Histogram principal components analysis is the generalization of the PCA. Histogram data are adapted to design complex and big data which histograms used as variables (big data adapter). Functions implemented provides numerical and graphical tools of an extension of PCA. Sun Makosso Kallyth (2016) <doi:10.1002/sam.11270>. Sun Makosso Kallyth and Edwin Diday (2012) <doi:10.1007/s11634-012-0108-0>.
| Version: | 1.1 |
| Depends: | R (≥ 2.15.1) |
| Imports: | ggplot2, FactoMineR, scatterplot3d, ggplot2movies |
| Published: | 2018-04-13 |
| Author: | Brahim Brahim and Sun Makosso-Kallyth |
| Maintainer: | Brahim Brahim <brahim.brahim at bigdatavisualizations.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | GraphPCA results |
| Reference manual: | GraphPCA.pdf |
| Package source: | GraphPCA_1.1.tar.gz |
| Windows binaries: | r-devel: GraphPCA_1.1.zip, r-release: GraphPCA_1.1.zip, r-oldrel: GraphPCA_1.1.zip |
| macOS binaries: | r-release (arm64): GraphPCA_1.1.tgz, r-oldrel (arm64): GraphPCA_1.1.tgz, r-release (x86_64): GraphPCA_1.1.tgz, r-oldrel (x86_64): GraphPCA_1.1.tgz |
| Old sources: | GraphPCA archive |
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