Comparing Observed and Imputed Values under MAR and MCAR
Moritz Marbach <moritz.marbach@tamu.edu> [aut, cre]
Implements the computation of discrepancy statistics summarizing differences between the density of imputed and observed values and the construction of weights to balance covariates that are part of the missing data mechanism as described in Marbach (2021) <arXiv:2107.05427>.

Nonparametric Missing Value Imputation using Random Forest
The function 'missForest' in this package is used to
impute missing values particularly in the case of mixed-type
data. It uses a random forest trained on the observed values of
a data matrix to predict the missing values. It can be used to
impute continuous and/or categorical data including complex
interactions and nonlinear relations. It yields an out-of-bag
(OOB) imputation error estimate without the need of a test set
or elaborate cross-validation.

The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and nonlinear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation.

The function 'missForest' in this package is used to
impute missing values particularly in the case of mixed-type
data. It uses a random forest trained on the observed values of
a data matrix to predict the missing values. It can be used to
impute continuous and/or categorical data including complex
interactions and non-linear relations. It yields an out-of-bag
(OOB) imputation error estimate without the need of a test set
or elaborate cross-validation.

The function 'missForest' in this package is used to
impute missing values particularly in the case of mixed-type
data. It uses a random forest trained on the observed values of
a data matrix to predict the missing values. It can be used to
impute continuous and/or categorical data including complex
interactions and non-linear relations. It yields an out-of-bag
(OOB) imputation error estimate without the need of a test set
or elaborate cross-validation. It can be run in parallel to
save computation time.