Efficient procedures for fitting conditional graphical lasso
    models that link a set of predictor variables to a set of response
    variables (or tasks), even when the response data may contain missing
    values. 'missoNet' simultaneously estimates the predictor
    coefficients for all tasks by leveraging information from one another,
    in order to provide more accurate predictions in comparison to
    modeling them individually. Additionally, 'missoNet' estimates the
    response network structure influenced by conditioning predictor
    variables using a L1-regularized conditional Gaussian graphical model.
    Unlike most penalized multi-task regression methods (e.g., MRCE),
    'missoNet' is capable of obtaining estimates even when the response
    data is corrupted by missing values. The method automatically enjoys
    the theoretical and computational benefits of convexity, and returns
    solutions that are comparable to the estimates obtained without
    missingness.

A framework that boosts the imputation of 'missForest' by Stekhoven, D.J. and Bühlmann, P. (2012) <doi:10.1093/bioinformatics/btr597> by harnessing parallel processing and through the fast Gradient Boosted Decision Trees (GBDT) implementation 'LightGBM' by Ke, Guolin et al.(2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>. 'misspi' has the following main advantages:
             1. Allows embrassingly parallel imputation on large scale data.
             2. Accepts a variety of machine learning models as methods with friendly user portal.
             3. Supports multiple initializations methods.
             4. Supports early stopping that prohibits unnecessary iterations.

A system for testing differential effects among treatments in case of Randomised Block Design and Latin Square Design when there is one missing observation. Methods for this process are as described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3).

Alternative implementation of the beautiful 'MissForest'
    algorithm used to impute mixed-type data sets by chaining random
    forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012)
    <doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the
    lightning fast random forest package 'ranger'. Between the iterative
    model fitting, we offer the option of using predictive mean matching.
    This firstly avoids imputation with values not already present in the
    original data (like a value 0.3334 in 0-1 coded variable).  Secondly,
    predictive mean matching tries to raise the variance in the resulting
    conditional distributions to a realistic level. This would allow,
    e.g., to do multiple imputation when repeating the call to
    missRanger(). Out-of-sample application is supported as well.