`covNNC()`

estimates robust covariance/dispersion matrices by the
nearest neighbor variance estimation (NNVE) or (rather)
“Nearest Neighbor Cleaning” (NNC) method of Wang and Raftery
(2002, *JASA*).

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`X` |
matrix in which each row represents an observation or point and each column represents a variable. |

`k` |
desired number of nearest neighbors (default is 12) |

`pnoise` |
percent of added noise |

`emconv` |
convergence tolerance for EM |

`bound` |
value used to identify surges in variance caused by
outliers wrongly included as signal points ( |

`extension` |
whether or not to continue after reaching the last
chi-square distance. The default is to continue,
which is indicated by setting |

`devsm` |
when |

A list with components

`cov` |
covariance matrix |

`mu` |
mean vector |

`postprob` |
posterior probability |

`classification` |
classification (0=noise otherwise 1) obtained
by rounding |

`innc` |
list of initial nearest neighbor cleaning results (components are the covariance, mean, posterior probability and classification) |

Terms of use: GPL version 2 or newer.

MM: Even though `covNNC()`

is backed by a serious scientific
publication, I cannot recommend its use at all.

Naisyin Wang nwang@stat.tamu.edu and Adrian Raftery raftery@stat.washington.edu with contributions from Chris Fraley fraley@stat.washington.edu.

`covNNC()`

, then named `cov.nnve()`

, used to be (the only
function) in CRAN package covRobust (2003), which was archived
in 2012.

Martin Maechler allowed `ncol(X) == 1`

,
sped up the original code, by reducing the amount of scaling;
further, the accuracy was increased (using internal `q.dDk()`

).
The original version is available, unexported as
`robustX:::covNNC1`

.

Wang, N. and Raftery, A. (2002)
Nearest neighbor variance estimation (NNVE):
Robust covariance estimation via nearest neighbor cleaning (with discussion).
*Journal of the American Statistical Association* **97**, 994–1019.

see also University of Washington Statistics Technical Report 368 (2000) http://www.stat.washington.edu/www/research/reports

`cov.mcd`

from package MASS;
`covMcd`

, and `covOGK`

from package robustbase.

The whole package rrcov.

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