PLNmodels: Poisson lognormal models <img src="man/figures/logo.png" align="right" width="155" height="180"/>

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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

<!-- #### CRAN dependencies --> <!-- **PLNmodels** needs the following CRAN R packages, so check that they are are installed on your computer. --> <!-- ```{r CRAN dependencies, eval = FALSE} --> <!-- required_CRAN <- c("R6", "glassoFast", "Matrix", "Rcpp", "RcppArmadillo", --> <!-- "nloptr", "igraph", "grid", "gridExtra", "dplyr", --> <!-- "tidyr", "ggplot2", "corrplot", "magrittr", "devtools") --> <!-- not_installed_CRAN <- setdiff(required_CRAN, rownames(installed.packages())) --> <!-- if (length(not_installed_CRAN) > 0) install.packages(not_installed_CRAN) --> <!-- ``` --> <!-- #### Bioconductor dependencies --> <!-- **PLNmodels** also needs two BioConductor packages --> <!-- ```{r Bioconductor dependencies, eval = FALSE} --> <!-- required_BioC <- c("phyloseq", "biomformat") --> <!-- not_installed_BioC <- setdiff(required_BioC, rownames(installed.packages())) --> <!-- if (length(not_installed_BioC) > 0) BiocManager::install(not_installed_BioC) --> <!-- ``` -->

Installing PLNmodels

• For the last stable version, use the CRAN version

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install.packages("PLNmodels")

• For the development version, use the github install

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remotes::install_github("pln-team/PLNmodels")

• For a specific tagged release, use

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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

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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)

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myPLN <- PLN(Abundance ~ 1, data = trichoptera)

Rank Constrained Poisson lognormal for Poisson Principal Component Analysis (aka PLNPCA)

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myPCA <- PLNPCA(Abundance ~ 1, data = trichoptera, ranks = 1:8)

Poisson lognormal discriminant analysis (aka PLNLDA)

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myLDA <- PLNLDA(Abundance ~ 1, grouping = Group, data = trichoptera)

Sparse Poisson lognormal model for sparse covariance inference for counts (aka PLNnetwork)

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myPLNnetwork <- PLNnetwork(Abundance ~ 1, data = trichoptera)

Mixture of Poisson lognormal models for model-based clustering of counts (aka PLNmixture)

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myPLNmixture <- PLNmixture(Abundance ~ 1, data = trichoptera)

References

Please cite our work using the following references:

• 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

• 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

• J. Chiquet, M. Mariadassou and S. Robin: Variational inference for
  probabilistic Poisson PCA, the Annals of Applied Statistics, 12:
  2674–2698, 2018. link