generalCorr: Generalized Correlations and Plausible Causal Paths

Since causal paths from data are important for all sciences, the package provides many sophisticated functions. causeSummBlk() gives easy-to-interpret causal paths. Let Z denote control variables and compare two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X,Z)+e2. Our criterion Cr1 says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent" than in Y and causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These inequalities between many absolute value are quantified by four orders of stochastic dominance. Our third criterion Cr3 for the causal path X to Y requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|. The function parcorBMany() reports generalized partials between the first variable and all others. The package provides additional R tools for causal assessment, "outlier detection," and for numerical integration by the trapezoidal rule, stochastic dominance, pillar 3D charts, etc. We also provide functions for bootstrap-based statistical inference for causal paths. causeSummary() and causeSummBlk() are easiest to use functions.

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