Merge pull request #12 from MindTheGap-ERC/dev

Dev

R/apply_niche.R

@@ -11,6 +11,8 @@ apply_niche = function(x, niche_def, gc){
     #' Models niches by removing events (fossil occurrences) when they are outside of their niche using the function `thin`.
     #' Combines the functions `niche_def` and `gc` ("gradient change") to determine how the taxons' collection probability changes with time/position. This is done by composing `niche_def` and `gc`. The result is then used as a thinning on the events `x`.
   #'
+  #' @returns numeric vector, events preserved after thinning
+  #'
   #' @examples
     #' \dontrun{
     #' ## setup

R/apply_taphonomy.R

@@ -13,7 +13,15 @@ apply_taphonomy = function(x, pres_potential, ctc){
     #'
   #' @seealso [apply_niche()] for modeling niche preferences based on the same principle, [thin()] for the underlying mathematical procedure.
   #'
+  #' @returns numeric vector, events preserved after taphonomic effects are applied
   #'
+  #' @examples
+    #' \dontrun{
+    #' # see
+    #' vignette("advanced_functionality")
+    #' # details on usage
+    #' }
+    #'
 
   # function that returns preservation potential as a function of input (e.g. time or position)
   change_pres_pot = function(y) pres_potential(ctc(y))

R/p3_var_rate.R

@@ -35,6 +35,8 @@ p3_var_rate = function(x, y = NULL, from = 0, to = 1, f_max = 1, n = NULL){
     #' }
     #'
   #' @seealso [p3()] for the constant rate implementation, [rej_samp()] for the underlying random number generation.
+  #'
+  #' @returns numeric vector, timing/location of events
 
   if (from >= to){
     stop("\"from\" must be smaller than \"to\".")

R/rej_samp.R

@@ -19,6 +19,8 @@ rej_samp = function(f, x_min, x_max, n = 1L, f_max = 1){
     #' hist(x) # note that no samples are drawn where sin is negative
     #' }
   #' @seealso [p3_var_rate()] for the derived variable rate Poisson point process implementation.
+  #'
+  #' @returns numeric vector, samples drawn from the (pseudo) pdf
   x = c()
   warn = FALSE
   if (f_max <= 0) {stop("`f_max` must be positive.")}

R/thin.R

@@ -23,6 +23,8 @@ thin = function(x, thin){
     #' }
     #'
   #' @seealso [apply_niche()] and [apply_taphonomy()] for use cases with biological meaning
+  #'
+  #' @returns numeric vector, events after thinning
 
   p = pmax(pmin(thin(x), rep(1, length(x))), rep(0, length(x))) # cut off at 0 and 1
   ind = stats::rbinom(n = length(x), size = 1, prob = p) # determine if preserved or not