Description: Analysis of count data with covariates. LORI uses a log-linear model where main row and column effects are decomposed as regression terms on known covariates. A residual low-rank interaction term is also fitted.
lattice, gridExtra, grid, grDevices, stats, graphics
Packaged: 2018-09-12 11:17:50 UTC; genevieverobin

Packaged: 2018-10-04 12:10:38 UTC; genevieverobin

Packaged: 2018-11-16 10:54:00 UTC; genevieverobin

Title: Low-Rank Interactions in Count Data with Covariates
Description: Analysis, imputation, and multiple imputation of count data using covariates. LORI uses a log-linear model where main row and column effects are decomposed as regression terms on known covariates. A residual low-rank interaction term is also fitted. LORI returns estimates of covariate effects and interactions, as well as an imputed count table. The package also contains a multiple imputation procedure.
lattice, gridExtra, grid, grDevices, stats, graphics,
data.table, doParallel, parallel, corpcor, foreach
Packaged: 2019-02-19 14:05:15 UTC; genevieverobin

Imports: svd, glmnet, stats, data.table, doParallel, parallel, corpcor,
Packaged: 2019-02-26 15:21:02 UTC; genevieverobin

Packaged: 2019-03-06 23:03:28 UTC; genevieverobin

Title: Imputation of Count Data using Side Information
Depends: stats, data.table, FactoMineR, rARPACK, svd
Packaged: 2019-03-29 08:49:25 UTC; genevieverobin

Authors@R: person("Genevieve", "Robin", email = "genevieve.robin@inria.fr",
Depends: stats, data.table, rARPACK, svd
Packaged: 2019-12-10 21:55:35 UTC; genevieverobin

Packaged: 2020-11-26 11:00:59 UTC; genevieverobin

Title: Imputation of High-Dimensional Count Data using Side Information
Authors@R: person("Genevieve", "Robin", email = "genevieve.robin@cnrs.fr",
Description: 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) <arXiv:1703.02296v4>.
Depends: stats, data.table, rARPACK, svd, R (>= 2.10)
Packaged: 2020-12-16 11:06:28 UTC; genevieverobin