fix description

Author: ja-thomas

DESCRIPTION

@@ -1,16 +1,16 @@
 Package: mlrMBO
-Title: mlrMBO: A Toolbox for Model-Based Optimization of Expensive Black-Box Functions
+Title: mlrMBO: A Toolbox for Model-Based Optimization of Expensive Black-Box Functions.
 Description: mlrMBO is a flexible and comprehensive R toolbox for model-based
-optimization (MBO), also known as Bayesian optimization. It is designed for
-both single- and multi-objective optimization with mixed continuous, categorical
-and conditional parameters. The machine learning toolbox mlr provide dozens
-of regression learners to model the performance of the target algorithm with
-respect to the parameter settings. It provides many different infill criteria to
-guide the search process. Additional features include multi-point batch proposal,
-parallelization as well as visualization and sophisticated logging mechanisms,
-which is especially useful for teaching and understanding of algorithm behavior.
-mlrMBO is implemented in a modular fashion, such that single components can
-be easily replaced or adapted by the user for specific use cases.
+	optimization (MBO), also known as Bayesian optimization. It is designed for
+	both single- and multi-objective optimization with mixed continuous, categorical
+	and conditional parameters. The machine learning toolbox mlr provide dozens
+	of regression learners to model the performance of the target algorithm with
+	respect to the parameter settings. It provides many different infill criteria to
+	guide the search process. Additional features include multi-point batch proposal,
+	parallelization as well as visualization and sophisticated logging mechanisms,
+	which is especially useful for teaching and understanding of algorithm behavior.
+	mlrMBO is implemented in a modular fashion, such that single components can
+	be easily replaced or adapted by the user for specific use cases.
 Authors@R: c(
     person("Bernd", "Bischl", email = "[email protected]", role = c("aut", "cre")), 
     person("Jakob", "Bossek", email = "[email protected]", role = "aut"),