Release mice 3.8.0 · amices/mice

Major changes

This version adds two new NARFCS methods for imputing data under the Missing Not at Random (MNAR) assumption. NARFCS is generalised version of the so-called delta-adjustment method. Margarita Moreno-Betancur and Ian White kindly contributed the functions mice.impute.mnar.norm() and mice.impute.mnar.logreg(). These functions aid in performing sensitivity analysis to investigate the impact of different MNAR assumptions on the conclusion of the study. An alternative for MNAR is the older mice.impute.ri() function.
Installation of mice is faster. External packages needed for imputation and analyses are now installed on demand. The number of dependencies as estimated by rsconnect::appDepencies() decreased from 132 to 83.
The name clash with the complete() function of tidyr should no longer be a problem.
There is now a more flexible pool() function that integrates better with the broom and broom.mixed packages.

Deprecates pool.compare(). Use D1() instead (#220)
Removes everything in utils::globalVariables()
Prevents name clashes with tidyr by defining complete.mids() as an S3 method for the tidyr::complete() generic (#212)
Extends the pool() function to deal with multiple sets of parameters. Currently supported keywords are: term (all broom functions), component (some broom.mixed functions) and y.values (for multinom() model) (#219)
Adds a new install.on.demand() function for lighter installation
Adds toenail2 and remove dependency on HSAUR3
Solves problem with ampute in extreme cases (#216)
Solves problem with pool with mgcv::gam (#218)
Adds .gitattributes for consistent line endings