Fit joint models of survival and multivariate longitudinal data. The longitudinal
    data is specified by generalised linear mixed models. The joint models are fit via maximum
    likelihood using an approximate expectation maximisation algorithm. 
    Bernhardt (2015) <doi:10.1016/j.csda.2014.11.011>.

Implements the Generalized Method of Wavelet Moments with Exogenous Inputs estimator (GMWMX) presented in Cucci, D. A., Voirol, L., Kermarrec, G., Montillet, J. P., and Guerrier, S. (2023) <doi:10.1007/s00190-023-01702-8>.
             The GMWMX estimator allows to estimate functional and stochastic parameters of linear models with correlated residuals. 
             The 'gmwmx' package provides functions to estimate, compare and analyze models, utilities to load and work with Global Navigation Satellite System (GNSS) data as well as methods to compare results with the Maximum Likelihood Estimator (MLE) implemented in Hector.

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>.

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>.