distance

Create a Gram matrix with squared Euclidean distances between
observations in the input matrix <code>X</code> and the input matrix <code>Y</code>

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

distance function

<dl><dt>X</dt>
<dd>A numeric input matrix</dd>
<dt>Y</dt>
<dd>A numeric input matrix with the same variables as <code>X</code></dd>
<dt>intercept</dt>
<dd>Logical indicating whether an intercept should be added to
the estimation procedure. In this case, the first column is an all-zero
column (which will be transformed into an all-ones column in the kernel).</dd></dl>

Arguments

Create a Gram matrix with squared Euclidean distances between
observations in the input matrix X and the input matrix Y — distance

<dl>

<dt>X</dt>
<dd>A numeric input matrix</dd>


<dt>Y</dt>
<dd>A numeric input matrix with the same variables as <code>X</code></dd>


<dt>intercept</dt>
<dd>Logical indicating whether an intercept should be added to
the estimation procedure. In this case, the first column is an all-zero
column (which will be transformed into an all-ones column in the kernel).</dd>

</dl>

distance: Create a Gram matrix with squared Euclidean distances between observations in the input matrix `X` and the input matrix `Y`

Description

Arguments