Stef van Buuren

Thanks for pointing out this problem. As far as I can see, there is a unwanted interaction between the regularisation techniques you've implemented in `mice.impute.norm()` and the separate internal function...

Thanks. I would like to be able to run `cbind(a_mids_object, a_data.frame_object)` and return a `mids` object, with the contents of the `a_data.frame_object` incorporated as new columns. Any imputations needed in...

There are actually multiple ways to create predictions from multiply imputed data. See [Obtaining Predictions from Models Fit to Multiply Imputed Data](http://journals.sagepub.com/doi/abs/10.1177/0049124115610345) for a comparison of two methods. I tend...

I have done some thinking on **mice prediction workflows**. Here are two possibilities, one involving only a *train* datasets, another involving both a *train* and *test* dataset. ### A One...

The script below uses the @ASKurz example in a more micy way. It is essentially Rubin's rules applied to model predictions instead of model parameters. ```r library(mice) library(dplyr) # generate...

@markdanese Thanks for flagging this. The code is an example. It works for ` lm()`, but needs tweaking for other models.

Yes, this is still one on the todo list

Note: I found this code on my machine. I didn't test, and it probably does not work, but can be used for inspiration. ``` ##' Conditional imputation helper ##' ##'...

PR #432 changed the random seed mechanism. For exact reproducibility, you may need to install a version prior to mice 3.13.11.

Hi Johanna, thanks for your contribution. Found some final glitches: - `class(mvma) == "try-error"` produces a note. Better use `inherits` - Please put package names in alphabetic order - Please...