**Symptom** Apple Clang's implementation of `std::shuffle` expects doesn't work
correctly when it is run with the random bit generator for R package:
```cpp
CustomGlobalRandomEngine::result_type
CustomGlobalRandomEngine::operator()() {
return static_cast<result_type>(
std::floor(unif_rand() * CustomGlobalRandomEngine::max()));
}
```
Minimial reproduction of failure (compile using Apple Clang 10.0):
```cpp
std::vector<int> feature_set(100);
std::iota(feature_set.begin(), feature_set.end(), 0);
// initialize with 0, 1, 2, 3, ..., 99
std::shuffle(feature_set.begin(), feature_set.end(), common::GlobalRandom());
// This returns 0, 1, 2, ..., 99, so content didn't get shuffled at all!!!
```
Note that this bug is platform-dependent; it does not appear when GCC or
upstream LLVM Clang is used.
**Diagnosis** Apple Clang's `std::shuffle` expects 32-bit integer
inputs, whereas `CustomGlobalRandomEngine::operator()` produces 64-bit
integers.
**Fix** Have `CustomGlobalRandomEngine::operator()` produce 32-bit integers.
Closes #3523.
XGBoost R Package for Scalable GBM
Resources
- XGBoost R Package Online Documentation
- Check this out for detailed documents, examples and tutorials.
Installation
We are on CRAN now. For stable/pre-compiled(for Windows and OS X) version, please install from CRAN:
install.packages('xgboost')
For more detailed installation instructions, please see here.
Examples
- Please visit walk through example.
- See also the example scripts for Kaggle Higgs Challenge, including speedtest script on this dataset and the one related to Otto challenge, including a RMarkdown documentation.
Development
- See the R Package section of the contributors guide.