Enhance a 'mice' imputation workflow with visualizations for
    incomplete and/or imputed data. The plotting functions produce
    'ggplot' objects which may be easily manipulated or extended. Use
    'ggmice' to inspect missing data, develop imputation models, evaluate
    algorithmic convergence, or compare observed versus imputed data.

Fit penalized multivariable linear mixed models with a single 
    random effect to control for population structure in genetic association 
    studies. The goal is to simultaneously fit many genetic variants at the 
    same time, in order to select markers that are independently associated 
    with the response. Can also handle prior annotation information, 
    for example, rare variants, in the form of variable weights. For more 
    information, see the website below and the accompanying paper: 
    Bhatnagar et al., "Simultaneous SNP selection and adjustment for 
    population structure in high dimensional prediction models", 2020, 
    <DOI:10.1371/journal.pgen.1008766>.

Estimate Gaussian graphical models with nonconvex penalties <doi:10.31234/osf.io/ad57p>, 
  including the atan Wang and Zhu (2016) <doi:10.1155/2016/6495417>, 
  seamless L0 Dicker, Huang, and Lin (2013) <doi:10.5705/ss.2011.074>,
  exponential Wang, Fan, and Zhu <doi:10.1007/s10463-016-0588-3>, 
  smooth integration of counting and absolute deviation Lv and Fan (2009) <doi:10.1214/09-AOS683>,
  logarithm Mazumder, Friedman, and Hastie (2011) <doi:10.1198/jasa.2011.tm09738>,
  Lq, smoothly clipped absolute deviation Fan and Li (2001) <doi:10.1198/016214501753382273>,
  and minimax concave penalty Zhang (2010) <doi:10.1214/09-AOS729>. There are also extensions
  for computing variable inclusion probabilities, multiple regression coefficients, and 
  statistical inference <doi:10.1214/15-EJS1031>.

Estimate non-regularized Gaussian graphical models, Ising models, 
  and mixed graphical models. The current methods consist of multiple regression, 
  a non-parametric bootstrap  <doi:10.1080/00273171.2019.1575716>, and Fisher z 
  transformed partial correlations <doi:10.1111/bmsp.12173>. Parameter uncertainty, 
  predictability, and network replicability <doi:10.31234/osf.io/fb4sa> are also implemented.