Random forests were formally introduced by Breiman in 2001. Due to his excellent performance and simple application,
random forests are getting a more and more popular modeling strategy in many different research areas.

Random forests are suitable in many different modeling cases, such as classification, regression, survival time analysis,
multivariate classification and regression, multilabel classification and quantile regression.

An overview of existing random forest implementations and their speed performance can be found in
the ranger documentation, altough this list is not exhaustive and many new implementations are comming up.
The performances of models build with different packages slightly differ, depending on how the random forest algorithm was
implemented.

Now I will present some random forest implementations in R. A good site to find all R packages to one specific topic is
Metacran.