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Multivariate Imputation by Chained Equations
Running mice on a data frame with about 11,000 rows and 42 columns, ~12 of which have missing values ranging to 10% of the observations. After running multiple tests, I...
For instance: `parallel::clusterEvalQ(cl, {library(mice); library(miceadds)})`
Hi! I am trying to setup parlMice in the following way: ``` mdf
I would like to use some functions in the package `miceadds` while running `mice` in parallel, but `parlmice` returns an error because they are not being exported. I can see...
Dear Stef, Another problem I encountered today showed itself when I was trying to reproduce some of your code from your book "Flexible Imputation of Missing Data" (fantastic book!). At...
`mice 3.1.3` overwrites the `base::cbind()` and `base::rbind()` functions. This is not elegant, and it throws a warning when the package is loaded. I am looking for a better way to...
This PR introduces `futuremice()`, a straightforward and flexibly scaleable approach to parallelising the `mice()` algorithm. `futuremice()` is much more robust than `parlmice()` and works on any machine and any operating...
I'm trying to run the ampute() function with bycases = FALSE, to the proportion of missingness is defined in terms of cells. I found the following error: "Proportion of missing...
I'm seeing an error where cbind() wounds up run without .Random.seed set, which this solves. Per `?.Random.seed`, no seed is set initially in the session until the first time `set.seed()`...
