Provides methods for imputation and visualization of 
    missing values. It includes graphical tools to explore the amount, structure 
    and patterns of missing and/or imputed values, supporting exploratory 
    data analysis and helping to investigate potential missingness mechanisms
    (details in Alfons, Templ and Filzmoser, <doi:10.1007/s11634-011-0102-y>. 
    The quality of imputations can be assessed visually using a wide range of 
    univariate, bivariate and multivariate plots. 
    The package further provides several imputation methods, 
    including efficient implementations of k-nearest neighbour and hot-deck 
    imputation (Kowarik and Templ 2013, <doi:10.18637/jss.v074.i07>, 
    iterative robust model-based multiple 
    imputation (Templ 2011, <doi:10.1016/j.csda.2011.04.012>; 
    Templ 2023, <doi:10.3390/math11122729>), and machine learning–based 
    approaches such as robust GAM-based multiple imputation 
    (Templ 2024, <doi:10.1007/s11222-024-10429-1>) as well as gradient boosting 
    (XGBoost) and transformer-based methods 
    (Niederhametner et al., <doi:10.1177/18747655251339401>). 
    General background and practical guidance on imputation are provided in the 
    Springer book by 
    Templ (2023) <doi:10.1007/978-3-031-30073-8>.

To computed the variability independent of mean (VIM) or variation
    independent of mean (VIM). The methodology can be found at
    Peter M Rothwell et al. (2010) <doi:10.1016/S1474-4422(10)70067-3>.

Collapsed Variational Inference for a Dirichlet Process (DP) mixture model with unknown covariance matrix structure and DP concentration parameter. It enables efficient clustering of high-dimensional data with significantly improved computational speed than traditional MCMC methods. The package incorporates 8 parameterisations and corresponding prior choices for the unknown covariance matrix, from which the user can choose and apply accordingly.

Calculate point estimates of and valid confidence intervals for
    nonparametric, algorithm-agnostic variable importance measures in high and low dimensions,
    using flexible estimators of the underlying regression functions. For more information
    about the methods, please see Williamson et al. (Biometrics, 2020),  Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).