update urls

Author: assignUser

README.Rmd

@@ -16,10 +16,10 @@ knitr::opts_chunk$set(
 <!-- badges: start -->
 [![R build status](https://github.com/kgoldfeld/simstudy/workflows/R-CMD-check/badge.svg?branch=main)](https://github.com/kgoldfeld/simstudy/actions){target="_blank"}
 [![CRAN status](https://www.r-pkg.org/badges/version/simstudy)](https://CRAN.R-project.org/package=simstudy){target="_blank"}
-[![status](https://joss.theoj.org/papers/640fd4333948933b2817343e86df3424/status.svg)](https://joss.theoj.org/papers/640fd4333948933b2817343e86df3424){target="_blank"}
+[![status](https://joss.theoj.org/papers/10.21105/joss.02763/status.svg)](https://joss.theoj.org/papers/10.21105/joss.02763){target="_blank"}
 [![CRAN downloads](https://cranlogs.r-pkg.org/badges/grand-total/simstudy)](https://CRAN.R-project.org/package=simstudy){target="_blank"}
-[![codecov](https://codecov.io/gh/kgoldfeld/simstudy/branch/main/graph/badge.svg)](https://codecov.io/gh/kgoldfeld/simstudy){target="_blank"}
-[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable){target="_blank"}
+[![codecov](https://app.codecov.io/gh/kgoldfeld/simstudy/branch/main/graph/badge.svg)](https://app.codecov.io/gh/kgoldfeld/simstudy){target="_blank"}
+[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html){target="_blank"}
 <!-- badges: end -->
 
 The `simstudy` package is a collection of functions that allow users to generate simulated data sets in order to explore modeling techniques or better understand data generating processes. The user defines the distributions of individual variables, specifies relationships between covariates and outcomes, and generates data based on these specifications. The final data sets can represent randomized control trials, repeated measure designs, cluster randomized trials, or naturally observed data processes. Other complexities that can be added include survival data, correlated data, factorial study designs, step wedge designs, and missing data processes.

README.md

@@ -9,12 +9,12 @@ simstudy
 status](https://github.com/kgoldfeld/simstudy/workflows/R-CMD-check/badge.svg?branch=main)](https://github.com/kgoldfeld/simstudy/actions)
 [![CRAN
 status](https://www.r-pkg.org/badges/version/simstudy)](https://CRAN.R-project.org/package=simstudy)
-[![status](https://joss.theoj.org/papers/640fd4333948933b2817343e86df3424/status.svg)](https://joss.theoj.org/papers/640fd4333948933b2817343e86df3424)
+[![status](https://joss.theoj.org/papers/10.21105/joss.02763/status.svg)](https://joss.theoj.org/papers/10.21105/joss.02763)
 [![CRAN
 downloads](https://cranlogs.r-pkg.org/badges/grand-total/simstudy)](https://CRAN.R-project.org/package=simstudy)
-[![codecov](https://codecov.io/gh/kgoldfeld/simstudy/branch/main/graph/badge.svg)](https://codecov.io/gh/kgoldfeld/simstudy)
+[![codecov](https://app.codecov.io/gh/kgoldfeld/simstudy/branch/main/graph/badge.svg)](https://app.codecov.io/gh/kgoldfeld/simstudy)
 [![Lifecycle:
-stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable)
+stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html)
 <!-- badges: end -->
 
 The `simstudy` package is a collection of functions that allow users to

vignettes/correlated.Rmd

@@ -101,7 +101,7 @@ dt[,round(sqrt(diag(var(cbind(a0, a1)))),1)]
 
 Two additional functions facilitate the generation of correlated data from *binomial*, *poisson*, *gamma*, and *uniform* distributions: `genCorGen` and `addCorGen`.
 
-`genCorGen` is an extension of `genCorData`. These functions draw on copula-based methods to generate the data. (This [Wikipedia page](https://bit.ly/31vAxFf) provides a general introduction and copula-based modeling can be conducted in `R` using package [copula](https://cran.r-project.org/web/packages/copula/index.html).) In the first example, we are generating data from a multivariate Poisson distribution. We start by specifying the mean of the Poisson distribution for each new variable, and then we specify the correlation structure, just as we did with the normal distribution.
+`genCorGen` is an extension of `genCorData`. These functions draw on copula-based methods to generate the data. (This [Wikipedia page](https://en.wikipedia.org/wiki/Copula_(probability_theory)) provides a general introduction and copula-based modeling can be conducted in `R` using package [copula](https://cran.r-project.org/package=copula).) In the first example, we are generating data from a multivariate Poisson distribution. We start by specifying the mean of the Poisson distribution for each new variable, and then we specify the correlation structure, just as we did with the normal distribution.
 
 ```{r}
 l <- c(8, 10, 12) # lambda for each new variable