Multiple Imputation by Chained Equations with Random Forests
person(given = "Sam",
family = "Wilson",
role = c("aut","cre"),
email = "samwilson303@gmail.com")
Multiple Imputation has been shown to
be a flexible method to impute missing values by
Van Buuren (2007) <doi:10.1177/0962280206074463>.
Expanding on this, random forests have been shown
to be an accurate model by Stekhoven and Buhlmann
<arXiv:1105.0828> to impute missing values in datasets.
They have the added benefits of returning out of bag
error and variable importance estimates, as well as
being simple to run in parallel.
ranger, data.table, stats, FNN, ggplot2, gridExtra, crayon,
corrplot, ggpubr, DescTools, foreach

ranger, data.table, stats, FNN, ggplot2, crayon, corrplot,
ggpubr, DescTools, foreach
knitr, rmarkdown, doParallel, testthat (>= 2.1.0)

Some additional multiple imputation functions, especially for
mice
c(person("Alexander", "Robitzsch", role =
c("aut","cre"), email = "a.robitzsch@bifie.at"))
The package miceadds contains some auxiliary functions for
multiple imputation which complements existing functionality in R.
In addition to some utility functions, main features include
plausible value imputation, multilevel imputation functions,
imputation using partial least squares (PLS) for high dimensional
predictors and two-way imputation.
sirt (>= 0.42), lme4, MBESS, pls, mitools, bayesm, Rcpp (>=
0.11-0)

sirt (>= 0.42), lme4, MBESS, pls, mitools, bayesm, Rcpp (>=
0.11-0), inline

sirt (>= 0.42), lme4, MBESS, pls, mitools, bayesm, Rcpp (>=
0.11-0), inline, car, foreign

Some Additional Multiple Imputation Functions, Especially for
'mice'
Contains some auxiliary functions for multiple imputation which
complements existing functionality in R.
In addition to some utility functions, main features include
plausible value imputation, multilevel imputation functions,
imputation using partial least squares (PLS) for high dimensional
predictors and two-way imputation.
Rcpp, sirt, TAM, lme4, MBESS, pls, mitools, bayesm, inline,
car, foreign