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>.

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