Moreover there are offered a few functions built to work with popular R packages such as 'data.table'.
Suggests: data.table, knitr, rmarkdown, pacman, testthat, mice, dplyr,

Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'.
Imports: methods, data.table, Rcpp (>= 0.12.12)
Suggests: dplyr, knitr, rmarkdown, pacman, testthat, mice, broom, car,

A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search.
Imports: methods, data.table, dplyr, magrittr, Rcpp (>= 0.12.12),
Suggests: knitr, rmarkdown, pacman, testthat, mice, broom, car, ggplot2

Suggests: knitr, rmarkdown, pacman, testthat, mice, broom, car
Packaged: 2020-12-13 15:08:17 UTC; maciejnasinski

Packaged: 2021-07-10 18:10:11 UTC; maciejnasinski

Imports: methods, Rcpp (>= 0.12.12), data.table
Suggests: knitr, rmarkdown, pacman, testthat, mice, magrittr, ggplot2,
Packaged: 2022-03-13 23:52:26 UTC; maciejnasinski

Packaged: 2022-11-17 20:43:40 UTC; maciejnasinski

Title: Missing not at Random Imputation Models for Multiple Imputation
Description: 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.
Imports: stats, mvtnorm, pbivnorm, GJRM, sampleSelection
Author: Jacques-Emmanuel Galimard [aut, cre] (INSERM, U1153, ECSTRA team),
Matthieu Resche-Rigon [aut] (INSERM, U1153, ECSTRA team)
Packaged: 2018-05-28 15:03:34 UTC; Jacques-Emmanuel

Description: 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).
Packaged: 2018-08-27 12:06:51 UTC; Jacques-Emmanuel