NonLinNormalZs: Generate High-dimensional Nonlinear Normal Data
In CCI: Computational Test for Conditional Independence
NonLinNormalZs R Documentation
Generate High-dimensional Nonlinear Normal Data
Description
Creates a Z-dimensional nonlinear dataset with complex dependencies between features and targets.
Usage
NonLinNormalZs(N, d = 0, Zs = 20)
Arguments
N
Integer. Sample size.
d
Numeric. Dependency strength. Default is 0.
Zs
Integer. Number of Z variables. Default is 10.
Value
A data frame with columns Z1-Z10, X, and Y.
Examples
head(NonLinNormalZs(N = 100, Zs = 20))
CCI documentation built on Aug. 29, 2025, 5:17 p.m.
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| NonLinNormalZs | R Documentation |
Generate High-dimensional Nonlinear Normal Data
Description
Creates a Z-dimensional nonlinear dataset with complex dependencies between features and targets.
Usage
NonLinNormalZs(N, d = 0, Zs = 20)
Arguments
N |
Integer. Sample size. |
d |
Numeric. Dependency strength. Default is 0. |
Zs |
Integer. Number of Z variables. Default is 10. |
Value
A data frame with columns Z1-Z10, X, and Y.
Examples
head(NonLinNormalZs(N = 100, Zs = 20))
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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