densityratio - Distribution Comparison Through Density Ratio Estimation

Fast, flexible and user-friendly functionality to directly estimate the ratio of two probability distributions from samples from these distributions without estimating the densities separately. Estimated density ratios can, among other things, be used for prediction, outlier detection, change-point detection in time-series, importance weighting under domain adaptation (i.e., sample selection bias) and evaluation of synthetic data utility. The rationale behind these use-cases is that differences between two data distributions can be captured in the ratio their density ratio, which is estimated over the entire multivariate space of the data. Computationally intensive code is executed in `C++` using `Rcpp` and `RcppArmadillo`. The package provides good default hyperparameters that can be optimized in cross-validation (we do recommend understanding those parameters before using `densityratio` in practice). Multiple density ratio estimation methods are implemented, such as unconstrained least-squares importance fitting (`ulsif()`), Kullback-Leibler importance estimation procedure (`kliep()`), spectral density ratio estimation (`spectral()`), and least-squares heterodistributional subspace search (`lhss()`).