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README.Rmd
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---
output:
github_document:
html_preview: false
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
options(tibble.print_min = 5, tibble.print_max = 5)
```
# baggr
<!-- badges: start -->
[](http://cran.r-project.org/package=baggr)
[](https://travis-ci.org/wwiecek/baggr)
[](https://codecov.io/gh/wwiecek/baggr)
[](https://cran.rstudio.com/web/packages/baggr/index.html)
<!-- badges: end -->
This is _baggr_, an [R package](https://www.r-project.org/) for Bayesian meta-analysis using [Stan](https://mc-stan.org/). _Baggr_ is intended to be user-friendly and transparent so that it's easier to understand the models you are building and criticise them.
_Baggr_ provides a suite of models that work with both summary data and full data sets, to synthesise evidence collected from different groups, contexts or time periods. The `baggr()` command automatically detects the data type and, by default, fits a partial pooling model (which you may know as [random effects models](https://stats.stackexchange.com/questions/4700/what-is-the-difference-between-fixed-effect-random-effect-and-mixed-effect-mode)) with weakly informative priors by calling [Stan](https://mc-stan.org/) to carry out Bayesian inference. Modelling of variances or quantiles, standardisation and transformation of data is also possible.
The current version (v0.5, June 2020) is a stable version of a tool that's in active development so we are counting on your feedback.
## Installation
Before starting, _baggr_ will not work if you don't have RStan, which is responsible for Bayesian inference in _baggr_. In that case, please follow [the installation instructions for RStan](https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started).
The package itself is available on CRAN:
```{r, eval = FALSE}
install.packages("baggr")
```
You can also install the most up-to-date version of `baggr` directly from GitHub; for this, you will need the `devtools` package.
```{r, eval = FALSE}
# compilation of models should take 5-10 minutes
devtools::install_github("wwiecek/baggr",
build_vignettes = TRUE,
build_opts = c("--no-resave-data", "--no-manual"))
```
## Basic use case
`baggr` is designed to work well with both individual-level ("full") and aggregate/summary ("group") data on treatment effect. In basic cases, only the summary information on treatment effects (such as means and their standard errors) is needed. Data are always specified in a single input data frame and the same `baggr()` function is used for different models.
For the "standard" cases of modelling means, the appropriate model is detected from the shape of data.
```{r, eval = FALSE}
library(baggr)
df_pooled <- data.frame("tau" = c(28,8,-3,7,-1,1,18,12),
"se" = c(15,10,16,11,9,11,10,18))
bg <- baggr(df_pooled, pooling = "partial")
```
You can specify the model type from several choices, the pooling type (`"none"`, `"partial"` or `"full"`), and certain aspects of the priors, as well as other options for data preparation, prediction and more. You can access the underlying `stanfit` object through `bg$fit`.
Flexible plotting methods are included, together with an automatic comparison of multiple models (e.g. comparing no, partial and full pooling) through `baggr_compare()` command. Various statistics can be calculated: in particular, `pooling()` for pooling metrics and `loocv()` for leave-one-group-out cross-validation, allowing us to then compare and select models via `loo_compare()`. Forest plots and plots of treatment effects are available.
Try `vignette('baggr')` for an overview of these functions and an example of meta-analysis workflow with `baggr`. If working with binary data, try `vignette("baggr_binary")`. Compiled vignettes are available [on CRAN](https://cran.r-project.org/web/packages/baggr/index.html).
## Current and future releases
Included in baggr v0.5 (June 2020):
* Meta-analysis and hierarchical models for continuous and binary outcomes
* Both full and aggregate data sets can be used
* Summaries and plots specific to meta-analysis, typical diagnostic plots
* Meta-regression / fixed effect modelling capabilities
* Compatibility with `rstan` and `bayesplot` features
* Automatic choice of priors or "plain-text" specification of priors
* Automatic calculation of pooling metrics
* Cross-validation (including leave-one-group-out)
* Prior and posterior predictive distributions
In the coming months (summer 2020) we will be including more models and more features such as:
* Modelling of quantiles and of variances
* Better modelling of log-normal data
* Automatic standardisation of variables
* More model diagnostics