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.

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.

A collection of statistical tests for genetic association studies and summary data based Mendelian randomization.

An implementation of the initial guided analytics for parameter testing and
    controlband extraction framework. Functions are available for continuous and 
    categorical target variables as well as for generating standardized reports of the
    conducted analysis. See <https://github.com/stefan-stein/igate> for more information
    on the technology.

Develops a General Equilibrium (GE) Model, which estimates key variables such as wages, the number of residents and workers, the prices of the floor space, and its distribution between commercial and residential use, as in Ahlfeldt et al., (2015) <doi:10.3982/ECTA10876>. By doing so, the model allows understanding the economic influence of different urban policies.

Compute distributional quantities for an
    Integrated Gamma (IG) or Integrated Gamma Limit (IGL) copula, such
    as a cdf and density. Compute corresponding conditional quantities
    such as the cdf and quantiles. Generate
    data from an IG or IGL copula. See the vignette for formulas,
    or for a derivation, see Coia, V (2017) "Forecasting of Nonlinear 
    Extreme Quantiles Using Copula Models." PhD Dissertation, 
    The University of British Columbia.