add version 1.1.5 to r-mlrmbo (#21102)

1 files changed, 15 insertions, 13 deletions

diff --git a/var/spack/repos/builtin/packages/r-mlrmbo/package.py b/var/spack/repos/builtin/packages/r-mlrmbo/package.py
index 23ae339e12..05e2f0447b 100644
--- a/var/spack/repos/builtin/packages/r-mlrmbo/package.py
+++ b/var/spack/repos/builtin/packages/r-mlrmbo/package.py
@@ -7,24 +7,26 @@ from spack import *
 
 
 class RMlrmbo(RPackage):
-    """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, parallel execution 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."""
+    """Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions
+
+    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, parallel execution 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."""
 
     homepage = "https://github.com/mlr-org/mlrMBO/"
     url      = "https://cloud.r-project.org/src/contrib/mlrMBO_1.1.1.tar.gz"
     list_url = "https://cloud.r-project.org/src/contrib/Archive/mlrMBO"
 
+    version('1.1.5', sha256='7ab9d108ad06f6c5c480fa4beca69e09ac89bb162ae6c85fe7d6d25c41f359b8')
     version('1.1.2', sha256='8e84caaa5d5d443d7019128f88ebb212fb095870b3a128697c9b64fe988f3efe')
     version('1.1.1', sha256='e87d9912a9b4a968364584205b8ef6f7fea0b5aa043c8d31331a7b7be02dd7e4')
     version('1.1.0', sha256='6ae82731a566333f06085ea2ce23ff2a1007029db46eea57d06194850350a8a0')