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. Since causal variables are
also exogenous in a model, we provide new exogeneity tests.  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 cross products
of regressor values and residuals (Cr1) and
absolute residuals (Cr2), are 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,z)|> |r*(y|x,z)| it suggests that y is more likely
the "kernel cause" of x. The usual partial correlations are generalized with
a new non-symmetric matrix 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.

Generalized Correlations and Plausible Causal Paths
Since causal paths from data are important for all sciences, the
package provides many sophisticated functions. causeSummary()
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 parcorMany() 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.