denominator_small

Subset of the denominator_data with three variables and 50 observations

datasets

data

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.

Thom Volker

densityratio

Distribution Comparison Through Density Ratio Estimation

Carlos Gonzalez Poses

Erik-Jan van Kesteren

denominator_small function

A data frame with 100 rows and 3 columns:<dl>
<dt>x1</dt>
<dd>Continuous variable (normally distributed given x1 and x2)</dd><dt>x2</dt>
<dd>Continuous variable (normally distributed)</dd><dt>x3</dt>
<dd>Continuous variable (normally distributed)</dd>
</dl>

Format

denominator_small — denominator_small

A data frame with 100 rows and 3 columns:<dl>
<dt>x1</dt>
<dd>Continuous variable (normally distributed given x1 and x2)</dd>

<dt>x2</dt>
<dd>Continuous variable (normally distributed)</dd>

<dt>x3</dt>
<dd>Continuous variable (normally distributed)</dd>


</dl>

denominator_small: denominator_small

Description

Arguments

Format