Interactive shiny application for running Item Response Theory
    analysis. Provides graphics for characteristic and information curves.

The ISA is a biclustering algorithm that finds modules 
  in an input matrix. A module or bicluster is a block of the
  reordered input matrix.

We provide data sets used in the textbook "Introduction to Sports Analytics using R" by Elmore and Urbaczweski (2025).

These are data and functions to support quantitative peace science
    research. The data are important state-year information on democracy and
    wealth, which require periodic updates and regular maintenance. The
    functions permit some exploratory and diagnostic assessment of the kinds of
    data in demand by the community, but do not impose many dependencies on the
    user.

Reads the output of the 'PerkinElmer InForm' software <http://www.perkinelmer.com/product/inform-cell-analysis-one-seat-cls135781>. In addition to cell-density count, it can derive statistics of intercellular spatial distance for each cell-type.

ISA is a metadata framework to manage an increasingly diverse set 
    of life science, environmental and biomedical experiments. In isatabr 
    methods for reading, modifying and writing of files in the ISA-Tab format
    are implemented. It also contains methods for processing assay data.

The Inductive Subgroup Comparison Approach ('ISCA') offers a way to compare groups that are internally differentiated and heterogeneous. It starts by identifying the social structure of a reference group against which a minority or another group is to be compared, yielding empirical subgroups to which minority members are then matched based on how similar they are. The modelling of specific outcomes then occurs within specific subgroups in which majority and minority members are matched. 'ISCA' is characterized by its data-driven, probabilistic, and iterative approach and combines fuzzy clustering, Monte Carlo simulation, and regression analysis. ISCA_random_assignments() assigns subjects probabilistically to subgroups. ISCA_clustertable() provides summary statistics of each cluster across iterations. ISCA_modeling() provides Ordinary Least Squares regression results for each cluster across iterations. For further details please see Drouhot (2021) <doi:10.1086/712804>.