I’ve found the package Impute.jl but it seems it’s only able to use these simple methods:
- drop - remove missing
- locf - last observation carried forward
- nocb - next observation carried backward
- interp - linear interpolation of values in vector
- fill - replace with a specific value or a function which returns a value given the existing vector with missing values dropped.
How can I do multiple imputation when I have several variables or I want to use more complex methods, for example using: fully conditional specification (chained equations, mice), bayesian methods, random forest, multilevel imputation, nested imputation, censored data, categorical data, survival data…