Functions for computing a standardized moderation effect
             in moderated regression and forming its confidence interval
             by nonparametric bootstrapping as proposed in
             Cheung, Cheung, Lau, Hui, and Vong (2022)
             <doi:10.1037/hea0001188>. Also includes simple-to-use
             functions for computing conditional effects (unstandardized
             or standardized) and plotting moderation effects.

Contains functionality for regression standardization. Four general classes of models are allowed; generalized linear models, conditional generalized estimating equation models, Cox proportional hazards models and shared frailty gamma-Weibull models. Sjolander, A. (2016) <doi:10.1007/s10654-016-0157-3>.

Contains more modern tools for causal inference using regression 
      standardization. Four general classes of models are implemented; generalized 
      linear models, conditional generalized estimating equation models, 
      Cox proportional hazards models, and shared frailty gamma-Weibull models. 
      Methodological details are described in Sjölander, A. (2016) <doi:10.1007/s10654-016-0157-3>. 
      Also includes functionality for doubly robust estimation for generalized linear models 
      in some special cases, and the ability to implement custom models.

Allows the creation and manipulation of C++ std::vector's in R.

Functions related to multivariate measures of independence and ICA:
 -estimate independent components by minimizing distance covariance;
 -conduct a test of mutual independence based on distance covariance; 
 -estimate independent components via infomax (a popular method but generally performs poorer than mdcovica, ProDenICA, and/or fastICA, but is useful for comparisons);
 -order indepedent components by skewness;
 -match independent components from multiple estimates;
 -other functions useful in ICA.

Provides estimation of Bayesian vector autoregression
    (BVAR) models with steady-state priors via 'Stan', along with
    functions for unconditional and conditional forecasting, as well as impulse
    response analysis. For details on the steady-state BVAR model
    see Villani (2009) <doi:10.1002/jae.1065>.