Implements a set of routines to perform structured matrix factorization with minimum volume constraints. The NMF procedure decomposes a matrix X into a product C * D. Given conditions such that the matrix C is non-negative and has sufficiently spread columns, then volume minimization of a matrix D delivers a correct and unique, up to a scale and permutation, solution (C, D). This package provides both an implementation of volume-regularized NMF and "anchor-free" NMF, whereby the standard NMF problem is reformulated in the covariance domain. This algorithm was applied in Vladimir B. Seplyarskiy Ruslan A. Soldatov, et al. "Population sequencing data reveal a compendium of mutational processes in the human germ line". Science, 12 Aug 2021. <doi:10.1126/science.aba7408>. This package interacts with data available through the 'simulatedNMF' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/vrnmf>. The size of the 'simulatedNMF' package is approximately 8 MB.

The goal of 'vroom' is to read and write data (like 'csv',
    'tsv' and 'fwf') quickly. When reading it uses a quick initial
    indexing step, then reads the values lazily , so only the data you
    actually use needs to be read.  The writer formats the data in
    parallel and writes to disk asynchronously from formatting.

A  collection of statistical tests for martingale difference hypothesis, including automatic portmanteau test (Escansiano and Lobato, 2009) <doi:10.1016/j.jeconom.2009.03.001> and automatic variance ratio test (Kim, 2009) <doi:10.1016/j.frl.2009.04.003>.

Performs variable selection/feature reduction under a clustering or 
  classification framework. In particular, it can be used in an automated fashion 
  using mixture model-based methods ('teigen' and 'mclust' are currently supported).
  Can account for mixtures of non-Gaussian distributions via Manly transform (via 'ManlyMix').
  See Andrews and McNicholas (2014) <doi:10.1007/s00357-013-9139-2> and Neal and McNicholas (2023)
  <doi:10.48550/arXiv.2305.16464>.