Documented more srr standards

DESCRIPTION

@@ -29,7 +29,8 @@ Imports:
     R6 (>= 2.5.0),
     raster (>= 3.6),
     trend (>= 1.1.4),
-    truncnorm (>= 1.0)
+    truncnorm (>= 1.0),
+    qs
 Collate: 
     'GenericClass.R'
     'Region.R'

R/DispersalFriction.R

@@ -1,5 +1,7 @@
 #' R6 class representing a dispersal friction.
-#'
+#' @srrstats {G4.0} WHen it writes to disk, this function writes .RData files 
+#' only and does not accept any other specification of file type/suffix.
+#' 
 #' @description
 #' \code{\link[R6:R6Class]{R6}} class functionality for modeling sea, ice and other
 #' frictional barriers to dispersal within a spatially-explicit population model. The

R/GenericManager.R

@@ -1,5 +1,7 @@
 #' R6 class representing a generic manager.
-#'
+#' @srrstats {G4.0} When a results file suffix is not provided, this class 
+#' defaults to .RData.
+#' 
 #' @description
 #' \code{\link[R6:R6Class]{R6}} class to represent a generic (abstract) manager for
 #' generating or processing simulation results, as well as optionally generating values

R/ResultsManager.R

@@ -1,4 +1,6 @@
 #' R6 class representing a results manager.
+#' @srrstats {G4.0} When a results file suffix is not provided, this class
+#' fills it in with .RData.
 #'
 #' @description
 #' \code{\link[R6:R6Class]{R6}} class to represent a manager for generating summary

R/SimulationManager.R

@@ -1,4 +1,6 @@
 #' R6 class representing a simulation manager.
+#' @srrstats {G4.0} When a results file suffix is not provided, this class
+#' fills it in with .RData.
 #'
 #' @description
 #' \code{\link[R6:R6Class]{R6}} class to represent a manager for running multiple model

R/Validator.R

@@ -1,6 +1,8 @@
 #' R6 class representing a pattern-oriented validator.
 #'
 #' @srrstats {G1.3} We clearly define here what pattern-oriented validation is.
+#' @srrstats {G4.0} When validation diagnostics are written to disk, they are
+#' always written as .pdf files. No alternative file suffix is accepted.
 #' 
 #' @description
 #' \code{\link[R6:R6Class]{R6}} class for pattern-oriented validation and simulation

README.Rmd

@@ -22,7 +22,7 @@ knitr::opts_chunk$set(
 #' @srrstats {G1.3} Here we define the statistical term "pattern-oriented modeling."
 #' @srrstats {G1.4} All documentation of functions and classes is done with roxygen2 tags. 
 #' There are no non-exported functions or classes.
-#' 
+#' @srrstats {SP2.1} `poems` uses `sf` and not `sp`.
 ```
 
 # poems: Pattern-oriented ensemble modeling system (for spatially explicit populations) <img src='man/figures/logo.svg' align="right" height="125" />

README.md

@@ -6,6 +6,7 @@
 <!-- badges: start -->
 
 [![Paper_doi](https://img.shields.io/badge/doi-10.1111/2041--210X.13720-orange.svg)](https://doi.org/10.1111/2041-210X.13720)
+[![CRAN_version](https://www.r-pkg.org/badges/version/poems)](https://cran.r-project.org/package=poems)
 [![Codecov test
 coverage](https://codecov.io/gh/GlobalEcologyLab/poems/branch/main/graph/badge.svg)](https://app.codecov.io/gh/GlobalEcologyLab/poems?branch=main)
 [![Last
@@ -110,47 +111,47 @@ results <- population_simulator(pop_model)
 results # examine
 #> $all
 #> $all$abundance
-#> [1] 1031 1226 1249 1409 1358
+#> [1]  977 1090 1238 1256 1341
 #> 
 #> $all$abundance_stages
 #> $all$abundance_stages[[1]]
-#> [1] 603 748 724 868 789
+#> [1] 574 643 755 731 847
 #> 
 #> $all$abundance_stages[[2]]
-#> [1] 428 478 525 541 569
+#> [1] 403 447 483 525 494
 #> 
 #> 
 #> 
 #> $abundance
 #>      [,1] [,2] [,3] [,4] [,5]
-#> [1,]   55  118  191  202  186
-#> [2,]  105  159  171  204  187
-#> [3,]  114  157  164  183  163
-#> [4,]  172  205  195  219  223
-#> [5,]  177  198  183  213  216
-#> [6,]  204  197  197  197  194
-#> [7,]  204  192  148  191  189
+#> [1,]   59  134  157  147  173
+#> [2,]   90  102  157  169  197
+#> [3,]  133  156  192  202  191
+#> [4,]  168  149  154  180  168
+#> [5,]  163  180  178  193  192
+#> [6,]  171  181  192  189  219
+#> [7,]  193  188  208  176  201
 #> 
 #> $abundance_stages
 #> $abundance_stages[[1]]
 #>      [,1] [,2] [,3] [,4] [,5]
-#> [1,]   31   74  111  128  111
-#> [2,]   65   94   95  121  104
-#> [3,]   68  104   95  133   81
-#> [4,]   97  113  118  120  142
-#> [5,]  100  134  106  125  118
-#> [6,]  120  116  110  122  114
-#> [7,]  122  113   89  119  119
+#> [1,]   28   76  100   83  108
+#> [2,]   55   56  103   94  133
+#> [3,]   83   91  108  121  115
+#> [4,]  101   96   82  129   86
+#> [5,]   95  109  113  102  141
+#> [6,]  103  105  115   93  132
+#> [7,]  109  110  134  109  132
 #> 
 #> $abundance_stages[[2]]
 #>      [,1] [,2] [,3] [,4] [,5]
-#> [1,]   24   44   80   74   75
-#> [2,]   40   65   76   83   83
-#> [3,]   46   53   69   50   82
-#> [4,]   75   92   77   99   81
-#> [5,]   77   64   77   88   98
-#> [6,]   84   81   87   75   80
-#> [7,]   82   79   59   72   70
+#> [1,]   31   58   57   64   65
+#> [2,]   35   46   54   75   64
+#> [3,]   50   65   84   81   76
+#> [4,]   67   53   72   51   82
+#> [5,]   68   71   65   91   51
+#> [6,]   68   76   77   96   87
+#> [7,]   84   78   74   67   69
 raster::plot(region$raster_from_values(results$abundance[, 5]),
   main = "Final abundance", xlab = "Longitude (degrees)",
   ylab = "Latitude (degrees)", zlim = c(0, 300), colNA = "blue"