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Computation of large covariance matrices having a block structure up to a permutation of their columns and rows from a small number of samples with respect to the dimension of the matrix. The method is described in the paper Perrot-Dockès et al. (2019) <doi:10.48550/arXiv.1806.10093>.
| Version: | 0.1.1 |
| Imports: | Matrix, stats, Rdpack, BBmisc, dplyr, tibble, magrittr, rlang |
| Suggests: | knitr |
| Published: | 2019-04-13 |
| Author: | M. Perrot-Dock\`es, C. Lévy-Leduc |
| Maintainer: | Marie Perrot-Dockès <marie.perrocks at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | BlockCov results |
| Reference manual: | BlockCov.pdf |
| Vignettes: |
BlockCov package |
| Package source: | BlockCov_0.1.1.tar.gz |
| Windows binaries: | r-devel: BlockCov_0.1.1.zip, r-release: BlockCov_0.1.1.zip, r-oldrel: BlockCov_0.1.1.zip |
| macOS binaries: | r-release (arm64): BlockCov_0.1.1.tgz, r-oldrel (arm64): BlockCov_0.1.1.tgz, r-release (x86_64): BlockCov_0.1.1.tgz, r-oldrel (x86_64): BlockCov_0.1.1.tgz |
| Old sources: | BlockCov archive |
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