Package: eimpute 0.2.4

Zhe Gao

eimpute: Efficiently Impute Large Scale Incomplete Matrix

Efficiently impute large scale matrix with missing values via its unbiased low-rank matrix approximation. Our main approach is Hard-Impute algorithm proposed in <https://www.jmlr.org/papers/v11/mazumder10a.html>, which achieves highly computational advantage by truncated singular-value decomposition.

Authors:Zhe Gao [aut, cre], Jin Zhu [aut], Junxian Zhu [aut], Xueqin Wang [aut], Yixuan Qiu [cph], Gael Guennebaud [cph, ctb], Jitse Niesen [cph, ctb], Ray Gardner [ctb]

eimpute_0.2.4.tar.gz
eimpute_0.2.4.tar.gz(r-4.5-noble)eimpute_0.2.4.tar.gz(r-4.4-noble)
eimpute_0.2.4.tgz(r-4.4-emscripten)eimpute_0.2.4.tgz(r-4.3-emscripten)
eimpute.pdf |eimpute.html
eimpute/json (API)
NEWS

# Install 'eimpute' in R:
install.packages('eimpute', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.00 score 7 scripts 315 downloads 5 exports 2 dependencies

Last updated 5 months agofrom:e1a037a34a. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-linux-x86_64OKNov 20 2024

Exports:biscalebiscale.controleimputeincomplete.generatorr.search

Dependencies:RcppRcppEigen

eimpute: Efficiently IMPUTE Large Scale Incomplete Matrix

Rendered fromeimpute.Rmdusingknitr::rmarkdownon Nov 20 2024.

Last update: 2022-05-02
Started: 2020-03-20