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index.Rmd
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---
output: github_document
---
<!-- badges: start -->
[](https://doi.org/10.5281/zenodo.2616951)
[](https://travis-ci.org/kkmann/adoptr)
[](https://codecov.io/gh/kkmann/adoptr?branch=master)
[](https://cran.r-project.org/package=adoptr)
[](https://cran.r-project.org/package=adoptr)
[](https://cran.r-project.org/package=adoptr)
<!-- badges: end -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/index-",
out.width = "100%"
)
```
# adoptr <a href='https://github.com/kkmann/adoptr'><img src='man/figures/logo.png' align="right" height="139" /></a>
The **adoptr** package enables exploring optimal adaptive two-stage designs for
clinical trials with one or two arms.
Currently, only normally distributed test statistics with known variance are
supported.
## Installation
**adoptr** is avilable on CRAN and the most recent release ca be installed using
`install.packages("adoptr")`.
Alternatively, the current development version can be installed directly from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("kkmann/adoptr")
```
## Validation
We provide an extensive validation suite for **adoptr** in the separate
package [**adoptrValidation**](https://github.com/kkmann/adoptrValidation).
The rationale behind externalizing the validation suit is to keep the main
package test suit lean and focused on checking technical correctness.
We also want to ensure that the validation suit is transparent and accessible.
It is thus implemented as a set of vignettes in the separate package
**adoptrValidation**.
The entire validation report is made accessible at
[https://kkmann.github.io/adoptrValidation/](https://kkmann.github.io/adoptrValidation/).
The website is re-build on a weekly basis to ensure that the presented
validation report is up-to-date with the master branch of **adoptr**.
To validate a specific version of **adoptr**, just download and install
the validation package an build the contained vignettes locally.
## References
A theoretical investigation of optimal two-stage designs is described in:
Pilz M, Kunzmann K, Herrmann C, Rauch G, Kieser M. A variational approach to
optimal two-stage designs. Statistics in Medicine. 2019;38(21):4159–4171.
https://doi.org/10.1002/sim.8291