  Prediction models are used to predict an outcome based on input variables.
Missing data in input variables often occurs at model development and at
prediction time. The missForestPredict R package proposes an adaptation of the
missForest imputation algorithm that is fast, user-friendly and tailored for
prediction settings. The algorithm iteratively imputes variables using random
forests until a convergence criterion (unified for continuous and categorical
variables and based on the out-of-bag error) is met. The imputation models are
saved for each variable and iteration and can be applied later to new
observations at prediction time. The missForestPredict package offers extended
error monitoring, control over variables used in the imputation and custom
initialization. This allows users to tailor the imputation to their specific
needs. The missForestPredict algorithm is compared to mean/mode imputation,
linear regression imputation, mice, k-nearest neighbours, bagging, miceRanger
and IterativeImputer on eight simulated datasets with simulated missingness (48
scenarios) and eight large public datasets using different prediction models.
missForestPredict provides competitive results in prediction settings within
short computation times.
