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.

In some situations where researchers would like to demonstrate
    causal effects, it is hard to obtain a sample size that would allow for a
    well-powered randomized controlled trial. Single case designs are experimental
    designs that can be used to demonstrate causal effects with only one participant
    or with only a few participants. The 'scdtb' package provides a suite of tools
    for analyzing data from studies that use single case designs. The nap() function
    can be used to compute the nonoverlap of all pairs as outlined by the What
    Works Clearinghouse (2022) <https://ies.ed.gov/ncee/wwc/Handbooks>. The
    package also offers the mixed_model_analysis() and cross_lagged() functions
    which implement mixed effects models and cross lagged analyses as described
    in Maric & van der Werff (2020) <doi:10.4324/9780429273872-9>. The
    randomization_test() function implements randomization tests based on methods
    presented in Onghena (2020) <doi:10.4324/9780429273872-8>. The scdtb() 'shiny'
    application can be used to upload single case design data and access various
    'scdtb' tools for plotting and analysis.