Since causal paths from data are important for all sciences, the package provides sophisticated functions. The idea is simply that if X causes Y (path: X to Y) then non-deterministic variation in X is more "original or independent" than similar variation in Y. We compare two flipped kernel regressions: X=f(Y, Z) and Y=g(X,Z), where Z are control variables. Our first two criteria compare absolute gradients (Cr1) and absolute residuals (Cr2), both quantified by stochastic dominance of four orders (SD1 to SD4). Our third criterion (Cr3) expects X to be better able to predict Y than the other way around using generalized partial correlation If |r*(x|y)|> |r*(y|x)| it suggests that y is more likely the "kernel cause" of x. The usual partial correlations are generalized for the asymmetric matrix of r*'s developed here. 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 directions 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.

Version 1.0.3 [2016-06-28] o New functions added for bootstrap and causal path assessment without much printing.

Version: 1.0.2 [2016-05-20] o New functions added to treat some variables as control variables, not part of causal paths.

Version: 1.0.1 [2016-05-20] o cosmetic changes to many functions, changed output titles in somePairs(), some0Pairs(), allPairs() with added example in parcor_ridg()