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.
Version: | 0.2.0 |
Depends: | R (≥ 2.10) |
Imports: | osqp, Rcpp, pbapply, ggplot2 |
LinkingTo: | Rcpp, RcppArmadillo, RcppProgress |
Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
Published: | 2025-05-19 |
DOI: | 10.32614/CRAN.package.densityratio |
Author: | Thom Volker |
Maintainer: | Thom Volker <thombenjaminvolker at gmail.com> |
BugReports: | https://github.com/thomvolker/densityratio/issues |
License: | GPL (≥ 3) |
URL: | https://thomvolker.github.io/densityratio/ |
NeedsCompilation: | yes |
Citation: | densityratio citation info |
Materials: | README NEWS |
CRAN checks: | densityratio results |
Reference manual: | densityratio.pdf |
Vignettes: |
Covariate shift adjustment (source) densityratio (source) High dimensional two-sample testing (source) |
Package source: | densityratio_0.2.0.tar.gz |
Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
macOS binaries: | r-release (arm64): densityratio_0.2.0.tgz, r-oldrel (arm64): densityratio_0.2.0.tgz, r-release (x86_64): not available, r-oldrel (x86_64): not available |
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