Single imputation based on the Ensemble Conditional Trees (i.e. Cforest algorithm Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007) <doi:10.1186/1471-2105-8-25>).

A four step change point detection method that can detect break points with the presence of missing values proposed by Liu and Safikhani (2023) <https://drive.google.com/file/d/1a8sV3RJ8VofLWikTDTQ7W4XJ76cEj4Fg/view?usp=drive_link>.

Implements two methods: a nonparametric risk adjustment and a
    data imputation method that use general population mortality tables to allow a
    correct analysis of time to disease recurrence. Also includes a powerful set of
    object oriented survival data simulation functions.

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>.

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 (via 'ranger' or 'randomForest') 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.

Missing data imputation based on the 'missForest' algorithm (Stekhoven, Daniel J (2012) <doi:10.1093/bioinformatics/btr597>)
    with adaptations for prediction settings. The function missForest() is used 
    to impute a (training) dataset with missing values and to learn imputation 
    models that can be later used for imputing new observations. 
    The function missForestPredict() is used to impute one or multiple new 
    observations (test set) using the models learned on the training data. For more details see 
    Albu, E., Gao, S., Wynants, L., & Van Calster, B. (2024). missForestPredict--Missing data imputation for prediction settings <doi:10.48550/arXiv.2407.03379>.