# Compute discrete wavelets.

### Description

For a given wavelet computes a list with each entry of the list containing that discrete wavelet at a different scale. The first entry corresponds to the finest wavelet, the next entry to the next finest, and so on.

### Usage

1 | ```
mkcoef(J, filter.number = 10, family = "DaubLeAsymm")
``` |

### Arguments

`J` |
A NEGATIVE integer. -J is the maximum number of levels to compute. |

`filter.number` |
The filter number (number of vanishing moments) of the underlying wavelet to use. |

`family` |
The family of the wavelet. See |

### Value

A list of length J. The first entry contains the discrete wavelet at the finest scale, the 2nd entry contains the next most finest wavelet, and so on.

### Author(s)

Guy Nason.

### References

Nason, G.P. (2013) A test for second-order stationarity and
approximate confidence intervals for localized autocovariances
for locally stationary time series. *J. R. Statist. Soc.* B,
**75**, 879-904.

### See Also

`Rvarlacf`

,
`whichlevel`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
#
# E.g. compute discrete Haar wavelets on scales 1, 2, 3.
#
mkcoef(-3, 1, "DaubExPhase")
#[[1]]
#[1] 0.7071068 -0.7071068
#
#[[2]]
#[1] 0.5 0.5 -0.5 -0.5
#
#[[3]]
#[1] 0.3535534 0.3535534 0.3535534 0.3535534 -0.3535534 -0.3535534 -0.3535534
#[8] -0.3535534
``` |