Create vector tile archives in 'PMTiles' format from 'sf' spatial
    data frames. Supports 'Mapbox Vector Tile' ('MVT') and 'MapLibre Tile'
    ('MLT') output formats. Uses a 'Rust' backend via 'extendr' for fast,
    in-memory tiling with zero external system dependencies.

Wrapper functions that interface with 'Freesurfer' 
    <https://surfer.nmr.mgh.harvard.edu/>, a powerful and 
    commonly-used 'neuroimaging'
    software, using system commands. The goal is to be able to interface with
    'Freesurfer' completely in R, where you pass R objects of class 'nifti',
    implemented by package 'oro.nifti', and the function executes an 'Freesurfer'
    command and returns an R object of class 'nifti' or necessary output.

Provides functions to read and write neuroimaging data in various file formats, with a focus on 'FreeSurfer' <http://freesurfer.net/> formats. This includes, but is not limited to, the following file formats: 1) MGH/MGZ format files, which can contain multi-dimensional images or other data. Typically they contain time-series of three-dimensional brain scans acquired by magnetic resonance imaging (MRI). They can also contain vertex-wise measures of surface morphometry data. The MGH format is named after the Massachusetts General Hospital, and the MGZ format is a compressed version of the same format. 2) 'FreeSurfer' morphometry data files in binary 'curv' format. These contain vertex-wise surface measures, i.e., one scalar value for each vertex of a brain surface mesh. These are typically values like the cortical thickness or brain surface area at each vertex. 3) Annotation file format. This contains a brain surface parcellation derived from a cortical atlas. 4) Surface file format. Contains a brain surface mesh, given by a list of vertices and a list of faces.

This tree-based method deals with high dimensional longitudinal 
	data with correlated features through the use of a piecewise random effect 
	model. FREE tree also exploits the network structure of the features, by 
	first clustering them using Weighted Gene Co-expression Network Analysis 
	('WGCNA'). It then conducts a screening step within each cluster of features 
	and a selecting step among the surviving features, which provides a relatively
	unbiased way to do feature selection. By using dominant principle components 
	as regression variables at each leaf and the original features as splitting 
	variables at splitting nodes, FREE tree delivers easily interpretable results
	while improving computational efficiency.