This function can estimate either the autocovariance / autocorrelation for univariate time series, or the partial autocovariance / autocorrelation for univariate time series.

```
auto_corr(
x,
lag.max = NULL,
pacf = FALSE,
type = "correlation",
demean = TRUE,
robust = FALSE
)
```

- x
A

`vector`

or`ts`

object (of length \(N > 1\)).- lag.max
An

`integer`

indicating the maximum lag up to which to compute the empirical ACF / PACF.- pacf
A

`boolean`

indicating whether to output the PACF. If it's`TRUE`

, then the function will only estimate the empirical PACF. If it's`FALSE`

(the default), then the function will only estimate the empirical ACF.- type
A

`character`

string giving the type of acf to be computed. Allowed values are "correlation" (the default) and "covariance".- demean
A

`boolean`

indicating whether the data should be detrended (`TRUE`

) or not (`FALSE`

). Defaults to`TRUE`

.- robust
A

`boolean`

indicating whether a robust estimator should be used (`TRUE`

) or not (`FALSE`

). Defaults to`FALSE`

. This only works when the function is estimating ACF.

An `array`

of dimensions \(N \times 1 \times 1\).

`lagmax`

default is \(10*log10(N/m)\) where \(N\) is the number of
observations and \(m\) is the number of time series being compared. If
`lagmax`

supplied is greater than the number of observations N, then one
less than the total will be taken (i.e. N - 1).

```
m = auto_corr(datasets::AirPassengers)
m = auto_corr(datasets::AirPassengers, pacf = TRUE)
```