Simultaneously estimates sparse regression coefficients and
    response network structure in multivariate models with missing data.
    Unlike traditional approaches requiring imputation, handles
    missingness natively through unbiased estimating equations (MCAR/MAR
    compatible). Employs dual L1 regularization with automated selection
    via cross-validation or information criteria. Includes parallel
    computation, warm starts, adaptive grids, publication-ready
    visualizations, and prediction methods.  Ideal for genomics,
    neuroimaging, and multi-trait studies with incomplete high-dimensional
    outcomes. See Zeng et al. (2025) <doi:10.48550/arXiv.2507.05990>.

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).

Classify missing data as missing completely at random (MCAR), missing
    at random (MAR), or missing not at random (MNAR). This step is required before
    handling missing data (e.g. mean imputation) so that bias is not introduced.
    See Little (1988) <doi:10.1080/01621459.1988.10478722> for the statistical
    rationale for the methods used.

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.