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README.Rmd
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---
output: github_document
---
<!-- TODO README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# ModelArray
<!-- badges: start -->
[](https://circleci.com/gh/PennLINC/ModelArray)
[](https://github.com/MShawon/github-clone-count-badge)
[](https://hub.docker.com/r/pennlinc/modelarray_confixel)
<!-- badges: end -->
`ModelArray` is an R package for statistical analysis of fixel-wise data and beyond. Its features include:
* Easy to use: set up your statistical analysis with just several lines of code;
* Supporting linear and nonlinear modeling, and extensible to more models:
* At present, `ModelArray` supports linear models as well as generalized additive models (GAMs) with and without penalized splines, which are particularly useful for studying nonlinear effects in lifespan data. `ModelArray` is also extensible to diverse models available in R;
* Scalable for large-scale datasets;
* Compatible with fixel-wise data, voxel-wise data, and greyordinate-wise data.
Please cite our [NeuroImage paper](https://doi.org/10.1016/j.neuroimage.2023.120037) if you use `ModelArray`:
> Zhao, C., Tapera, T. M., Bagautdinova, J., Bourque, J., Covitz, S., Gur, R. E., Gur, R. C., Larsen, B., Mehta, K., Meisler, S. L., Murtha, K., Muschelli, J., Roalf, D. R., Sydnor, V. J., Valcarcel, A. M., Shinohara, R. T., Cieslak, M. & Satterthwaite, T. D. (2023). ModelArray: an R package for statistical analysis of fixel-wise data. _NeuroImage_, _271_, 120037. https://doi.org/10.1016/j.neuroimage.2023.120037
## Overview
<center>
</center>
ModelArray is packaged with the companion software [ConFixel](https://github.com/PennLINC/ConFixel) for converting fixel-wise data, voxel-wise data or greyordinate-wise data to the expected file format that ModelArray uses. Specifically, [ConFixel](https://github.com/PennLINC/ConFixel) is Python-based command-line interface software, and it converts between the original image format (`.mif` for fixel-wise data, NIfTI for voxel-wise data, CIFTI-2 for greyordinate-wise data) and the HDF5 file format (`.h5`) used for ModelArray.
<!-- if there is any changes in this overview section, please also update ConFixel's frontpage! -->
## Installation
Please refer to webpage [Installation](https://pennlinc.github.io/ModelArray/articles/installations.html) for a full guidance of installation of `ModelArray` and its companion python package [ConFixel](https://github.com/PennLINC/ConFixel). The most important steps for installing `ModelArray` are:
* Make sure you have necessary libraries for HDF5 - see [this section](https://pennlinc.github.io/ModelArray/articles/installations.html#install-hdf5-libraries-in-the-system)
* Install `ModelArray` from GitHub - see [this section](https://pennlinc.github.io/ModelArray/articles/installations.html#install-modelarray-r-package-from-github)
Additionally, we also provide a [container image](https://hub.docker.com/r/pennlinc/modelarray_confixel) that includes `ModelArray` and `ConFixel`. With this container image, there is no need for the user to install `ModelArray`, `ConFixel`, and dependent R and Python packages. Please see [this webpage](https://pennlinc.github.io/ModelArray/articles/container.html) for how to use this container image.
<!-- check above links work, esp those with section titles!!! -->
## How to use
Load the `ModelArray` package into R via:
```{r example}
library(ModelArray)
```
We provide a walkthrough [here](https://pennlinc.github.io/ModelArray/articles/walkthrough.html) with example fixel-wise data. For additional notes on application to voxel-wise data, please refer to [here](https://pennlinc.github.io/ModelArray/articles/voxel-wise_data.html).
For documentation of `ModelArray` functions, you can:
* Either go to [this webpage](https://pennlinc.github.io/ModelArray/reference/index.html);
* Or in R console, type: `help(<function_name>)`. For example: `help(ModelArray.lm)`
Full documentation of `ModelArray` can be found [here](https://pennlinc.github.io/ModelArray/).
Source code of `ModelArray` can be found [here](https://github.com/PennLINC/ModelArray).
```{r, include = FALSE}
# 1. Confirm links above are up-to-date!!
# 2. You'll still need to render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this. You could also use GitHub Actions to re-render `README.Rmd` every time you push. An example workflow can be found here: <https://github.com/r-lib/actions/tree/master/examples>.
# You can also embed plots, for example:
# ```{r pressure, echo = FALSE}
# plot(pressure)
# ```
# In that case, don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN.
```