Using automated machine learning, the package fine-tunes an Elastic Net or Gradient Boosting Machine model for imputing the missing observations of each variable. This procedure has been implemented for the first time by this package and is expected to outperform other packages for imputing missing data that do not fine-tune their models.
| Version: | 0.0.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | h2o, VIM, missRanger, memuse, md.log |
| Published: | 2022-08-13 |
| Author: | E. F. Haghish [aut, cre, cph] |
| Maintainer: | E. F. Haghish <haghish at uio.no> |
| BugReports: | https://github.com/haghish/mlim/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/haghish/mlim, https://www.sv.uio.no/psi/english/people/aca/haghish/ |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | mlim results |
| Reference manual: | mlim.pdf |
| Package source: | mlim_0.0.1.tar.gz |
| Windows binaries: | r-devel: mlim_0.0.1.zip, r-release: mlim_0.0.1.zip, r-oldrel: not available |
| macOS binaries: | r-release (arm64): mlim_0.0.1.tgz, r-oldrel (arm64): mlim_0.0.1.tgz, r-release (x86_64): not available, r-oldrel (x86_64): not available |
Please use the canonical form https://CRAN.R-project.org/package=mlim to link to this page.