Package: gmwmx2 0.0.5

Lionel Voirol

gmwmx2: Estimate Functional and Stochastic Parameters of Linear Models with Correlated Residuals and Missing Data

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) <doi:10.48550/arXiv.2409.05160>. The GMWMX estimator allows to estimate functional and stochastic parameters of linear models with correlated residuals in presence of missing data. The 'gmwmx2' package provides functions to load and plot Global Navigation Satellite System (GNSS) data from the Nevada Geodetic Laboratory and functions to estimate linear model model with correlated residuals in presence of missing data.

Authors:Lionel Voirol [aut, cre], Haotian Xu [aut], Yuming Zhang [aut], Luca Insolia [aut], Roberto Molinari [aut], Stéphane Guerrier [aut]

gmwmx2_0.0.5.tar.gz
gmwmx2_0.0.5.zip(r-4.7)gmwmx2_0.0.5.zip(r-4.6)gmwmx2_0.0.5.zip(r-4.5)
gmwmx2_0.0.5.tgz(r-4.6-x86_64)gmwmx2_0.0.5.tgz(r-4.6-arm64)gmwmx2_0.0.5.tgz(r-4.5-x86_64)gmwmx2_0.0.5.tgz(r-4.5-arm64)
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gmwmx2_0.0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
gmwmx2/json (API)

# Install 'gmwmx2' in R:
install.packages('gmwmx2', repos = c('https://smac-group.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/smac-group/gmwmx2/issues

Pkgdown/docs site:https://smac-group.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

openblascpp

5.98 score 2 stars 17 scripts 518 downloads 13 exports 43 dependencies

Last updated from:3c114773d2. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK214
linux-devel-x86_64OK203
source / vignettesOK332
linux-release-arm64OK193
linux-release-x86_64OK196
macos-release-arm64OK138
macos-release-x86_64OK268
macos-oldrel-arm64OK121
macos-oldrel-x86_64OK303
windows-develOK198
windows-releaseOK206
windows-oldrelOK180
wasm-releaseOK162

Exports:ar1download_all_stations_ngldownload_estimated_velocities_ngldownload_station_nglflickergenerategmwm2gmwmx2markov_two_statesmaternplrwwn

Dependencies:askpassbackportsbroomclicodacpp11curldata.tabledplyrfarvergenericsgluehttr2labelinglatticelifecyclelongmemomagrittrMatrixopensslpillarpkgconfigpurrrR6rappdirsRColorBrewerRcppRcppArmadillorlangrobcorscalessimtsstringistringrsystibbletidyrtidyselectutf8vctrsviridisLitewithrwv

Estimate geodetic time series from the Nevada Geodetic Laboratory
Model and estimator | Estimating tectonic velocities and crustal uplift | Trajectory model | Stochastic model | Example of estimation | Download a station from Nevada Geodetic Laboratory | Plot Station | Plot Northing component | Estimate models on station data | References
Rendered fromestimate_geodetic_time_series_from_ngl.Rmdusingknitr::rmarkdown

Last update: 2026-06-10
Started: 2026-02-17

GMWMX: Estimate linear models with dependent errors
Build an arbitrary design matrix X | Example 1: White noise + AR(1) | Generate signal | Fit model | Monte Carlo simulation | Plot empirical distributions of estimated parameters | Compute empirical coverage of confidence intervals | Example 2: White noise + stationary power-law | Example 3: White noise + flicker
Rendered fromestimate_linear_models_with_dependent_errors.Rmdusingknitr::rmarkdown

Last update: 2026-06-10
Started: 2026-02-18

GMWMX: Estimate linear models with dependent errors and missing observations
Build an arbitrary design matrix X | Example 1: White noise + AR(1) | Generate signal | Fit model | Monte Carlo simulation | Plot empirical distributions of estimated parameters | Compute empirical coverage of confidence intervals | Example 2: White noise + stationary power-law | Example 3: White noise + flicker
Rendered fromestimate_linear_models_with_dependent_errors_and_missing_observations.Rmdusingknitr::rmarkdown

Last update: 2026-06-10
Started: 2026-02-18

Load and plot data from Nevada Geodetic Laboratory
Download all available stations from NGL | Download one station | Extract GNSS position time series for the station | Extract equipment or software change steps | Extract earthquake steps | Plot GNSS position time series
Rendered fromload_plot_data_ngl.Rmdusingknitr::rmarkdown

Last update: 2026-06-10
Started: 2024-11-05

Data generation
Available models | Stochastic models | White noise | AR(1) | Power-law | Matérn | Random walk | Flicker | Reproducible generation | Composite models (sum of processes)
Rendered fromdata_generation.Rmdusingknitr::rmarkdown

Last update: 2026-06-10
Started: 2026-02-05

Estimate composite stochastic processes
Example 1 (White noise + AR(1)) | Monte Carlo simulation | Example 2 (White noise + stationary powerlaw) | Example 3 (White noise + AR(1) + random walk)
Rendered fromestimate_composite_stochastic_models.Rmdusingknitr::rmarkdown

Last update: 2026-06-10
Started: 2026-02-07

Estimate a small network of GNSS stations
Load packages | Define some functions for plotting | Estimate a small network in France | Load elevation data and create raster for plot | Plot estimated N-E velocities and associated uncertainty
Rendered fromestimate_small_network.Rmdusingknitr::rmarkdown

Last update: 2026-06-10
Started: 2024-11-15

Plot a large network of GNSS data
Load the data | Plot estimated North/East velocities for all estimated stations | Zoom in the United States
Rendered fromplot_large_network.Rmdusingknitr::rmarkdown

Last update: 2026-02-18
Started: 2024-11-15

Readme and manuals

Help Manual

Help pageTopics
Add to a 'sum_model' object+.sum_model
Add to a 'time_series_model' object+.time_series_model
AR(1) process ('time_series_model')ar1
Estimated northward and eastward velocity and their standard deviation using the GMWMX estimatordf_estimated_velocities_gmwmx
Download all stations name and location from the Nevada Geodetic Laboratorydownload_all_stations_ngl
Download estimated velocities using the MIDAS estimator provided by the Nevada Geodetic Laboratory for all stations.download_estimated_velocities_ngl
Download GNSS position time series and steps reference from the Nevada Geodetic Laboratory with IGS14 or IGS20 reference frame.download_station_ngl
Flicker noise process ('time_series_model')flicker
Generate a time series from a 'time_series_model' or 'sum_model' objectgenerate
GMWM estimatorgmwm2
GMWMX estimatorgmwmx2 gmwmx2.default gmwmx2.gnss_ts_ngl
Markov two-state missingness model ('missingness_model')markov_two_states
Matern process ('time_series_model')matern
Stationary Power-Law process ('time_series_model')pl
Plot a 'generated_composite_model_time_series' objectplot.generated_composite_model_time_series
Plot a 'generated_missingness' objectplot.generated_missingness
Plot a 'generated_time_series' objectplot.generated_time_series
Plot method for a 'gmwm2_fit' objectplot.gmwm2_fit
Plot a 'gmwmx2_fit_gnss_ts_ngl' objectplot.gmwmx2_fit_gnss_ts_ngl
Plot a 'gnss_ts_ngl' objectplot.gnss_ts_ngl
Print method for a 'gmwm2_fit' objectprint.gmwm2_fit
Print method for a 'gmwmx2_fit' objectprint.gmwmx2_fit
Print method for a 'gmwmx2_fit_gnss_ts_ngl' objectprint.gmwmx2_fit_gnss_ts_ngl
Random walk process ('time_series_model')rw
White noise process ('time_series_model')wn