gmwmx2 Overview

The gmwmx2 R package implements the Generalized Method of Wavelet Moments with Exogenous Inputs estimator (GMWMX) presented in Voirol, L., Xu, H., Zhang, Y., Insolia, L., Molinari, R. and Guerrier, S. (2024). The GMWMX estimator is a computationally efficient estimator to estimate large scale regression problems with complex dependence structure in presence of missing data. The gmwmx2 R package allows to estimate (i) functional/structural parameters, (ii) stochastic parameters describing the dependence structure and (iii) nuisance parameters of the missingness process of large regression models with dependent observations and missing data. To illustrate the capability of the GMWMX estimator, the gmwmx2 R package provides functions to download an plot Global Navigation Satellite System (GNSS) position time series from the Nevada Geodetic Laboratory and allow to estimate linear model with a specific dependence structure modeled by composite stochastic processes, allowing to estimate tectonic velocities and crustal uplift from GNSS position time series.

Find vignettes with detailed examples as well as the user's manual at the package website.

Below are instructions on how to install and make use of the gmwmx2 package.

Installation Instructions

The gmwmx2 package is currently only available on GitHub. You can install the gmwmx2 package with:

# Install dependencies
install.packages(c("devtools"))

# Install/Update the package from GitHub
devtools::install_github("SMAC-Group/gmwmx2")

# Install the package with Vignettes/User Guides 
devtools::install_github("SMAC-Group/gmwmx2", build_vignettes = TRUE)

External R libraries

The gmwmx2 package relies on a limited number of external libraries, but notably on Rcpp and RcppArmadillo which require a C++ compiler for installation, such as for example gcc.

License

This source code is released under is the GNU AFFERO GENERAL PUBLIC LICENSE (AGPL) v3.0.

References

Voirol, L., Xu, H., Zhang, Y., Insolia, L., Molinari, R., and Guerrier, S. (2024). Inference for Large Scale Regression Models with Dependent Errors. doi:10.48550/arXiv.2409.05160.

Guerrier, S., Skaloud, J., Stebler, Y. and Victoria-Feser, M.P., 2013. Wavelet-variance-based estimation for composite stochastic processes. Journal of the American Statistical Association, 108(503), pp.1021-1030. doi:10.1080/01621459.2013.799920