Specification of the number of boosting iterations, step size and other parameters for boosting algorithms.

1 2 3 4 | ```
bst_control(mstop = 50, nu = 0.1, twinboost = FALSE, twintype=1, threshold=c("standard",
"adaptive"), f.init = NULL, coefir = NULL, xselect.init = NULL, center = FALSE,
trace = FALSE, numsample = 50, df = 4, s = NULL, sh = NULL, q = NULL, qh = NULL,
fk = NULL, iter = 10, intercept = FALSE, trun=FALSE)
``` |

`mstop` |
an integer giving the number of boosting iterations. |

`nu` |
a small number (between 0 and 1) defining the step size or shrinkage parameter. |

`twinboost` |
a logical value: |

`twintype` |
for |

`threshold` |
if |

`f.init` |
the estimate from the first round of twin boosting. Only useful when |

`coefir` |
the estimated coefficients from the first round of twin boosting. Only useful when |

`xselect.init` |
the variable selected from the first round of twin boosting. Only useful when |

`center` |
a logical value: |

`trace` |
a logical value for printout of more details of information during the fitting process. |

`numsample` |
number of random sample variable selected in the first round of twin boosting. This is potentially useful in the future implementation. |

`df` |
degree of freedom used in smoothing splines. |

`s,q` |
truncation parameter |

`sh, qh` |
threshold value or frequency |

`fk` |
used for robust classification. A function estimate used in difference of convex algorithm |

`iter` |
number of iteration in difference of convex algorithm |

`intercept` |
logical value, if TRUE, estimation of intercept with linear predictor model |

`trun` |
logical value, if TRUE, predicted value in each boosting iteration is truncated at -1, 1, for |

Objects to specify parameters of the boosting algorithms
implemented in `bst`

, via the `ctrl`

argument. The default value of `s`

is -1 if `family="thinge"`

, -log(3) if `family="tbinom"`

, and 4 if `family="binomd"`

An object of class `bst_control`

, a list. Note `fk`

may be updated for robust boosting.

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