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 set of decision making tools to conduct missing person searches.  Particularly, it allows computing optimal LR threshold for declaring potential matches in DNA-based database search. More recently 'mispitools' incorporates preliminary investigation data based LRs. Statistical weight of different traces of evidence such as biological sex, age and hair color are presented. For citing mispitools please use the following references: Marsico and Caridi, 2023 <doi:10.1016/j.fsigen.2023.102891> and  Marsico, Vigeland et al. 2021 <doi:10.1016/j.fsigen.2021.102519>.

Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.

Partial Replacement Imputation Estimation (PRIME) can overcome problems caused by missing covariates in additive partially linear model. PRIME conducts imputation and regression simultaneously with known and unknown model structure. More details can be referred to 
    Zishu Zhan, Xiangjie Li and Jingxiao Zhang. (2022) <arXiv:2205.14994>.