GitLab

* Update lgb.Booster.R

Added method to call LGBM_BoosterLoadModelFromString_R if model_str is provided for initialization, added option to load from model_str in lgb.load

* Update lightgbm_R.cpp

Adding LGBM_BoosterLoadModelFromString_R

* Update lightgbm_R.cpp

Added LGBM_BoosterSaveModelToString_R

* Update lightgbm_R.cpp

* Update lgb.Booster.R

Added save_model_to_string method

* Update lgb.Booster.R

Implemented @Laurae2 comments

* Update lgb.Booster.R

* Update lightgbm_R.h

Added load and save model from/to string exports

* Add weight adjustment tutorial

* Update 00Index

* update README to reflect changes

* Add linear separation

* User can safely ignore warning message

* Fix some English stuff

* More information for installation on Linux for beginners.

* Add leaf stability example

* 00index leaf stability

* Add leaf stability to README

* Define environment in examples (xgboost clash)

* Large R code changes

* Update lgb.Booster.R

* Add saveRDS (manual)

* Add documentation

* Change arguments passed from debug.

* Add readRDS, change way of saving.

* Change documentation.

* Add better documentation.

* add scripts for build self-contain R-package in windows.

* add unix build.

* Update README.md

* Add multiclass example in R

* Update subsequent files

* Add lgb.Booster parsing function

* Add data.table, magrittr, jsonlite to Imports

* Attempt to fix categorical issue in R package

* Add small fix for categorical IDs

* Add error for unknown numbers/columns

* Added possibility to compile without openmp (useful for clang on Mac)

* Added CMake option to create dylib on MacOS

* src & callbacks

* lgb.Booster and utils

* cv

* wip lgb.Dataset

* lgb.Dataset

* lgb.Predictor

* lgb.train

* typos

* add flags to facilitate macosx compilation

* fix basic_string template error with clang

* most unfortunate mode of development

* fixup tests

* last test

* roxygen

* roxygen v5.x.x

* Fix to avoid issue #176 for R package installation

* Provide installation step using devtools

* finish R's c_api

* clean code

* fix sizeof pointer in 32bit system.

* add predictor class

* add Dataset class

* format code

* add booster

* add type check for expose function

* add a simple callback

* add all callbacks

* finish the basic training logic

* update docs

* add an simple training interface

* add basic test

* adapt the changes in c_api

* add test for Dataset

* add test for custom obj/eval functions

* fix python test

* fix bug in metadata init

* fix R CMD check