## MVR: Mean-Variance Regularization

MVR is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm), among which are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom.
Key features include:
(i) Normalization and/or variance stabilization of the data,
(ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow),
(iii) Generation of diverse diagnostic plots,
(iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.

Version: |
1.20.0 |

Depends: |
R (≥ 2.15.0), parallel, statmod |

Published: |
2013-11-13 |

Author: |
Jean-Eudes Dazard [aut, cre],
Hua Xu [ctb],
Alberto Santana [ctb] |

Maintainer: |
Jean-Eudes Dazard <jxd101 at case.edu> |

License: |
GPL (≥ 3) | file LICENSE |

URL: |
http://www.r-project.org |

NeedsCompilation: |
yes |

Citation: |
NA |

Materials: |
NA |

CRAN checks: |
MVR results |

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