv.extlasso Man page
cv.normal Man page
cv.poisson Man page
extlasso Man page
extlasso.binomial Man page
extlasso.binom.lambda Man page
extlasso.normal Man page
extlasso.norm.lambda Man page
extlasso.pois.lambda Man page
extlasso.poisson Man page
fl.lambda Man page
fold Man page
fusedlasso Man page
kfold Man page
msefun.binomial Man page
msefun.normal Man page
msefun.poisson Man page
plot.extlasso Man page
predict.extlasso Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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