* [R] MSVC compatibility
* [GPU] allow seed in BernoulliRng up to size_t and scale to uint32_t
* R package build with cmake and CUDA
* R package CUDA build fixes and cleanups
* always export the R package native initialization routine on windows
* update the install instructions doc
* fix lint
* use static_cast directly to set BernoulliRng seed
* [R] demo for GPU accelerated algorithm
* tidy up the R package cmake stuff
* R pack cmake: installs main dependency packages if needed
* [R] version bump in DESCRIPTION
* update NEWS
* added short missing/sparse values explanations to FAQ
for loop in create.new.tree.features was referencing length(trees) as the upper bound of the loop. trees is a base R dataset and not the model that the code is generating. Changed loop boundary to model$niter which should be the number of trees.
* fix cran check
* change required R version because of utils::globalVariables
* temporary commit, monotone not working
* fix test
* fix doc
* fix doc
* fix cran note and warning
* improve checks
* fix urls
* add support for tweedie regression
* added back readme line that was accidentally deleted
* fixed linting errors
* add support for tweedie regression
* added back readme line that was accidentally deleted
* fixed linting errors
* rebased with upstream master and added R example
* changed parameter name to tweedie_variance_power
* linting error fix
* refactored tweedie-nloglik metric to be more like the other parameterized metrics
* added upper and lower bound check to tweedie metric
* add support for tweedie regression
* added back readme line that was accidentally deleted
* fixed linting errors
* added upper and lower bound check to tweedie metric
* added back readme line that was accidentally deleted
* rebased with upstream master and added R example
* rebased again on top of upstream master
* linting error fix
* added upper and lower bound check to tweedie metric
* rebased with master
* lint fix
* removed whitespace at end of line 186 - elementwise_metric.cc
Issue in "demo(package="xgboost", custom_objective)"
> bst <- xgb.train(param, dtrain, num_round, watchlist,
+ objective=logregobj, eval_metric=evalerror)
Error in xgb.train(param, dtrain, num_round, watchlist, objective = logregobj, :
Duplicated term in parameters. Please check your list of params.
