pareto_scale

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

centering

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

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

pareto_scale: Pareto Scaling

Description

Usage

Arguments

Value

References

Examples