Computes and visualizes empirical joint distribution of multivariate data with optimized algorithms and multi-thread computation. There is a faster algorithm using dynamic programming to compute the whole empirical joint distribution of a bivariate data. There are optimized algorithms for computing empirical joint CDF function values for other multivariate data. Visualization is focused on bivariate data. Levelplots and wireframes are included.
| Version: | 0.1.2 |
| Depends: | R (≥ 3.2.5), lattice |
| Imports: | Rcpp (≥ 0.12.8), methods |
| LinkingTo: | Rcpp |
| Published: | 2018-01-13 |
| Author: | En-shuo Hsu, Jeffrey C. Miecznikowski |
| Maintainer: | En-shuo Hsu <daviden1013 at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| CRAN checks: | Emcdf results |
| Reference manual: | Emcdf.pdf |
| Package source: | Emcdf_0.1.2.tar.gz |
| Windows binaries: | r-devel: Emcdf_0.1.2.zip, r-release: Emcdf_0.1.2.zip, r-oldrel: Emcdf_0.1.2.zip |
| OS X binaries: | r-release: Emcdf_0.1.2.tgz, r-oldrel: Emcdf_0.1.2.tgz |
| Old sources: | Emcdf archive |
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