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 |

**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...

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
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