README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# TrendLSW

<!-- badges: start -->
[![R-CMD-check](https://github.com/EuanMcGonigle/TrendLSW/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/EuanMcGonigle/TrendLSW/actions/workflows/R-CMD-check.yaml)
[![Codecov test coverage](https://codecov.io/gh/EuanMcGonigle/TrendLSW/branch/main/graph/badge.svg)](https://app.codecov.io/gh/EuanMcGonigle/TrendLSW?branch=main)
<!-- badges: end -->

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.

## Installation

You can install the released version of `TrendLSW` from [CRAN](https://CRAN.R-project.org) with:

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

You can install the development version of `TrendLSW` from [GitHub](https://github.com/) with:

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

## Usage

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


```{r}
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:

```{r}
x.TLSW <- TLSW(x)
```

Visualise the estimated trend and spectrum:
```{r TLSW-plot}
plot(x.TLSW)
```