z_ig_random_soft

Generates an initialization matrix of size n times g using random soft.

multivariate

cluster

classif

An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.

Paul McNicholas

mixture

Mixture Models for Clustering and Classification

Nik Pocuca

Ryan P. Browne

Paul D. McNicholas

z_ig_random_soft function

<dl> <dt>n</dt>
<dd>Number of rows, must be positive.</dd> <dt>g</dt>
<dd>Number of columns, must be positive.</dd>
</dl>

Arguments

Nik Pocuca, Ryan P. Browne and Paul D. McNicholas.
Maintainer: Paul D. McNicholas &lt;mcnicholas@math.mcmaster.ca&gt;

Author

Random Soft Initialization — z_ig_random_soft

<dl>

 <dt>n</dt>
<dd>Number of rows, must be positive.</dd>

 <dt>g</dt>
<dd>Number of columns, must be positive.</dd>


</dl>

z_ig_random_soft: Random Soft Initialization

Description

Usage

Arguments

Author

References

Examples