gerbil: Generalized Efficient Regression-Based Imputation with Latent Processes (original) (raw)
Implements a new multiple imputation method that draws imputations from a latent joint multivariate normal model which underpins generally structured data. This model is constructed using a sequence of flexible conditional linear models that enables the resulting procedure to be efficiently implemented on high dimensional datasets in practice. See Robbins (2021) <doi:10.48550/arXiv.2008.02243>.
| Version: | 0.1.9 |
|---|---|
| Depends: | R (≥ 2.10) |
| Imports: | base, DescTools, graphics, grDevices, lattice, MASS, mvtnorm, openxlsx, parallel, pbapply, stats, truncnorm, utils |
| Suggests: | dplyr, knitr, mice, rmarkdown, testthat (≥ 2.1.0) |
| Published: | 2023-01-12 |
| DOI: | 10.32614/CRAN.package.gerbil |
| Author: | Michael Robbins [aut, cre], Max Griswold [ctb], Pedro Nascimento de Lima [ctb] |
| Maintainer: | Michael Robbins |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | README, NEWS |
| In views: | MissingData |
| CRAN checks: | gerbil results |
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