Relaxation of glmnet models (as in relaxed lasso, Meinshausen
2007)
Extends the glmnet package with "relaxation", done by
running glmnet once on the entire predictor matrix, then again
on each different subset of variables from along the
regularization path. Penalty may be lasso (alpha = 1) or
elastic net (0 < alpha < 1). For this version, family may be
"gaussian" or "binomial" only. Takes advantage of fast fortran
code from the glmnet package.

Extends the glmnet package with "relaxation", done by
running glmnet once on the entire predictor matrix, then again
on each different subset of variables from along the
regularization path. Relaxation may lead to improved prediction
accuracy for truly sparse data generating models, as well as
fewer false positives (i.e. fewer noncontributing predictors in
the final model). Penalty may be lasso (alpha = 1) or elastic
net (0 < alpha < 1). For this version, family may be "gaussian"
or "binomial" only. Takes advantage of fast fortran code from
the glmnet package.

Extends the glmnet package with "relaxation", done by running glmnet once on the entire predictor matrix, then again on each different subset of variables from along the regularization path. Relaxation may lead to improved prediction accuracy for truly sparse data generating models, as well as fewer false positives (i.e. fewer noncontributing predictors in the final model). Penalty may be lasso (alpha = 1) or elastic net (0 < alpha < 1). For this version, family may be "gaussian" or "binomial" only. Takes advantage of fast FORTRAN code from the glmnet package.

Relaxed Lasso is a generalisation of the Lasso shrinkage technique for linear regression. Both variable selection and parameter estimation is achieved by regular Lasso, yet both steps do not necessarily use the same penalty parameter. The results include all standard Lasso solutions but allow often for sparser models while having similar or even slightly better predictive performance if many predictor variables are present. The package depends on the LARS package.