Merge pull request #184 from pepaaran/master

Amends after CRAN feedback

DESCRIPTION

@@ -6,7 +6,7 @@ Authors@R: c(
               family = "Benjamin",
               given = "Stocker",
               email = "[email protected]",
-              comment = c(ORCID = "0000-0002-5070-8109"),
+              comment = c(ORCID = "0000-0003-2697-9096"),
               role = c("aut", "cre")),
             person(
                family = "Hufkens",
@@ -25,7 +25,7 @@ Authors@R: c(
            )
 Description: Implements the Simulating Optimal FUNctioning framework for 
               site-scale simulations of ecosystem processes, including model 
-              calibration. It contains Fortran modules for the P-model (Stocker
+              calibration. It contains 'Fortran 90' modules for the P-model (Stocker
               et al. (2020) <doi:10.5194/gmd-13-1545-2020>), SPLASH
               (Davis et al. (2017) <doi:10.5194/gmd-10-689-2017>)
               and BiomeE (Weng et al. (2015)

NEWS.md

@@ -11,6 +11,7 @@
 * documentation parameter sensitivity analysis
 * documentation on the data format
 * document calibration diagnostic and model uncertainty calculation
+* more transparent licensing and copyright statements in COPYING file
 
 # rsofun v4.3
 

R/init_dates_dataframe.R

@@ -27,7 +27,7 @@
 #' @export
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #'  ddf <- init_dates_dataframe( 2000, 2003, startmoy=1, startdoy=1,
 #'   freq="days", endmoy=12, enddom=31, noleap=FALSE )
 #' }

R/runread_biomee_f.R

@@ -3,7 +3,7 @@
 #' Runs the BiomeE model and loads output in once.
 #'
 #' @param drivers A nested data frame with one row for each site and columns
-#'  named according to thearguments of function `runread_biomee_f_bysite()`
+#'  named according to the arguments of function `runread_biomee_f_bysite()`
 #' @param makecheck A logical specifying whether checks are performed to verify
 #'  forcings.
 #' @param parallel A logical specifying whether simulations are to be 
@@ -12,17 +12,18 @@
 #' @param ncores An integer specifying the number of cores used for parallel 
 #' computing. Defaults to 2.
 #'
-#' @return A tibble with one row for each site and outputs stored 
-#' in the nested column \code{data}.
+#' @return A tibble with one row for each site and model outputs stored 
+#' in the nested column \code{data}. See `run_biomee_f_bysite()` for a
+#' description of the BiomeE output variables.
 #' @export
 #' 
 #' @examples 
-#' \dontrun{ \donttest{
+#' \donttest{
 #' # Example BiomeE model run
 #' 
 #' mod_output <- runread_biomee_f(
 #'   drivers = biomee_gs_leuning_drivers)
-#' }}
+#' }
 
 runread_biomee_f <- function(
   drivers,

analysis/paper_results.R

@@ -228,8 +228,8 @@ par_calib <- calib_sofun(
 
 toc() # took 1555.601 sec to run
 
-# # Save result
-# saveRDS(par_calib, file = "analysis/calibration_exploration_files/par_calib_12000iterations.rsd")
+# Save result
+saveRDS(par_calib, file = "analysis/calibration_exploration_files/par_calib_12000iterations.rda")
 
 # Define functions for plotting (re-use BayesianTools hidden code)
 getSetup <- function(x) {
@@ -362,6 +362,12 @@ plot_gpp_error <- ggplot(data = pmodel_runs |>
                              # Merge GPP validation data (first year)
                              p_model_validation$data[[1]][1:365, ] |>
                                dplyr::rename(gpp_obs = gpp),
+                             by = "date") |>
+                           dplyr::left_join(
+                             # Merge GPP before calibration
+                             output$data[[1]][1:365, ] |>
+                               dplyr::select(date, gpp) |>
+                               dplyr::rename(gpp_no_calib = gpp),
                              by = "date")
                          ) +             # Plot only first year
   geom_ribbon(
@@ -374,7 +380,15 @@ plot_gpp_error <- ggplot(data = pmodel_runs |>
         ymax = gpp_q95,
         x = date,
         fill = "Parameter uncertainty")) +
-
+
+  # geom_line(
+  #   aes(
+  #     date,
+  #     gpp_no_calib,
+  #     color = "Non-calibrated predictions"
+  #   ), 
+  #   lty = 2
+  # ) +
   geom_line(
     aes(
       date,
@@ -401,9 +415,11 @@ plot_gpp_error <- ggplot(data = pmodel_runs |>
 plot_gpp_error +  
   scale_color_manual(name = "",
                      breaks = c("Observations",
-                                "Predictions"),
+                                "Predictions",
+                                "Non-calibrated predictions"),
                      values = c(t_col("black", 10),
-                                t_col("#009E73", 10))) +
+                                t_col("#E69F00", 10),
+                                t_col("#56B4E9", 10))) +
   scale_fill_manual(name = "",
                      breaks = c("Model uncertainty",
                                 "Parameter uncertainty"),

cran-comments.md

@@ -1,6 +1,20 @@
 Dear CRAN team,
 
-This is the re-submission of the {rsofun} package. We have addressed your concerns about the DOIs in the description.
+This is the re-submission of the {rsofun} package. We have addressed the following
+concerns voiced by Benjamin Altman:
+
+- Corrected the software naming in package title and description.
+- Added a \value field to all the exported functions, explaining the object returned
+  by each function.
+- Removed \dontrun statements in the documentation examples, now \donttest
+  is used for examples that have long runtimes.
+- Included license and copyright statements in a COPYING file. The original authors
+  of the software borrowed for this package were contacted and permissions to use
+  parts of their code were discussed, as well as the copyright and license that
+  accompany their code. The direct authors of this package are listed in the
+  DESCRIPTION file.
+
+---
 
 The {rsofun} package provides the implementation of a modelling framework for site-scale simulations of ecosystem processes, with low level routines in Fortran 90. It contains the following models:
 - P-model for leaf-level acclimation of photosynthesis from Stocker et al. (2019).