Fast imputations under the object-oriented programming paradigm.
Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'.
The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used.
A single evaluation of a quantitative model for the multiple imputations is another major enhancement.
A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search.
methods, data.table, dplyr, magrittr, Rcpp (>= 0.12.12),
lifecycle
knitr, rmarkdown, pacman, testthat, mice, broom, car, ggplot2

methods, data.table, dplyr, magrittr, Rcpp (>= 0.12.12),
UpSetR, ggplot2, tidyr, assertthat
knitr, rmarkdown, pacman, testthat, mice, broom, car

knitr, rmarkdown, pacman, testthat, mice, magrittr, ggplot2,
UpSetR, dplyr

Missing not at Random Imputation Models for Multiple Imputation
by Chained Equation
Provides imputation models and functions for binary or continuous Missing Not At Random (MNAR) outcomes through the use of the 'mice' package. The mice.impute.hecknorm() function provides imputation model for continuous outcome based on Heckman's model also named sample selection model as described in Galimard et al (2016) <doi:10.1002/sim.6902>. The mice.impute.heckprob() function provides imputation model for binary outcome based on bivariate probit model.
stats, mvtnorm, pbivnorm, GJRM, sampleSelection
Jacques-Emmanuel Galimard [aut, cre] (INSERM, U1153, ECSTRA team),
Matthieu Resche-Rigon [aut] (INSERM, U1153, ECSTRA team)

Provides imputation models and functions for binary or continuous Missing Not At Random (MNAR) outcomes through the use of the 'mice' package. The mice.impute.hecknorm() function provides imputation model for continuous outcome based on Heckman's model also named sample selection model as described in Galimard et al (2018) and Galimard et al (2016) <doi:10.1002/sim.6902>. The mice.impute.heckprob() function provides imputation model for binary outcome based on bivariate probit model as described in Galimard et al (2018).