The Ensemble Quadratic and Affine Invariant Markov chain Monte 
    Carlo algorithms provide an efficient way to perform Bayesian inference in 
    difficult parameter space geometries. The Ensemble Quadratic Monte Carlo 
    algorithm was developed by Militzer (2023) <doi:10.3847/1538-4357/ace1f1>. 
    The Ensemble Affine Invariant algorithm was developed by Goodman and Weare 
    (2010) <doi:10.2140/camcos.2010.5.65> and it was implemented in Python by 
    Foreman-Mackey et al (2013) <doi:10.48550/arXiv.1202.3665>. The Quadratic 
    Monte Carlo method was shown to perform better than the Affine Invariant 
    method in the paper by Militzer (2023) <doi:10.3847/1538-4357/ace1f1> and 
    the Quadratic Monte Carlo method is the default method used. The Chen-Shao 
    Highest Posterior Density Estimation algorithm is used for obtaining 
    credible intervals and the potential scale reduction factor diagnostic is 
    used for checking the convergence of the chains.

A tool for automatic generation of sibling items from a parent item model defined by
           the user. It is an implementation of the process automatic item generation (AIG) focused
           on generating quantitative multiple-choice type of items (see Embretson, Kingston
           (2018) <doi:10.1111/jedm.12166>).

Implements heuristics for the Quadratic Assignment Problem (QAP). Although, the QAP was introduced as a combinatorial optimization problem for the facility location problem in operations research, it also has many applications in data analysis. The problem is NP-hard and the package implements a simulated annealing heuristic.

Estimates QAPE using bootstrap procedures. The residual, parametric and double bootstrap is used. The test of normality using Cholesky decomposition is added. Y pop is defined.

Calculates the right-tail probability of quadratic forms of Gaussian variables using the skewness-kurtosis ratio matching method, modified Liu-Tang-Zhang method and Satterthwaite-Welch method. The technical details can be found in Hong Zhang, Judong Shen and Zheyang Wu (2020) <arXiv:2005.00905>.