Software

synimu: Scale-wise Variance Optimization

synimu is an R package which contains the functions and datasets that allow to replicate the examples considered in Zhang et al. (2022). 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.

simts: Time Series Analysis Tools

simts is an R package that contains various tools for time series analysis. Indeed, this R package provides a series of tools to simulate, plot, estimate, select and forecast different time series models. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013). This R package is originally conceived as a support to the online textbook "Applied Time Series Analysis with R".

wv: Wavelet Variance

wv is an R package that provides a series of tools to compute and plot quantities related to classical and robust wavelet variance for time series and regular lattices. More details can be found, for example, in Serroukh et al. (2000) and Guerrier et al. (2021).

avar: Allan Variance

avar is an R package that implements the Allan variance and Allan variance linear regression estimator for latent time series models. More details can be found, for example in Guerrier et al. (2016).