Package: RcppPlanc 1.0.0

Andrew Robbins

RcppPlanc: Parallel Low-Rank Approximation with Nonnegativity Constraints

'Rcpp' bindings for 'PLANC', a highly parallel and extensible NMF/NTF (Non-negative Matrix/Tensor Factorization) library. Wraps algorithms described in Kannan et. al (2018) <doi:10.1109/TKDE.2017.2767592> and Eswar et. al (2021) <doi:10.1145/3432185>. Implements algorithms described in Welch et al. (2019) <doi:10.1016/j.cell.2019.05.006>, Gao et al. (2021) <doi:10.1038/s41587-021-00867-x>, and Kriebel & Welch (2022) <doi:10.1038/s41467-022-28431-4>.

Authors:Andrew Robbins [aut, cre], Yichen Wang [aut], Joshua Welch [cph], Ramakrishnan Kannan [cph], UT-Batelle [cph]

# Install 'RcppPlanc' in R:

install.packages('RcppPlanc', repos = c('https://welch-lab.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

Datasets:

On CRAN:

Last updated 2 months agofrom:4f705f90d4. Checks:OK: 1 NOTE: 1 WARNING: 7. Indexed: yes.

Yichen Wang

Last update: 2023-10-02
Started: 2023-10-02

Help page	Topics
Block Principal Pivoted Non-Negative Least Squares	bppnnls bppnnls_prod
Example single-cell transcriptomic data in sparse form	ctrl.sparse stim.sparse
Retrieve the dimension of H5SpMat argument list	dim.H5SpMat dim<-.H5SpMat
Prepare character information of a H5Mat object	format.H5Mat
prepare character information of a H5SpMat object	format.H5SpMat
Argument list object for using a dense matrix stored in HDF5 file	as.H5Mat as.H5Mat.default as.H5Mat.dgCMatrix as.H5Mat.matrix H5Mat
Argument list object for using a sparse matrix stored in HDF5 file	as.H5SpMat as.H5SpMat.default as.H5SpMat.dgCMatrix as.H5SpMat.matrix H5SpMat
Perform Integrative Non-negative Matrix Factorization	inmf
Perform Non-negative Matrix Factorization	nmf
Perform Integrative Non-negative Matrix Factorization Using Online Learning	onlineINMF
Show information of a H5Mat object	print.H5Mat
Show information of a H5SpMat object	print.H5SpMat
Perform Symmetric Non-negative Matrix Factorization	symNMF
Perform Mosaic Integrative Non-negative Matrix Factorization with Unshared Features	uinmf