For fitting Bayesian joint latent class and regression models using
    Gibbs sampling. See the documentation for the model.
    The technical details of the model implemented here are described in Elliott,
    Michael R., Zhao, Zhangchen, Mukherjee, Bhramar, Kanaya, Alka, Needham,
    Belinda L., "Methods to account for uncertainty in latent class assignments when
    using latent classes as predictors in regression models, with application to
    acculturation strategy measures" (2020) In press at Epidemiology
    <doi:10.1097/EDE.0000000000001139>.

Helper functions to implement univariate and bivariate latent change score models in R using the 'lavaan' package.
  For details about Latent Change Score Modeling (LCSM) see McArdle (2009) <doi:10.1146/annurev.psych.60.110707.163612> and Grimm, An, McArdle, Zonderman and Resnick (2012) <doi:10.1080/10705511.2012.659627>.
  The package automatically generates 'lavaan' syntax for different model specifications and varying timepoints.
  The 'lavaan' syntax generated by this package can be returned and further specifications can be added manually.
  Longitudinal plots as well as simplified path diagrams can be created to visualise data and model specifications.
  Estimated model parameters and fit statistics can be extracted as data frames.
  Data for different univariate and bivariate LCSM can be simulated by specifying estimates for model parameters to explore their effects.
  This package combines the strengths of other R packages like 'lavaan', 'broom', and 'semPlot' by generating 'lavaan' syntax that helps these packages work together.

Provides researchers and educators with easy-to-learn user friendly tools for calculating 
    key spatial statistics and to apply simple as well as advanced methods of spatial analysis in real data. 
    These include: Local Pearson and Geographically Weighted Pearson Correlation Coefficients, 
    Spatial Inequality Measures (Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation 
    (Global and Local Moran's I), several Geographically Weighted Regression techniques and other 
    Spatial Analysis tools (other geographically weighted statistics). This package also contains functions for 
    measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations.