# entropy

##### Shannon Entropy

KNN Shannon Entropy Estimators.

- Keywords
- manip

##### Usage

`entropy(X, k = 10, algorithm = c("kd_tree", "brute"))`

##### Arguments

- X
an input data matrix.

- k
the maximum number of nearest neighbors to search. The default value is set to 10.

- algorithm
nearest neighbor search algorithm.

##### Value

a vector of length `k`

for entropy estimates using `1:k`

nearest neighbors, respectively.

##### References

H. Singh, N. Misra, V. Hnizdo, A. Fedorowicz and E. Demchuk (2003). “Nearest neighbor
estimates of entropy”. *American Journal of Mathematical and Management Sciences*, **23**, 301-321.

M.N. Goria, N.N.Leonenko, V.V. Mergel and P.L. Novi Inverardi (2005).
“A new class of random vector entropy estimators and its applications in testing statistical hypotheses”.
*Journal of Nonparametric Statistics*, **17**:3, 277--297.

R.M. Mnatsakanov, N. Misra, S. Li and E.J. Harner (2008). “K_n-nearest neighbor estimators of entropy”.
*Mathematical Methods of Statistics*, **17**:3, 261-277.

*Documentation reproduced from package FNN, version 1.1.3, License: GPL (>= 2)*