Are the RandomForest and Ranger libraries the same? After being stuck in this question, I found another way to perform random forest.
I'm trying to compare a normal random forest model (train & test/predicted) and time series data. My data have date columns, my target variable plus another predictor. I used the train() function from the ranger package for my target variable with the predictor. Based on my understanding ranger is a random forest.
However, after converting my data to time series (ts) I got error when I ran train() from ranger on my time series data, but it worked when I ran the randomForest() function.
Are ranger and randomForest the same?
I know in my case if I applied random forest and train() from ranger I won't get the same results because one is time series and one have more variables but If I applied the same dataset to randomForest() and train() will I get the same result?

The title of the paper introducing it is literally

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

(highlighting by myself). But as said by
usεr11852, random forest is randomized and there may be implementational differences, so exactly the same results are not guaranteed.
