This package is considered a duplicate. The official version of this package is found at:https://welch-lab.r-universe.dev/RcppPlanc

Package: RcppPlanc 2.0.15

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], Conrad Sanderson [cph], Blue Brain Project/EPFL [cph], UT-Batelle [cph]

RcppPlanc_2.0.15.tar.gz
RcppPlanc_2.0.15.tar.gz(r-4.7-arm64)RcppPlanc_2.0.15.tar.gz(r-4.7-x86_64)RcppPlanc_2.0.15.tar.gz(r-4.6-arm64)RcppPlanc_2.0.15.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
RcppPlanc/json (API)

# Install 'RcppPlanc' in R:

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

Bug tracker:https://github.com/welch-lab/rcppplanc/issues

Uses libs:

openblas– Optimized BLAS
hwloc– Hierarchical view of the machine
hdf5– HDF5 C runtime files - serial version
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

ctrl.sparse - Example single-cell transcriptomic data in sparse form
stim.sparse - Example single-cell transcriptomic data in sparse form

On CRAN:

openblas hwloc hdf5 glibc cpp openmp

4.62 score 3 packages 31 scripts 1.5k downloads 23 exports 34 dependencies

Last updated from:3bd80069cd. Checks:5 OK, 1 FAIL. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	244
linux-devel-x86_64	OK	294
source / vignettes	OK	362
linux-release-arm64	OK	257
linux-release-x86_64	OK	287
wasm-release	FAIL	165

Exports:as.H5Mat as.H5Mat.default as.H5Mat.dgCMatrix as.H5Mat.matrix as.H5SpMat as.H5SpMat.default as.H5SpMat.dgCMatrix as.H5SpMat.matrix bppnnls bppnnls_prod dim.H5SpMat dim<-.H5SpMat format.H5Mat format.H5SpMat H5Mat H5SpMat inmf nmf onlineINMF print.H5Mat print.H5SpMat symNMF uinmf

Dependencies:backports bit bit64 checkmate cli dplyr easy.utils farver fastmatch generics glue hdf5r hdf5r.Extra HighFive labeling lattice lifecycle magrittr Matrix pillar pkgconfig R6 RColorBrewer Rcpp RcppArmadillo RcppProgress rlang scales tibble tidyselect utf8 vctrs viridisLite withr

RcppPlanc, Fast NMF and iNMF Implementation with C++

Running Non-negative Matrix Factorization (NMF) | Running Integrative Non-negative Matrix Factorization (iNMF) | Using Data Stored in HDF5 Files for iNMF

Rendered fromRcppPlanc.Rmdusingknitr::rmarkdown

Last update: 2025-03-25
Started: 2025-03-25

Citation

Readme and manuals

Help Manual

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

Package: RcppPlanc 2.0.15

RcppPlanc: Parallel Low-Rank Approximation with Nonnegativity Constraints

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)