c(person("François", "Rousset", role = c("aut", "cre", "cph"),
email = "francois.rousset@umontpellier.fr",
comment = c(ORCID = "0000-0003-4670-0371")),
person("Jimmy","Lopez", role="ctb"),
person("Khalid","Belkhir", role="ctb"))
François Rousset [aut, cre, cph]
(<https://orcid.org/0000-0003-4670-0371>),
Jimmy Lopez [ctb],
Khalid Belkhir [ctb]

Generalized Correlations and Initial Causal Path
Prof. H. D. Vinod, Fordham University, NY.
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 a function for bootstrap-based statistical inference and one
for a heuristic t-test.

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.