docs: typos

Author: sumny

README.Rmd

@@ -10,7 +10,7 @@ lgr::get_logger("bbotk")$set_threshold("warn")
 
 # mlr3mbo
 
-A new R6 and much more modular implementation for single- and multi-objective Bayesian optimization.
+A new R6 and much more modular implementation for single- and multi-objective Bayesian Optimization.
 
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 [![tic](https://github.com/mlr-org/mlr3mbo/workflows/tic/badge.svg?branch=main)](https://github.com/mlr-org/mlr3mbo/actions)
@@ -31,7 +31,7 @@ An overview and gentle introduction is given in [this vignette](https://mlr3mbo.
 * `AcqFunction`: Acquisition Function
 * `AcqOptimizer`: Acquisition Function Optimizer
 
-Based on these, Bayesian optimization loops can be written, see, e.g., `bayesopt_ego` for sequential single-objective BO.
+Based on these, Bayesian Optimization loops can be written, see, e.g., `bayesopt_ego` for sequential single-objective BO.
 
 `mlr3mbo` also provides an `OptimizerMbo` class behaving like any other `Optimizer` from the [bbotk](https://cran.r-project.org/package=bbotk) package as well as
 a `TunerMbo` class behaving like any other `Tuner` from the [mlr3tuning](https://cran.r-project.org/package=mlr3tuning) package.

README.md

@@ -2,7 +2,7 @@
 # mlr3mbo
 
 A new R6 and much more modular implementation for single- and
-multi-objective Bayesian optimization.
+multi-objective Bayesian Optimization.
 
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@@ -26,7 +26,7 @@ vignette](https://mlr3mbo.mlr-org.com/articles/mlr3mbo.html).
 - `AcqFunction`: Acquisition Function
 - `AcqOptimizer`: Acquisition Function Optimizer
 
-Based on these, Bayesian optimization loops can be written, see, e.g.,
+Based on these, Bayesian Optimization loops can be written, see, e.g.,
 `bayesopt_ego` for sequential single-objective BO.
 
 `mlr3mbo` also provides an `OptimizerMbo` class behaving like any other

vignettes/mlr3mbo.Rmd

@@ -176,7 +176,7 @@ lgr = NULL
 
 # Intro
 
-`r ref_pkg("mlr3mbo")` makes model Bayesian optimization (BO) available within the `r ref_pkg("mlr3")` ecosystem.
+`r ref_pkg("mlr3mbo")` makes Bayesian Optimization (BO) available within the `r ref_pkg("mlr3")` ecosystem.
 BO can be used for optimizing any black box function, and is very suitable for hyperparameter optimization of machine learning models.
 `r ref_pkg("mlr3mbo")` allows for building custom BO algorithms relying on building blocks in a modular fashion, but also provides a variety of standard single- and multi-objective BO algorithms that can be used in a straightforward manner.