In maxlinde/baymedr: Computation of Bayes Factors for Common Biomedical Designs

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baymedr: BAYesian inference for MEDical designs in R

baymedr is an R package with the goal of providing researchers with easy-to-use tools for the computation of Bayes factors for common biomedical research designs. Implemented are functions to test the equivalence (equiv_bf()), non-inferiority (infer_bf()), and superiority (super_bf()) of an experimental group (e.g., a new medication) compared to a control group (e.g., a placebo or an already existing medication) on a continuous dependent variable, as well as functions for simulating survival data (coxph_data_sim()) and calculating a Bayes factor for Cox proportional hazards models (coxph_bf()). A special focus of baymedr lies on a user-friendly interface, so that a wide variety or researchers (i.e., not only statisticians) can utilize baymedr for their analyses.

Installation and attaching

To install baymedr use:

``` {r, eval = FALSE} install.packages("baymedr")

You can install the latest development version of `baymedr` from [GitHub](https://github.com/), using the `devtools` package, with:

``` {r, eval = FALSE}
# install.packages("devtools")
devtools::install_github("maxlinde/baymedr")

Subsequently, you can load baymedr, so that it is ready to use:

{r, eval = FALSE} library(baymedr)

maxlinde/baymedr documentation built on Oct. 4, 2022, 6:27 a.m.