gllvm: Generalized Linear Latent Variable Models

Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either Laplace approximation method or variational approximation method implemented via TMB (Kristensen et al., (2016), <doi:10.18637/jss.v070.i05>). For details see Niku et al. (2019) <doi:10.1371/journal.pone.0216129>.

Version: 1.1.7
Depends: TMB, mvabund
Imports: MASS, Matrix, mvtnorm, statmod, fishMod, mgcv
LinkingTo: TMB, RcppEigen
Suggests: knitr, rmarkdown, testthat, gclus, corrplot, lattice
Published: 2019-09-17
Author: Jenni Niku, Wesley Brooks, Riki Herliansyah, Francis K.C. Hui, Sara Taskinen, David I. Warton
Maintainer: Jenni Niku <jenni.m.e.niku at jyu.fi>
License: GPL-2
URL: https://github.com/JenniNiku/gllvm.git
NeedsCompilation: yes
CRAN checks: gllvm results

Downloads:

Reference manual: gllvm.pdf
Vignettes: Analysing multivariate abundance data using gllvm
Analysing high-dimensional microbial community data using gllvm
Package source: gllvm_1.1.7.tar.gz
Windows binaries: r-devel: gllvm_1.1.6.zip, r-release: gllvm_1.1.6.zip, r-oldrel: gllvm_1.1.6.zip
OS X binaries: r-release: gllvm_1.1.7.tgz, r-oldrel: gllvm_1.1.7.tgz
Old sources: gllvm archive

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