Functions for the input/output and visualization of
    medical imaging data in the form of 'CIFTI' files 
    <https://www.nitrc.org/projects/cifti/>.

CIFTI files contain brain imaging data in "grayordinates," which 
    represent the gray matter as cortical surface vertices (left and right) and
    subcortical voxels (cerebellum, basal ganglia, and other deep gray matter). 
    'ciftiTools' provides a unified environment for reading, writing, 
    visualizing and manipulating CIFTI-format data. It supports the "dscalar," 
    "dlabel," and "dtseries" intents. Grayordinate data is read in as a "xifti" 
    object, which is structured for convenient access to the data and metadata,
    and includes support for surface geometry files to enable
    spatially-dependent functionality such as static or interactive 
    visualizations and smoothing.

Produces statistical indicators of the impact of migration on the socio-demographic
	composition of an area. Three measures can be used: ratios, percentages and the Duncan index
	of dissimilarity. The input data files are assumed to be in an  origin-destination matrix format,
	with each cell representing a flow count between an origin and a destination area. Columns are expected
	to represent origins, and rows are expected to represent destinations. The first row and column are
	assumed to contain labels for each area. See Rodriguez-Vignoli and Rowe (2018)
	<doi:10.1080/00324728.2017.1416155> for technical details.

Connect to the California Irrigation Management 
    Information System (CIMIS) Web API. See the CIMIS main page 
    <https://cimis.water.ca.gov> and web API documentation
    <https://et.water.ca.gov> for more information.

DNA methylation signatures are usually based on multivariate
    approaches that require hundreds of sites for predictions.  'CimpleG'
    is a method for the detection of small CpG methylation signatures used
    for cell-type classification and deconvolution.  'CimpleG' is time
    efficient and performs as well as top performing methods for cell-type
    classification of blood cells and other somatic cells, while basing
    its prediction on a single DNA methylation site per cell type (but
    users can also select more sites if they so wish).  Users can train
    cell type classifiers ('CimpleG' based, and others) and directly apply
    these in a deconvolution of cell mixes context.  Altogether, 'CimpleG'
    provides a complete computational framework for the delineation of
    DNAm signatures and cellular deconvolution.  For more details see Maié
    et al. (2023) <doi:10.1186/s13059-023-03000-0>.