Merge pull request #39 from m-muecke/pkg-docs

docs: remove trailing whitespace in package docs

Author: davidruegamer

R/FDboost-package.R

@@ -1,87 +1,87 @@
 #################################################################################
 #' FDboost: Boosting Functional Regression Models
-#' 
-#' @description 
-#' Regression models for functional data, i.e., scalar-on-function, 
-#' function-on-scalar and function-on-function regression models, are fitted 
+#'
+#' @description
+#' Regression models for functional data, i.e., scalar-on-function,
+#' function-on-scalar and function-on-function regression models, are fitted
 #' by a component-wise gradient boosting algorithm.
-#' 
-#' @details 
-#' This package is intended to fit regression models with functional variables. 
-#' It is possible to fit models with functional response and/or functional covariates, 
-#' resulting in scalar-on-function, function-on-scalar and function-on-function regression. 
+#'
+#' @details
+#' This package is intended to fit regression models with functional variables.
+#' It is possible to fit models with functional response and/or functional covariates,
+#' resulting in scalar-on-function, function-on-scalar and function-on-function regression.
 #' Furthermore, the package can be used to fit density-on-scalar regression models.
 #' Details on the functional regression models that can be fitted with \pkg{FDboost}
-#' can be found in Brockhaus et al. (2015, 2017, 2018) and Ruegamer et al. (2018).  
-#' A hands-on tutorial for the package can be found 
+#' can be found in Brockhaus et al. (2015, 2017, 2018) and Ruegamer et al. (2018).
+#' A hands-on tutorial for the package can be found
 #' in Brockhaus, Ruegamer and Greven (2020), see <doi:10.18637/jss.v094.i10>.
 #' For density-on-scalar regression models see Maier et al. (2021).
-#' 
-#' Using component-wise gradient boosting as fitting procedure, \pkg{FDboost} relies on 
-#' the R package \pkg{mboost} (Hothorn et al., 2017). 
-#' A comprehensive tutorial to \pkg{mboost} is given in Hofner et al. (2014). 
-#' 
-#' The main fitting function is \code{\link{FDboost}}. 
-#' The model complexity is controlled by the number of boosting iterations (mstop).   
-#' Like the fitting procedures in \pkg{mboost}, the function \code{FDboost} DOES NOT 
-#' select an appropriate stopping iteration. This must be chosen by the user.  
-#' The user can determine an adequate stopping iteration by resampling methods like 
-#' cross-validation or bootstrap. 
-#' This can be done using the function \code{\link{applyFolds}}. 
-#' 
-#' Aside from common effect surface plots, tensor product factorization via the 
-#' function \code{\link{factorize}} presents an alternative tool for visualization 
-#' of estimated effects for non-linear function-on-scalar models 
-#' (Stoecker, Steyer and Greven (2022), \url{https://arxiv.org/abs/2109.02624}). 
-#' After factorization, effects are decomposed multiple scalar effects into 
-#' functional main effect directions, which can be separately plotted allowing to 
-#' visualize more complex effect structures.  
-#' 
-#' 
-#' @references 
+#'
+#' Using component-wise gradient boosting as fitting procedure, \pkg{FDboost} relies on
+#' the R package \pkg{mboost} (Hothorn et al., 2017).
+#' A comprehensive tutorial to \pkg{mboost} is given in Hofner et al. (2014).
+#'
+#' The main fitting function is \code{\link{FDboost}}.
+#' The model complexity is controlled by the number of boosting iterations (mstop).
+#' Like the fitting procedures in \pkg{mboost}, the function \code{FDboost} DOES NOT
+#' select an appropriate stopping iteration. This must be chosen by the user.
+#' The user can determine an adequate stopping iteration by resampling methods like
+#' cross-validation or bootstrap.
+#' This can be done using the function \code{\link{applyFolds}}.
+#'
+#' Aside from common effect surface plots, tensor product factorization via the
+#' function \code{\link{factorize}} presents an alternative tool for visualization
+#' of estimated effects for non-linear function-on-scalar models
+#' (Stoecker, Steyer and Greven (2022), \url{https://arxiv.org/abs/2109.02624}).
+#' After factorization, effects are decomposed multiple scalar effects into
+#' functional main effect directions, which can be separately plotted allowing to
+#' visualize more complex effect structures.
+#'
+#'
+#' @references
 #' Brockhaus, S., Ruegamer, D. and Greven, S. (2020):
-#' Boosting Functional Regression Models with FDboost. 
+#' Boosting Functional Regression Models with FDboost.
 #' Journal of Statistical Software, 94(10), 1–50.
 #' <doi:10.18637/jss.v094.i10>
-#' 
-#' Brockhaus, S., Scheipl, F., Hothorn, T. and Greven, S. (2015): 
-#' The functional linear array model. Statistical Modelling, 15(3), 279-300. 
-#' 
-#' Brockhaus, S., Melcher, M., Leisch, F. and Greven, S. (2017): 
-#' Boosting flexible functional regression models with a high number of functional historical effects,  
-#' Statistics and Computing, 27(4), 913-926.   
-#' 
-#' Brockhaus, S., Fuest, A., Mayr, A. and Greven, S. (2018): 
-#' Signal regression models for location, scale and shape with an application to stock returns. 
+#'
+#' Brockhaus, S., Scheipl, F., Hothorn, T. and Greven, S. (2015):
+#' The functional linear array model. Statistical Modelling, 15(3), 279-300.
+#'
+#' Brockhaus, S., Melcher, M., Leisch, F. and Greven, S. (2017):
+#' Boosting flexible functional regression models with a high number of functional historical effects,
+#' Statistics and Computing, 27(4), 913-926.
+#'
+#' Brockhaus, S., Fuest, A., Mayr, A. and Greven, S. (2018):
+#' Signal regression models for location, scale and shape with an application to stock returns.
 #' Journal of the Royal Statistical Society: Series C (Applied Statistics), 67, 665-686.
-#' 
-#' Hothorn T., Buehlmann P., Kneib T., Schmid M., and Hofner B. (2017). mboost: Model-Based Boosting, 
+#'
+#' Hothorn T., Buehlmann P., Kneib T., Schmid M., and Hofner B. (2017). mboost: Model-Based Boosting,
 #' R package version 2.8-1, \url{https://cran.r-project.org/package=mboost}
-#' 
-#' Hofner, B., Mayr, A., Robinzonov, N., Schmid, M. (2014). Model-based Boosting in R: 
-#' A Hands-on Tutorial Using the R Package mboost. Computational Statistics, 29, 3-35. 
+#'
+#' Hofner, B., Mayr, A., Robinzonov, N., Schmid, M. (2014). Model-based Boosting in R:
+#' A Hands-on Tutorial Using the R Package mboost. Computational Statistics, 29, 3-35.
 #' \url{https://cran.r-project.org/package=mboost/vignettes/mboost_tutorial.pdf}
-#' 
+#'
 #' Maier, E.-M., Stoecker, A., Fitzenberger, B., Greven, S. (2021):
 #' Additive Density-on-Scalar Regression in Bayes Hilbert Spaces with an Application to Gender Economics.
 #' arXiv preprint arXiv:2110.11771.
-#' 
-#' Ruegamer D., Brockhaus, S., Gentsch K., Scherer, K., Greven, S. (2018). 
-#' Boosting factor-specific functional historical models for the detection of synchronization in bioelectrical signals. 
+#'
+#' Ruegamer D., Brockhaus, S., Gentsch K., Scherer, K., Greven, S. (2018).
+#' Boosting factor-specific functional historical models for the detection of synchronization in bioelectrical signals.
 #' Journal of the Royal Statistical Society: Series C (Applied Statistics), 67, 621-642.
-#' 
+#'
 #' Stoecker A., Steyer L., Greven S. (2022):
 #' Functional Additive Models on Manifolds of Planar Shapes and Forms.
 #' arXiv preprint arXiv:2109.02624.
-#' 
-#' @author 
+#'
+#' @author
 #' Sarah Brockhaus, David Ruegamer and Almond Stoecker
-#' 
+#'
 #' @aliases FDboost_package package-FDboost FDboost-package
-#' 
-#' @seealso 
-#' \code{\link{FDboost}} for the main fitting function and 
-#' \code{\link{applyFolds}} for model tuning via resampling methods. 
-#' 
+#'
+#' @seealso
+#' \code{\link{FDboost}} for the main fitting function and
+#' \code{\link{applyFolds}} for model tuning via resampling methods.
+#'
 "_PACKAGE"
-#> [1] "_PACKAGE"
+