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.

Author
B N Mandal <mandal.stat@gmail.com> and Jun Ma <jun.ma@mq.edu.au>
Date of publication
2014-08-19 07:54:21
Maintainer
B N Mandal <mandal.stat@gmail.com>
License
GPL (>= 2)
Version
0.2

View on CRAN

Man pages

bars
Error bars
coef.extlasso
Extract coefficients from a fitted extlasso object
cv.binomial
k-fold cross validation for penalized generalized linear...
cv.extlasso
k-fold cross validation for penalized generalized linear...
cv.normal
k-fold cross validation for penalized generalized linear...
cv.poisson
k-fold 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
k-fold 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...

Files in this package

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