README.rmd

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

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


[![Licence](https://img.shields.io/badge/licence-AGPL--3.0-blue.svg)](https://opensource.org/licenses/AGPL-3.0)
[![Last-changedate](https://img.shields.io/badge/last%20change-`r gsub('-', '--', Sys.Date())`-yellowgreen.svg)](https://github.com/SMAC-Group/wv)


# `synimu` Overview

This `synimu` package contains the functions and datasets that allow to replicate the examples considered in [Zhang et al. (2022)](https://ieeexplore.ieee.org/abstract/document/9899741). In particular, this package implements a non-parametric method that makes use of the wavelet cross-covariance at different scales to combine the measurements coming from an array of gyroscopes in order to deliver an optimal measurement signal with weak assumptions on the processes underlying the individual error signals. Although the method is illustrated with the applications of gyroscopes, it can be applied to any sensor or signal where one aims to compute an average signal having optimal properties in terms of its resulting wavelet variance. In this package we also provide a rigorous non-parametric approach for the estimation of the asymptotic covariance matrix of the wavelet cross-covariance estimator which has various important applications.

Below are instructions on how to install and make use of the `synimu` package.

## Installation Instructions

The `wv` package is available only on GitHub at the moment. The latest version can be installed with:

```{r, eval = F}
# Install dependencies
install.packages(c("devtools"))

# Install/Update the package from GitHub
devtools::install_github("Yuming-Zhang/synimu")

# Install the package with Vignettes/User Guides 
devtools::install_github("Yuming-Zhang/synimu", build_vignettes = TRUE)
```