person("Genevieve", "Robin", email = "genevieve.robin@polytechnique.edu",
role = c("aut", "cre"))
Estimation and visualization of low-rank interaction contingency tables. Low-rank interaction is a two-fold extension of the log-bilinear model. First, instead of a fixed rank constraint, the log-likelihood is penalized by the nuclear norm of the interaction matrix. Second, available row and column covariates can be incorporated in the model. The main function can be applied to contingency tables with missing values. G. Robin, J. Josse, E. Moulines, S. Sardy (2017) <arXiv:1703.02296>.
psych, svd, NLRoot, FactoMineR, glmnet, pdist, lars, ade4,
lattice, logmult

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.
psych, svd, NLRoot, FactoMineR, glmnet, pdist, lars, ade4,
lattice, gridExtra, grid, grDevices, stats, graphics

Low-Rank Interactions in Count Data with Covariates
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.
psych, svd, NLRoot, FactoMineR, glmnet, pdist, lars, ade4,
lattice, gridExtra, grid, grDevices, stats, graphics,
data.table, doParallel, parallel, corpcor, foreach

svd, glmnet, stats, data.table, doParallel, parallel, corpcor,
foreach, FactoMineR

svd, glmnet, stats, data.table, doParallel, parallel, corpcor,
foreach, FactoMineR, rARPACK

Imputation of Count Data using Side Information
stats, data.table, FactoMineR, rARPACK, svd

person("Genevieve", "Robin", email = "genevieve.robin@inria.fr",
role = c("aut", "cre"))

Imputation of High-Dimensional Count Data using Side Information
person("Genevieve", "Robin", email = "genevieve.robin@cnrs.fr",
role = c("aut", "cre"))
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>.
stats, data.table, rARPACK, svd, R (>= 2.10)