Package: mlim
Type: Package
Title: Multiple Imputation with Automated Machine Learning
Version: 0.0.9
Authors@R: 
    person("E. F. Haghish",
           role = c("aut", "cre", "cph"),
           email = "haghish@uio.no")
Depends: R (>= 3.5.0)
Description: Using automated machine learning, the package fine-tunes an Elastic 
    Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting,
    or Stacked Ensemble machine learning 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. The main idea is 
    to allow the model to set its own parameters for imputing each variable 
    instead of setting fixed predefined parameters to impute all variables 
    of the dataset.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: h2o (>= 3.34.0.0), curl (>= 4.3.0), mice, missRanger, memuse,
        md.log (>= 0.2.0)
RoxygenNote: 7.2.1
LazyData: true
URL: https://github.com/haghish/mlim,
        https://www.sv.uio.no/psi/english/people/aca/haghish/
BugReports: https://github.com/haghish/mlim/issues
NeedsCompilation: no
Packaged: 2022-09-06 09:19:02 UTC; OJO
Author: E. F. Haghish [aut, cre, cph]
Maintainer: E. F. Haghish <haghish@uio.no>
Repository: CRAN
Date/Publication: 2022-09-07 07:50:08 UTC
