This function predicts item response probabilities and item 
  responses using the item-focused tree model. The item-focused tree model
  combines logistic regression with recursive partitioning to detect 
  Differential Item Functioning in dichotomous items. The model applies 
  partitioning rules to the data, splitting it into homogeneous subgroups, and 
  uses logistic regression within each subgroup to explain the data. 
  Differential Item Functioning detection is achieved by examining potential 
  group differences in item response patterns. This method is useful for 
  understanding how different predictors, such as demographic or psychological 
  factors, influence item responses across subgroups.

Implements the "Smith-Pittman" community detection algorithm  
    for network analysis using 'igraph' objects. This algorithm combines node 
    degree and betweenness centrality measures to identify communities within 
    networks, with a gradient evident in social partitioning. The package 
    provides functions for community detection, visualization, and analysis of 
    the resulting community structure. Methods are based on results from Smith, 
    Pittman and Xu (2024) <doi:10.48550/arXiv.2411.01394>.

A dataset of the top colours of photos from Instagram 
 taken in 2014 in the city of Vancouver, British Columbia, Canada.
 It consists of: top colour and counts data. This data was
 obtained using the Instagram API. Instagram is a web photo 
 sharing service. It can be found at: <https://instagram.com>.
 The Instagram API is documented at: <https://instagram.com/developer/>.

Functions to calculate indices used to score immunoglobulin A (IgA) binding of bacteria in IgA sequencing (IgA-Seq) experiments.
  This includes the original Kau and Palm indices and more recent methods as described in Jackson et al. (2020) <doi:10.1101/2020.08.19.257501>.
  Additionally the package contains a function to simulate IgA-Seq data and an example experimental data set for method testing.