Semi-Binary and Semi-Ternary Matrix Decomposition are performed based on Non-negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD). For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/dcTensor>.

Dynamic CUR (dCUR) boosts the CUR decomposition (Mahoney MW., Drineas P. (2009) <doi:10.1073/pnas.0803205106>) varying the k, the number of columns and rows used, and its final purposes to help find the stage, which minimizes the relative error to reduce matrix dimension.
    The goal of CUR Decomposition is to give a better interpretation of the matrix decomposition employing proper variable selection in the data matrix, in a way that yields a simplified structure. Its origins come from analysis in genetics. 
    The goal of this package is to show an alternative to variable selection (columns) or individuals (rows). The idea proposed consists of adjusting the probability distributions to the leverage scores and selecting the best columns and rows that minimize the reconstruction error of the matrix approximation ||A-CUR||. It also includes a method that recalibrates the relative importance of the leverage scores according to an external variable of the user's interest.

A Davidian curve defines a seminonparametric density, whose shape and flexibility can be tuned by
  easy to estimate parameters. Since a special case of a Davidian curve is the standard normal density,
  Davidian curves can be used for relaxing normality assumption in statistical applications (Zhang & Davidian, 2001)
  <doi:10.1111/j.0006-341X.2001.00795.x>. This package provides the density function, the gradient of
  the loglikelihood and a random generator for Davidian curves.

Diagnostic and prognostic models are typically evaluated with
    measures of accuracy that do not address clinical consequences.
    Decision-analytic techniques allow assessment of clinical outcomes,
    but often require collection of additional information may be
    cumbersome to apply to models that yield a continuous result. Decision
    curve analysis is a method for evaluating and comparing prediction
    models that incorporates clinical consequences, requires only the data
    set on which the models are tested, and can be applied to models that
    have either continuous or dichotomous results. See the following references 
    for details on the methods: Vickers (2006) <doi:10.1177/0272989X06295361>,
    Vickers (2008) <doi:10.1186/1472-6947-8-53>, 
    and Pfeiffer (2020) <doi:10.1002/bimj.201800240>.