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 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>.

Many times, you will not find data for all dates. After first January, 2011 you may have next data on 20th January, 2011 and so on. Also available dates may have zero values. Try to gather all such kinds of data in different excel sheets of a single excel file. Every sheet will contain two columns (1st one is dates and second one is the data). After loading all the sheets into different elements of a list, using this you can fill the gaps for all the sheets and mark all the corresponding values as zeros. Here I am talking about daily data. Finally, it will combine all the filled results into one data frame (first column is date and other columns will be corresponding values of your sheets) and give one combined data frame. Number of columns in the data frame will be number of sheets plus one. Then imputation will be done. Daily to monthly and weekly conversion is also possible.  More details can be found in Garai and others (2023) <doi:10.13140/RG.2.2.11977.42087>.