Latent Class Item Response Theory Models Under ‘Within-Item Multi-Dimensionality’ (MLCIRTwithin)
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multi-dimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version.

Blowfish’ Password Hashing Algorithm (bcrypt)
An R interface to the ‘OpenBSD Blowfish’ password hashing algorithm, as described in ‘A Future-Adaptable Password Scheme’ by ‘Niels Provos’. The implementation is derived from the ‘py-bcrypt’ module for Python which is a wrapper for the ‘OpenBSD’ implementation.

Robust Mixture Modeling Fitted via Spatial-EM Algorithm for Model-Based Clustering and Outlier Detection (RobustEM)
The Spatial-EM is a new robust EM algorithm for the finite mixture learning procedures. The algorithm utilizes median- based location and rank-based scatter estimators to replace sample mean and sample covariance matrix in each M step, hence enhancing stability and robustness of the algorithm. To understand more about this algorithm, read the article ‘Yu, K., Dang, X., Bart Jr, H. and Chen, Y. (2015). Robust Model- based Learning via Spatial-EM Algorithm. IEEE Transactions on Knowledge and Data Engineering, 27(6), 1670-1682. doi:10.1109/TKDE.2014.2373355’.