Description: An R code implementation of the maximum likelihood (fixed
point) algorithm of Hyvaerinen, Karhuna, and Oja for

Title: Missing Data Imputation with Automated Machine Learning
Description: 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.
Imports: h2o, VIM, missRanger, memuse, md.log

Title: Multiple Imputation with Automated Machine Learning
Imports: h2o (>= 3.34.0.0), mice, missRanger, memuse, md.log (>= 0.2.0)

Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting,
or Stacked Ensemble machine learning model for imputing the missing
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
Imports: h2o (>= 3.34.0.0), curl (>= 4.3.0), mice, missRanger, memuse,

Description: Provides an R API wrapper for 'mljar.com', a web service allowing for on-line training for machine learning models (see <https://mljar.com> for more information).

Title: Examples from Multilevel Modelling Software Review
Description: Data and examples from a multilevel modelling software review
Packaged: Fri Jan 21 22:51:08 2005; bates

as well as other well-known data sets from the multilevel modelling
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Packaged: Thu Jan 19 12:49:57 2006; bates

Packaged: Wed Jun 25 12:29:46 2008; bates

Description: Data and examples from a multilevel modelling software
review as well as other well-known data sets from the