CRAN checks

orf: ordered random forests


The R package orf is an implementation of the Ordered Forest estimator as developed in Lechner & Okasa (2019). The Ordered Forest flexibly estimates the conditional probabilities of models with ordered categorical outcomes (so-called ordered choice models). Additionally to common machine learning algorithms the orf package provides functions for estimating marginal effects as well as statistical inference thereof and thus provides similar output as in standard econometric models for ordered choice. The core forest algorithm relies on the fast C++ forest implementation from the ranger package (Wright & Ziegler, 2017).


In order to install the latest CRAN released version use:

install.packages("orf", dependencies = c("Imports", "Suggests"))

to make sure all the needed packages are installed as well. Note that if you install the package directly from the source a C++ compiler is required. For Windows users Rtools collection is required too.


The examples below demonstrate the basic functionality of the orf package.

## Ordered Forest

# load example data

# specify response and covariates
Y <- as.numeric(odata[, 1])
X <- as.matrix(odata[, -1])

# estimate Ordered Forest with default settings
orf_fit <- orf(X, Y, num.trees = 1000, mtry = 2, min.node.size = 5,
                     replace = FALSE, sample.fraction = 0.5,
                     honesty = TRUE, honesty.fraction = 0.5,
                     inference = FALSE, importance = FALSE)

# print output of the Ordered Forest estimation

# show summary of the Ordered Forest estimation
summary(orf_fit, latex = FALSE)

# plot the estimated probability distributions

# predict with the estimated Ordered Forest
predict(orf_fit, newdata = NULL, type = "probs", inference = FALSE)

# estimate marginal effects of the Ordered Forest
margins(orf_fit, newdata = NULL, eval = "mean", window = 0.1, inference = FALSE)

For a more detailed examples see the package vignette.