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+# README
+
+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:
+```
+R> devtools::install_git("https://git.joeltherrien.ca/joel/largeRCRF.git")
+```
+
+## System Requirements
+
+You need:
+
+* R version 3.4.2 or greater
+* The `rJava` package version 0.9-9 or greater
+* A Java runtime version 1.8 or greater
+
+## Troubleshooting
+
+### I get an `OutOfMemoryException` error but I have plenty of RAM
+
+`largeRCRF` makes use of the Java virtual machine, which unfortunately restricts itself by default to a quarter of your system memory. 
+You can override the default by including **before** loading `largeRCRF` or any other `rJava` based package the following line:
+
+```
+R> options(java.parameters <- c("-Xmx13G", "-Xms13G"))
+```
+
+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.