Implements an algorithm for variable selection in high-dimensional linear regression using the "tilted correlation", a new way of measuring the contribution of each variable to the response which takes into account high correlations among the variables in a data-driven way.

Author | Haeran Cho [aut, cre], Piotr Fryzlewicz [aut] |

Date of publication | 2016-12-26 12:25:13 |

Maintainer | Haeran Cho <haeran.cho@bristol.ac.uk> |

License | GPL (>= 2) |

Version | 1.1.1 |

**col.norm:** Compute the L2 norm of each column

**get.thr:** Select a threshold for sample correlation matrix

**lse.beta:** Compute the least squares estimate on a given index set

**projection:** Compute the projection matrix onto a given set of variables

**select.model:** Select the final model

**thresh:** Hard-threshold a matrix

**tilting:** Variable selection via Tilted Correlation Screening algorithm

**tilting-package:** Variable Selection via Tilted Correlation Screening Algorithm

tilting

tilting/NAMESPACE

tilting/R

tilting/R/package.R
tilting/MD5

tilting/DESCRIPTION

tilting/man

tilting/man/lse.beta.Rd
tilting/man/select.model.Rd
tilting/man/thresh.Rd
tilting/man/projection.Rd
tilting/man/col.norm.Rd
tilting/man/tilting-package.Rd
tilting/man/get.thr.Rd
tilting/man/tilting.Rd
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