colvR: Poisson Lognormal with Dimension Reduction for Count Matrices
with Missing Data
Impute missing data in count tables using a Poisson lognormal
model with dimension reduction, with and without zero inflation, as
described in Bricout et al. (2026) <doi:10.48550/arXiv.2602.10673>.
Parameters are estimated with a variational expectation-maximization
algorithm. The package also provides the asymptotic variance of the
estimators, prediction intervals for the imputed counts, and model
selection criteria (Bayesian information criterion, BIC, and
integrated completed likelihood, ICL) to choose the dimension
of the latent space.
| Version: |
0.2.2 |
| Depends: |
R (≥ 3.5) |
| Imports: |
Rcpp, PLNmodels, Matrix, mvtnorm, pbapply, lori, MASS |
| LinkingTo: |
Rcpp, RcppArmadillo, nloptr |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown, spelling |
| Published: |
2026-11-13 |
| DOI: |
10.32614/CRAN.package.colvR |
| Author: |
Barbara Bricout [aut, cre] |
| Maintainer: |
Barbara Bricout <barbara.bricout at gmail.com> |
| BugReports: |
https://github.com/BBricout/colvR/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/BBricout/colvR,
https://bbricout.github.io/colvR/ |
| NeedsCompilation: |
yes |
| SystemRequirements: |
NLopt, C++17 |
| Language: |
en-US |
| Citation: |
colvR citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
colvR results |
Documentation:
Downloads:
Linking:
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