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.

This is a compendium of 'C++' routines useful for Bayesian statistics. We steal 
  other people's 'C++' code, repurpose it, and export it so developers of 'R' 
  packages can use it in their 'C++' code. We actually don't steal anything, or 
  claim that Thomas Bayes did, but copy code that is compatible with our GPL 3 
  licence, fully acknowledging the authorship of the original code.

The steepness package computes steepness as a
        property of dominance hierarchies. Steepness is defined as the
        absolute slope of the straight line fitted to the normalized
        David's scores. The normalized David's scores can be obtained
        on the basis of dyadic dominance indices corrected for chance
        or by means of proportions of wins. Given an observed
        sociomatrix, it computes hierarchy's steepness and estimates
        statistical significance by means of a randomization test.