The stantargets R package

The stantargets R package: a workflow framework for efficient reproducible Stan-powered Bayesian data analysis pipelines - Published in JOSS (2021)

https://github.com/ropensci/stantargets

Science Score: 93.0%

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Published in Journal of Open Source Software

Keywords

bayesian high-performance-computing make r r-targetopia reproducibility rstats rstats-package stan statistics targets

Keywords from Contributors

drake makefile ropensci

Last synced: 4 months ago · JSON representation

Repository

Reproducible Bayesian data analysis pipelines with targets and cmdstanr

Basic Info

Host: GitHub
Owner: ropensci
License: other
Language: R
Default Branch: main
Homepage: https://docs.ropensci.org/stantargets
Size: 1.43 MB

Statistics

Stars: 50
Watchers: 7
Forks: 10
Open Issues: 3
Releases: 8

Topics

bayesian high-performance-computing make r r-targetopia reproducibility rstats rstats-package stan statistics targets

Created about 5 years ago · Last pushed 4 months ago

Metadata Files

Readme Changelog Contributing License Codemeta

README.Rmd

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
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)
```

# stantargets 

[![ropensci](https://badges.ropensci.org/430_status.svg)](https://github.com/ropensci/software-review/issues/430)
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[![R Targetopia](https://img.shields.io/badge/R_Targetopia-member-blue?style=flat&labelColor=gray)](https://wlandau.github.io/targetopia/)

[![active](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![check](https://github.com/ropensci/stantargets/workflows/check/badge.svg)](https://github.com/ropensci/stantargets/actions?query=workflow%3Acheck)
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[![lint](https://github.com/ropensci/stantargets/workflows/lint/badge.svg)](https://github.com/ropensci/stantargets/actions?query=workflow%3Alint)


Bayesian data analysis usually incurs long runtimes and cumbersome custom code, and the process of prototyping and deploying custom [Stan](https://mc-stan.org) models can become a daunting software engineering challenge. To ease this burden, the `stantargets` R package creates [Stan](https://mc-stan.org) pipelines that are concise, efficient, scalable, and tailored to the needs of Bayesian statisticians. Leveraging [`targets`](https://docs.ropensci.org/targets/), `stantargets` pipelines automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom user-side code is required, and there is no need to manually configure branching, so `stantargets` is easier to use than [`targets`](https://docs.ropensci.org/targets/) and [`CmdStanR`](https://mc-stan.org/cmdstanr/) directly. `stantargets` can access all of [`cmdstanr`](https://github.com/stan-dev/cmdstanr)'s major algorithms (MCMC, variational Bayes, and optimization) and it supports both single-fit workflows and multi-rep simulation studies.

## Prerequisites

1. The [prerequisites of the `targets` R package](https://docs.ropensci.org/targets/index.html#prerequisites).
1. Basic familiarity with [`targets`](https://docs.ropensci.org/targets/): watch minutes 7 through 40 of [this video](https://youtu.be/Gqn7Xn4d5NI?t=439), then read [this chapter](https://books.ropensci.org/targets/walkthrough.html) of the [user manual](https://books.ropensci.org/targets/).
1. Familiarity with Bayesian Statistics and [Stan](https://mc-stan.org/). Prior knowledge of [`cmdstanr`](https://mc-stan.org/cmdstanr/) helps.

## How to get started

Read the `stantargets` [introduction](https://docs.ropensci.org/stantargets/articles/introduction.html) and [simulation](https://docs.ropensci.org/stantargets/articles/simulation.html) vignettes, and use  as a reference while constructing your own workflows. Visit  for an example project based on the [simulation vignette](https://docs.ropensci.org/stantargets/articles/simulation.html). The example has an [RStudio Cloud workspace](https://rstudio.cloud/project/2466069) which allows you to run the project in a web browser.

## Example projects

Description | Link
---|---
Validating a minimal Stan model | 
Using Target Markdown and `stantargets` to validate a Bayesian longitudinal model for clinical trial data analysis | 

## Installation

Install the GitHub development version to access the latest features and patches.

```{r, eval = FALSE}
remotes::install_github("ropensci/stantargets")
```

The [CmdStan](https://github.com/stan-dev/cmdstan) command line interface is also required.

```{r, eval = FALSE}
cmdstanr::install_cmdstan()
```

If you have problems installing [CmdStan](https://github.com/stan-dev/cmdstan), please consult the [installation guide of `cmdstanr`](https://mc-stan.org/cmdstanr/articles/cmdstanr.html) and the [installation guide of CmdStan](https://mc-stan.org/docs/2_26/cmdstan-guide/cmdstan-installation.html). Alternatively, the [Stan discourse](https://discourse.mc-stan.org) is a friendly place to ask Stan experts for help.

## Usage

First, write a [`_targets.R` file](https://books.ropensci.org/targets/walkthrough.html) that loads your packages, defines a function to generate [Stan](https://mc-stan.org/) data, and lists a pipeline of targets. The target list can call target factories like [`tar_stan_mcmc()`](https://docs.ropensci.org/stantargets/reference/tar_stan_mcmc.html) as well as ordinary targets with [`tar_target()`](https://docs.ropensci.org/targets/reference/tar_target.html). The following minimal example is simple enough to contain entirely within the `_targets.R` file, but for larger projects, you may wish to store functions in separate files as in the [`targets-stan`](https://github.com/wlandau/targets-stan) example.

```{r, eval = FALSE}
# _targets.R
library(targets)
library(stantargets)

generate_data <- function() {
  true_beta <- stats::rnorm(n = 1, mean = 0, sd = 1)
  x <- seq(from = -1, to = 1, length.out = n)
  y <- stats::rnorm(n, x * true_beta, 1)
  list(n = n, x = x, y = y, true_beta = true_beta)
}

list(
  tar_stan_mcmc(
    name = example,
    stan_files = "x.stan",
    data = generate_data()
  )
)
```

Run [`tar_visnetwork()`](https://docs.ropensci.org/targets/reference/tar_visnetwork.html) to check `_targets.R` for correctness, then call [`tar_make()`](https://docs.ropensci.org/targets/reference/tar_make.html) to run the pipeline. Access the results using [`tar_read()`](https://docs.ropensci.org/targets/reference/tar_read.html), e.g. `tar_read(example_summary_x)`. Visit the [introductory vignette](https://docs.ropensci.org/stantargets/articles/introduction.html) to read more about this example.

## How it works behind the scenes

`stantargets` supports specialized [target factories](https://ropensci.org/blog/2021/02/03/targets/#target-factories) that create ensembles of [target objects](https://docs.ropensci.org/targets/reference/tar_target.html) for [`cmdstanr`](https://github.com/stan-dev/cmdstanr) workflows. These [target factories](https://ropensci.org/blog/2021/02/03/targets/#target-factories) abstract away the details of [`targets`](https://docs.ropensci.org/targets/) and [`cmdstanr`](https://github.com/stan-dev/cmdstanr) and make both packages easier to use. For details, please read the [introductory vignette](https://docs.ropensci.org/stantargets/articles/introduction.html).

## Help

Please first read the [help guide](https://books.ropensci.org/targets/help.html) to learn how best to ask for help.

If you have trouble using `stantargets`, you can ask for help in the [GitHub discussions forum](https://github.com/ropensci/stantargets/discussions/categories/help). Because the purpose of `stantargets` is to combine [`targets`](https://docs.ropensci.org/targets/) and [`cmdstanr`](https://github.com/stan-dev/cmdstanr), your issue may have something to do with one of the latter two packages, a [dependency of `targets`](https://github.com/ropensci/targets/blob/4e3ef2a6c986f558a25e544416f480fc01236b6b/DESCRIPTION#L49-L88), or [Stan](https://mc-stan.org) itself. When you troubleshoot, peel back as many layers as possible to isolate the problem. For example, if the issue comes from [`cmdstanr`](https://github.com/stan-dev/cmdstanr), create a [reproducible example](https://reprex.tidyverse.org) that directly invokes [`cmdstanr`](https://github.com/stan-dev/cmdstanr) without invoking `stantargets`. The GitHub discussion and issue forums of those packages, as well as the [Stan discourse](https://discourse.mc-stan.org), are great resources.

## Participation

Development is a community effort, and we welcome discussion and contribution. Please note that this package is released with a [Contributor Code of Conduct](https://ropensci.org/code-of-conduct/). By contributing to this project, you agree to abide by its terms.

## Citation

```{r, warning = FALSE}
citation("stantargets")
```

Owner

Name: rOpenSci
Login: ropensci
Kind: organization
Email: info@ropensci.org
Location: Berkeley, CA

Website: https://ropensci.org/
Twitter: rOpenSci
Repositories: 307
Profile: https://github.com/ropensci

JOSS Publication

The stantargets R package: a workflow framework for efficient reproducible Stan-powered Bayesian data analysis pipelines

Published

April 21, 2021

DOI

10.21105/joss.03193

Volume 6, Issue 60, Page 3193

Authors

William Michael Landau

Eli Lilly and Company

Editor

Kyle Niemeyer

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Last synced: 4 months ago

All Time

Total issues: 32
Total pull requests: 7
Average time to close issues: 20 days
Average time to close pull requests: 19 days
Total issue authors: 6
Total pull request authors: 4
Average comments per issue: 1.5
Average comments per pull request: 0.71
Merged pull requests: 5
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 2
Pull requests: 0
Average time to close issues: 2 days
Average time to close pull requests: N/A
Issue authors: 2
Pull request authors: 0
Average comments per issue: 2.0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

wlandau (24)
maelle (3)
stuvet (2)
adammwilson (1)
ihuff (1)
odaniel1 (1)

Pull Request Authors

wlandau (3)
maelle (2)
stuvet (2)
wlandau-lilly (1)

Top Labels

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type: new feature (11) order: later (4) type: potential direction (4) type: bug (2) type: maintenance (1) topic: style (1) topic: documentation (1) type: trouble (1)

Pull Request Labels

type: new feature (1)

Dependencies

DESCRIPTION cran

R >= 3.5.0 depends
cmdstanr >= 0.5.0 imports
digest >= 0.6.25 imports
fs >= 1.5.0 imports
fst >= 0.9.2 imports
posterior >= 1.0.1 imports
purrr >= 0.3.4 imports
qs >= 0.23.2 imports
rlang >= 0.4.10 imports
stats * imports
tarchetypes >= 0.6.0 imports
targets >= 0.12.0 imports
tibble >= 3.0.1 imports
withr >= 2.1.2 imports
R.utils >= 2.10.1 suggests
dplyr >= 1.0.2 suggests
knitr >= 1.30 suggests
rmarkdown >= 2.3 suggests
testthat >= 3.0.0 suggests
visNetwork >= 2.0.9 suggests

.github/workflows/check.yaml actions

actions/checkout v3 composite
r-lib/actions/check-r-package v2 composite
r-lib/actions/setup-pandoc v2.2.4 composite
r-lib/actions/setup-r v2.2.4 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/cover.yaml actions

actions/checkout v3 composite
r-lib/actions/setup-pandoc v1 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/lint.yaml actions

actions/checkout v3 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

The stantargets R package

Science Score: 93.0%

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README.Rmd

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JOSS Publication

The stantargets R package: a workflow framework for efficient reproducible Stan-powered Bayesian data analysis pipelines

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CodeMeta (codemeta.json)

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