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).

Last 30 days

This package has been downloaded 5,167 times in the last 30 days. A solid achievement! Enough downloads to get noticed at department meetings. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 81 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 81,263 times in the last 365 days. This work is reaching a lot of screens. A significant achievement indeed! The day with the most downloads was May 01, 2024 with 1,443 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.