Fast masked KNN imputation for tabular data with support for single and multiple imputation.

Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.

To test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR). 
    For detailed description see Jamshidian, M. Jalal, S., and Jansen, C. (2014). "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)", Journal of Statistical Software, 56(6), 1-31. <https://www.jstatsoft.org/v56/i06/> <doi:10.18637/jss.v056.i06>.

Supply functions for the creation and handling of missing
    data as well as tools to evaluate missing data methods. Nearly all
    possibilities of generating missing data discussed by Santos et al.
    (2019) <doi:10.1109/ACCESS.2019.2891360> and some additional are
    implemented.  Functions are supplied to compare parameter estimates
    and imputed values to true values to evaluate missing data methods.
    Evaluations of these types are done, for example, by Cetin-Berber et
    al. (2019) <doi:10.1177/0013164418805532> and Kim et al. (2005)
    <doi:10.1093/bioinformatics/bth499>.

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>.