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
require(orf)
# load example data
data(odata)
# specify response and covariates
<- as.numeric(odata[, 1])
Y <- as.matrix(odata[, -1])
X
# estimate Ordered Forest with default settings
<- orf(X, Y, num.trees = 1000, mtry = 2, min.node.size = 5,
orf_fit replace = FALSE, sample.fraction = 0.5,
honesty = TRUE, honesty.fraction = 0.5,
inference = FALSE, importance = FALSE)
# print output of the Ordered Forest estimation
print(orf_fit)
# show summary of the Ordered Forest estimation
summary(orf_fit, latex = FALSE)
# plot the estimated probability distributions
plot(orf_fit)
# 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.

- Lechner, M., & Okasa, G. (2019). Random Forest Estimation of the Ordered Choice Model. arXiv preprint arXiv:1907.02436. https://arxiv.org/abs/1907.02436
- Wright, M. N. & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. J Stat Softw 77:1-17. https://doi.org/10.18637/jss.v077.i01