From f2f40f571c4f5b7314d085aa100cbd086baef40c Mon Sep 17 00:00:00 2001 From: mihaiconstantin Date: Tue, 10 Aug 2021 13:40:17 +0200 Subject: [PATCH] Build: fix broken URLs --- R/exports.R | 2 +- R/powerly-package.R | 2 +- README.md | 20 ++++++++++---------- inst/CITATION | 2 +- 4 files changed, 13 insertions(+), 13 deletions(-) diff --git a/R/exports.R b/R/exports.R index f3098ae..a935759 100644 --- a/R/exports.R +++ b/R/exports.R @@ -116,7 +116,7 @@ #' #' @details #' This function represents the implementation of the method introduced by -#' [Constantin et al. (2021)](https://arxiv.org/abs/) for performing a priori +#' [Constantin et al. (2021)](https://arxiv.org) for performing a priori #' sample size analysis in the context of network models. The method 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 diff --git a/R/powerly-package.R b/R/powerly-package.R index 7d52067..1d803b2 100644 --- a/R/powerly-package.R +++ b/R/powerly-package.R @@ -20,7 +20,7 @@ #' #' @description #' `powerly` is a package that implements the method by [Constantin et al. -#' (2021)](https://arxiv.org/abs/) for conducting sample size analysis for +#' (2021)](https://arxiv.org) for conducting sample size analysis for #' network models. #' #' @details diff --git a/README.md b/README.md index c29f9da..d0b97db 100644 --- a/README.md +++ b/README.md @@ -11,16 +11,16 @@ ## Description `powerly` is an `R` package that implements the method by [Constantin, -Schuurman, & Vermunt (2021)](https://) 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 +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 poster](https://github.com/mihaiconstantin/powerly#poster). --- diff --git a/inst/CITATION b/inst/CITATION index 7bff54d..939c314 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -6,6 +6,6 @@ citEntry( author = personList(as.person("Mihai A. Constantin"), as.person("Noemi N. K. Schuurman"), as.person("Jeroen K. Vermunt")), journal = "Journal", year = "2021", - url = "https://arxiv.org/abs/", + 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." )