Travis-CI Build Status Project Status: Active Licence minimal R version CRAN RStudio mirror downloads CRAN RStudio mirror downloads Last-changedate

Welcome to the avar package

This package provides the tools necessary to compute the empirical Allan Variance (AV) and use it to estimate the parameters of (latent) time series models. The estimation of the Allan Variance is performed through the estimator proposed by Allan (1966) and, based on this quantity, the Allan Variance Linear Regression (AVLR) approach (or Allan Variance Slope Method) is often used by engineers to retrieve the parameters of time series models which are assumed to underlie the observed signals (see for example Guerrier, Molinari, and Stebler 2016). These estimators are implemented in this package along with the relevant plotting and summary functions.

Install Instructions

The avar package is available on both CRAN and GitHub. The CRAN version is considered stable while the GitHub version is subject to modifications/updates which may lead to installation problems or broken functions. You can install the stable version of the avar package with:


For users who are interested in having the latest developments, the GitHub version is ideal although more dependencies are required to run a stable version of the package. Most importantly, users must have a (C++) compiler installed on their machine that is compatible with R (e.g. Clang). Once you’ve made sure that you have a compatible C++ compiler installed on your computer, run the following code in an R session and you will be ready to use the devlopment version of avar.

# Install dependencies

# Install/Update the package from GitHub

# Install the package with Vignettes/User Guides 
devtools::install_github("SMAC-Group/avar", build_vignettes = TRUE)


Allan, David W. 1966. “Statistics of Atomic Frequency Standards.” Proceedings of the IEEE 54 (2). IEEE: 221–30.

Guerrier, Stéphane, Roberto Molinari, and Yannick Stebler. 2016. “Theoretical Limitations of Allan Variance-Based Regression for Time Series Model Estimation.” IEEE Signal Processing Letters 23 (5). IEEE: 597–601.