## Abstract

dglars is a publicly available R package that implements the method proposed in Augugliaro, Mineo, and Wit (2013), developed to study the sparse structure of a generalized linear model. This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method proposed in Efron, Hastie, Johnstone, and Tibshirani (2004). The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve: a predictor-corrector algorithm, proposed in Augugliaro et al. (2013), and a cyclic coordinate descent algorithm, proposed in Augugliaro, Mineo, and Wit (2012). The latter algorithm, as shown here, is significantly faster than the predictor-corrector algorithm. For comparison purposes, we have implemented both algorithms.

Original language | English |
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Pages (from-to) | 1-40 |

Number of pages | 40 |

Journal | Journal of Statistical Software |

Volume | 59 |

Issue number | 8 |

DOIs | |

Publication status | Published - Aug-2014 |

## Keywords

- differential geometry
- generalized linear models
- dgLARS
- predictor-corrector algorithm
- cyclic coordinate descent algorithm
- sparse models
- variable selection
- LEAST ANGLE REGRESSION
- BREAST-CANCER RISK
- VARIABLE SELECTION
- DANTZIG SELECTOR
- EXPRESSION
- MARKER
- HAPLOTYPES
- LIKELIHOOD
- SHRINKAGE
- TISSUES