Juan C. Laria
2 packages on CRAN
Implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models. We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 <doi:10.1080/10618600.2012.657139> for references on the Joint Trained Elastic-Net.
Implements the Merged Tree-CAT method (Javier Rodriguez-Cuadrado et al., 2020, <doi:10.1016/j.eswa.2019.113066>) to generate Computerized Adaptive Tests (CATs) based on a decision tree. The tree growth is controlled by merging branches with similar trait distributions and estimations. This package has the necessary tools for creating CATs and estimate the subject's ability level.