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.

Analyzes longitudinal Electronic Health Record (EHR) data with possibly informative observational time. These methods are grouped into two classes depending on the inferential task. One group focuses on estimating the effect of an exposure on a longitudinal biomarker while the other group assesses the impact of a longitudinal biomarker on time-to-diagnosis outcomes. The accompanying paper is Du et al (2024) <doi:10.48550/arXiv.2410.13113>.

Different methods to conduct causal inference for multiple treatments with a binary outcome, including regression adjustment, vector matching, Bayesian additive regression trees, targeted maximum likelihood and inverse probability of treatment weighting using different generalized propensity score models such as multinomial logistic regression, generalized boosted models and super learner. For more details, see the paper by Hu et al. <doi:10.1177/0962280220921909>.