r-cran-rsvd
Randomized Singular Value Decomposition
Low-rank matrix decompositions are fundamental tools and widely used for
data analysis, dimension reduction, and data compression. Classically,
highly accurate deterministic matrix algorithms are used for this task.
However, the emergence of large-scale data has severely challenged our
computational ability to analyze big data. The concept of randomness has
been demonstrated as an effective strategy to quickly produce
approximate answers to familiar problems such as the singular value
decomposition (SVD). The rsvd package provides several randomized matrix
algorithms such as the randomized singular value decomposition (rsvd),
randomized principal component analysis (rpca), randomized robust
principal component analysis (rrpca), randomized interpolative
decomposition (rid), and the randomized CUR decomposition (rcur). In
addition several plot functions are provided. The methods are discussed
in detail by Erichson et al. (2016) <arXiv:1608.02148>.
gccgo-8-mipsisa32r6el-linux-gnu
GNU Go compiler
libgo13-mipsr6el-cross
Runtime library for GNU Go applications
librte-pmd-skeleton-event18.11
Data Plane Development Kit (librte_pmd_skeleton_event runtime library)
r-cran-parmigene
Parallel Mutual Information to establish Gene Networks
The package provides a parallel estimation of the mutual
information based on entropy estimates from k-nearest neighbors
distances and algorithms for the reconstruction of gene
regulatory networks.
libqgis-server2.14.22
QGIS - shared server library