Penalized regression methods, such as lasso and elastic net, are used in
    many biomedical applications when simultaneous regression coefficient
    estimation and variable selection is desired. However, missing data
    complicates the implementation of these methods, particularly when
    missingness is handled using multiple imputation. Applying a variable
    selection algorithm on each imputed dataset will likely lead
    to different sets of selected predictors, making it difficult
    to ascertain a final active set without resorting to ad hoc
    combination rules. 'miselect' presents Stacked Adaptive Elastic Net (saenet)
    and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes,
    developed by Du et al (2022) <doi:10.1080/10618600.2022.2035739>. They, 
    by construction, force selection of the same variables across multiply 
    imputed data. 'miselect' also provides cross validated variants of these 
    methods.

A toolkit for analysis of genomic data. The 'misha' package
    implements an efficient data structure for storing genomic data, and
    provides a set of functions for data extraction, manipulation and
    analysis. Some of the 2D genome algorithms were described in Yaffe and Tanay
    (2011) <doi:10.1038/ng.947>.

Performs multiple imputation of missing data using an ensemble
    super learner built with the tidymodels framework. For each incomplete
    column, a stacked ensemble of candidate learners is trained on a bootstrap
    sample of the observed data and used to generate imputations via predictive
    mean matching (continuous), probability draws (binary), or cumulative
    probability draws (categorical). Supports parallelism across imputed
    datasets via the future framework.

A comprehensive toolkit for missing person identification combining
    genetic and non-genetic evidence within a Bayesian framework. Computes
    likelihood ratios (LRs) for DNA profiles, biological sex, age, hair color,
    and birthdate evidence. Provides decision analysis tools including optimal
    LR thresholds, error rate calculations, and ROC curve visualization.
    Includes interactive Shiny applications for exploring evidence combinations.
    For methodological details see Marsico et al. (2023) <doi:10.1016/j.fsigen.2023.102891>
    and Marsico, Vigeland et al. (2021) <doi:10.1016/j.fsigen.2021.102519>.