Implement weighted higher-order initialization and angle-based iteration for multi-way spherical clustering under degree-corrected tensor block model. See reference Jiaxin Hu and Miaoyan Wang (2023) <doi:10.1109/TIT.2023.3239521>.

A system for combining two diagnostic tests using various approaches
              that include statistical and machine-learning-based methodologies. 
              These approaches are divided into four groups: linear combination 
              methods, non-linear combination methods, mathematical operators, 
              and machine learning algorithms. See 
              the <https://biotools.erciyes.edu.tr/dtComb/> website 
              for more information, documentation, and examples.

Comparison of the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.

Provides fast methods to work with Merton's distance to default 
  model introduced in Merton (1974) <doi:10.1111/j.1540-6261.1974.tb03058.x>. 
  The methods includes simulation and estimation of the parameters.

Implementation of different algorithms for analyzing
        randomly truncated data, one-sided and two-sided (i.e. doubly)
        truncated data. It serves to compute empirical cumulative 
        distributions and also kernel density and hazard functions 
        using different bandwidth selectors.
      Several real data sets are included.

Nonparametric estimator of the cumulative incidences of competing risks under double truncation. The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting. Efron, B. and Petrosian, V. (1999) <doi:10.2307/2669997>.

Non-iterative estimator for the cumulative distribution of a doubly truncated variable. de Uña-Álvarez J. (2018) <doi:10.1007/978-3-319-73848-2_37>.

The Discrete Transmuted Generalized Inverse Weibull (DTGIW) distribution is a new distribution for count data analysis. The DTGIW is discrete distribution based on Atchanut and Sirinapa (2021). <DOI: 10.14456/sjst-psu.2021.149>.