Competence-based knowledge space theory (CbKST) is
  an extension of knowledge space theory (KST) modeling the latent 
  skills and competencies underlying the observable response 
  behaviour as described by Heller & Stefanutti (2024) 
  <doi:10.1142/9789811280481_0001>. The package focuses on the mappings
  between competence and performance level (skill (multi) map, 
  problem function etc.).

Methods for learning causal relationships among a set of
    foreground variables X based on signals from a (potentially much
    larger) set of background variables Z, which are known non-descendants
    of X. The confounder blanket learner (CBL) uses sparse regression
    techniques to simultaneously perform many conditional independence
    tests, with complementary pairs stability selection to guarantee
    finite sample error control. CBL is sound and complete with respect to
    a so-called "lazy oracle", and works with both linear and nonlinear
    systems. For details, see Watson & Silva (2022) <arXiv:2205.05715>.

A function that facilitates fitting three types of models 
    for contrast-based Bayesian Network Meta Analysis.  The first model is that which
    is described in Lu and Ades (2006) <doi:10.1198/016214505000001302>.  The other two 
    models are based on a Bayesian nonparametric methods that permit ties when comparing 
    treatment or for a treatment effect to be exactly equal to zero. In addition to the 
    model fits, the package provides a summary of the interplay  between treatment 
    effects based on the procedure described in Barrientos, Page, and Lin (2023)
    <doi:10.48550/arXiv.2207.06561>.

Provides correlation-based penalty estimators for both linear and logistic regression models by implementing a new regularization method that incorporates correlation structures within the data. This method encourages a grouping effect where strongly correlated predictors tend to be in or out of the model together. See Tutz and Ulbricht (2009) <doi:10.1007/s11222-008-9088-5> and Algamal and Lee (2015) <doi:10.1016/j.eswa.2015.08.016>.