Package: generalCorr
Type: Package
Title: Generalized Correlations and Various Causal Paths
Version: 1.1.6
Date: 2020-11-17
Author: Prof. H. D. Vinod, Fordham University, NY.
Maintainer: H. D. Vinod <vinod@fordham.edu>
Encoding: UTF-8
Depends: R (>= 3.0.0), np (>= 0.60), xtable (>= 1.8), meboot (>= 1.4),
psych, lattice
Suggests: R.rsp
VignetteBuilder: R.rsp
Description: 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 values 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 several R functions including
get0outliers() for outlier detection, bigfp() for numerical integration by the
trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts,
canonRho() for generalized canonical correlations, depMeas() measures nonlinear
dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns
is easiest to use. Several functions whose names begin with 'boot' provide bootstrap
statistical inference including a new bootGcRsq() test for "Granger-causality"
allowing nonlinear relations. See Vinod (2019) <doi:10.1080/03610918.2015.1122048>.
License: GPL (>= 2)
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-11-20 22:13:19 UTC; vinod
Repository: CRAN
Date/Publication: 2020-11-20 23:30:02 UTC

Package: generalCorr
Type: Package
Title: Generalized Correlations and Various Causal Paths
Version: 1.1.7
Date: 2020-11-29
Author: Prof. H. D. Vinod, Fordham University, NY.
Maintainer: H. D. Vinod <vinod@fordham.edu>
Encoding: UTF-8
Depends: R (>= 3.0.0), np (>= 0.60), xtable (>= 1.8), meboot (>= 1.4),
psych, lattice
Suggests: R.rsp
VignetteBuilder: R.rsp
Description: 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 values 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 several R functions including
get0outliers() for outlier detection, bigfp() for numerical integration by the
trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts,
canonRho() for generalized canonical correlations, depMeas() measures nonlinear
dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns
is easiest to use. Several functions whose names begin with 'boot' provide bootstrap
statistical inference including a new bootGcRsq() test for "Granger-causality"
allowing nonlinear relations. See Vinod (2019) <doi:10.1080/03610918.2015.1122048>.
License: GPL (>= 2)
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-11-29 21:21:11 UTC; vinod
Repository: CRAN
Date/Publication: 2020-11-29 21:50:02 UTC