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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# bbr <a href='https:/metrumresearchgroup.github.io/bbr'><img src='man/figures/logo.png' align="right" height="120" /></a>
<!-- badges: start -->
[![Build Status](https://github.com/metrumresearchgroup/bbr/actions/workflows/main.yaml/badge.svg)](https://github.com/metrumresearchgroup/bbr/actions/workflows/main.yaml)
[![codecov](https://codecov.io/gh/metrumresearchgroup/bbr/branch/main/graph/badge.svg)](https://codecov.io/gh/metrumresearchgroup/bbr)
<!-- badges: end -->
`bbr` helps manage the entire modeling workflow from within R. Users can submit models, inspect output and diagnostics, and iterate on models. Furthermore, workflow tools---such as simple tagging of models and model inheritance trees---make reproducibility and external review more streamlined.
`bbr` supports running NONMEM models via the [bbi](https://github.com/metrumresearchgroup/bbi) command-line tool, with a focus on non-Bayesian methods. The [bbr.bayes](https://github.com/metrumresearchgroup/bbr.bayes) package extends `bbr` to enable Bayesian estimation through either NONMEM or [Stan](https://mc-stan.org/).
## Installation
You can install the latest released version of `bbr` via [MPN snapshots](https://mpn.metworx.com/docs/snapshots) from any snapshot date in 2021 or later. (An earlier version of this package was available under the name `rbabylon` in snapshot dates 2020-03-07 through 2020-12-21.)
You can also install development versions of `bbr` by downloading the source files for the latest version from `https://s3.amazonaws.com/mpn.metworx.dev/releases/bbr/` or get the latest development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("metrumresearchgroup/bbr", ref = "main")
```
## Documentation
You can find documentation and a "Getting Started" vignette that shows users how to set up `bbr` and demonstrates the basic modeling workflow [here](http://metrumresearchgroup.github.io/bbr/).
There are several other vignettes, and more are being added as new functionality is rolled out. A complete list can be found [here](https://metrumresearchgroup.github.io/bbr/articles/).
### Cheat Sheet
<a href="https://metrumresearchgroup.github.io/cheatsheets/bbr_nonmem_cheat_sheet.pdf"><img src="https://metrumresearchgroup.github.io/cheatsheets/thumbnails/bbr_nonmem_cheat_sheet_thumbnail.png" width="700" height="395"/></a>
### Featured Vignettes
* [Getting Started with bbr](https://metrumresearchgroup.github.io/bbr/articles/getting-started.html) -- Some basic scenarios for modeling with NONMEM using `bbr`, introducing you to its standard workflow and functionality.
* [Using the based_on field](https://metrumresearchgroup.github.io/bbr/articles/using-based-on.html) -- How to use the `based_on` field to track a model's ancestry through the model development process, as well how to leverage `config_log()` to check whether older models are still up-to-date.
* [Creating a Model Summary Log](https://metrumresearchgroup.github.io/bbr/articles/using-summary-log.html) -- How to use `summary_log()` to extract model diagnostics like the objective function value, condition number, and parameter counts.
## Development
`bbr` uses [pkgr](https://github.com/metrumresearchgroup/pkgr) to manage
development dependencies and [renv](https://rstudio.github.io/renv/) to provide
isolation. To replicate this environment,
1. clone the repo
2. install pkgr
3. open package in an R session and run `renv::init(bare = TRUE)`
- install `renv` > 0.8.3-4 into default `.libPaths()` if not already installed
3. run `pkgr install` in terminal within package directory
4. restart session
Then, launch R with the repo as the working directory (open the project in
RStudio). renv will activate and find the project library.