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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
# mimosa <img src="sticker.png" width = "150" align="right" />
The `mimosa` package trains and makes predictions from the MIMoSA method. Access to the full papers can be found [here](https://onlinelibrary.wiley.com/doi/abs/10.1111/jon.12506) and [here](https://doi.org/10.1016/j.nicl.2018.10.013). Additionally, it allows for implementation of some common segmentation metrics such as true positive rate, false positive rate, false negative rate, false positive count, and sensitivity based on lesion count.
## Installation
To install the package from Neuroconductor, type:
```{r, eval = FALSE}
source("https://neuroconductor.org/neurocLite.R")
neuro_install("mimosa")
```
To get the latest development version from GitHub:
```{r, eval = FALSE}
devtools::install_github('avalcarcel9/mimosa')
```
avalcarcel9 badges:
[](https://travis-ci.org/avalcarcel9/mimosa)
<!-- [](https://ci.appveyor.com/project/avalcarcel9/mimosa) -->
[](https://coveralls.io/r/avalcarcel9/mimosa?branch=master)
muschellij2 badges:
[](https://travis-ci.org/muschellij2/mimosa)
<!-- [](https://ci.appveyor.com/project/muschellij2/mimosa) -->
[](https://coveralls.io/r/muschellij2/mimosa?branch=master)
## Package Documentation
Check out our `pkgdown` site [here](https://avalcarcel9.github.io/mimosa/).
Full functions and documentation references are available [here](https://avalcarcel9.github.io/mimosa/reference/index.html).
## Vignette
For a full implementation of the methods with output please see our [vignette](https://avalcarcel9.github.io/mimosa/articles/mimosa.html).