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.
The gmwmx2 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
gmwmx2 package with:
install.packages("gmwmx2")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).
# 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)R librariesThe 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.
gmwmx2 vs
gmwmxThe original gmwmx
package was designed to compare estimated parameters obtained from the
GMWMX with the ones obtained with the Maximum Likelihood Estimator (MLE)
implemented in Hector. This allowed
for the replication of examples and simulations discussed in Cucci, D. A., Voirol,
L., Kermarrec, G., Montillet, J. P., and Guerrier, S. (2022).
However, as we advanced in the methodological and computational
development of the GMWMX method, we sought a standalone implementation
that did not include Hector. Additionally,
many of the new computational techniques and structural improvements
would have been challenging to incorporate into the previous
gmwmx package. Therefore, we will now exclusively support
and develop the gmwmx2 package.
The gmwmx2 package is currently in the early stages of
development. While the supported features are stable, we have numerous
additional methods and computational enhancements planned for gradual
integration. These include:
This source code is released under is the GNU AFFERO GENERAL PUBLIC LICENSE (AGPL) v3.0.
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