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DESCRIPTION
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DESCRIPTION
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Package: genDA
Type: Package
Title: Multi-distributional Discriminant Analysis using Generalised Linear Latent Variable Modelling
Version: 1.1.3
Author: Sarah Romanes, John Ormerod
Maintainer: Sarah Romanes <sarah.romanes@sydney.edu.au>
Description: genDA is a Discriminant Analysis (DA) algorithm capable for use in multi-distributional response data - generalising the capabilities of DA beyond Gaussian response.
It utilises Generalised Linear Latent Variable Models (GLLVMs) to capture the covariance structure between the different response types and provide an efficient classifier for such datasets.
This package leverages the highly resourcful TMB package for fast and accurate gradient calculation in C++.
License: GPL-2
Imports: mvtnorm, matrixStats, MASS, purrr
Depends: TMB, mvabund, RcppEigen
Encoding: UTF-8
LazyData: true
LinkingTo: TMB, RcppEigen
RoxygenNote: 6.1.1
NeedsCompilation: yes
URL: https://github.com/sarahromanes/genDA.git