Performs augmented backward elimination and checks the stability of the obtained model. Augmented backward elimination combines significance or information based criteria with the change in estimate to either select the optimal model for prediction purposes or to serve as a tool to obtain a practically sound, highly interpretable model. More details can be found in Dunkler et al. (2014) <doi:10.1371/journal.pone.0113677>.

Detect several types of aberrant behavior, including answer
    copying, answer similarity, nonparametric misfit, parametric misfit,
    preknowledge, rapid guessing, and test tampering.

Extremely efficient toolkit for solving the best subset selection problem <https://www.jmlr.org/papers/v23/21-1060.html>. This package is its R interface. The package implements and generalizes algorithms designed in <doi:10.1073/pnas.2014241117> that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection <doi:10.1287/ijoc.2022.1241> and sure independence screening <doi:10.1111/j.1467-9868.2008.00674.x> are also provided.

Implements a Bayesian adaptive graphical lasso data-augmented block Gibbs sampler. The sampler simulates the posterior distribution of precision matrices of a Gaussian Graphical Model. This sampler was adapted from the original MATLAB routine proposed in Wang (2012) <doi:10.1214/12-BA729>.

Easy to use functions to visualize marker data
    from biparental populations. Useful for both analyzing and
    presenting genotypes in the ABH format.

Combine multidimensional arrays into a single array.
  This is a generalization of 'cbind' and 'rbind'.  Works with
  vectors, matrices, and higher-dimensional arrays (aka tensors).
  Also provides functions 'adrop', 'asub', and 'afill' for
  manipulating, extracting and replacing data in arrays.