PLNmodels: Poisson lognormal models 
The Poisson lognormal model and variants can be used for a variety of multivariate problems when count data are at play (including PCA, LDA and network inference for count data). This package implements efficient algorithms to fit such models accompanied with a set of functions for visualization and diagnostic. See this deck of slides for a comprehensive introduction.
Installation
PLNmodels is available on CRAN. The development version is available on Github.
R Package installation
Installing PLNmodels
- For the last stable version, use the CRAN version
install.packages("PLNmodels")- For the development version, use the github install
remotes::install_github("pln-team/PLNmodels")- For a specific tagged release, use
remotes::install_github("pln-team/PLNmodels@tag_number")Usage and main fitting functions
The package comes with an ecological data set to present the functionality
library(PLNmodels)
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)The main fitting functions work with the usual R formula notations,
with mutivariate responses on the left hand side. You probably want to
start by one of them. Check the corresponding vignette and documentation
page. There is a dedicated vignettes for each model in the package (See
https://pln-team.github.io/PLNmodels/articles/).
Unpenalized Poisson lognormal model (aka PLN)
myPLN <- PLN(Abundance ~ 1, data = trichoptera)Rank Constrained Poisson lognormal for Poisson Principal Component Analysis (aka PLNPCA)
myPCA <- PLNPCA(Abundance ~ 1, data = trichoptera, ranks = 1:8)Poisson lognormal discriminant analysis (aka PLNLDA)
myLDA <- PLNLDA(Abundance ~ 1, grouping = Group, data = trichoptera)Sparse Poisson lognormal model for sparse covariance inference for counts (aka PLNnetwork)
myPLNnetwork <- PLNnetwork(Abundance ~ 1, data = trichoptera)Mixture of Poisson lognormal models for model-based clustering of counts (aka PLNmixture)
myPLNmixture <- PLNmixture(Abundance ~ 1, data = trichoptera)References
Please cite our work using the following references:
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J. Chiquet, M. Mariadassou and S. Robin: The Poisson-lognormal model as a versatile framework for the joint analysis of species abundances, Frontiers in Ecology and Evolution, 2021. link
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J. Chiquet, M. Mariadassou and S. Robin: Variational inference for sparse network reconstruction from count data, Proceedings of the 36th International Conference on Machine Learning (ICML), 2019. link
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J. Chiquet, M. Mariadassou and S. Robin: Variational inference for probabilistic Poisson PCA, the Annals of Applied Statistics, 12: 2674–2698, 2018. link