Finite Gaussian mixture models with parsimonious extended ultrametric covariance structures estimated via a grouped coordinate ascent algorithm, which is equivalent to the Expectation-Maximization algorithm. The thirteen ultrametric covariance structures implemented allow for the inspection of different hierarchical relationships among variables. The estimation of an ultrametric correlation matrix is included as a function. The methodologies are described in Cavicchia, Vichi, Zaccaria (2024) <doi:10.1007/s11222-024-10405-9>, Cavicchia, Vichi, Zaccaria (2022) <doi:10.1007/s11634-021-00488-x> and Cavicchia, Vichi, Zaccaria (2020) <doi:10.1007/s11634-020-00400-z>.

Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) <arXiv:1711.08129>.

R Interface to Pullword Service for natural language processing
    in Chinese. It enables users to extract valuable words from text by deep learning models. 
    For more details please visit the official site (in Chinese) <http://www.pullword.com/>.

A method of clustering functional data using
    subregion information of the curves. It is intended to supplement the
    'fda' and 'fda.usc' packages in functional data object clustering. It
    also facilitates the printing and plotting of the results in a tree
    format and limits the partitioning candidates into a specific set of
    subregions.

Model selection for penalized graphical models using the Stability Approach to Regularization Selection ('StARS'), with options for speed-ups including Bounded StARS (B-StARS), batch computing, and other stability metrics (e.g., graphlet stability G-StARS). Christian L. Müller, Richard Bonneau, Zachary Kurtz (2016) <arXiv:1605.07072>.

R package to query and get data out of a Pumilio sound archive system (http://ljvillanueva.github.io/pumilio/).