Provides a framework to perform Non-negative Matrix
    Factorization (NMF). The package implements a set of already published algorithms
    and seeding methods, and provides a framework to test, develop and plug
    new/custom algorithms. Most of the built-in algorithms have been optimized
    in C++, and the main interface function provides an easy way of performing
    parallel computations on multicore machines.

Factorize binary matrices into rank-k components using the logistic function in the updating process. See e.g. Tomé et al (2015) <doi:10.1007/s11045-013-0240-9> .

Performs Non-negative Matrix Factorization (NMF)
    with Kernel Covariates. Given an observation matrix and kernel
    covariates, it optimizes both a basis matrix and a parameter matrix.
    Notably, if the kernel matrix is an identity matrix, the method
    simplifies to standard NMF. Also provides NMF with Random Effects
    (NMF-RE) via nmfre(), which estimates a mixed-effects model combining
    covariate-driven scores with unit-specific random effects together
    with wild bootstrap inference, and NMF-based Structural Equation
    Modeling (NMF-SEM) via nmf.sem(), which fits a two-block input-output
    model for blind source separation and path analysis.
    References: Satoh (2025) <doi:10.48550/arXiv.2403.05359>;
    Satoh (2025) <doi:10.48550/arXiv.2510.10375>;
    Satoh (2025) <doi:10.48550/arXiv.2512.18250>;
    Satoh (2026) <doi:10.48550/arXiv.2603.01468>;
    Satoh (2026) <doi:10.1007/s42081-025-00314-0>.

Contains functions useful for debugging, set operations on vectors,
    and 'UTC' date and time functionality. It adds a few vector manipulation 
    verbs to 'purrr' and 'dplyr' packages. It can also generate an R file to 
    install and update packages to simplify deployment into production. The 
    functions were developed at the data science firm 'Numeract LLC' and are 
    used in several packages and projects.

A program for Bayesian analysis of univariate normal mixtures with an unknown number
  of components, following the approach of Richardson and Green (1997) <doi:10.1111/1467-9868.00095>.
  This makes use of reversible jump Markov chain Monte Carlo methods that are capable of jumping
  between the parameter sub-spaces corresponding to different numbers of components in the mixture.
  A sample from the full joint distribution of all unknown variables is thereby generated,
  and this can be used as a basis for a thorough presentation of many aspects of the posterior
  distribution.