Docs: update citation information and mentions

R/exports.R

@@ -192,8 +192,9 @@
 #' }}
 #'
 #' @references
-#' Constantin et al. (2021). A General Monte Carlo Method for Sample Size
-#' Analysis in the Context of Network Models.
+#' Constantin, M. A., Schuurman, N. K., & Vermunt, J. (2021). A General Monte
+#' Carlo Method for Sample Size Analysis in the Context of Network Models.
+#' PsyArXiv. [https://doi.org/10.31234/osf.io/j5v7u](https://doi.org/10.31234/osf.io/j5v7u)
 #'
 #' @examples
 #' \dontrun{

README.md

@@ -15,17 +15,17 @@
 
 ## Description
 
-`powerly` is an `R` package that implements the method by [Constantin,
-Schuurman, & Vermunt (2021)](https://arxiv.org) for conducting sample size
-analysis for cross-sectional network models. The method implemented is
-implemented in the main function `powerly()`. The implementation takes the form
-of a three-step recursive algorithm designed to find an optimal sample size
-value given a model specification and an outcome measure of interest. It starts
-with a Monte Carlo simulation step for computing the outcome at various sample
-sizes. It continues with a monotone curve-fitting step for interpolating the
-outcome. The final step employs stratified bootstrapping to quantify the
-uncertainty around the fitted curve. For more details on how the method works,
-check the manuscript linked above. Moreover, consult the [method
+`powerly` is an `R` package that implements the method by [Constantin et al.
+(2021)](https://psyarxiv.com/j5v7u) for conducting sample size analysis for
+cross-sectional network models. The method implemented is implemented in the
+main function `powerly()`. The implementation takes the form of a three-step
+recursive algorithm designed to find an optimal sample size value given a model
+specification and an outcome measure of interest. It starts with a Monte Carlo
+simulation step for computing the outcome at various sample sizes. It continues
+with a monotone curve-fitting step for interpolating the outcome. The final step
+employs stratified bootstrapping to quantify the uncertainty around the fitted
+curve. For more details on how the method works, check the manuscript linked
+above. Moreover, consult the [method
 poster](https://github.com/mihaiconstantin/powerly#poster).
 
 ---
@@ -155,5 +155,5 @@ plot(validation)
 
 The code in this repository is licensed under the [MIT license](https://opensource.org/licenses/MIT).
 
-To cite `powerly` please use:
-- Constantin, M. A., Schuurman, N. K., & Vermunt, K. (2021). A General Monte Carlo Method for Sample Size Analysis in the Context of Network Models.
+To use `powerly` please cite:
+- Constantin, M. A., Schuurman, N. K., & Vermunt, J. (2021). A General Monte Carlo Method for Sample Size Analysis in the Context of Network Models. PsyArXiv. [https://doi.org/10.31234/osf.io/j5v7u](https://doi.org/10.31234/osf.io/j5v7u)

inst/CITATION

@@ -3,9 +3,9 @@ citHeader("To cite powerly in publications please use:")
 citEntry(
   entry    = "Article",
   title    = "A General Monte Carlo Method for Sample Size Analysis in the Context of Network Models",
-  author   = personList(as.person("Mihai A. Constantin"), as.person("Noemi N. K. Schuurman"), as.person("Jeroen K. Vermunt")),
+  author   = personList(as.person("Mihai A. Constantin"), as.person("Noemi K. Schuurman"), as.person("Jeroen K. Vermunt")),
   journal  = "Journal",
   year     = "2021",
-  url      = "https://arxiv.org",
-  textVersion = "Constantin, M. A., Schuurman, N. K., & Vermunt, K. (2021). A General Monte Carlo Method for Sample Size Analysis in the Context of Network Models."
+  url      = "https://doi.org/10.31234/osf.io/j5v7u",
+  textVersion = "Constantin, M. A., Schuurman, N. K., & Vermunt, J. (2021). A General Monte Carlo Method for Sample Size Analysis in the Context of Network Models. PsyArXiv. https://doi.org/10.31234/osf.io/j5v7u"
 )