Fast computation of weights needed for adaptive lasso based on Gaussian family data.

1 2 | ```
adaptive.weights(x, y, nu = 1, weight.method = c("multivariate",
"univariate"))
``` |

`x` |
input matrix, of dimension nobs x nvars; each row is an observation vector. |

`y` |
response variable. |

`nu` |
non-negative tuning parameter |

`weight.method` |
Should the weights be computed for multivariate regression model (only possible when the number of observations is larger than the number of parameters) or by individual marginal/"univariate" regression coefficients. |

The weights returned are 1/abs(beta_hat)^nu where the beta-parameters are estimated from the corresponding linear model (either multivariate or univariate).

Returns a list with two elements:

`weights ` |
the computed weights |

`nu ` |
the value of nu used for the computations |

Claus Ekstrom claus@rprimer.dk

Xou, H (2006). The Adaptive Lasso and Its Oracle Properties. JASA, Vol. 101.

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