Link to example project and bump version

DESCRIPTION

@@ -14,7 +14,7 @@ Description: Bayesian data analysis usually incurs long runtimes
   both single-fit workflows and multi-rep simulation studies.
   For the statistical methodology, please refer to 'Stan' documentation
   (Stan Development Team 2020) <https://mc-stan.org/>.
-Version: 0.0.0.9003
+Version: 0.0.1
 License: MIT + file LICENSE
 URL: https://docs.ropensci.org/stantargets/, https://github.com/ropensci/stantargets
 BugReports: https://github.com/ropensci/stantargets/issues

NEWS.md

@@ -1,4 +1,4 @@
-# stantargets 0.0.0.9003
+# stantargets 0.0.1
 
 * Skip tests if CmdStan is not installed (@sakrejda).
 * Use custom `generate_data()` function in the docs, as opposed to `tar_stan_example_data()` directly (@sakrejda).
@@ -12,6 +12,7 @@
 * Use `@family` go cross-reference functions (@mattwarkentin).
 * Elaborate on the roles and return values of specific targets generated by target factories (@mattwarkentin).
 * Undergo rOpenSci peer review and transition to rOpenSci.
+* Link to an example project.
 
 # stantargets 0.0.0.9002
 

README.Rmd

@@ -37,7 +37,7 @@ The `stantargets` R package is an extension to [`targets`](https://docs.ropensci
 
 ## How to get started
 
-Read the `stantargets` tutorial vignettes [here](https://docs.ropensci.org/stantargets/articles/mcmc.html) and [here](https://docs.ropensci.org/stantargets/articles/mcmc_rep.html), then use <https://docs.ropensci.org/stantargets/> as a reference while constructing your own workflows.
+Read the `stantargets` tutorial vignettes [here](https://docs.ropensci.org/stantargets/articles/mcmc.html) and [here](https://docs.ropensci.org/stantargets/articles/mcmc_rep.html), and use <https://docs.ropensci.org/stantargets/> as a reference while constructing your own workflows. Visit <https://github.com/wlandau/stantargets-example-validation> for an example project based on <https://docs.ropensci.org/stantargets/articles/mcmc_rep.html>. The example has an [RStudio Cloud workspace](https://rstudio.cloud/project/2466069) which allows you to run the project in a web browser.
 
 ## Installation
 

README.md

@@ -59,8 +59,14 @@ pipelines](https://docs.ropensci.org/stantargets/articles/mcmc.html).
 Read the `stantargets` tutorial vignettes
 [here](https://docs.ropensci.org/stantargets/articles/mcmc.html) and
 [here](https://docs.ropensci.org/stantargets/articles/mcmc_rep.html),
-then use <https://docs.ropensci.org/stantargets/> as a reference while
-constructing your own workflows.
+and use <https://docs.ropensci.org/stantargets/> as a reference while
+constructing your own workflows. Visit
+<https://github.com/wlandau/stantargets-example-validation> for an
+example project based on
+<https://docs.ropensci.org/stantargets/articles/mcmc_rep.html>. The
+example has an [RStudio Cloud
+workspace](https://rstudio.cloud/project/2466069) which allows you to
+run the project in a web browser.
 
 ## Installation
 

inst/WORDLIST

@@ -352,3 +352,4 @@ SBC
 stdout
 stderr
 HMC
+mcmc

vignettes/mcmc_rep.Rmd

@@ -30,12 +30,22 @@ if (identical(Sys.getenv("IN_PKGDOWN"), "true")) {
 }
 ```
 
+## Background
+
 It is sometimes desirable to run one or more Bayesian models repeatedly across multiple simulated datasets. Examples:
 
 1. Validate the implementation of a Bayesian model using simulation.
 2. Simulate a randomized controlled experiment to explore frequentist properties such as power and Type I error.
 
-This vignette focuses on (1). The goal of this particular example to simulate multiple datasets from the model below, analyze each dataset, and assess how often the estimated posterior intervals cover the true parameters from the prior predictive simulations. The quantile method by @cook2006 generalizes this concept, and simulation-based calibration [@talts2020] generalizes further. The interval-based technique featured in this vignette is not as robust as SBC, but it may be more expedient for large models because it does not require visual inspection of multiple histograms.
+This vignette focuses on (1).
+
+## Example project
+
+Visit <https://github.com/wlandau/stantargets-example-validation> for an example project based on this vignette. The example has an [RStudio Cloud workspace](https://rstudio.cloud/project/2466069) which allows you to run the project in a web browser.
+
+## Simulation-based validation study
+
+The goal of this particular example to simulate multiple datasets from the model below, analyze each dataset, and assess how often the estimated posterior intervals cover the true parameters from the prior predictive simulations. The quantile method by @cook2006 generalizes this concept, and simulation-based calibration [@talts2020] generalizes further. The interval-based technique featured in this vignette is not as robust as SBC, but it may be more expedient for large models because it does not require visual inspection of multiple histograms.
 
 ```{r}
 lines <- "data {