A collection of functions that enable easy access and updating of a database of data over time.
             More specifically, the package facilitates type-2 history for data-warehouses and provides a number
             of Quality of life improvements for working on SQL databases with R.
             For reference see Ralph Kimball and Margy Ross (2013, ISBN 9781118530801).

Streamlined workflow from deconvolution of bulk RNA-seq data to downstream differential expression and gene-set enrichment analysis. Provide various visualization functions.

Implements methods for obtaining kernel density estimates
    subject to a variety of shape constraints (unimodality, bimodality, 
    symmetry, tail monotonicity, bounds, and constraints on the number of 
    inflection points). Enforcing constraints can eliminate unwanted waves or 
    kinks in the estimate, which improves its subjective appearance and can 
    also improve statistical performance. The main function scdensity() is 
    very similar to the density() function in 'stats', allowing 
    shape-restricted estimates to be obtained with little effort. The 
    methods implemented in this package are described in Wolters and Braun 
    (2017) <doi:10.1080/03610918.2017.1288247>, Wolters (2012) 
    <doi:10.18637/jss.v047.i06>, and Hall and Huang (2002) 
    <http://www3.stat.sinica.edu.tw/statistica/j12n4/j12n41/j12n41.htm>.  
    See the scdensity() help for for full citations.

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.