Function to run kernel regression with options for residuals and gradients.

1 2 |

`dep.y` |
Data on the dependent (response) variable |

`reg.x` |
Data on the regressor (stimulus) variable |

`tol` |
Tolerance on the position of located minima of the cross-validation function (default =0.1) |

`ftol` |
Fractional tolerance on the value of cross validation function evaluated at local minima (default =0.1) |

`gradients` |
Set to TRUE if gradients computations are desired |

`residuals` |
Set to TRUE if residuals are desired |

Creates a model object ‘mod’ containing the entire kernel regression output.
Type `names(mod)`

to reveal the variety of outputs produced by ‘npreg’ of the ‘np’ package.
The user can access all of them at will by using the dollar notation of R.

This is a work horse for causal identification.

Prof. H. D. Vinod, Economics Dept., Fordham University, NY

Vinod, H. D.'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, http://dx.doi.org/10.1080/03610918.2015.1122048

1 2 3 4 5 6 7 |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.