fixes from CRAN

Author: jakob-r

DESCRIPTION

@@ -1,89 +1,49 @@
 Package: mlrMBO
 Title: Bayesian Optimization and Model-Based Optimization of Expensive
-    Black-Box Functions
-Version: 1.1.5-9000
-Authors@R: 
-    c(person(given = "Bernd",
-             family = "Bischl",
-             role = "aut",
-             email = "[email protected]",
-             comment = c(ORCID = "0000-0001-6002-6980")),
-      person(given = "Jakob",
-             family = "Richter",
-             role = c("aut", "cre"),
-             email = "[email protected]",
-             comment = c(ORCID = "0000-0003-4481-5554")),
-      person(given = "Jakob",
-             family = "Bossek",
-             role = "aut",
-             email = "[email protected]",
-             comment = c(ORCID = "0000-0002-4121-4668")),
-      person(given = "Daniel",
-             family = "Horn",
-             role = "aut",
-             email = "[email protected]"),
-      person(given = "Michel",
-             family = "Lang",
-             role = "aut",
-             email = "[email protected]",
-             comment = c(ORCID = "0000-0001-9754-0393")),
-      person(given = "Janek",
-             family = "Thomas",
-             role = "aut",
-             email = "[email protected]",
-             comment = c(ORCID = "0000-0003-4511-6245")))
-Description: Flexible and comprehensive R toolbox for model-based
-    optimization ('MBO'), also known as Bayesian optimization. It
-    implements the Efficient Global Optimization Algorithm and 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.
+        Black-Box Functions
+Version: 1.1.5.1
+Description: Flexible and comprehensive R toolbox for model-based optimization
+    ('MBO'), also known as Bayesian optimization. It implements the Efficient
+    Global Optimization Algorithm and 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.
+Authors@R: c(
+    person("Bernd", "Bischl", email = "[email protected]", role = c("aut"), comment = c(ORCID = "0000-0001-6002-6980")),
+    person("Jakob", "Richter", email = "[email protected]", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4481-5554")),
+    person("Jakob", "Bossek", email = "[email protected]", role = "aut", comment = c(ORCID = "0000-0002-4121-4668")),
+    person("Daniel", "Horn", email = "[email protected]", role = "aut"),
+    person("Michel", "Lang", email = "[email protected]", role = "aut", comment = c(ORCID = "0000-0001-9754-0393")),
+    person("Janek", "Thomas", email = "[email protected]", role = "aut", comment = c(ORCID = "0000-0003-4511-6245")))
 License: BSD_2_clause + file LICENSE
 URL: https://github.com/mlr-org/mlrMBO
 BugReports: https://github.com/mlr-org/mlrMBO/issues
-Depends:
-    mlr (>= 2.10),
-    ParamHelpers (>= 1.10),
-    smoof (>= 1.5.1)
-Imports:
-    backports (>= 1.1.0),
-    BBmisc (>= 1.11),
-    checkmate (>= 1.8.2),
-    data.table,
-    lhs,
-    parallelMap (>= 1.3)
-Suggests:
-    akima,
-    cmaesr (>= 1.0.3),
-    covr,
-    DiceKriging,
-    earth,
-    emoa,
-    GGally,
-    ggplot2,
-    gridExtra,
-    kernlab,
-    kknn,
-    knitr,
-    mco,
-    nnet,
-    party,
-    randomForest,
-    reshape2,
-    rgenoud,
-    rmarkdown,
-    rpart,
-    testthat
-VignetteBuilder: 
-    knitr
-ByteCompile: yes
+Depends: mlr (>= 2.10), ParamHelpers (>= 1.10), smoof (>= 1.5.1)
+Imports: backports (>= 1.1.0), BBmisc (>= 1.11), checkmate (>= 1.8.2),
+        data.table, lhs, parallelMap (>= 1.3)
+Suggests: cmaesr (>= 1.0.3), ggplot2, DiceKriging, earth, emoa, GGally,
+        gridExtra, kernlab, kknn, knitr, mco, nnet, party,
+        randomForest, reshape2, rmarkdown, rgenoud, rpart, testthat,
+        covr
 Encoding: UTF-8
+ByteCompile: yes
 RoxygenNote: 7.1.1
+VignetteBuilder: knitr
+NeedsCompilation: yes
+Packaged: 2022-07-04 07:35:16 UTC; ripley
+Author: Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>),
+  Jakob Richter [aut, cre] (<https://orcid.org/0000-0003-4481-5554>),
+  Jakob Bossek [aut] (<https://orcid.org/0000-0002-4121-4668>),
+  Daniel Horn [aut],
+  Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>),
+  Janek Thomas [aut] (<https://orcid.org/0000-0003-4511-6245>)
+Maintainer: Jakob Richter <[email protected]>
+Repository: CRAN
+Date/Publication: 2022-07-04 08:50:50 UTC

R/doc_mbo_OptPath.R

@@ -11,10 +11,10 @@
 #' \item{prop.type}{Type of point proposal. Possible values are
 #'   \describe{
 #'   \item{initdesign}{Points actually not proposed, but in the initial design.}
-#'   \item{infill\_x}{Here x is a placeholder for the selected infill criterion, e.g., infill\_ei for expected improvement.}
-#'   \item{random\_interleave}{Uniformly sampled points added additionally to the proposed points.}
-#'   \item{random\_filtered}{If filtering of proposed points located too close to each other is active, these are replaced by random points.}
-#'   \item{final\_eval}{If \code{final.evals} is set in \code{\link{makeMBOControl}}: Final evaluations of the proposed solution to reduce noise in y.}
+#'   \item{infill_x}{Here x is a placeholder for the selected infill criterion, e.g., infill_ei for expected improvement.}
+#'   \item{random_interleave}{Uniformly sampled points added additionally to the proposed points.}
+#'   \item{random_filtered}{If filtering of proposed points located too close to each other is active, these are replaced by random points.}
+#'   \item{final_eval}{If \code{final.evals} is set in \code{\link{makeMBOControl}}: Final evaluations of the proposed solution to reduce noise in y.}
 #'   }
 #' }
 #' \item{parego.weight}{Weight vector sampled for multi-point ParEGO}