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Semi-Binary and Semi-Ternary Matrix Decomposition are performed based on Non-negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD). For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/dcTensor>.
| Version: | 1.3.0 |
| Depends: | R (≥ 3.4.0) |
| Imports: | methods, MASS, fields, rTensor, nnTensor |
| Suggests: | knitr, rmarkdown, testthat |
| Published: | 2024-05-11 |
| DOI: | 10.32614/CRAN.package.dcTensor |
| Author: | Koki Tsuyuzaki [aut, cre] |
| Maintainer: | Koki Tsuyuzaki <k.t.the-answer at hotmail.co.jp> |
| License: | MIT + file LICENSE |
| URL: | https://github.com/rikenbit/dcTensor |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | dcTensor results |
| Package source: | dcTensor_1.3.0.tar.gz |
| Windows binaries: | r-devel: dcTensor_1.3.0.zip, r-release: dcTensor_1.3.0.zip, r-oldrel: dcTensor_1.3.0.zip |
| macOS binaries: | r-release (arm64): dcTensor_1.3.0.tgz, r-oldrel (arm64): dcTensor_1.3.0.tgz, r-release (x86_64): dcTensor_1.3.0.tgz, r-oldrel (x86_64): dcTensor_1.3.0.tgz |
| Old sources: | dcTensor archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.