miceadds

Contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, doi:10.18637/jss.v045.i03) are implemented. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, doi:10.1007/BF02294457), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, doi:10.1177/1094428117703686; van Buuren, 2018, Ch.7, doi:10.1201/9780429492259), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, doi:10.1111/1467-9574.00217), substantive model compatible imputation (Bartlett et al., 2015, doi:10.1177/0962280214521348), and features for the generation of synthetic datasets (Reiter, 2005, doi:10.1111/j.1467-985X.2004.00343.x; Nowok, Raab, & Dibben, 2016, doi:10.18637/jss.v074.i11).