# Auto- and Cross- Covariance and -Correlation Function Estimation

### Description

This function calls the acf function in the stats package and processes to drop lag-0 of the acf. It only works for univariate time series, so x below should be 1-dimensional.

### Usage

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### Arguments

`x` |
a univariate or multivariate (not ccf) numeric time series object or a numeric vector or matrix, or an "acf" object. |

`lag.max` |
maximum number of lags at which to calculate the acf. Default is 10*log10(N/m) where N is the number of observations and m the number of series. |

`type` |
character string giving the type of acf to be computed. Allowed values are "correlation" (the default), "covariance" or "partial". |

`plot` |
logical. If TRUE (the default) the acf is plotted. |

`na.action` |
function to be called to handle missing values. na.pass can be used. |

`demean` |
logical. Should the covariances be about the sample means? |

`drop.lag.0` |
logical. Should lag 0 be dropped |

`...` |
further arguments to be passed to plot.acf. |

### Value

An object of class "acf", which is a list with the following elements:

`lag` |
A three dimensional array containing the lags at which the acf is estimated. |

`acf` |
An array with the same dimensions as lag containing the estimated acf. |

`type` |
The type of correlation (same as the type argument). |

`n.used` |
The number of observations in the time series. |

`series` |
The name of the series x. |

`snames` |
The series names for a multivariate time series. |

### Author(s)

Original authors of stats:::acf are: Paul Gilbert, Martyn Plummer, B.D. Ripley. This wrapper is written by Kung-Sik Chan

### References

~put references to the literature/web site here ~

### See Also

`plot.acf`

, `ARMAacf`

for the exact autocorrelations of a given ARMA process.

### Examples

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