pareto_scale

Pareto scaling of a numeric matrix, with or without centering. Observations are scaled by the square-root of their column-wise standard deviations.

utility

Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.

Keefe Murphy

IMIFA

Infinite Mixtures of Infinite Factor Analysers and Related
Models

Cinzia Viroli

Isobel Claire Gormley

pareto_scale function

<dl><dt>x</dt>
<dd>A numeric matrix-like object.</dd>
<dt>centering</dt>
<dd>A logical vector indicating whether centering is to be applied (default=<code>TRUE</code>).</dd></dl>

Arguments

Keefe Murphy - &lt;<a href="/link/keefe.murphy%40mu.ie?package=IMIFA&version=2.2.0" data-mini-rdoc="IMIFA::keefe.murphy@mu.ie">keefe.murphy@mu.ie</a>&gt;

Author

Pareto Scaling — pareto_scale

<dl>

<dt>x</dt>
<dd>A numeric matrix-like object.</dd>


<dt>centering</dt>
<dd>A logical vector indicating whether centering is to be applied (default=<code>TRUE</code>).</dd>

</dl>

Keefe Murphy - &lt;<a href='mailto:keefe.murphy@mu.ie'>keefe.murphy@mu.ie</a>&gt;

pareto_scale: Pareto Scaling

Description

Usage

Value

Arguments

Author

References

Examples