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A mutual information estimator based on k-nearest neighbor method proposed by A. Kraskov, et al. (2004) <doi:10.1103/PhysRevE.69.066138> to measure general dependence and the time complexity for our estimator is only squared to the sample size, which is faster than other statistics. Besides, an implementation of mutual information based independence test is provided for analyzing multivariate data in Euclidean space (T B. Berrett, et al. (2019) <doi:10.1093/biomet/asz024>); furthermore, we extend it to tackle datasets in metric spaces.
| Version: | 0.1.1 |
| Imports: | Rcpp, stats |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | testthat |
| Published: | 2019-12-30 |
| Author: | Shiyun Lin [aut, cre], Jin Zhu [aut], Wenliang Pan [aut], Xueqin Wang [aut], SC2S2 [cph] |
| Maintainer: | Shiyun Lin <linshy27 at mail2.sysu.edu.cn> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | fastmit results |
| Reference manual: | fastmit.pdf |
| Package source: | fastmit_0.1.1.tar.gz |
| Windows binaries: | r-devel: fastmit_0.1.1.zip, r-release: fastmit_0.1.1.zip, r-oldrel: fastmit_0.1.1.zip |
| macOS binaries: | r-release (arm64): fastmit_0.1.1.tgz, r-oldrel (arm64): fastmit_0.1.1.tgz, r-release (x86_64): fastmit_0.1.1.tgz, r-oldrel (x86_64): fastmit_0.1.1.tgz |
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