From a547222157b16f67edeb2bb4e6214b348dc24120 Mon Sep 17 00:00:00 2001 From: wlandau Date: Tue, 20 Apr 2021 15:36:27 -0400 Subject: [PATCH] Link to example project and bump version --- DESCRIPTION | 2 +- NEWS.md | 3 ++- README.Rmd | 2 +- README.md | 10 ++++++++-- inst/WORDLIST | 1 + vignettes/mcmc_rep.Rmd | 12 +++++++++++- 6 files changed, 24 insertions(+), 6 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 5a14a5c..05d7c36 100644 --- a/DESCRIPTION +++ b/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) . -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 diff --git a/NEWS.md b/NEWS.md index 722fb3b..4637791 100644 --- a/NEWS.md +++ b/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 diff --git a/README.Rmd b/README.Rmd index 61c2082..d0ecbc2 100644 --- a/README.Rmd +++ b/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 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 as a reference while constructing your own workflows. Visit for an example project based on . The example has an [RStudio Cloud workspace](https://rstudio.cloud/project/2466069) which allows you to run the project in a web browser. ## Installation diff --git a/README.md b/README.md index 9cadff3..bd8ad1c 100644 --- a/README.md +++ b/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 as a reference while -constructing your own workflows. +and use as a reference while +constructing your own workflows. Visit + for an +example project based on +. The +example has an [RStudio Cloud +workspace](https://rstudio.cloud/project/2466069) which allows you to +run the project in a web browser. ## Installation diff --git a/inst/WORDLIST b/inst/WORDLIST index bc4540f..5b2dfed 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -352,3 +352,4 @@ SBC stdout stderr HMC +mcmc diff --git a/vignettes/mcmc_rep.Rmd b/vignettes/mcmc_rep.Rmd index cf4b7da..f317dae 100644 --- a/vignettes/mcmc_rep.Rmd +++ b/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 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 {