# Non-parametric Multiple Change-points Detection

### Description

Detect multiple change-points using a non-parametric maximum likelihood approach.

### Usage

1 |

### Arguments

`x` |
data vector |

`kmax` |
upper bound of the number of change-points |

`cpp` |
positions of candidate change-points. usually returned by function |

`ncp` |
the number of the candidate change-points. |

`n` |
length of the data. |

### Details

NMCD use DP algorithm to select change-points, while the true number of change-points is determined by the Bayesian information criterion(BIC).

### Value

a list with class `nmcd`

is returned with elements:

`npp` |
the true number of change-points |

`cpp` |
positions of true change-points |

`data` |
raw data, this is not printed on screen by default |

`bic` |
minimal BIC value gained. |

### Note

memory consume may be significant with large data.

### References

Changliang Zou, Guosheng Yin, Long Feng, Zhaojun Wang. Non-parametric Maximum Likelihood Approach to Multiple Change-points Problem

### Examples

1 2 3 |