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* [R-package] fix issue where early stopping thinks higher MAPE is desirable (fixes #3099)

* fix linting

* only use main metrics

* fix tests

[R-package] fixed best_iter and best_score when training data is passed (fixes #2295, #2525) (#2961)

* [R-package] fixed best_iter and best_score when early_stopping is not used (fixes #2295)

* fixed tests

* [R-package] [ci] added more linters

* Update R-package/tests/testthat/test_parameters.R
Co-Authored-By: Nikita Titov <nekit94-08@mail.ru>

* added cbind() to undesirable_function_linter

* fixed linting

* Update .ci/lint_r_code.R

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* removed duplicated code from language wrappers

* removed check for resetting metric

* [R-package] removed TODOs in code

* [ci] added to-do to TODO comment linter

* Added more linters on R code

* started working on implicit integers

* finished style changes to handle implicit integers

* regenned documentation and added concatenation linter

* changed channel for r-lintr

* try building stringi before lintr

* trying to get libicui18n

* trying another thing

* trying conda-forge again

* added re-install of stringi

* uncommented other stages

* Update .ci/test.sh
Co-Authored-By: Nikita Titov <nekit94-08@mail.ru>

* removed apt update and changed lintr version floor

* get lintr from CRAN

* R needs to come before C++ linting

* testing lintr install from CRAN

* trying one more thing

* more verbose

* order might matter

* removed commented code

* cleaned up linting block in test.sh

* grouped conda install calls and fixed a few integer array things

* fixed miscellaneous typos in documentation

* fix typo introduced in typo-fixing PR

* Callback for NA handling

* lgb.Booster default score => NA

* lgb.cv default best score => NA

* Fix back callback

* lgb.train with booster check at the end

manual tests done: 
* With early stopping + with validation set
* With early stopping + without validation set
* Without early stopping + with validation set
* Without early stopping + without validation set

And with multiple metrics / validation sets.

* lgb.cv with booster check at the end

manual tests done: 
* With early stopping + with validation set
* With early stopping + without validation set
* Without early stopping + with validation set
* Without early stopping + without validation set

And with multiple metrics / validation sets.

* [R-package] removed horizontal whitespace (fixes #1642)

* [R-package] fixed missing newline in test file

* Fixed typos in docs

* Fixed inconsistencies in documentation

* Updated strategy for registering routines

* Fixed issues caused by smashing multiple functions into one Rd

* Fixed issues with documentation

* Removed VignetteBuilder and updated Rbuildignore

* Added R build artefacts to gitignore

* Added namespacing on data.table set function. Updated handling of CMakeLists file to get around CRAN check.

* Updated build instructions

* Added R build script

* Removed build_r.sh script and updated R-package install instructions

* fix early stopping edge case

* fix message.

* fix tests

* fix GPU tests.

fix typos in callback.R and lgb.cv.R, closing #946 and #947

* minor changes for robustness and efficiency

* fix charcter typo

* don't use lgb.is.* in filter

* revert to cat instead of message; don't use mget

* fix bugs in previous commits

* should be good to go now

* Try to store best scores in R package Microsoft/LightGBM#686

* Add binding for best_score (unlock environment)

* Fix double comma

* Add best_score to lgb.Booster

* Define environment in examples (xgboost clash)

* Large R code changes

* 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

* 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