Showing 6 of total 6 results (show query)
yixuan
RSpectra:Solvers for Large-Scale Eigenvalue and SVD Problems
R interface to the 'Spectra' library <https://spectralib.org/> for large-scale eigenvalue and SVD problems. It is typically used to compute a few eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues, which is usually more efficient than eigen() if k << n. This package provides the 'eigs()' function that does the similar job as in 'Matlab', 'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function to calculate the largest k singular values and corresponding singular vectors of a real matrix. The matrix to be computed on can be dense, sparse, or in the form of an operator defined by the user.
Maintained by Yixuan Qiu. Last updated 9 months ago.
eigenvaluesspectrasvdopenblascpp
81 stars 12.40 score 394 scripts 433 dependentsyixuan
rARPACK:Solvers for Large Scale Eigenvalue and SVD Problems
Previously an R wrapper of the 'ARPACK' library <http://www.caam.rice.edu/software/ARPACK/>, and now a shell of the R package 'RSpectra', an R interface to the 'Spectra' library <http://yixuan.cos.name/spectra/> for solving large scale eigenvalue/vector problems. The current version of 'rARPACK' simply imports and exports the functions provided by 'RSpectra'. New users of 'rARPACK' are advised to switch to the 'RSpectra' package.
Maintained by Yixuan Qiu. Last updated 9 years ago.
45 stars 9.25 score 177 scripts 51 dependents
bnprks
BPCells:Single Cell Counts Matrices to PCA
> Efficient operations for single cell ATAC-seq fragments and RNA counts matrices. Interoperable with standard file formats, and introduces efficient bit-packed formats that allow large storage savings and increased read speeds.
Maintained by Benjamin Parks. Last updated 2 months ago.
184 stars 7.48 score 172 scripts
privefl
bigutilsr:Utility Functions for Large-scale Data
Utility functions for large-scale data. For now, package 'bigutilsr' mainly includes functions for outlier detection and unbiased PCA projection.
Maintained by Florian Privé. Last updated 5 months ago.
10 stars 5.84 score 39 scripts 5 dependents
rohelab
fastRG:Sample Generalized Random Dot Product Graphs in Linear Time
Samples generalized random product graphs, a generalization of a broad class of network models. Given matrices X, S, and Y with with non-negative entries, samples a matrix with expectation X S Y^T and independent Poisson or Bernoulli entries using the fastRG algorithm of Rohe et al. (2017) <https://www.jmlr.org/papers/v19/17-128.html>. The algorithm first samples the number of edges and then puts them down one-by-one. As a result it is O(m) where m is the number of edges, a dramatic improvement over element-wise algorithms that which require O(n^2) operations to sample a random graph, where n is the number of nodes.
Maintained by Alex Hayes. Last updated 7 months ago.
adjacency-matrixgraph-samplinglatent-factors
5 stars 4.52 score 22 scriptsrohelab
sparseLRMatrix:Represent and Use Sparse + Low Rank Matrices
Provides an S4 class for representing and interacting with sparse plus rank matrices. At the moment the implementation is quite spare, but the plan is eventually subclass Matrix objects.
Maintained by Alex Hayes. Last updated 4 years ago.
1 stars 3.48 score 2 scripts 2 dependents