Fast, flexible and user-friendly tools for distribution comparison
  through direct density ratio estimation. The estimated density ratio can be 
  used for covariate shift adjustment, outlier-detection, change-point detection,
  classification and evaluation of synthetic data quality. The package implements
  multiple non-parametric estimation techniques (unconstrained least-squares
  importance fitting, ulsif(), Kullback-Leibler importance estimation procedure,
  kliep(), spectral density ratio estimation, spectral(), kernel mean matching,
  kmm(), and least-squares hetero-distributional subspace search, lhss()).
  with automatic tuning of hyperparameters. Helper functions are available for
  two-sample testing and visualizing the density ratios. For an overview on 
  density ratio estimation, see Sugiyama et al. (2012) <doi:10.1017/CBO9781139035613>
  for a general overview, and the help files for references on the specific 
  estimation techniques.

Provide a Dens-based method for estimating functional connection in large scale brain networks using partial correlation.

Density ratio estimation.  The estimated density ratio
    function can be used in many applications such as anomaly detection,
    change-point detection, covariate shift adaptation.  The implemented
    methods are uLSIF (Hido et al. (2011)
    <doi:10.1007/s10115-010-0283-2>), RuLSIF (Yamada et al. (2011)
    <doi:10.1162/NECO_a_00442>), and KLIEP (Sugiyama et al. (2007)
    <doi:10.1007/s10463-008-0197-x>).

Methods for testing the equality between groups of estimated
    density functions. The package implements FDET (Fourier-based Density Equality
    Testing) and MDET (Moment-based Density Equality Testing), two new approaches
    introduced by the author. Both methods extend an earlier testing approach
    by Delicado (2007), "Functional k-sample problem when data are density functions"
    <doi:10.1007/s00180-007-0047-y>, which is referred to as DET (Density Equality
    Testing) in this package for clarity. FDET compares groups of densities
    based on their global shape using Fourier transforms, while MDET tests for
    differences in distributional moments. All methods are described in Anarat,
    Krutmann and Schwender (2025), "Testing for Differences in Extrinsic Skin Aging
    Based on Density Functions" (Submitted).