Random forest implementation with probability of choosing column or guarantee of choosing set of columns Are there random forest implementations which allows for choosing set of columns which will be always selected for every tree in the forest ? or implementations allowing for specifying probabilities of choosing each column ? 

Both cases could be simulated by choosing variables (partially or fully randomly) and building tree containing all variables in new temporal dataset, then repeating this procedure until we obtain assumed number of trees and then merging trees into forest,

but there are drawbacks like 
moving large number of data in scripting language until they are sent to low level implementation of forest for building tree or lack of merging procedures, which makes work with set of trees harder.
Ranger in R can do the first request. The always.split.variables argument defines which columns should always be included.