A collection of dimensionality reduction
    techniques from R packages and a common
    interface for calling the methods.

Estimate the Deterministic Input, Noisy "And" Gate (DINA)
    cognitive diagnostic model parameters using the Gibbs sampler described
    by Culpepper (2015) <doi:10.3102/1076998615595403>.

In tumor tissue, underlying genomic instability can lead to DNA copy number alterations,
	e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the
	genome, whereas recurrent alterations are observed in the same genomic region across multiple
	independent samples, perhaps because they provide a selective growth advantage.
	This package implements the DiNAMIC procedure for assessing the statistical significance of 
	recurrent DNA copy number aberrations (Bioinformatics (2011) 27(5) 678 - 685).

In tumor tissue, underlying genomic instability can lead to DNA copy number alterations,
	e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the
	genome, whereas recurrent alterations are observed in the same genomic region across multiple
	independent samples, perhaps because they provide a selective growth advantage. Here we use
	cyclic shift permutations to identify recurrent copy number alterations in a single cohort or
	recurrent copy number differences in two cohorts based on a common set of genomic markers.
	Additional functionality is provided to perform downstream analyses, including the creation of
	summary files and graphics. DiNAMIC.Duo builds upon the original DiNAMIC package of Walter et al.
	(2011) <doi:10.1093/bioinformatics/btq717> and leverages the theory developed in Walter et al.
	(2015) <doi:10.1093/biomet/asv046>. An article describing DiNAMIC.Duo by Walter et al. (2022)
	can be found at <doi: 10.1093/bioinformatics/btac542>.

Decomposition of (income) inequality by population sub groups. 
    For a decomposition on a single variable the mean log deviation can be used
      (see Mookherjee Shorrocks (1982) <DOI:10.2307/2232673>).
    For a decomposition on multiple variables a regression based technique can be 
      used (see Fields (2003) <DOI:10.1016/s0147-9121(03)22001-x>).
    Recentered influence function regression for marginal effects of the (income 
      or wealth) distribution  (see Firpo et al. (2009) <DOI:10.3982/ECTA6822>).
    Some extensions to inequality functions to handle weights and/or missings.