fix spelling (#398)

R/MBOResult.R

@@ -87,7 +87,7 @@ print.MBOResult = function(x, ...) {
 #' \itemize{
 #'   \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}]}{Indizes of the Pareto-optimal points in the opt.path}
+#'   \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}.}

R/OptProblem.R

@@ -1,7 +1,7 @@
 #' @title OptProblem object.
 #' @description
-#' The OptProblem contains all the constants values wich define a OptProblem within our MBO Steps.
-#' It is an enviroment and is always pointed at by the OptState.
+#' 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.
 #' @name OptProblem
 #' @rdname OptProblem
 NULL
@@ -107,4 +107,4 @@ print.OptProblem = function(x, ...) {
   catf("Objective Function: %s", getName(getOptProblemFun(x)))
   catf("Surrogate Learner:")
   print(getOptProblemLearner(x))
-} 
+}

R/OptState.R

@@ -1,18 +1,18 @@
 #' @title OptState object.
 #' @description
 #' The OptState is the central component of the mbo iterations.
-#' This enviroment contains every necessary information needed during optimization in MBO.
+#' This environment contains every necessary information needed during optimization in MBO.
 #' It also links to the \code{\link{OptProblem}} and to the \code{\link{OptResult}}.
 #' @name OptState
 #' @rdname OptState
 NULL
 
 # @param loop \code{integer()} \cr
-#   Tells us in what loop we are at the moment. 0 means we are in the inital phase.
+#   Tells us in what loop we are at the moment. 0 means we are in the initial phase.
 #   The loop i should change to i+1 as soon as the i-th point is evaluated
 # @param tasks \code{list()} \cr
-#   List of \code{RegrTask} which together formulate data neccessary for the surrogate.
-#   Caching is done to not neccessarly regenerate tasks if the \code{loop} has not changed yes
+#   List of \code{RegrTask} which together formulate data necessary for the surrogate.
+#   Caching is done to not necessarily regenerate tasks if the \code{loop} has not changed yes
 #  @param models \code{list()} \cr
 #    List of \code{WrappedModel} which are trained on the \code{tasks} and formulate the surrogate.
 #    Caching is done as above.
@@ -22,7 +22,7 @@ NULL
 #    Pointer to the OptResult Object.
 #  @param state \code{character(1)} \cr
 #    Tells us in what state we are in text. So far we know:
-#    init - right after iniitilization of mbo
+#    init - right after initialization of mbo
 #    iter - within an iteration
 #    term.iter - maximal number of iterations reached
 #    term.time - maximal running time exceeded
@@ -31,13 +31,13 @@ NULL
 #    term.fevals - maximal number of function evaluations reached
 #    term.custom - terminated due to custom termination condition
 #  @param opt.path \code{OptPath} \cr
-#    Here we keep the opt.path. It delivers the data for the tasks and other usefull information.
+#    Here we keep the opt.path. It delivers the data for the tasks and other useful information.
 #  @param time.last.saved \code{POSIXct} \cr
 #    The \code{Sys.time()} when the last save on disk was done.
 #  @param loop.starttime \code{POSIXct} \cr
 #    The \code{Sys.time()} when the mbo iteration was started.
 #  @param time.used \code{integer(1)} \cr
-#    The time in seconds we are alrady used for optimization since the verry start.
+#    The time in seconds we are already used for optimization since the very start.
 #    This counts all iterations together and is necessary for continuation with a given time budget.
 
 # IMPORTANT NOTE:

R/doc_error_handling.R

@@ -37,7 +37,7 @@
 #'
 #'
 #' \strong{Mechanism II - The mlr's on.learner.error}
-#' This procdues a FailureModel for the surrogate, if it crashed (issue 6). A random point (or multiple ones)
+#' This produces a FailureModel for the surrogate, if it crashed (issue 6). A random point (or multiple ones)
 #' are proposed now for the current iteration. And we pray that we can fit the model again in the
 #' next iteration.
 #
@@ -46,7 +46,7 @@
 #'
 #' \strong{Mechanism III - Filtering of proposed point which are too close}
 #'
-#' Issue 6 is solved by filtering points that are to close to other porposed points or points already
+#' Issue 6 is solved by filtering points that are to close to other proposed points or points already
 #' proposed in preceding iterations. Filtering in this context means replacing the proposed points by
 #' a randomly generated new point. The heuristics mechanism is (de)activated via the logical
 #' \code{filter.proposed.points.tol} parameter of the \code{\link{setMBOControlInfill}} function, which defaults to

R/doc_mbo_parallel.R

@@ -1,7 +1,7 @@
 #' @title Parallelization in mlrMBO
 #'
 #' @description
-#' In mlrMBO you can parallize the tuning on two different levels to speed up computation:
+#' In mlrMBO you can parallelize the tuning on two different levels to speed up computation:
 #' \itemize{
 #'   \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)}

R/exampleRun.R

@@ -12,7 +12,7 @@
 #' Please note the following things:
 #' - The true objective function (and later everything which is predicted from our surrogate model)
 #'   is evaluated on a regular spaced grid. These evaluations are stored in the result object.
-#'   You can control the resultion of this grid via \code{points.per.dim}.
+#'   You can control the resolution of this grid via \code{points.per.dim}.
 #'   Parallelization of these evaluations is possible with the R package parallelMap on the level \code{mlrMBO.feval}.
 #' - In every iteration the fitted, approximating surrogate model is stored in the result object
 #'   (via \code{store.model.at} in \code{control}) so we can later visualize it quickly.

R/infill_crits.R

@@ -180,7 +180,7 @@ makeMBOInfillCritAEI = function(aei.use.nugget = FALSE, se.threshold = 1e-6) {
       }
       return(res)
     },
-    name = "Augmeted expected improvement",
+    name = "Augmented expected improvement",
     id = "aei",
     components = c("se", "mean", "tau"),
     params = list(aei.use.nugget = aei.use.nugget),

R/initCrit.R

@@ -6,7 +6,7 @@
 #'   To actually set these default values, this function is called, which returns a fully
 #'   initialized [\code{\link{MBOInfillCrit}}].
 #'   This function is mainly for internal use. If a custom infill criterion is created, it may be
-#'   required to create a seperate method \code{initCrit.InfillCritID} where \code{ID} is the
+#'   required to create a separate method \code{initCrit.InfillCritID} where \code{ID} is the
 #'   \code{id} of the custom \link{MBOInfillCrit}.
 #'
 #' @param crit [\code{\link{MBOInfillCrit}}]\cr

R/makeMBOControl.R

@@ -37,7 +37,7 @@
 #'   Default is \code{NULL} which means to stop if the objective function did not produce the desired
 #'   result.
 #' @param trafo.y.fun [\code{MBOTrafoFunction}]\cr
-#'   Sometimes it is favourable to transform the target function values before modelling. Provide a
+#'   Sometimes it is favorable to transform the target function values before modeling. Provide a
 #'   MBO transformation function to do so.
 #' @param suppress.eval.errors [\code{logical(1)}]\cr
 #'   Should reporting of error messages during target function evaluations be suppressed?

R/makeMBOLearner.R

@@ -42,7 +42,7 @@
 #'   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).
+#' Ding et.al. (2010).
 #'
 #' @references
 #' Ding, Yufeng, and Jeffrey S. Simonoff. An investigation of missing data methods for

R/plotExampleRun.R

@@ -45,7 +45,7 @@
 #'   and \code{yhat(x) +- se.factor2 * se(x)} both in the plot. Note that this heuristic might
 #'   change the \code{ylim} setting between plot iterations.
 #' @param point.size [\code{numeric(1)}]\cr
-#'   Point size for ploted points. Default ist 3.
+#'   Point size for plotted points. Default is 3.
 #' @param line.size [\code{numeric(1)}]\cr
 #'   Line width of the graphs of plotted functions.
 #' @param trafo [\code{list}]\cr
@@ -59,8 +59,8 @@
 #'   If a single function is provided, this function is used for all plots.
 #' @param colors [\code{character(3)}]
 #'   Specify colors for point in the plots. Must be a vector of length 3,
-#'   each element a color for the type design, prop and seq respectivly.
-#'   Default is red for the initial design, blue for allready proposed points
+#'   each element a color for the type design, prop and seq respectively.
+#'   Default is red for the initial design, blue for already proposed points
 #'   and green for the actual iteration.
 #' @param gg.objects [\code{list)}]
 #'   List of \code{gg} objects that should be added to all ggplots.

R/plot_MBOResult.R

@@ -1,7 +1,7 @@
 #' @title MBO Result Plotting
 #'
 #' @description
-#' Plots any MBO result objects. Plots for X-Space, Y-Space and any coloumn in
+#' Plots any MBO result objects. Plots for X-Space, Y-Space and any column in
 #' the optimization path are available. This function uses
 #' \code{\link[ParamHelpers]{plotOptPath}} from package \code{ParamHelpers}.
 #'

R/renderExampleRunPlot.R

@@ -41,7 +41,7 @@
 #'   and \code{yhat(x) +- se.factor2 * se(x)} both in the plot. Note that this heuristic might
 #'   change the \code{ylim} setting between plot iterations.
 #' @param point.size [\code{numeric(1)}]\cr
-#'   Point size for ploted points. Default ist 3.
+#'   Point size for plotted points. Default is 3.
 #' @param line.size [\code{numeric(1)}]\cr
 #'   Line width of the graphs of plotted functions.
 #' @param trafo [\code{list}]\cr
@@ -55,12 +55,12 @@
 #'   If a single function is provided, this function is used for all plots.
 #' @param colors [\code{character(3)}]
 #'   Specify colors for point in the plots. Must be a vector of length 3,
-#'   each element a color for the type design, prop and seq respectivly.
-#'   Default is red for the initial design, blue for allready proposed points
+#'   each element a color for the type design, prop and seq respectively.
+#'   Default is red for the initial design, blue for already proposed points
 #'   and green for the actual iteration.
 #' @param ... [any]\cr
 #'   Currently not used.
-#' @return [\code{list}]. List containing seperate ggplot object. The number of plots depends on
+#' @return [\code{list}]. List containing separate ggplot object. The number of plots depends on
 #'   the type of MBO problem. See the description for details.
 #' @export
 renderExampleRunPlot =  function(object, iter, densregion = TRUE,

R/setMBOControlInfill.R

@@ -39,7 +39,7 @@
 #'   to optimize infill criterion. If all CMA-ES runs fail, a random point is generated
 #'   instead and a warning informs about it.
 #'   \dQuote{ea}: Use an (mu+1) EA to optimize infill criterion.
-#'   \dQuote{nsga2}: NSGA2 for multi obj. optimizationen. Needed for mspot.
+#'   \dQuote{nsga2}: NSGA2 for multi obj. optimizations. Needed for mspot.
 #'   Default is \dQuote{focussearch}.
 #'   Alternatively, you may pass a function name as string.
 #' @param opt.restarts [\code{integer(1)}]\cr

R/setMBOControlMultiObj.R

@@ -9,7 +9,7 @@
 #'   Which multi-objective method should be used?
 #'   \dQuote{parego}: The ParEGO algorithm.
 #'   \dQuote{dib}: Direct indicator-based method. Subsumes SMS-EGO and epsilon-EGO.
-#'   \dQuote{mspot}: Directly optimizes multcrit problem where we substitute the true
+#'   \dQuote{mspot}: Directly optimizes multicrit problem where we substitute the true
 #'   objectives with model-based infill crits via an EMOA.
 #'   All methods can also propose multiple points in parallel.
 #'   Default is \dQuote{dib}.
@@ -40,8 +40,8 @@
 #'   Default is 1, if only 1 point is proposed each iteration, otherwise 5.
 #' @param parego.use.margin.points [\code{logical}]\cr
 #'   For each target function: Should the weight vector (0, ..., 0, 1, 0, ..., 0),
-#'   i.e. the weight vector with only 0 and a single 1 at the i.th position for
-#'   the i.th target function, be drawn with probability 1? Number of TRUE entries
+#'   i.e. the weight vector with only 0 and a single 1 at the i-th position for
+#'   the i-th target function, be drawn with probability 1? Number of TRUE entries
 #'   must be less or equal to \code{propose.points}
 #'   Default is not to do this.
 #' @param parego.normalize [\code{character}] \cr
@@ -53,7 +53,7 @@
 #' @param mspot.select.crit [\code{\link{MBOInfillCrit}}]\cr
 #'   Which infill.crit to use in the candidate selection. After the NSGA2
 #'   proposed a set of candidates, \dQuote{propose.points} are selected via
-#'   the hypervoume contribution of this infill.crit.
+#'   the hypervolume contribution of this infill.crit.
 #'   Possible values are \dQuote{crit.mr} and \dQuote{crit.cb} (or any other InfillCrit generated with \code{\link{makeMBOInfillCritCB}}), default is \dQuote{crit.mr}.
 #' @return [\code{\link{MBOControl}}].
 #'
@@ -89,7 +89,7 @@
 #' Jeong, S.; Obayashi, S.: Efficient global optimization (EGO) for Multi-Objective Problem and Data Mining.
 #' In: Proc. IEEE Congress on
 #' Evolutionary Computation (CEC 2005), Edinburgh, UK, Corne, D.;
-#' et al. (eds.), IEEE, 2005, ISBN 0-7803-9363-5, pp. 2138-2145
+#' et.al. (eds.), IEEE, 2005, ISBN 0-7803-9363-5, pp. 2138-2145
 #'
 #' @family MBOControl
 #' @export

R/setMBOControlMultiPoint.R

@@ -16,7 +16,7 @@
 #'   The selection criterion is \code{moimbo.selection}.
 #'   If this method is selected the infill criterion in \code{setMBOInfillCrit} is ignored.
 #'   \dQuote{cl}: Proposes points by constant liar strategy.
-#'   Only meaningfull if \code{infill.crit == "cb"}
+#'   Only meaningful if \code{infill.crit == "cb"}
 #'   In the first step the kriging model is fitted based on the real data and the best point is calculated
 #'   according to the regular EI-criterion.
 #'   Then, the function value of the best point is simply guessed by the worst seen function evaluation.
@@ -31,9 +31,9 @@
 #'   Default is \dQuote{ei.dist}.
 #' @param moimbo.dist [\code{character(1)}]\cr
 #'   Distance function used in multi-objective EA.
-#'   Possible values are: \dQuote{nearest.neigbor}, \dQuote{nearest.better}.
+#'   Possible values are: \dQuote{nearest.neighbor}, \dQuote{nearest.better}.
 #'   Default is \dQuote{nearest.better}.
-#FIXME: a link to the definition of nearest.better and nearest.neigbor?
+#FIXME: a link to the definition of nearest.better and nearest.neighbor?
 #' @param moimbo.selection [\code{character(1)}]\cr
 #'   Method used for selecting 1 element for removal from the population
 #'   in each iteration of the multi-objective EA.

R/zzz.R

@@ -27,7 +27,7 @@ NULL
 #' \item{crit.cb}{Confidence bound with lambda automatically chosen, see \code{\link{infillcrits}}}
 #' \item{crit.cb1}{Confidence bound with lambda=1}
 #' \item{crit.cb2}{Confidence bound with lambda=2}
-#' \item{crit.aei}{Augmeted expected improvement}
+#' \item{crit.aei}{Augmented expected improvement}
 #' \item{crit.eqi}{Expected quantile improvement}
 #' \item{crit.dib1}{Direct indicator-based with lambda=1}
 #' }

man-roxygen/arg_plot_MBO.R

@@ -1,14 +1,14 @@
 #' @param crit.plot [\code{logical(1)}]\cr
 #'   Should the infill criterion versus the iteration be plotted? For multi objective
-#'   and method = MSPOT multiple infill criterion values per iterion do exist,
+#'   and method = MSPOT multiple infill criterion values per iteration do exist,
 #'   so a multi-dimensional plot is used. Default is TRUE
 #' @param hv.plot [\code{logical(1)}]\cr
 #'   Should the hypervolume development be plotted? Default is FALSE for single
-#'   objective und TRUE for multi objective.
+#'   objective and TRUE for multi objective.
 #' @param extra.measures [\code{character}]\cr
 #'   Additional measures to be plotted versus the iteration. Must be the name
-#'   of a coloumn available in the optimization path. Notice that at most 4 plost
-#'   can be renderd at the same time. Since X and Y Space plots will allways be
+#'   of a column available in the optimization path. Notice that at most 4 plots
+#'   can be rendered at the same time. Since X and Y Space plots will always be
 #'   rendered, at most 2 addition plots are possible. You must deactivate crit.plot
 #'   or hv.plot, if you want to add additional plots via extra.measure.
 #'  @param ref.point [\code{numeric}]\cr