docs: prepare new CRAN release

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: contsurvplot
 Title: Visualize the Effect of a Continuous Variable on a Time-to-Event Outcome
-Version: 0.2.0.9000
+Version: 0.2.1
 Authors@R: 
     person("Robin", "Denz", , "robin.denz@rub.de", role = c("aut", "cre"))
 Maintainer: Robin Denz <robin.denz@rub.de>

NEWS.md

@@ -24,3 +24,9 @@ New Features:
 * Added support for plotting Kaplan-Meier curves as reference in `plot_surv_area`, `plot_surv_animated`, `plot_surv_lines`
 * Added the `monotonic` argument in the `plot_surv_area` function to allow plotting curved relationships
 * Vignette now includes `plot_surv_matrix` function
+
+# contsurvplot 0.2.1
+
+Documentation:
+
+* Updated citation information to include published manuscript

codemeta.json

@@ -2,13 +2,13 @@
   "@context": "https://doi.org/10.5063/schema/codemeta-2.0",
   "@type": "SoftwareSourceCode",
   "identifier": "contsurvplot",
-  "description": "Graphically display the (causal) effect of a continuous variable on a time-to-event outcome using multiple different types of plots based on g-computation. Those functions include, among others, survival area plots, survival contour plots, survival quantile plots and 3D surface plots. Due to the use of g-computation, all plot allow confounder-adjustment naturally. For details, see Robin Denz, Nina Timmesfeld (2022) <arXiv:2208.04644v1>.",
+  "description": "Graphically display the (causal) effect of a continuous variable on a time-to-event outcome using multiple different types of plots based on g-computation. Those functions include, among others, survival area plots, survival contour plots, survival quantile plots and 3D surface plots. Due to the use of g-computation, all plot allow confounder-adjustment naturally. For details, see Robin Denz, Nina Timmesfeld (2023) <doi:10.1097/EDE.0000000000001630>.",
   "name": "contsurvplot: Visualize the Effect of a Continuous Variable on a Time-to-Event Outcome",
   "relatedLink": "https://robindenz1.github.io/contsurvplot/",
   "codeRepository": "https://github.com/RobinDenz1/contsurvplot",
   "issueTracker": "https://github.com/RobinDenz1/contsurvplot/issues",
   "license": "https://spdx.org/licenses/GPL-3.0",
-  "version": "0.2.0",
+  "version": "0.2.1",
   "programmingLanguage": {
     "@type": "ComputerLanguage",
     "name": "R",
@@ -249,11 +249,11 @@
     },
     "SystemRequirements": null
   },
-  "fileSize": "12127.977KB",
+  "fileSize": "15256.628KB",
   "citation": [
     {
       "@type": "ScholarlyArticle",
-      "datePublished": "2022",
+      "datePublished": "2023",
       "author": [
         {
           "@type": "Person",
@@ -266,14 +266,16 @@
           "familyName": "Timmesfeld"
         }
       ],
-      "name": "Visualizing the Causal Effect of a Continuous Variable on a Time-To-Event Outcome",
-      "description": "R package version 0.2.0",
+      "name": "Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome",
+      "url": "https://doi.org/10.1097/EDE.0000000000001630",
       "isPartOf": {
         "@type": "PublicationIssue",
-        "datePublished": "2022",
+        "issueNumber": "5",
+        "datePublished": "2023",
         "isPartOf": {
           "@type": ["PublicationVolume", "Periodical"],
-          "name": "arXiv:2208.04644v1"
+          "volumeNumber": "34",
+          "name": "Epidemiology"
         }
       }
     }