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Background  Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalization approaches are often the methods of choice. They are especially useful in case of multicollinearity, which appears if the number of explanatory variables exceeds the number of observations or for some biological reason. Then, the model goodness of fit is penalized by some suitable function of interest. Prominent examples are the lasso, group lasso and sparse-group lasso. Here, we offer a fast and numerically cheap implementation of these operators via proximal gradient descent. The grid search for the penalty parameter is realized by warm starts. The step size between consecutive iterations is determined with backtracking line search. Finally,  seagull  -the R package presented here- produces complete regularization paths.    Results  Publicly available high-dimensional methylation data are used to compare  seagull  to the established R package  SGL . The results of both packages enabled a precise prediction of biological age from DNA methylation status. But even though the results of  seagull  and  SGL  were very similar ( R  2  > 0.99),  seagull  computed the solution in a fraction of the time needed by  SGL . Additionally,  seagull  enables the incorporation of weights for each penalized feature.    Conclusions  The following operators for linear regression models are available in  seagull : lasso, group lasso, sparse-group lasso and Integrative LASSO with Penalty Factors (IPF-lasso). Thus,  seagull  is a convenient envelope of lasso variants.

Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent