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>.">

RcppPlanc: Parallel Low-Rank Approximation with Nonnegativity Constraints (original) (raw)

Version:

2.0.12

Depends:

R (≥ 3.5)

Imports:

methods, Rcpp, Matrix, hdf5r.Extra

LinkingTo:

Rcpp, RcppArmadillo, RcppProgress

Suggests:

knitr, withr, rmarkdown, testthat (≥ 3.0.0)

Published:

2025-05-29

DOI:

10.32614/CRAN.package.RcppPlanc

Author:

Andrew Robbins ORCID iD [aut, cre], Yichen Wang [aut], Joshua Welch ORCID iD [cph], Ramakrishnan KannanORCID iD [cph], Conrad Sanderson ORCID iD [cph], Blue Brain Project/EPFL [cph] (HighFive Headers), UT-Batelle [cph] (The original PLANC code)

Maintainer:

Andrew Robbins

BugReports:

https://github.com/welch-lab/RcppPlanc/issues/

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

Copyright:

see file

URL:

https://github.com/welch-lab/RcppPlanc/

NeedsCompilation:

yes

SystemRequirements:

C++17, cmake >= 3.21.0, hdf5, git, patch, gnumake, hwloc, GNU make

Materials:

README NEWS

CRAN checks:

RcppPlanc results