fixing CRAN notes

Author: tokami

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: TropFishR
 Type: Package
 Title: Tropical Fisheries Analysis
-Version: 1.6.4
-Date: 2024-01-29
+Version: 1.6.5
+Date: 2024-10-21
 Author: Tobias K. Mildenberger, Marc H. Taylor, Matthias Wolff
 Maintainer: Tobias K. Mildenberger <[email protected]>
 Depends:
@@ -31,7 +31,7 @@ NeedsCompilation: no
 BugReports: https://github.com/tokami/TropFishR/issues
 URL: https://github.com/tokami/TropFishR
 Encoding: UTF-8
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Suggests:
     graphics,
     grDevices,

R/growth_length_age.R

@@ -58,7 +58,7 @@
 #' to be omitted. Non-linear least squares fitting is the preferred method to estimate
 #' growth parameters according to Sparre and Venema (1998). If \code{CI = TRUE} the
 #' confidence interval of parameters is calculated and plotted. For plotting the
-#' confidence interval the \code{\link{predictNLS}} from the \link{propagate} package
+#' confidence interval the \code{\link[propagate]{predictNLS}} from the propagate package
 #' is applied.
 #'
 #' @return A list with the input parameters and following parameters:

R/ypr_sel.R

@@ -19,35 +19,27 @@
 #' @keywords function prediction ypr
 #'
 #'
-#' @details The Thompson and Bell model incorporates an iteration step simulating the
-#'    stock by means
-#'    of the \code{\link{stock_sim}} function. In case changes in gear
-#'    characteristics -
-#'    here measured in terms of Lc or tc, the length or age at first capture,
-#'    respectively -
-#'    should be explored, a list with selectivity information about the gear has
-#'    to be provided and
-#'    the prediction models make use of the selectivity \code{\link{select_ogive}}
-#'    function.
-#'    Sparre and Venema (1998) recommend to treat the last length class always as plus group.
-#'    This model is very sensitive to zero observations in the ultimate length
-#'    classes. If unrealistic results are returned,
-#'    it is recommended to cut length classes with zero observations, group
-#'    them in a plus group or to change the interval between
-#'    length classes.
-#'    Equations which are used in this function assume isometric growth, an
-#'    assumption often not met. Further, the assumption that there is no relationship
-#'    between the parental stock size and progeny
-#'    over a wide range of fishing mortalities or exploitation values,
-#'    respectively, is also said to be untrue. By default, the functions assume
-#'    knife-edge recruitment and selection of gears (Sparre and Venema, 1998).
-#'
-#'
-#' @return A list with the input parameters and dependent on the model type following
-#'    list objects:
-#' \itemize{
-#'   \item
-#' }
+#' @details The Thompson and Bell model incorporates an iteration step
+#'     simulating the stock by means of the \code{\link{stock_sim}} function. In
+#'     case changes in gear characteristics - here measured in terms of Lc or
+#'     tc, the length or age at first capture, respectively - should be
+#'     explored, a list with selectivity information about the gear has to be
+#'     provided and the prediction models make use of the selectivity
+#'     \code{\link{select_ogive}} function. Sparre and Venema (1998) recommend
+#'     to treat the last length class always as plus group. This model is very
+#'     sensitive to zero observations in the ultimate length classes. If
+#'     unrealistic results are returned, it is recommended to cut length classes
+#'     with zero observations, group them in a plus group or to change the
+#'     interval between length classes. Equations which are used in this
+#'     function assume isometric growth, an assumption often not met. Further,
+#'     the assumption that there is no relationship between the parental stock
+#'     size and progeny over a wide range of fishing mortalities or exploitation
+#'     values, respectively, is also said to be untrue. By default, the
+#'     functions assume knife-edge recruitment and selection of gears (Sparre
+#'     and Venema, 1998).
+#'
+#'
+#' @return A list with the input parameters and dependent on the model type.
 #'
 #'
 #' @references
@@ -217,5 +209,3 @@ ypr_sel <- function(param, FM_change, Lt, P){
 
   return(ret)
 }
-
-