dnn.weights

<p>A list of weights from four different neural network models:
random vs. non-random model (<code>r_nr_weights</code>),
low correlation factor vs. network model (<code>lf_n_weights</code>),
high correlation with variables less than or equal to factors vs. network model (<code>hlf_n_weights</code>), and
high correlation with variables greater than factors vs. network model (<code>hgf_n_weights</code>)</p>

datasets

Implements the Exploratory Graph Analysis (EGA) framework for dimensionality
and psychometric assessment. EGA estimates the number of dimensions in
psychological data using network estimation methods and community detection
algorithms. A bootstrap method is provided to assess the stability of dimensions
and items. Fit is evaluated using the Entropy Fit family of indices. Unique
Variable Analysis evaluates the extent to which items are locally dependent (or
redundant). Network loadings provide similar information to factor loadings and
can be used to compute network scores. A bootstrap and permutation approach are
available to assess configural and metric invariance. Hierarchical structures
can be detected using Hierarchical EGA. Time series and intensive longitudinal
data can be analyzed using Dynamic EGA, supporting individual, group, and
population level assessments.

dnn.weights: Loadings Comparison Test Deep Learning Neural Network Weights

Description

Usage

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Format

Examples