An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space.

Author | Yi Yang, Teng Zhang, Hui Zou |

Date of publication | 2015-08-28 00:35:09 |

Maintainer | Yi Yang <yiyang@umn.edu> |

License | GPL-2 |

Version | 1.0.0 |

**as.kernelMatrix:** Assing kernelMatrix class to matrix objects

**cv.KERE:** Cross-validation for KERE

**dots:** Kernel Functions

**KERE:** Fits the regularization paths for the kernel expectile...

**kernel-class:** Class "kernel" "rbfkernel" "polykernel", "tanhkernel",...

**kernelMatrix:** Kernel Matrix functions

**plot.KERE:** Plot coefficients from a "KERE" object

**predict.KERE:** make predictions from a "KERE" object.

KERE

KERE/COPYING

KERE/src

KERE/src/expkern_precision.f90

KERE/src/expkern_fast.f90

KERE/NAMESPACE

KERE/R

KERE/R/predict.KERE.R
KERE/R/utilities.R
KERE/R/aobjects.R
KERE/R/kernels.R
KERE/R/cv.KERE.R
KERE/R/KERE.R
KERE/R/kernelmatrix.R
KERE/R/plot.KERE.R
KERE/MD5

KERE/DESCRIPTION

KERE/man

KERE/man/cv.KERE.Rd
KERE/man/KERE.Rd
KERE/man/as.kernelMatrix.Rd
KERE/man/dots.Rd
KERE/man/kernelMatrix.Rd
KERE/man/kernel-class.Rd
KERE/man/predict.KERE.Rd
KERE/man/plot.KERE.Rd
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.