lori: Imputation of High-Dimensional Count Data using Side Information

Analysis, imputation, and multiple imputation of count data using covariates. LORI uses a log-linear Poisson model where main row and column effects, as well as effects of known covariates and interaction terms can be fitted. The estimation procedure is based on the convex optimization of the Poisson loss penalized by a Lasso type penalty and a nuclear norm. LORI returns estimates of main effects, covariate effects and interactions, as well as an imputed count table. The package also contains a multiple imputation procedure. The methods are described in Robin, Josse, Moulines and Sardy (2019) <doi:10.48550/arXiv.1703.02296>.

Version: 2.2.2
Depends: stats, data.table, rARPACK, svd, R (≥ 2.10)
Suggests: knitr, rmarkdown, testthat
Published: 2020-12-16
DOI: 10.32614/CRAN.package.lori
Author: Genevieve Robin [aut, cre]
Maintainer: Genevieve Robin <genevieve.robin at cnrs.fr>
License: GPL-3
NeedsCompilation: no
Materials: README
In views: MissingData
CRAN checks: lori results


Reference manual: lori.pdf
Vignettes: aravo_data_analysis


Package source: lori_2.2.2.tar.gz
Windows binaries: r-devel: lori_2.2.2.zip, r-release: lori_2.2.2.zip, r-oldrel: lori_2.2.2.zip
macOS binaries: r-release (arm64): lori_2.2.2.tgz, r-oldrel (arm64): lori_2.2.2.tgz, r-release (x86_64): lori_2.2.2.tgz, r-oldrel (x86_64): lori_2.2.2.tgz
Old sources: lori archive


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