R package implement the Generalized Method of Wavelet Moments with Exogenous Inputs estimator (GMWMX) introduced in Cucci, D. A., Voirol, L., Kermarrec, G., Montillet, J. P., and Guerrier, S. (2022) and provides functions to estimate times series models that can be expressed as linear models with correlated residuals. Moreover, the
gmwmx package provides tools to compare and analyze estimated models and methods to easily compare results with the Maximum Likelihood Estimator (MLE) implemented in Hector, allowing to replicate the examples and simulations considered in Cucci, D. A., Voirol, L., Kermarrec, G., Montillet, J. P., and Guerrier, S. (2022). In particular, this package implements a statistical inference framework for the functional and stochastic parameters of models such as those used to model Global Navigation Satellite System (GNSS) observations, enabling the comparison of the proposed method to the standard MLE estimates implemented in Hector.
Find the package vignettes and user’s manual at the package website.
Below are instructions on how to install and make use of the
gmwmx 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
gmwmx 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.
# Install dependencies install.packages(c("devtools")) # Install/Update the package from GitHub devtools::install_github("SMAC-Group/gmwmx") # Install the package with Vignettes/User Guides devtools::install_github("SMAC-Group/gmwmx", build_vignettes = TRUE)
In order to runs successfully functions that execute
Hector, we assume that
Hector is installed and available in the
PATH of the installation where these functions are called. More precisely, when running either
PBO_get_offsets(), we assume that
Hector’s binaries executable
date2mjd are located in a folder available in the
In order to make sure that these functions are available in the
PATH, you can run
Sys.getenv("PATH") and ensure that the directory that contains the executable binaries of
Hector is listed in the
For Linux users that are on distributions supported by
Hector, this can be easily done by:
Hector’s binaries for the corresponding OS here.
PATHenvironment variable by modifying
Sys.getenv("PATH")after running the script and reassigning the new
PATHenvironment variable with
. /etc/environmentor equivalently with
> Sys.getenv("PATH")  "$HOME/app/hector/bin:..."
Some users have reported that the procedure described above did not work on their installation and that even after completing these steps, the path containing the executable binaries of
Hector was not accessible to the
PATH recognized by
R. In this case, a strategy that seems to work is to directly indicate the path where
Hector is located by executing the following command before executing a function that runs
Sys.setenv(PATH = "$HOME/app/hector/bin")
"$HOME/app/hector/bin" is the path where are located
It is possible to execute functions from the
R package directly from a
MATLAB environment and to save estimated models in the
MATLAB environment thanks to
Rcall is an interface which runs in
MATLAB and provides direct access to methods and software packages implemented in
R. Refer to issue #1 for the detailed procedure and to the official
Rcall project for support.
We thank Dr. Machiel Bos for his helpful advises and constructive comments that helped us to improve the implementation of the
gmwmx package and to ensure the correct integration of
Hector into the
Cucci, D. A., Voirol, L., Kermarrec, G., Montillet, J. P., & Guerrier, S. (2023). The Generalized Method of Wavelet Moments with eXogenous inputs: a fast approach for the analysis of GNSS position time series. Journal of Geodesy, 97(2), 14.
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.