Description Usage Arguments References Examples

Local Nearest Neighbor entropy estimator using Gaussian kernel and kNN selected bandwidth. Entropy is estimated by taking a Monte Carlo estimate using local kernel density estimate of the negative-log density.

1 | ```
lnn_entropy(data, k = 5, tr = 30, bw = NULL)
``` |

`data` |
Matrix of sample observations, each row is an observation. |

`k` |
Order of the local kNN bandwidth selection. |

`tr` |
Order of truncation (number of neighbors to include in entropy). |

`bw` |
Bandwidth (optional) manually fix bandwidth instead of using local kNN bandwidth selection. |

Loader, C. (1999). Local regression and likelihood. Springer Science & Business Media.

Gao, W., Oh, S., & Viswanath, P. (2017). Density functional estimators with k-nearest neighbor bandwidths. IEEE International Symposium on Information Theory - Proceedings, 1, 1351–1355.

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