Fit Cox models using glmBayesMfp

Interface to the internal C++ optimization routine "bfgs"

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`x0` |
the start value |

`f_` |
the target function |

`min.x` |
minimum bound on x (default |

`max.x` |
maximum bound on x (default |

`prec` |
precision (default |

`verbose` |
be verbose? (not default) |

A list with the following elements:

- par
the minimum abscissa found by the algorithm

- inv.hessian
the inverse Hessian at

`par`

- evaluations
list of the function evaluation pairs:

`args`

and`vals`

- code
the convergence code. 0 is “OK”, -1 is “lost precision”, and +1 is “change not large enough”

Daniel Sabanes Bove daniel.sabanesbove@ifspm.uzh.ch

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