Implements wavelet methods for analysis of nonstationary time series. See

McGonigle, E. T., Killick, R., and Nunes, M. (2022). Trend locally stationary wavelet processes.

Journal of Time Series Analysis, 43(6), 895-917.McGonigle, E. T., Killick, R., and Nunes, M. (2022). Modelling time-varying first and second-order structure of time series via wavelets and differencing.

Electronic Journal of Statistics, 6(2), 4398-4448.

for full details.

You can install the released version of `TrendLSW`

from
CRAN with:

```
install.packages("TrendLSW")
```

You can install the development version of `TrendLSW`

from
GitHub with:

```
devtools::install_github("https://github.com/EuanMcGonigle/TrendLSW")
```

For detailed examples, see the help files within the package. We can generate a small example for performing trend and spectrum estimation as follows:

```
library(TrendLSW)
set.seed(1)
noise <- rnorm(512) * c(seq(from = 1, to = 3, length = 256), seq(from = 3, to = 1, length = 256))
trend <- seq(from = 0, to = 5, length = 512)
x <- trend + noise
```

Apply the `TLSW`

function:

```
x.TLSW <- TLSW(x)
```

Visualise the estimated trend and spectrum:

```
plot(x.TLSW)
```

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