---
title: "Advanced usage & future plans"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Advanced usage & future plans}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  eval = freesurfer::have_fs(),
  collapse = TRUE,
  comment = "#>"
)
options(freesurfer.verbose = FALSE)
```

```{r setup}
library(freesurfer)
```

## What's Next? Additional Features and Future Plans

For the initial release of `freesurfer`, we focused on implementing core functionalities.
However, not all Freesurfer output file types, such as annotation files and other surface-based files, were fully implemented for direct reading.
Rest assured, support for these file types is planned for future releases and may be integrated with existing functions to enhance their utility.
Additionally, while Freesurfer offers a suite of tools for Diffusion Tensor Imaging (DTI) data analysis, some of these functions have been adapted for `freesurfer`.
We are actively working on thoroughly testing and refining these DTI-related functions to ensure their robustness and reliability.

# Conclusion

The neuroimaging community has developed a vast array of powerful tools for image processing and analysis.
Many of these tools offer functionalities not natively present in R, such as Freesurfer's advanced surface-based registration and comprehensive processing pipelines.
By providing a seamless interface to these external tools, the `freesurfer` package effectively bridges this gap, much like how `fslr` integrates FSL functionalities.
This integration allows R users to leverage cutting-edge neuroimaging capabilities directly within their preferred statistical environment.

The increasing popularity of similar interfacing tools within the Python community, such as Nipype [@gorgolewski_nipype:_2011], further highlights the immense value of such integrations.
For many R users who may not have extensive experience with Python or bash scripting, `freesurfer` significantly lowers the barrier to entry for performing complex neuroimaging analyses.

Lowering this threshold is incredibly important because it empowers more R users to take full control of every aspect of their image analysis workflow.
This includes everything from raw image processing to the final statistical analysis.
Interfacing R with existing, robust software like Freesurfer provides R users with expanded functionality and access to a broader support community that would not be available if all these functions were rewritten from scratch in R.
While relying on an external software dependency might initially seem like a disadvantage, the integrated software benefits from years of rigorous testing and continuous development by dedicated communities.
Most importantly, because `freesurfer` is built entirely within the R framework, all the inherent benefits of using R are readily available to you.
This includes creating dynamic documents, developing interactive Shiny applications, generating customized figures, and employing state-of-the-art statistical methods.
This powerful synergy between the neuroimaging and R communities allows for comprehensive analyses within a single, unified framework, leveraging the strengths of both.
