From 8cad6b5c78ed204fd85a3044ede46a55517ef085 Mon Sep 17 00:00:00 2001
From: James Lamb <jaylamb20@gmail.com>
Date: Sat, 28 Sep 2019 22:17:51 -0500
Subject: [PATCH] updated roxygen files

---
 R-package/man/getinfo.Rd                 |  4 +-
 R-package/man/lgb.Dataset.Rd             | 13 +++--
 R-package/man/lgb.cv.Rd                  | 64 +++++++++++++++---------
 R-package/man/lgb.dump.Rd                | 16 +++---
 R-package/man/lgb.get.eval.result.Rd     | 25 +++++----
 R-package/man/lgb.importance.Rd          | 11 ++--
 R-package/man/lgb.load.Rd                | 16 +++---
 R-package/man/lgb.model.dt.tree.Rd       | 11 ++--
 R-package/man/lgb.plot.importance.Rd     |  9 +++-
 R-package/man/lgb.plot.interpretation.Rd | 24 ++++++---
 R-package/man/lgb.prepare.Rd             |  7 ++-
 R-package/man/lgb.prepare2.Rd            | 10 +++-
 R-package/man/lgb.prepare_rules.Rd       |  8 ++-
 R-package/man/lgb.prepare_rules2.Rd      | 46 ++++-------------
 R-package/man/lgb.save.Rd                | 16 +++---
 R-package/man/lgb.train.Rd               | 56 +++++++++++++--------
 R-package/man/lgb.unloader.Rd            | 26 ++++++----
 R-package/man/lgb_shared_params.Rd       | 10 ++--
 R-package/man/lightgbm.Rd                | 28 +++++++----
 R-package/man/predict.lgb.Booster.Rd     | 34 ++++++++-----
 R-package/man/readRDS.lgb.Booster.Rd     | 16 +++---
 R-package/man/saveRDS.lgb.Booster.Rd     | 33 ++++++++----
 R-package/man/setinfo.Rd                 |  4 +-
 R-package/man/slice.Rd                   |  4 +-
 24 files changed, 294 insertions(+), 197 deletions(-)

diff --git a/R-package/man/getinfo.Rd b/R-package/man/getinfo.Rd
index b5b8112bd2d3..8bdca02ca940 100644
--- a/R-package/man/getinfo.Rd
+++ b/R-package/man/getinfo.Rd
@@ -12,9 +12,9 @@ getinfo(dataset, ...)
 \arguments{
 \item{dataset}{Object of class \code{lgb.Dataset}}
 
-\item{...}{other parameters}
-
 \item{name}{the name of the information field to get (see details)}
+
+\item{...}{other parameters}
 }
 \value{
 info data
diff --git a/R-package/man/lgb.Dataset.Rd b/R-package/man/lgb.Dataset.Rd
index dd74fdf6626f..1aa7197f8237 100644
--- a/R-package/man/lgb.Dataset.Rd
+++ b/R-package/man/lgb.Dataset.Rd
@@ -4,9 +4,16 @@
 \alias{lgb.Dataset}
 \title{Construct \code{lgb.Dataset} object}
 \usage{
-lgb.Dataset(data, params = list(), reference = NULL, colnames = NULL,
-  categorical_feature = NULL, free_raw_data = TRUE, info = list(),
-  ...)
+lgb.Dataset(
+  data,
+  params = list(),
+  reference = NULL,
+  colnames = NULL,
+  categorical_feature = NULL,
+  free_raw_data = TRUE,
+  info = list(),
+  ...
+)
 }
 \arguments{
 \item{data}{a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename}
diff --git a/R-package/man/lgb.cv.Rd b/R-package/man/lgb.cv.Rd
index 5be785fa18d2..a419122592ff 100644
--- a/R-package/man/lgb.cv.Rd
+++ b/R-package/man/lgb.cv.Rd
@@ -4,13 +4,29 @@
 \alias{lgb.cv}
 \title{Main CV logic for LightGBM}
 \usage{
-lgb.cv(params = list(), data, nrounds = 10, nfold = 3,
-  label = NULL, weight = NULL, obj = NULL, eval = NULL,
-  verbose = 1, record = TRUE, eval_freq = 1L, showsd = TRUE,
-  stratified = TRUE, folds = NULL, init_model = NULL,
-  colnames = NULL, categorical_feature = NULL,
-  early_stopping_rounds = NULL, callbacks = list(),
-  reset_data = FALSE, ...)
+lgb.cv(
+  params = list(),
+  data,
+  nrounds = 10,
+  nfold = 3,
+  label = NULL,
+  weight = NULL,
+  obj = NULL,
+  eval = NULL,
+  verbose = 1,
+  record = TRUE,
+  eval_freq = 1L,
+  showsd = TRUE,
+  stratified = TRUE,
+  folds = NULL,
+  init_model = NULL,
+  colnames = NULL,
+  categorical_feature = NULL,
+  early_stopping_rounds = NULL,
+  callbacks = list(),
+  reset_data = FALSE,
+  ...
+)
 }
 \arguments{
 \item{params}{List of parameters}
@@ -27,7 +43,7 @@ lgb.cv(params = list(), data, nrounds = 10, nfold = 3,
 
 \item{obj}{objective function, can be character or custom objective function. Examples include
 \code{regression}, \code{regression_l1}, \code{huber},
-\code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}}
+ \code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}}
 
 \item{eval}{evaluation function, can be (list of) character or custom eval function}
 
@@ -54,17 +70,15 @@ the \code{nfold} and \code{stratified} parameters are ignored.}
 type int represents index,
 type str represents feature names}
 
-\item{early_stopping_rounds}{int
-Activates early stopping.
-Requires at least one validation data and one metric
-If there's more than one, will check all of them except the training data
-Returns the model with (best_iter + early_stopping_rounds)
-If early stopping occurs, the model will have 'best_iter' field}
+\item{early_stopping_rounds}{int. Activates early stopping. Requires at least one validation data
+and one metric. If there's more than one, will check all of them
+except the training data. Returns the model with (best_iter + early_stopping_rounds).
+If early stopping occurs, the model will have 'best_iter' field.}
 
-\item{callbacks}{list of callback functions
-List of callback functions that are applied at each iteration.}
+\item{callbacks}{List of callback functions that are applied at each iteration.}
 
-\item{reset_data}{Boolean, setting it to TRUE (not the default value) will transform the booster model into a predictor model which frees up memory and the original datasets}
+\item{reset_data}{Boolean, setting it to TRUE (not the default value) will transform the booster model
+into a predictor model which frees up memory and the original datasets}
 
 \item{...}{other parameters, see Parameters.rst for more information. A few key parameters:
 \itemize{
@@ -89,11 +103,13 @@ data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
 params <- list(objective = "regression", metric = "l2")
-model <- lgb.cv(params,
-                dtrain,
-                10,
-                nfold = 3,
-                min_data = 1,
-                learning_rate = 1,
-                early_stopping_rounds = 5)
+model <- lgb.cv(
+  params = params
+  , data = dtrain
+  , nrounds = 10
+  , nfold = 3
+  , min_data = 1
+  , learning_rate = 1
+  , early_stopping_rounds = 5
+)
 }
diff --git a/R-package/man/lgb.dump.Rd b/R-package/man/lgb.dump.Rd
index 73226a36b1b2..c03dcdd7fc6b 100644
--- a/R-package/man/lgb.dump.Rd
+++ b/R-package/man/lgb.dump.Rd
@@ -27,13 +27,15 @@ test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
 params <- list(objective = "regression", metric = "l2")
 valids <- list(test = dtest)
-model <- lgb.train(params,
-                  dtrain,
-                   10,
-                   valids,
-                   min_data = 1,
-                   learning_rate = 1,
-                   early_stopping_rounds = 5)
+model <- lgb.train(
+  params = params
+  , data = dtrain
+  , nrounds = 10
+  , valids = valids
+  , min_data = 1
+  , learning_rate = 1
+  , early_stopping_rounds = 5
+)
 json_model <- lgb.dump(model)
 
 }
diff --git a/R-package/man/lgb.get.eval.result.Rd b/R-package/man/lgb.get.eval.result.Rd
index b13c151c9b57..0b124ffb62cb 100644
--- a/R-package/man/lgb.get.eval.result.Rd
+++ b/R-package/man/lgb.get.eval.result.Rd
@@ -4,8 +4,13 @@
 \alias{lgb.get.eval.result}
 \title{Get record evaluation result from booster}
 \usage{
-lgb.get.eval.result(booster, data_name, eval_name, iters = NULL,
-  is_err = FALSE)
+lgb.get.eval.result(
+  booster,
+  data_name,
+  eval_name,
+  iters = NULL,
+  is_err = FALSE
+)
 }
 \arguments{
 \item{booster}{Object of class \code{lgb.Booster}}
@@ -34,12 +39,14 @@ test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
 params <- list(objective = "regression", metric = "l2")
 valids <- list(test = dtest)
-model <- lgb.train(params,
-                   dtrain,
-                   10,
-                   valids,
-                   min_data = 1,
-                   learning_rate = 1,
-                   early_stopping_rounds = 5)
+model <- lgb.train(
+  params = params
+  , data = dtrain
+  , nrounds = 10
+  , valids = valids
+  , min_data = 1
+  , learning_rate = 1
+  , early_stopping_rounds = 5
+)
 lgb.get.eval.result(model, "test", "l2")
 }
diff --git a/R-package/man/lgb.importance.Rd b/R-package/man/lgb.importance.Rd
index 6955fad34129..2a75e1c14eb3 100644
--- a/R-package/man/lgb.importance.Rd
+++ b/R-package/man/lgb.importance.Rd
@@ -29,9 +29,14 @@ data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
 
-params <- list(objective = "binary",
-               learning_rate = 0.01, num_leaves = 63, max_depth = -1,
-               min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1)
+params <- list(
+  objective = "binary"
+  , learning_rate = 0.01
+  , num_leaves = 63
+  , max_depth = -1
+  , min_data_in_leaf = 1
+  , min_sum_hessian_in_leaf = 1
+)
 model <- lgb.train(params, dtrain, 10)
 
 tree_imp1 <- lgb.importance(model, percentage = TRUE)
diff --git a/R-package/man/lgb.load.Rd b/R-package/man/lgb.load.Rd
index 943eb81ee2e3..ebe14d0e39bc 100644
--- a/R-package/man/lgb.load.Rd
+++ b/R-package/man/lgb.load.Rd
@@ -29,13 +29,15 @@ test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
 params <- list(objective = "regression", metric = "l2")
 valids <- list(test = dtest)
-model <- lgb.train(params,
-                   dtrain,
-                   10,
-                   valids,
-                   min_data = 1,
-                   learning_rate = 1,
-                   early_stopping_rounds = 5)
+model <- lgb.train(
+  params = params
+  , data = dtrain
+  , nrounds = 10
+  , valids = valids
+  , min_data = 1
+  , learning_rate = 1
+  , early_stopping_rounds = 5
+)
 lgb.save(model, "model.txt")
 load_booster <- lgb.load(filename = "model.txt")
 model_string <- model$save_model_to_string(NULL) # saves best iteration
diff --git a/R-package/man/lgb.model.dt.tree.Rd b/R-package/man/lgb.model.dt.tree.Rd
index c25d139fa12c..fc85105eee50 100644
--- a/R-package/man/lgb.model.dt.tree.Rd
+++ b/R-package/man/lgb.model.dt.tree.Rd
@@ -44,9 +44,14 @@ data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
 
-params <- list(objective = "binary",
-               learning_rate = 0.01, num_leaves = 63, max_depth = -1,
-               min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1)
+params <- list(
+  objective = "binary"
+  , learning_rate = 0.01
+  , num_leaves = 63
+  , max_depth = -1
+  , min_data_in_leaf = 1
+  , min_sum_hessian_in_leaf = 1
+)
 model <- lgb.train(params, dtrain, 10)
 
 tree_dt <- lgb.model.dt.tree(model)
diff --git a/R-package/man/lgb.plot.importance.Rd b/R-package/man/lgb.plot.importance.Rd
index 84493b1e9e6c..cc419ab0ae8d 100644
--- a/R-package/man/lgb.plot.importance.Rd
+++ b/R-package/man/lgb.plot.importance.Rd
@@ -4,8 +4,13 @@
 \alias{lgb.plot.importance}
 \title{Plot feature importance as a bar graph}
 \usage{
-lgb.plot.importance(tree_imp, top_n = 10, measure = "Gain",
-  left_margin = 10, cex = NULL)
+lgb.plot.importance(
+  tree_imp,
+  top_n = 10,
+  measure = "Gain",
+  left_margin = 10,
+  cex = NULL
+)
 }
 \arguments{
 \item{tree_imp}{a \code{data.table} returned by \code{\link{lgb.importance}}.}
diff --git a/R-package/man/lgb.plot.interpretation.Rd b/R-package/man/lgb.plot.interpretation.Rd
index 6d3637e9ea41..a026d619c8f9 100644
--- a/R-package/man/lgb.plot.interpretation.Rd
+++ b/R-package/man/lgb.plot.interpretation.Rd
@@ -4,8 +4,13 @@
 \alias{lgb.plot.interpretation}
 \title{Plot feature contribution as a bar graph}
 \usage{
-lgb.plot.interpretation(tree_interpretation_dt, top_n = 10, cols = 1,
-  left_margin = 10, cex = NULL)
+lgb.plot.interpretation(
+  tree_interpretation_dt,
+  top_n = 10,
+  cols = 1,
+  left_margin = 10,
+  cex = NULL
+)
 }
 \arguments{
 \item{tree_interpretation_dt}{a \code{data.table} returned by \code{\link{lgb.interprete}}.}
@@ -25,8 +30,8 @@ The \code{lgb.plot.interpretation} function creates a \code{barplot}.
 Plot previously calculated feature contribution as a bar graph.
 }
 \details{
-The graph represents each feature as a horizontal bar of length proportional to the defined contribution of a feature.
-Features are shown ranked in a decreasing contribution order.
+The graph represents each feature as a horizontal bar of length proportional to the defined
+contribution of a feature. Features are shown ranked in a decreasing contribution order.
 }
 \examples{
 library(lightgbm)
@@ -39,9 +44,14 @@ setinfo(dtrain, "init_score", rep(Logit(mean(train$label)), length(train$label))
 data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
 
-params <- list(objective = "binary",
-               learning_rate = 0.01, num_leaves = 63, max_depth = -1,
-               min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1)
+params <- list(
+  objective = "binary"
+  , learning_rate = 0.01
+  , num_leaves = 63
+  , max_depth = -1
+  , min_data_in_leaf = 1
+  , min_sum_hessian_in_leaf = 1
+)
 model <- lgb.train(params, dtrain, 10)
 
 tree_interpretation <- lgb.interprete(model, test$data, 1:5)
diff --git a/R-package/man/lgb.prepare.Rd b/R-package/man/lgb.prepare.Rd
index fd38d5046eb6..262a52aa1f21 100644
--- a/R-package/man/lgb.prepare.Rd
+++ b/R-package/man/lgb.prepare.Rd
@@ -10,10 +10,13 @@ lgb.prepare(data)
 \item{data}{A data.frame or data.table to prepare.}
 }
 \value{
-The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
+The cleaned dataset. It must be converted to a matrix format (\code{as.matrix})
+        for input in \code{lgb.Dataset}.
 }
 \description{
-Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. Factors and characters are converted to numeric without integers. Please use \code{lgb.prepare_rules} if you want to apply this transformation to other datasets.
+Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
+Factors and characters are converted to numeric without integers. Please use
+\code{lgb.prepare_rules} if you want to apply this transformation to other datasets.
 }
 \examples{
 library(lightgbm)
diff --git a/R-package/man/lgb.prepare2.Rd b/R-package/man/lgb.prepare2.Rd
index 106ab21d587f..1d8400220b64 100644
--- a/R-package/man/lgb.prepare2.Rd
+++ b/R-package/man/lgb.prepare2.Rd
@@ -10,10 +10,16 @@ lgb.prepare2(data)
 \item{data}{A data.frame or data.table to prepare.}
 }
 \value{
-The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
+The cleaned dataset. It must be converted to a matrix format (\code{as.matrix})
+        for input in \code{lgb.Dataset}.
 }
 \description{
-Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. Factors and characters are converted to numeric (specifically: integer). Please use \code{lgb.prepare_rules2} if you want to apply this transformation to other datasets. This is useful if you have a specific need for integer dataset instead of numeric dataset. Note that there are programs which do not support integer-only input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
+Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
+Factors and characters are converted to numeric (specifically: integer).
+Please use \code{lgb.prepare_rules2} if you want to apply this transformation to other datasets.
+This is useful if you have a specific need for integer dataset instead of numeric dataset.
+Note that there are programs which do not support integer-only input. Consider this as a half
+memory technique which is dangerous, especially for LightGBM.
 }
 \examples{
 library(lightgbm)
diff --git a/R-package/man/lgb.prepare_rules.Rd b/R-package/man/lgb.prepare_rules.Rd
index 939cb1fd58ef..d8caa74475aa 100644
--- a/R-package/man/lgb.prepare_rules.Rd
+++ b/R-package/man/lgb.prepare_rules.Rd
@@ -12,10 +12,14 @@ lgb.prepare_rules(data, rules = NULL)
 \item{rules}{A set of rules from the data preparator, if already used.}
 }
 \value{
-A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
+A list with the cleaned dataset (\code{data}) and the rules (\code{rules}).
+        The data must be converted to a matrix format (\code{as.matrix}) for input
+        in \code{lgb.Dataset}.
 }
 \description{
-Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. Factors and characters are converted to numeric. In addition, keeps rules created so you can convert other datasets using this converter.
+Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
+Factors and characters are converted to numeric. In addition, keeps rules created
+so you can convert other datasets using this converter.
 }
 \examples{
 library(lightgbm)
diff --git a/R-package/man/lgb.prepare_rules2.Rd b/R-package/man/lgb.prepare_rules2.Rd
index 5e2d909e9d6f..64ccd2041e1e 100644
--- a/R-package/man/lgb.prepare_rules2.Rd
+++ b/R-package/man/lgb.prepare_rules2.Rd
@@ -12,43 +12,15 @@ lgb.prepare_rules2(data, rules = NULL)
 \item{rules}{A set of rules from the data preparator, if already used.}
 }
 \value{
-A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
+A list with the cleaned dataset (\code{data}) and the rules (\code{rules}).
+        The data must be converted to a matrix format (\code{as.matrix}) for input in
+        \code{lgb.Dataset}.
 }
 \description{
-Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. Factors and characters are converted to numeric (specifically: integer). In addition, keeps rules created so you can convert other datasets using this converter. This is useful if you have a specific need for integer dataset instead of numeric dataset. Note that there are programs which do not support integer-only input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
-}
-\examples{
-library(lightgbm)
-data(iris)
-
-str(iris)
-
-new_iris <- lgb.prepare_rules2(data = iris) # Autoconverter
-str(new_iris$data)
-
-data(iris) # Erase iris dataset
-iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
-
-# Use conversion using known rules
-# Unknown factors become 0, excellent for sparse datasets
-newer_iris <- lgb.prepare_rules2(data = iris, rules = new_iris$rules)
-
-# Unknown factor is now zero, perfect for sparse datasets
-newer_iris$data[1, ] # Species became 0 as it is an unknown factor
-
-newer_iris$data[1, 5] <- 1 # Put back real initial value
-
-# Is the newly created dataset equal? YES!
-all.equal(new_iris$data, newer_iris$data)
-
-# Can we test our own rules?
-data(iris) # Erase iris dataset
-
-# We remapped values differently
-personal_rules <- list(Species = c("setosa" = 3L,
-                                   "versicolor" = 2L,
-                                   "virginica" = 1L))
-newest_iris <- lgb.prepare_rules2(data = iris, rules = personal_rules)
-str(newest_iris$data) # SUCCESS!
-
+Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
+Factors and characters are converted to numeric (specifically: integer).
+In addition, keeps rules created so you can convert other datasets using this converter.
+This is useful if you have a specific need for integer dataset instead of numeric dataset.
+Note that there are programs which do not support integer-only input.
+Consider this as a half memory technique which is dangerous, especially for LightGBM.
 }
diff --git a/R-package/man/lgb.save.Rd b/R-package/man/lgb.save.Rd
index f0ab4c593b42..df483f6513bb 100644
--- a/R-package/man/lgb.save.Rd
+++ b/R-package/man/lgb.save.Rd
@@ -29,13 +29,15 @@ test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
 params <- list(objective = "regression", metric = "l2")
 valids <- list(test = dtest)
-model <- lgb.train(params,
-                   dtrain,
-                   10,
-                   valids,
-                   min_data = 1,
-                   learning_rate = 1,
-                   early_stopping_rounds = 5)
+model <- lgb.train(
+  params = params
+  , data = dtrain
+  , nrounds = 10
+  , valids = valids
+  , min_data = 1
+  , learning_rate = 1
+  , early_stopping_rounds = 5
+)
 lgb.save(model, "model.txt")
 
 }
diff --git a/R-package/man/lgb.train.Rd b/R-package/man/lgb.train.Rd
index 79b16ef88910..f7096389d428 100644
--- a/R-package/man/lgb.train.Rd
+++ b/R-package/man/lgb.train.Rd
@@ -4,11 +4,24 @@
 \alias{lgb.train}
 \title{Main training logic for LightGBM}
 \usage{
-lgb.train(params = list(), data, nrounds = 10, valids = list(),
-  obj = NULL, eval = NULL, verbose = 1, record = TRUE,
-  eval_freq = 1L, init_model = NULL, colnames = NULL,
-  categorical_feature = NULL, early_stopping_rounds = NULL,
-  callbacks = list(), reset_data = FALSE, ...)
+lgb.train(
+  params = list(),
+  data,
+  nrounds = 10,
+  valids = list(),
+  obj = NULL,
+  eval = NULL,
+  verbose = 1,
+  record = TRUE,
+  eval_freq = 1L,
+  init_model = NULL,
+  colnames = NULL,
+  categorical_feature = NULL,
+  early_stopping_rounds = NULL,
+  callbacks = list(),
+  reset_data = FALSE,
+  ...
+)
 }
 \arguments{
 \item{params}{List of parameters}
@@ -39,17 +52,16 @@ lgb.train(params = list(), data, nrounds = 10, valids = list(),
 type int represents index,
 type str represents feature names}
 
-\item{early_stopping_rounds}{int
-Activates early stopping.
-Requires at least one validation data and one metric
-If there's more than one, will check all of them except the training data
-Returns the model with (best_iter + early_stopping_rounds)
-If early stopping occurs, the model will have 'best_iter' field}
+\item{early_stopping_rounds}{int. Activates early stopping. Requires at least one validation data
+and one metric. If there's more than one, will check all of them
+except the training data. Returns the model with (best_iter + early_stopping_rounds).
+If early stopping occurs, the model will have 'best_iter' field.}
 
-\item{callbacks}{list of callback functions
-List of callback functions that are applied at each iteration.}
+\item{callbacks}{List of callback functions that are applied at each iteration.}
 
-\item{reset_data}{Boolean, setting it to TRUE (not the default value) will transform the booster model into a predictor model which frees up memory and the original datasets}
+\item{reset_data}{Boolean, setting it to TRUE (not the default value) will transform the
+booster model into a predictor model which frees up memory and the
+original datasets}
 
 \item{...}{other parameters, see Parameters.rst for more information. A few key parameters:
 \itemize{
@@ -78,11 +90,13 @@ test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
 params <- list(objective = "regression", metric = "l2")
 valids <- list(test = dtest)
-model <- lgb.train(params,
-                   dtrain,
-                   10,
-                   valids,
-                   min_data = 1,
-                   learning_rate = 1,
-                   early_stopping_rounds = 5)
+model <- lgb.train(
+  params = params
+  , data = dtrain
+  , nrounds = 10
+  , valids = valids
+  , min_data = 1
+  , learning_rate = 1
+  , early_stopping_rounds = 5
+)
 }
diff --git a/R-package/man/lgb.unloader.Rd b/R-package/man/lgb.unloader.Rd
index ab12dfe2cf23..b77046cf5644 100644
--- a/R-package/man/lgb.unloader.Rd
+++ b/R-package/man/lgb.unloader.Rd
@@ -7,11 +7,15 @@
 lgb.unloader(restore = TRUE, wipe = FALSE, envir = .GlobalEnv)
 }
 \arguments{
-\item{restore}{Whether to reload \code{LightGBM} immediately after detaching from R. Defaults to \code{TRUE} which means automatically reload \code{LightGBM} once unloading is performed.}
+\item{restore}{Whether to reload \code{LightGBM} immediately after detaching from R.
+Defaults to \code{TRUE} which means automatically reload \code{LightGBM} once
+unloading is performed.}
 
-\item{wipe}{Whether to wipe all \code{lgb.Dataset} and \code{lgb.Booster} from the global environment. Defaults to \code{FALSE} which means to not remove them.}
+\item{wipe}{Whether to wipe all \code{lgb.Dataset} and \code{lgb.Booster} from the global
+environment. Defaults to \code{FALSE} which means to not remove them.}
 
-\item{envir}{The environment to perform wiping on if \code{wipe == TRUE}. Defaults to \code{.GlobalEnv} which is the global environment.}
+\item{envir}{The environment to perform wiping on if \code{wipe == TRUE}. Defaults to
+\code{.GlobalEnv} which is the global environment.}
 }
 \value{
 NULL invisibly.
@@ -29,13 +33,15 @@ test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
 params <- list(objective = "regression", metric = "l2")
 valids <- list(test = dtest)
-model <- lgb.train(params,
-                   dtrain,
-                   10,
-                   valids,
-                   min_data = 1,
-                   learning_rate = 1,
-                   early_stopping_rounds = 5)
+model <- lgb.train(
+  params = params
+  , data = dtrain
+  , nrounds = 10
+  , valids = valids
+  , min_data = 1
+  , learning_rate = 1
+  , early_stopping_rounds = 5
+)
 
 \dontrun{
 lgb.unloader(restore = FALSE, wipe = FALSE, envir = .GlobalEnv)
diff --git a/R-package/man/lgb_shared_params.Rd b/R-package/man/lgb_shared_params.Rd
index 0a40c2ae31ed..3dcad6f47544 100644
--- a/R-package/man/lgb_shared_params.Rd
+++ b/R-package/man/lgb_shared_params.Rd
@@ -9,12 +9,10 @@ List of callback functions that are applied at each iteration.}
 
 \item{data}{a \code{lgb.Dataset} object, used for training}
 
-\item{early_stopping_rounds}{int
-Activates early stopping.
-Requires at least one validation data and one metric
-If there's more than one, will check all of them except the training data
-Returns the model with (best_iter + early_stopping_rounds)
-If early stopping occurs, the model will have 'best_iter' field}
+\item{early_stopping_rounds}{int. Activates early stopping. Requires at least one validation data
+and one metric. If there's more than one, will check all of them
+except the training data. Returns the model with (best_iter + early_stopping_rounds).
+If early stopping occurs, the model will have 'best_iter' field.}
 
 \item{eval_freq}{evaluation output frequency, only effect when verbose > 0}
 
diff --git a/R-package/man/lightgbm.Rd b/R-package/man/lightgbm.Rd
index d0582110a156..3f424dfc7542 100644
--- a/R-package/man/lightgbm.Rd
+++ b/R-package/man/lightgbm.Rd
@@ -4,10 +4,20 @@
 \alias{lightgbm}
 \title{Train a LightGBM model}
 \usage{
-lightgbm(data, label = NULL, weight = NULL, params = list(),
-  nrounds = 10, verbose = 1, eval_freq = 1L,
-  early_stopping_rounds = NULL, save_name = "lightgbm.model",
-  init_model = NULL, callbacks = list(), ...)
+lightgbm(
+  data,
+  label = NULL,
+  weight = NULL,
+  params = list(),
+  nrounds = 10,
+  verbose = 1,
+  eval_freq = 1L,
+  early_stopping_rounds = NULL,
+  save_name = "lightgbm.model",
+  init_model = NULL,
+  callbacks = list(),
+  ...
+)
 }
 \arguments{
 \item{data}{a \code{lgb.Dataset} object, used for training}
@@ -24,12 +34,10 @@ lightgbm(data, label = NULL, weight = NULL, params = list(),
 
 \item{eval_freq}{evaluation output frequency, only effect when verbose > 0}
 
-\item{early_stopping_rounds}{int
-Activates early stopping.
-Requires at least one validation data and one metric
-If there's more than one, will check all of them except the training data
-Returns the model with (best_iter + early_stopping_rounds)
-If early stopping occurs, the model will have 'best_iter' field}
+\item{early_stopping_rounds}{int. Activates early stopping. Requires at least one validation data
+and one metric. If there's more than one, will check all of them
+except the training data. Returns the model with (best_iter + early_stopping_rounds).
+If early stopping occurs, the model will have 'best_iter' field.}
 
 \item{save_name}{File name to use when writing the trained model to disk. Should end in ".model".}
 
diff --git a/R-package/man/predict.lgb.Booster.Rd b/R-package/man/predict.lgb.Booster.Rd
index 480311889c76..e2359c225390 100644
--- a/R-package/man/predict.lgb.Booster.Rd
+++ b/R-package/man/predict.lgb.Booster.Rd
@@ -4,9 +4,17 @@
 \alias{predict.lgb.Booster}
 \title{Predict method for LightGBM model}
 \usage{
-\method{predict}{lgb.Booster}(object, data, num_iteration = NULL,
-  rawscore = FALSE, predleaf = FALSE, predcontrib = FALSE,
-  header = FALSE, reshape = FALSE, ...)
+\method{predict}{lgb.Booster}(
+  object,
+  data,
+  num_iteration = NULL,
+  rawscore = FALSE,
+  predleaf = FALSE,
+  predcontrib = FALSE,
+  header = FALSE,
+  reshape = FALSE,
+  ...
+)
 }
 \arguments{
 \item{object}{Object of class \code{lgb.Booster}}
@@ -16,8 +24,8 @@
 \item{num_iteration}{number of iteration want to predict with, NULL or <= 0 means use best iteration}
 
 \item{rawscore}{whether the prediction should be returned in the for of original untransformed
-sum of predictions from boosting iterations' results. E.g., setting \code{rawscore=TRUE} for
-logistic regression would result in predictions for log-odds instead of probabilities.}
+sum of predictions from boosting iterations' results. E.g., setting \code{rawscore=TRUE}
+for logistic regression would result in predictions for log-odds instead of probabilities.}
 
 \item{predleaf}{whether predict leaf index instead.}
 
@@ -53,13 +61,15 @@ test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
 params <- list(objective = "regression", metric = "l2")
 valids <- list(test = dtest)
-model <- lgb.train(params,
-                   dtrain,
-                   10,
-                   valids,
-                   min_data = 1,
-                   learning_rate = 1,
-                   early_stopping_rounds = 5)
+model <- lgb.train(
+  params = params
+  , data = dtrain
+  , nrounds = 10
+  , valids = valids
+  , min_data = 1
+  , learning_rate = 1
+  , early_stopping_rounds = 5
+)
 preds <- predict(model, test$data)
 
 }
diff --git a/R-package/man/readRDS.lgb.Booster.Rd b/R-package/man/readRDS.lgb.Booster.Rd
index cef1679ce53d..a8fc219ffd95 100644
--- a/R-package/man/readRDS.lgb.Booster.Rd
+++ b/R-package/man/readRDS.lgb.Booster.Rd
@@ -27,13 +27,15 @@ test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
 params <- list(objective = "regression", metric = "l2")
 valids <- list(test = dtest)
-model <- lgb.train(params,
-                   dtrain,
-                   10,
-                   valids,
-                   min_data = 1,
-                   learning_rate = 1,
-                   early_stopping_rounds = 5)
+model <- lgb.train(
+  params = params
+  , data = dtrain
+  , nrounds = 10
+  , valids = valids
+  , min_data = 1
+  , learning_rate = 1
+  , early_stopping_rounds = 5
+)
 saveRDS.lgb.Booster(model, "model.rds")
 new_model <- readRDS.lgb.Booster("model.rds")
 
diff --git a/R-package/man/saveRDS.lgb.Booster.Rd b/R-package/man/saveRDS.lgb.Booster.Rd
index 54fd1d07867c..afa639d4245c 100644
--- a/R-package/man/saveRDS.lgb.Booster.Rd
+++ b/R-package/man/saveRDS.lgb.Booster.Rd
@@ -4,19 +4,31 @@
 \alias{saveRDS.lgb.Booster}
 \title{saveRDS for \code{lgb.Booster} models}
 \usage{
-saveRDS.lgb.Booster(object, file = "", ascii = FALSE, version = NULL,
-  compress = TRUE, refhook = NULL, raw = TRUE)
+saveRDS.lgb.Booster(
+  object,
+  file = "",
+  ascii = FALSE,
+  version = NULL,
+  compress = TRUE,
+  refhook = NULL,
+  raw = TRUE
+)
 }
 \arguments{
 \item{object}{R object to serialize.}
 
 \item{file}{a connection or the name of the file where the R object is saved to or read from.}
 
-\item{ascii}{a logical. If TRUE or NA, an ASCII representation is written; otherwise (default), a binary one is used. See the comments in the help for save.}
+\item{ascii}{a logical. If TRUE or NA, an ASCII representation is written; otherwise (default),
+a binary one is used. See the comments in the help for save.}
 
-\item{version}{the workspace format version to use. \code{NULL} specifies the current default version (2). Versions prior to 2 are not supported, so this will only be relevant when there are later versions.}
+\item{version}{the workspace format version to use. \code{NULL} specifies the current default
+version (2). Versions prior to 2 are not supported, so this will only be relevant
+when there are later versions.}
 
-\item{compress}{a logical specifying whether saving to a named file is to use "gzip" compression, or one of \code{"gzip"}, \code{"bzip2"} or \code{"xz"} to indicate the type of compression to be used. Ignored if file is a connection.}
+\item{compress}{a logical specifying whether saving to a named file is to use "gzip" compression,
+or one of \code{"gzip"}, \code{"bzip2"} or \code{"xz"} to indicate the type of
+compression to be used. Ignored if file is a connection.}
 
 \item{refhook}{a hook function for handling reference objects.}
 
@@ -26,7 +38,8 @@ saveRDS.lgb.Booster(object, file = "", ascii = FALSE, version = NULL,
 NULL invisibly.
 }
 \description{
-Attempts to save a model using RDS. Has an additional parameter (\code{raw}) which decides whether to save the raw model or not.
+Attempts to save a model using RDS. Has an additional parameter (\code{raw}) which decides
+whether to save the raw model or not.
 }
 \examples{
 library(lightgbm)
@@ -39,10 +52,10 @@ dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
 params <- list(objective = "regression", metric = "l2")
 valids <- list(test = dtest)
 model <- lgb.train(
-    params
-    , dtrain
-    , 10
-    , valids
+    params = params
+    , data = dtrain
+    , nrounds = 10
+    , valids = valids
     , min_data = 1
     , learning_rate = 1
     , early_stopping_rounds = 5
diff --git a/R-package/man/setinfo.Rd b/R-package/man/setinfo.Rd
index 861e5f0219ef..5b0a23f6992b 100644
--- a/R-package/man/setinfo.Rd
+++ b/R-package/man/setinfo.Rd
@@ -12,11 +12,11 @@ setinfo(dataset, ...)
 \arguments{
 \item{dataset}{Object of class \code{lgb.Dataset}}
 
-\item{...}{other parameters}
-
 \item{name}{the name of the field to get}
 
 \item{info}{the specific field of information to set}
+
+\item{...}{other parameters}
 }
 \value{
 passed object
diff --git a/R-package/man/slice.Rd b/R-package/man/slice.Rd
index 10040d11a2bc..c759ee06c989 100644
--- a/R-package/man/slice.Rd
+++ b/R-package/man/slice.Rd
@@ -12,9 +12,9 @@ slice(dataset, ...)
 \arguments{
 \item{dataset}{Object of class \code{lgb.Dataset}}
 
-\item{...}{other parameters (currently not used)}
-
 \item{idxset}{a integer vector of indices of rows needed}
+
+\item{...}{other parameters (currently not used)}
 }
 \value{
 constructed sub dataset