This R package is used to train random competing risks forests, ideally for large data.
It's based heavily off of [randomForestSRC](https://github.com/kogalur/randomForestSRC/), although there are some differences which are described in (TODO - link to paper here).
This package is still in a pre-release state and so it not yet available on CRAN.
To install it now, in R install the `devtools` package and run the following command:
with `13G` replaced with a little less than your available system memory.
### I get an `OutOfMemoryException` error and I'm short on RAM
Obviously if you're short on RAM there is a limit on how large of a dataset you can train,
but there are some techniques you can use to limit how much `largeRCRF` needs.
* If your training dataset is large you might not want both R and `largeRCRF` to have their own separate copies
(limitations due to Java require `largeRCRF` have its own copy). When specifying the `data` parameter into `train`,
instead provide an environment containing one object called `data` which is the dataset. `largeRCRF` will delete that variable
after importing it into the Java environment.
Example:
```
R> data.env <-new.env()
R> data.env$data <-trainingData
R> rm(trainingData)
R> model <-train(...,data=data.env,...)
```
* Each core that is training trees requires its own memory; you can try limiting `largeRCRF` to train only one tree at a time by specifiying `cores=1`.
* By default `largeRCRF` keeps the entire forest loaded in memory during training,
when in practice only the trees being trained on need to be loaded.
You can specify `savePath` to give a directory for `largeRCRF` to save trees in during training,
which will allow to `largeRCRF` to conserve memory for only those trees being currently trained.
