Fix CRAN issue "lost braces"

R/MBOResult.R

@@ -1,8 +1,8 @@
-#' @title Single-Objective result object.
+#' @title Single-Objective result object
 #'
 #' @description
 #'
-#' \itemize{
+#' \describe{
 #'   \item{x [\code{list}]}{Named list of proposed optimal parameters.}
 #'   \item{y [\code{numeric(1)}]}{Value of objective function at \code{x},
 #'     either from evals during optimization or from requested final evaluations,
@@ -12,7 +12,7 @@
 #'     Includes all evaluated points and additional information as documented in \link{mbo_OptPath}.
 #'     You can convert it via \code{as.data.frame}.}
 #'   \item{resample.results [List of \code{\link[mlr]{ResampleResult}}]}{List of the desired \code{resample.results} if \code{resample.at} is set in \code{makeMBOControl}.}
-#'   \item{final.state [\code{character}] The final termination state. Gives information why the optimization ended. Possible values are
+#'   \item{final.state [\code{character}]}{The final termination state. Gives information why the optimization ended. Possible values are
 #'      \describe{
 #'        \item{term.iter}{Maximal number of iterations reached.}
 #'        \item{term.time}{Maximal running time exceeded.}
@@ -23,7 +23,7 @@
 #'     }
 #'   }
 #'   \item{models [List of \code{\link[mlr]{WrappedModel}}]}{List of saved regression models if \code{store.model.at} is set in \code{makeMBOControl}. The default is that it contains the model generated after the last iteration.}
-#'   \item{control [\code{MBOControl}] Control object used in optimization}
+#'   \item{control [\code{MBOControl}]}{Control object used in optimization}
 #' }
 #' @name MBOSingleObjResult
 #' @rdname MBOSingleObjResult
@@ -83,20 +83,20 @@ print.MBOResult = function(x, ...) {
   print(tail(as.data.frame(op), 10))
 }
 
-#' @title Multi-Objective result object.
+#' @title Multi-Objective result object
 #'
 #' @description
 #'
-#' \itemize{
+#' \describe{
 #'   \item{pareto.front [\code{matrix}]}{Pareto front of all evaluated points.}
 #'   \item{pareto.set [\code{list} of \code{list}s]}{Pareto set of all evaluated points.}
 #'   \item{pareto.inds [\code{numeric}]}{Indices of the Pareto-optimal points in the opt.path}
 #'   \item{opt.path [\code{\link[ParamHelpers]{OptPath}}]}{Optimization path.
 #'     Includes all evaluated points and additional information as documented in \link{mbo_OptPath}.
 #'     You can convert it via \code{as.data.frame}.}
-#'   \item{final.state [\code{character}] The final termination state. Gives information why the optimization ended}
+#'   \item{final.state [\code{character}]}{The final termination state. Gives information why the optimization ended}
 #'   \item{models [List of \code{\link[mlr]{WrappedModel}}]}{List of saved regression models.}
-#'   \item{control[\code{MBOControl}] Control object used in optimization}
+#'   \item{control[\code{MBOControl}]}{Control object used in optimization}
 #' }
 #' @name MBOMultiObjResult
 #' @rdname MBOMultiObjResult

R/OptProblem.R

@@ -1,4 +1,4 @@
-#' @title OptProblem object.
+#' @title OptProblem object
 #' @description
 #' The OptProblem contains all the constants values which define a OptProblem within our MBO Steps.
 #' It is an environment and is always pointed at by the OptState.

R/OptResult.R

@@ -1,4 +1,4 @@
-#' @title OptResult object.
+#' @title OptResult object
 #' @description
 #' The OptResult stores all entities which are not needed while optimizing but are needed to build the final result.
 #' It can contains fitted surrogate models at certain times as well as resample objects.

R/OptState.R

@@ -1,4 +1,4 @@
-#' @title OptState object.
+#' @title OptState object
 #' @description
 #' The OptState is the central component of the mbo iterations.
 #' This environment contains every necessary information needed during optimization in MBO.

R/SMBO.R

@@ -1,4 +1,4 @@
-#' @title Initialize a manual sequential MBO run.
+#' @title Initialize a manual sequential MBO run
 #' @description When you want to run a human-in-the-loop MBO run you need to initialize it first.
 #'
 #' @inheritParams mbo

R/doc_error_handling.R

@@ -5,17 +5,17 @@
 #' process. mlrMBO tries to handle most of them as smart as possible.
 #'
 #' The target function could
-#' \itemize{
-#'   \item{1}{The target function returns NA(s) or NaN(s) (plural for the multi-objective case).}
-#'   \item{2}{The target function stops with an error.}
-#'   \item{3}{The target function does not return at all (infinite or very long execution time).}
-#'   \item{4}{The target function crashes the whole R process.}
-#'   \item{5}{The surrogate machine learning model might crash.
-#'     Kriging quite often can run into numerical problems.}
-#'   \item{6}{The proposal mechanism - in multi-point or single point mode - produces
+#' \enumerate{
+#'   \item The target function returns NA(s) or NaN(s) (plural for the multi-objective case).
+#'   \item The target function stops with an error.
+#'   \item The target function does not return at all (infinite or very long execution time).
+#'   \item The target function crashes the whole R process.
+#'   \item The surrogate machine learning model might crash.
+#'     Kriging quite often can run into numerical problems.
+#'   \item The proposal mechanism - in multi-point or single point mode - produces
 #'     a point which is either close to another candidate point in the same iteration or
-#'     an already visited point in a previous iteration.}
-#'   \item{7}{The mbo process exits / stops / crashes itself. Maybe because it hit a walltime.}
+#'     an already visited point in a previous iteration.
+#'   \item The mbo process exits / stops / crashes itself. Maybe because it hit a walltime.
 #' }
 #'
 #'

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}

R/doc_mbo_parallel.R

@@ -2,7 +2,7 @@
 #'
 #' @description
 #' In mlrMBO you can parallelize the tuning on two different levels to speed up computation:
-#' \itemize{
+#' \describe{
 #'   \item{\code{mlrMBO.feval}}{Multiple evaluations of the target function.}
 #'   \item{\code{mlrMBO.propose.points}}{Optimization of the infill criteria if multiple are used (e.g. ParEGO and ParallelLCB)}
 #' }
@@ -11,8 +11,8 @@
 #' The different levels of parallelization can be specified in \code{parallelStart*}.
 #' Details for the levels mentioned above are given below:
 #' \itemize{
-#' \item{Evaluation of the objective function can be parallelized in cases multiple points are to be evaluated at once. These are: evaluation of the initial design, multiple proposed points per iteration and evaluation of the target function in \code{\link{exampleRun}}. (Level: \code{mlrMBO.feval})}
-#' \item{Model fitting / point proposal - in some cases where independent, expensive operations are performed. (Level: \code{mlrMBO.propose.points})}
+#' \item Evaluation of the objective function can be parallelized in cases multiple points are to be evaluated at once. These are: evaluation of the initial design, multiple proposed points per iteration and evaluation of the target function in \code{\link{exampleRun}}. (Level: \code{mlrMBO.feval})
+#' \item Model fitting / point proposal - in some cases where independent, expensive operations are performed. (Level: \code{mlrMBO.propose.points})
 #' }
 #' Details regarding the latter:
 #' \describe{

R/exampleRun.R

@@ -1,4 +1,4 @@
-#' @title Perform an mbo run on a test function and and visualize what happens.
+#' @title Perform an mbo run on a test function and and visualize what happens
 #'
 #' @description
 #' Usually used for 1D or 2D examples,
@@ -164,7 +164,7 @@ evaluate = function(fun, par.set, n.params, par.types, noisy, noisy.evals, point
   }
 }
 
-#' Helper function which returns the (estimated) global optimum.
+#' Helper function which returns the (estimated) global optimum
 #'
 #' @param run [\code{MBOExampleRun}]\cr
 #'   Object of type \code{MBOExampleRun}.

R/exampleRunMultiObj.R

@@ -1,4 +1,4 @@
-#' @title Perform an MBO run on a multi-objective test function and and visualize what happens.
+#' @title Perform an MBO run on a multi-objective test function and and visualize what happens
 #'
 #' @description
 #' Only available for 2D -> 2D examples,

R/getMBOInfillCrit.R

@@ -1,4 +1,4 @@
-#' @title Get properties of MBO infill criterion.
+#' @title Get properties of MBO infill criterion
 #'
 #' @description
 #' Returns properties of an infill criterion, e.g., name or id.

R/getSupportedInfillOptFunctions.R

@@ -1,4 +1,4 @@
-#' @title Get names of supported infill-criteria optimizers.
+#' @title Get names of supported infill-criteria optimizers
 #' @description
 #' None.
 #' @return [\code{character}]

R/getSupportedMultipointInfillOptFunctions.R

@@ -1,4 +1,4 @@
-#' @title Get names of supported multi-point infill-criteria optimizers.
+#' @title Get names of supported multi-point infill-criteria optimizers
 #'
 #' @description
 #' Returns all names of supported multi-point infill-criteria optimizers.

R/infill_crits.R

@@ -1,4 +1,4 @@
-#' @title Infill criteria.
+#' @title Infill criteria
 #'
 #' @description
 #' \pkg{mlrMBO} contains most of the most popular infill criteria, e.g., expected

R/initCrit.R

@@ -1,4 +1,4 @@
-#' @title Initialize an MBO infill criterion.
+#' @title Initialize an MBO infill criterion
 #'
 #' @description
 #'   Some infill criteria have parameters that are dependent on values in the parameter set, design,

R/makeMBOControl.R

@@ -1,4 +1,4 @@
-#' @title Set MBO options.
+#' @title Set MBO options
 #'
 #' @description
 #' Creates a control object for MBO optimization.
@@ -78,7 +78,7 @@
 #'   Possible values are:
 #'   \dQuote{stop}: R exception is generated.
 #'   \dQuote{warn}: The error will be converted to a waring and a random point will be proposed.
-#'   \dQuote{quiet}: Same as “warn” but without the warning.
+#'   \dQuote{quiet}: Same as "warn" but without the warning.
 #'   This will overwrite the mlr setting \code{on.learner.error} for the surrogate learner.
 #'   Default is: \dQuote{stop}.
 #'
@@ -165,7 +165,7 @@ makeMBOControl = function(n.objectives = 1L,
   return(control)
 }
 
-#' Print mbo control object.
+#' Print mbo control object
 #'
 #' @param x [\code{\link{MBOControl}}]\cr
 #'   Control object.

R/makeMBOInfillCrit.R

@@ -1,4 +1,4 @@
-#' @title Create an infill criterion.
+#' @title Create an infill criterion
 #'
 #' @description The infill criterion guides the model based search process.
 #' The most prominent infill criteria, e.g., expected improvement, lower

R/makeMBOLearner.R

@@ -1,44 +1,43 @@
-#' @title Generate default learner.
+#' @title Generate default learner
 #'
 #' @description
 #' This is a helper function that generates a default surrogate, based on properties of the objective
 #' function and the selected infill criterion.
 #'
 #' For numeric-only (including integers) parameter spaces without any dependencies:
 #' \itemize{
-#' \item{A Kriging model \dQuote{regr.km} with kernel \dQuote{matern3_2} is created.}
-#' \item{If the objective function is deterministic we add a small nugget effect (10^-8*Var(y),
+#' \item A Kriging model \dQuote{regr.km} with kernel \dQuote{matern3_2} is created.
+#' \item If the objective function is deterministic we add a small nugget effect (10^-8*Var(y),
 #'   y is vector of observed outcomes in current design) to increase numerical stability to
-#'   hopefully prevent crashes of DiceKriging.}
-#' \item{If the objective function is noisy the nugget effect will be estimated with
-#'   \code{nugget.estim = TRUE} (but you can override this in \code{...}.}
+#'   hopefully prevent crashes of DiceKriging.
+#' \item If the objective function is noisy the nugget effect will be estimated with
+#'   \code{nugget.estim = TRUE} (but you can override this in \code{...}.
 #'   Also \code{jitter} is set to \code{TRUE} to circumvent a problem with DiceKriging where already
 #'   trained input values produce the exact trained output.
 #'   For further information check the \code{$note} slot of the created learner.
-#' \item{Instead of the default \code{"BFGS"} optimization method we use rgenoud (\code{"gen"}),
+#' \item Instead of the default \code{"BFGS"} optimization method we use rgenoud (\code{"gen"}),
 #'   which is a hybrid algorithm, to combine global search based on genetic algorithms and local search
 #'   based on gradients.
 #'   This may improve the model fit and will less frequently produce a constant surrogate model.
-#'   You can also override this setting in \code{...}.}
+#'   You can also override this setting in \code{...}.
 #' }
 #'
 #' For mixed numeric-categorical parameter spaces, or spaces with conditional parameters:
 #' \itemize{
-#' \item{A random regression forest \dQuote{regr.randomForest} with 500 trees is created.}
-#' \item{The standard error of a prediction (if required by the infill criterion) is estimated
+#' \item A random regression forest \dQuote{regr.randomForest} with 500 trees is created.
+#' \item The standard error of a prediction (if required by the infill criterion) is estimated
 #'   by computing the jackknife-after-bootstrap.
 #'   This is the \code{se.method = "jackknife"} option of the \dQuote{regr.randomForest} Learner.
-#'   }
 #' }
 #'
 #' If additionally dependencies are in present in the parameter space, inactive conditional parameters
 #' are represented by missing \code{NA} values in the training design data.frame.
 #' We simply handle those with an imputation method, added to the random forest:
 #' \itemize{
-#' \item{If a numeric value is inactive, i.e., missing, it will be imputed by 2 times the
-#'   maximum of observed values}
-#' \item{If a categorical value is inactive, i.e., missing, it will be imputed by the
-#'   special class label \code{"__miss__"}}
+#' \item If a numeric value is inactive, i.e., missing, it will be imputed by 2 times the
+#'   maximum of observed values
+#' \item If a categorical value is inactive, i.e., missing, it will be imputed by the
+#'   special class label \code{"__miss__"}
 #' }
 #' Both of these techniques make sense for tree-based methods and are usually hard to beat, see
 #' Ding et.al. (2010).

R/makeMBOTrafoFunction.R

@@ -1,4 +1,4 @@
-#' @title Create a transformation function for MBOExampleRun.
+#' @title Create a transformation function for MBOExampleRun
 #'
 #' @description
 #' Creates a transformation function for MBOExampleRun.
@@ -17,11 +17,11 @@ makeMBOTrafoFunction = function(name, fun) {
   return(fun)
 }
 
-#' Transformation methods.
+#' Transformation methods
 #'
-#' \itemize{
-#'   \item{\bold{logTrafo}}{\cr Natural logarithm.}
-#'   \item{\bold{sqrtTrafo}}{\cr Square root.}
+#' \describe{
+#'   \item{\bold{logTrafo}}{Natural logarithm.}
+#'   \item{\bold{sqrtTrafo}}{Square root.}
 #' }
 #' If negative values occur and the trafo function can handle only positive values,
 #' a shift of the form x - min(x) + 1 is performed prior to the transformation if the

R/mbo.R

@@ -1,4 +1,4 @@
-#' @title Optimizes a function with sequential model based optimization.
+#' @title Optimizes a function with sequential model based optimization
 #'
 #' @description
 #' See \link{mbo_parallel} for all parallelization options.

R/mboContinue.R

@@ -1,4 +1,4 @@
-#' @title Continues an mbo run from a save-file.
+#' @title Continues an mbo run from a save-file
 #'
 #' @description
 #' Useful if your optimization is likely to crash,

R/mboFinalize.R

@@ -1,4 +1,4 @@
-#' @title Finalizes an mbo run from a save-file.
+#' @title Finalizes an mbo run from a save-file
 #'
 #' @description
 #' Useful if your optimization didn't terminate but you want a results nonetheless.

R/plotExampleRun.R

@@ -1,4 +1,4 @@
-#' @title Renders plots for exampleRun objects and displays them.
+#' @title Renders plots for exampleRun objects and displays them
 #'
 #' @description
 #' The graphical output depends on the target function at hand.

R/renderExampleRunPlot.R

@@ -1,5 +1,5 @@
 #' @title Renders plots for exampleRun objects, either in 1D or 2D, or
-#' exampleRunMultiObj objects.
+#' exampleRunMultiObj objects
 #'
 #' @description
 #' The graphical output depends on the target function at hand.

R/setMBOControlInfill.R

@@ -1,4 +1,4 @@
-#' @title Extends mbo control object with infill criteria and infill optimizer options.
+#' @title Extends mbo control object with infill criteria and infill optimizer options
 #'
 #' @description
 #' Please note that internally all infill criteria are minimized. So for some of them,

R/setMBOControlMultiObj.R

@@ -1,6 +1,6 @@
 #FIXME: briefly explain multi-point proposal for all three methods
 
-#' @title Set multi-objective options.
+#' @title Set multi-objective options
 #' @description
 #' Extends MBO control object with multi-objective specific options.
 #'
@@ -60,6 +60,7 @@
 #' @references
 #' For more information on the implemented multi-objective procedures the following
 #' sources might be helpful:
+#'
 #' Knowles, J.: ParEGO: A hybrid algorithm with on-line landscape
 #' approximation for expensive multiobjective optimization problems. IEEE
 #' Transactions on Evolutionary Computation, 10 (2006) 1, pp. 50-66
@@ -83,8 +84,8 @@
 #' 7th International. Conf. Evolutionary Multi-Criterion Optimization (EMO
 #' 2013), March 19-22, Sheffield, UK, R. Purshouse; P. J. Fleming;
 #' C. M. Fonseca; S. Greco; J. Shaw, eds., 2013, vol. 7811 of Lecture
-#' Notes in Computer Science, ISBN 978-3-642-37139-4, pp. 756{770,
-#' doi:10.1007/978-3-642-37140-0 56}
+#' Notes in Computer Science, ISBN 978-3-642-37139-4, pp. 756-770,
+#' doi:10.1007/978-3-642-37140-0 56
 #'
 #' Jeong, S.; Obayashi, S.: Efficient global optimization (EGO) for Multi-Objective Problem and Data Mining.
 #' In: Proc. IEEE Congress on

R/setMBOControlMultiPoint.R

@@ -1,4 +1,4 @@
-#' @title Set multipoint proposal options.
+#' @title Set multipoint proposal options
 #' @description
 #' Extends an MBO control object with options for multipoint proposal.
 #' @template arg_control

R/setMBOControlTermination.R

@@ -1,4 +1,4 @@
-#' @title Set termination options.
+#' @title Set termination options
 #'
 #' @description
 #' Extends an MBO control object with infill criteria and infill optimizer options.