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.

When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 
  due to missing information between node pairs), it is possible to account for the underlying process
  that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) <doi:10.18637/jss.v101.i12>,
  adjusts the popular stochastic block model from network data sampled under various missing data conditions, 
  as described in 'Tabouy, Barbillon and Chiquet' (2019) <doi:10.1080/01621459.2018.1562934>.

The Self-Organizing Maps with Built-in Missing Data Imputation. Missing values are imputed and regularly updated during the online Kohonen algorithm. Our method can be used for data visualisation, clustering or imputation of missing data. It is an extension of the online algorithm of the 'kohonen' package. The method is described
    in the article "Self-Organizing Maps for Exploration of Partially Observed Data and Imputation of Missing Values" by S. Rejeb, C. Duveau, T. Rebafka (2022) <arXiv:2202.07963>.

A flexible computational framework for mixture distributions with the focus on the composite models.

Various reliability analysis methods for rare event inference (computing failure probability and quantile from model/function outputs).