Provides a fast and accurate pipeline for single-cell analyses. 
    The 'scDHA' software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) <DOI:10.1038/s41467-021-21312-2>).

Provides a set of tools for estimating hierarchical linear
    models and effect sizes based on data from single-case designs. 
    Functions are provided for calculating standardized mean difference effect sizes that 
    are directly comparable to standardized mean differences estimated from between-subjects randomized experiments,
    as described in Hedges, Pustejovsky, and Shadish (2012) <DOI:10.1002/jrsm.1052>; 
    Hedges, Pustejovsky, and Shadish (2013) <DOI:10.1002/jrsm.1086>; 
    Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>; 
    and Chen, Pustejovsky, Klingbeil, and Van Norman (2023) <DOI:10.1016/j.jsp.2023.02.002>. 
    Includes an interactive web interface.

Analysis tools to investigate changes in intercellular
    communication from scRNA-seq data. Using a Seurat object as input,
    the package infers which cell-cell interactions are present in the dataset
    and how these interactions change between two conditions of interest
    (e.g. young vs old). It relies on an internal database of ligand-receptor
    interactions (available for human, mouse and rat) that have been gathered
    from several published studies. Detection and differential analyses
    rely on permutation tests. The package also contains several tools
    to perform over-representation analysis and visualize the results. See
    Lagger, C. et al. (2023) <doi:10.1038/s43587-023-00514-x> for a full 
    description of the methodology.

Detection of item-wise Differential Item Functioning (DIF) 
             in fitted 'mirt', 'multipleGroup' or 'bfactor' models  
             using score-based structural change tests. Under the hood 
             the sctest() function from the 'strucchange' package is used.