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.

Fits semiparametric joint models for longitudinal electronic health record (EHR) data that addresses two-stage hierarchical missingness mechanism. The first stage is the visiting process, and the second stage is the observation process. The core CIMEHR method (Clinical Informative Missingness for Electronic Health Records) uses a three-stage procedure: partial likelihood with log-normal frailty for visit intensity, probit regression with shared latent factor-linked random effects for observation, and weighted least squares with risk-set centering for the outcome. These three stages are connected through a shared latent factor that induces dependence across all three processes. A data simulator and implementations of common benchmark methods (linear mixed models, multiple imputation, and others) are included for comparative studies. Detailed methods are described in Yang, Shi, and Mukherjee (2026) <doi:10.48550/arXiv.2602.15374>.