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.
Author  B N Mandal <mandal.stat@gmail.com> and Jun Ma <jun.ma@mq.edu.au> 
Date of publication  20140819 07:54:21 
Maintainer  B N Mandal <mandal.stat@gmail.com> 
License  GPL (>= 2) 
Version  0.2 
Package repository  View on CRAN 
Installation  Install the latest version of this package by entering the following in R:

All man pages Function index File listing
Man pages  

bars: Error bars  
coef.extlasso: Extract coefficients from a fitted extlasso object  
cv.binomial: kfold cross validation for penalized generalized linear...  
cv.extlasso: kfold cross validation for penalized generalized linear...  
cv.normal: kfold cross validation for penalized generalized linear...  
cv.poisson: kfold cross validation for penalized generalized linear...  
extlasso: Entire regularization path of penalized generalized linear...  
extlasso.binomial: Entire regularization path of penalized generalized linear...  
extlasso.binom.lambda: Coefficients of penalized generalized linear models for a...  
extlasso.normal: Entire regularization path of penalized generalized linear...  
extlasso.norm.lambda: Coefficients of penalized generalized linear models for a...  
extlasso.pois.lambda: Coefficients of penalized generalized linear models for a...  
extlasso.poisson: Entire regularization path of penalized generalized linear...  
fl.lambda: Coefficients of fused lasso penalized regression for a given...  
fold: Particular fold of a data after k fold partition  
fusedlasso: Fused lasso penalized linear regression  
kfold: kfold partition of data at random  
msefun.binomial: Deviances for hold out data in cross validation  
msefun.normal: Prediction means squared errors for hold out data in cross...  
msefun.poisson: Deviances for hold out data in cross validation  
plot.extlasso: Plot of regularization path  
predict.extlasso: Prediction of coefficients of a penalized linear regression... 
Functions  

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

NAMESPACE
 
R
 
R/extlasso.R  
MD5
 
DESCRIPTION
 
man
 
man/extlasso.binomial.Rd  
man/extlasso.normal.Rd  
man/cv.extlasso.Rd  
man/extlasso.pois.lambda.Rd  
man/kfold.Rd  
man/fl.lambda.Rd  
man/fusedlasso.Rd  
man/msefun.poisson.Rd  
man/msefun.binomial.Rd  
man/coef.extlasso.Rd  
man/predict.extlasso.Rd  
man/msefun.normal.Rd  
man/extlasso.Rd  
man/extlasso.poisson.Rd  
man/bars.Rd  
man/fold.Rd  
man/cv.binomial.Rd  
man/cv.normal.Rd  
man/plot.extlasso.Rd  
man/extlasso.norm.lambda.Rd  
man/cv.poisson.Rd  
man/extlasso.binom.lambda.Rd 
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