Implements proper and so-called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>. A number of different imputation methods are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using Rubin's rules (for proper imputation), or a modified version for maximum likelihood imputation. For maximum likelihood imputations a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935> is also available.

Maximum likelihood estimates (MLE) of the proportions
    of 5-mC and 5-hmC in the DNA using information from BS-conversion,
    TAB-conversion, and oxBS-conversion methods. One can use information from all three methods 
    or any combination of two of them. Estimates are based on Binomial model by
    Qu et al. (2013) <doi:10.1093/bioinformatics/btt459> and
    Kiihl et al. (2019) <doi:10.1515/sagmb-2018-0031>.

Provides a unified workflow for probing, plotting, and assessing
    the robustness of cross-level interaction effects in two-level mixed-effects
    models fitted with 'lme4' (Bates et al., 2015)
    <doi:10.18637/jss.v067.i01>. Implements simple slopes analysis following
    Aiken and West (1991, ISBN:9780761907121), Johnson-Neyman intervals
    following Johnson and Fay (1950) <doi:10.1007/BF02288864> and Bauer
    and Curran (2005) <doi:10.1207/s15327906mbr4003_5>, and grand- or
    group-mean centering as described in Enders and Tofighi (2007)
    <doi:10.1037/1082-989X.12.2.121>. Includes a slope variance decomposition
    that separates fixed-effect uncertainty from random-slope variance
    (tau11), a contour surface plot of predicted outcomes over the full
    predictor-by-moderator space, and robustness diagnostics comprising
    intraclass correlation coefficient shift analysis and
    leave-one-cluster-out (LOCO) stability checks. Designed for researchers
    in education, psychology, biostatistics, epidemiology, organizational
    science, and other fields where outcomes are clustered within
    higher-level units.