generalCorr: Generalized Correlations and Initial Causal Path

Asymmetric generalized correlations r*(x|y) measure strength of the dependence of x on y. If |r*(x|y)|> |r*(y|x)| it suggests that y is more likely the "kernel cause" of x. There are at least two additional ways of comparing two kernel regressions helping identify the ‘cause’. In simultaneous equation models where endogeneity of regressors is feared, we can use Prof. Koopmans' method to ignore endogeneity problems when it kernel causes the dependent variable. The usual partial correlations can be generalized for the asymmetric matrix of r*'s. Partial correlations help asses effect of x on y after removing the effect of a set of variables. The package provides additional tools for causal assessment, for printing the causal detections in a clear, comprehensive compact summary form, for matrix algebra, for outlier detection, and for numerical integration by the trapezoidal rule, stochastic dominance, etc. The package has functions for bootstrap-based statistical inference and one for a heuristic t-test.

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