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extlasso
View on CRAN: Click
here
Download and install extlasso package within the R console
Install from CRAN:
install.packages("extlasso")
Install from Github:
library("remotes")
install_github("cran/extlasso") Install by package version:
library("remotes")
install_version("extlasso", "0.3") Attach the package and use:
library("extlasso")
Maintained by
B N Mandal
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-07-30
Latest Update: 2022-05-13
Description:
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. This 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. This package also contains functions for cross validation to select a suitable lambda value given the data. Also provides a function for estimation in fused lasso penalized linear regression. For more details, see Mandal, B. N.(2014). Computational methods for L1 penalized GLM model fitting, unpublished report submitted to Macquarie University, NSW, Australia.
How to cite:
B N Mandal (2014). extlasso: Maximum Penalized Likelihood Estimation with Extended Lasso Penalty. R package version 0.3, https://cran.r-project.org/web/packages/extlasso. Accessed 26 Aug. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for extlasso
Functions, R codes and Examples using
the extlasso R package
Some associated functions: bars . coef.extlasso . cv.binomial . cv.extlasso . cv.normal . cv.poisson . extlasso.binom.lambda . extlasso.binomial . extlasso.norm.lambda . extlasso.normal . extlasso . extlasso.pois.lambda . extlasso.poisson . fl.lambda . fold . fusedlasso . kfold . msefun.binomial . msefun.normal . msefun.poisson . plot.extlasso . predict.extlasso .
Some associated R codes: extlasso.R . Full extlasso package functions and examples
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