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

## Documentation for package ‘gmwmx2’ version 0.0.5

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

+.sum_model | Add to a 'sum_model' object
+.time_series_model | Add to a 'time_series_model' object
ar1 | AR(1) process ('time_series_model')
df_estimated_velocities_gmwmx | Estimated northward and eastward velocity and their standard deviation using the GMWMX estimator
download_all_stations_ngl | Download all stations name and location from the Nevada Geodetic Laboratory
download_estimated_velocities_ngl | Download estimated velocities using the MIDAS estimator provided by the Nevada Geodetic Laboratory for all stations.
download_station_ngl | Download GNSS position time series and steps reference from the Nevada Geodetic Laboratory with IGS14 or IGS20 reference frame.
flicker | Flicker noise process ('time_series_model')
generate | Generate a time series from a 'time_series_model' or 'sum_model' object
gmwm2 | GMWM estimator
gmwmx2 | GMWMX estimator
gmwmx2.default | GMWMX estimator
gmwmx2.gnss_ts_ngl | GMWMX estimator
markov_two_states | Markov two-state missingness model ('missingness_model')
matern | Matern process ('time_series_model')
pl | Stationary Power-Law process ('time_series_model')
plot.generated_composite_model_time_series | Plot a 'generated_composite_model_time_series' object
plot.generated_missingness | Plot a 'generated_missingness' object
plot.generated_time_series | Plot a 'generated_time_series' object
plot.gmwm2_fit | Plot method for a 'gmwm2_fit' object
plot.gmwmx2_fit_gnss_ts_ngl | Plot a 'gmwmx2_fit_gnss_ts_ngl' object
plot.gnss_ts_ngl | Plot a 'gnss_ts_ngl' object
print.gmwm2_fit | Print method for a 'gmwm2_fit' object
print.gmwmx2_fit | Print method for a 'gmwmx2_fit' object
print.gmwmx2_fit_gnss_ts_ngl | Print method for a 'gmwmx2_fit_gnss_ts_ngl' object
rw | Random walk process ('time_series_model')
wn | White noise process ('time_series_model')