Bain stands for Bayesian informative hypothesis evaluation. It computes Bayes factors for informative hypotheses in a wide variety of statistical models. Just run your analysis as usual, and then apply bain to the output. A tutorial is available at DOI:10.31234/osf.io/v3shc. A sequel with the focus on Structural Equation Models is available at https://doi.org/10.1080/10705511.2020.1745644.

Install the latest release version of `bain`

from CRAN:

```
install.packages("bain")
```

You can also install the latest development version of `bain`

from
GitHub. This requires a working toolchain, to compile the Fortran source
code. Step 3 in this
tutorial
explains how to set up the toolchain. Then, run:

```
install.packages("devtools")
devtools::install_github("cjvanlissa/bain")
```

Add bain to your existing R workflow, and obtain Bayes factors for your familiar R analyses! Bain is compatible with the pipe operator. Here is an example for testing an informative hypothesis about mean differences in an ANOVA:

```
# Load bain
library(bain)
# dplyr to access the %>% operator
library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.0.5
# Iris as example data
iris %>%
# Select outcome and predictor variables
select(Sepal.Length, Species) %>%
# Add -1 to the formula to estimate group means, as in ANOVA
lm(Sepal.Length ~ -1 + Species, .) %>%
bain("Speciessetosa < Speciesversicolor = Speciesvirginica;
Speciessetosa < Speciesversicolor < Speciesvirginica")
#> Bayesian informative hypothesis testing for an object of class lm (ANOVA):
#>
#> Fit Com BF.u BF.c PMPa PMPb PMPc
#> H1 0.000 0.224 0.000 0.000 0.000 0.000 0.000
#> H2 1.000 0.171 5.863 118577263118.841 1.000 0.854 1.000
#> Hu 0.146
#> Hc 0.000 0.829 0.000 0.000
#>
#> Hypotheses:
#> H1: Speciessetosa<Speciesversicolor=Speciesvirginica
#> H2: Speciessetosa<Speciesversicolor<Speciesvirginica
#>
#> Note: BF.u denotes the Bayes factor of the hypothesis at hand versus the unconstrained hypothesis Hu. BF.c denotes the Bayes factor of the hypothesis at hand versus its complement. PMPa contains the posterior model probabilities of the hypotheses specified. PMPb adds Hu, the unconstrained hypothesis. PMPc adds Hc, the complement of the union of the hypotheses specified.
```

Every user-facing function in the package is documented, and the
documentation can be accessed by running `?function_name`

in the R
console, e.g., `?bain`

.

Moreover, you can read the *Introduction to bain* vignette by running
`vignette("Introduction_to_bain", package = "bain")`

You can cite the R-package with the following citation:

Gu, X., Hoijtink, H., Mulder, J., & van Lissa, C. (2019). bain: Bayes factors for informative hypotheses. (Version 0.2.3) [R package]. https://CRAN.R-project.org/package=bain

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