reference Brockhaus, Ruegamer, Greven, JSS in FDboost-package.R updated

Author: Almond-S

R/FDboost-package.R

@@ -14,7 +14,7 @@
 #' 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 
-#' in Brockhaus, Ruegamer and Greven (2017), see \url{https://arxiv.org/abs/1705.10662}.
+#' in Brockhaus, Ruegamer and Greven (2020), see \url{https://doi.org/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 
@@ -39,8 +39,9 @@
 #' 
 #' 
 #' @references 
-#' Brockhaus, S., Ruegamer, D. and Greven, S. (2017):
-#' Boosting Functional Regression Models with FDboost.
+#' Brockhaus, S., Ruegamer, D. and Greven, S. (2020):
+#' 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):