Penalized Likelihood Factor Analysis via Nonconvex Penalty

Computes the penalized maximum likelihood estimates of factor loadings and unique variances for various tuning parameters. The pathwise coordinate descent along with EM algorithm is used. This package also includes a graphical tool which outputs path diagrams, heatmaps, goodness-of-fit indices and model selection criteria for each regularization parameter (Yamamoto, M., Hirose, K. and Nagata, H., 2017 ). The user can change the regularization parameter interactively with a built-in self-contained HTML viewer (no additional packages required), which is helpful to find a suitable value of regularization parameter. As a penalty, we can choose either the minimax concave penalty (Hirose, K. and Yamamoto, M., 2015 ; Hirose, K. and Yamamoto, M., 2014 ) or the product-based elastic net penalty (Hirose, K. and Terada, Y., 2023 ).