It is a heuristic procedure which tries to figure out positions of
period and cohort effects in the data. It also uses a few steps to estimate
model's parameters. The procedure is supposed to outperform `autoSmoothAPC`

slightly.

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`data` |
Demographic data presented as a matrix. |

`p.value` |
P-value used to test the period and the cohort effects for significance. The lower the value the fewer diagonals and years will be used to find cohort and period effects. |

`cornerLength` |
Sets the smallest length of a diagonal to be considered for cohort effects. |

`lower` |
Lowest possible values for the optimization procedure. |

`upper` |
Highest possible values for the optimization procedure. |

`init` |
Initial values for the optimization procedure. |

`reltol` |
Relative tolerance parameter to be supplied to |

`trace` |
Controls if tracing is on. |

`control` |
The control data passed directly to |

A list of six components: smooth surface, period effects, cohort effects, parameters used for smoothing, diagonals used for cohort effects and years used for period effects.

Alexander Dokumentov

http://robjhyndman.com/working-papers/mortality-smoothing/

`autoSmoothAPC`

, `smoothAPC`

.

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