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.

Generalized Mann-Whitney type tests based on probabilistic
        indices and new diagnostic plots, for the underlying manuscript see Fischer, Oja (2015) <doi:10.18637/jss.v065.i09>.

The function plotLRT() draws pairwise graphical model checks for
      the Rasch Model (RM; Rasch, 1960), 
      the Partial Credit Model (PCM; Masters, 1982), and 
      the Rating Scale Model (RSM; Andrich, 1978)
  using the output object of eRm::LRtest().
  The function cLRT() provides a conditional Likelihood Ratio Test
  (Andersen, 1973), using the routines of 'psychotools' (Zeileis et al., 2026).
  Users may choose to plot
      the threshold parameters, 
      the cumulative thresholds, 
      the average thresholds per item, or
      the person parameters.
  Extended coloring options allow for automated item-wise or
  threshold-wise coloring. For multi-group splits, all pairwise
  group comparisons are drawn automatically.
  The function plotMLT() draws a graphical model check
  for the Martin-Loef-Test (MLT).
  If type='persons', it draws scatterplots of the person parameter 
  estimates of each pair of item sub-groups.
  If type='items', it draws a scatterplot of the item/threshold 
  parameter estimates of each of the two sub-groups vertically 
  against the estimates of the total sample horizontally.

Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR) time series models which take account of network structure, potentially with exogenous variables.  Such models are described in Knight et al. (2020) <doi:10.18637/jss.v096.i05> and Nason and Wei (2021) <doi:10.1111/rssa.12875>.  Diagnostic tools for GNAR(X) models can be found in Nason et al. (2023) <doi:10.48550/arXiv.2312.00530>.