Learn R Programming

PLNmodels (version 1.3.1)

Poisson Lognormal Models

Description

The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 ) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.

Copy Link

Version

Install

install.packages('PLNmodels')

Monthly Downloads

461

Version

1.3.1

License

GPL (>= 3)

Maintainer

Julien Chiquet

Last Published

August 29th, 2026

Functions in PLNmodels (1.3.1)

PLNLDAfit_diagonal

An R6 Class to represent a PLNfit in a LDA framework with diagonal covariance

PLNfit_diagonal

An R6 Class to represent a PLNfit in a standard, general framework, with diagonal residual covariance

An R6 Class to represent a collection of PLNfit

PLNfit_fixedcov

An R6 Class to represent a PLNfit in a standard, general framework, with fixed (inverse) residual covariance

Poisson lognormal model towards Principal Component Analysis

Control of PLNPCA fit

An R6 Class to represent a PLNfit in a PCA framework

An R6 Class to represent a collection of PLNPCAfit

An R6 Class to represent a PLNfit in a standard, general framework

Control of a PLN fit

PLNmixture_param

Control of a PLNmixture fit

Poisson lognormal mixture model

Sparse Poisson lognormal model for network inference

An R6 Class to represent a PLNfit with a residual covariance structured by a fixed correlation matrix (e.g. a genetic relationship matrix), motivated by population genetics

PLNfit_spherical

An R6 Class to represent a PLNfit in a standard, general framework, with spherical residual covariance

PLNnetworkfamily

An R6 Class to represent a collection of PLNnetworkfits

PLNnetwork_param

Control of PLNnetwork fit

PLNmodels-package

PLNmodels: Poisson Lognormal Models

An R6 Class to represent a PLNfit in a mixture framework

PLNmixturefamily

An R6 Class to represent a collection of PLNmixturefit

ZIPLNfit_sparse

An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance

An R6 Class to represent a PLNfit in a sparse inverse covariance framework

Control of a ZIPLN fit

Zero Inflated Poisson lognormal model

An R6 Class to represent a ZIPLNfit

ZIPLNnetwork_param

Control of ZIPLNnetwork fit

Zero Inflated Sparse Poisson lognormal model for network inference

coefficient_path

Extract the regularization path of a PLNnetwork fit

Extracts model coefficients from objects returned by PLNLDA()

Extract model coefficients

coef.PLNmixturefit

Extract model coefficients

compute_ZIPLN_starting_point

Helper function for ZIPLN initialization.

ZIPLNfit_diagonal

An R6 Class to represent a ZIPLNfit in a standard, general framework, with diagonal residual covariance

Compute offsets from a count data using one of several normalization schemes

ZIPLNfit_spherical

An R6 Class to represent a ZIPLNfit in a standard, general framework, with spherical residual covariance

An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance

Extract edge selection frequency in bootstrap subsamples

getBestModel.PLNPCAfamily

Best model extraction from a collection of models

ZIPLNnetworkfamily

An R6 Class to represent a collection of ZIPLNnetwork

Extracts model fitted values from objects returned by PLN() and its variants

Barents fish data set

Extract model coefficients

plot.PLNmixturefit

Mixture visualization of a PLNmixturefit object

PCA visualization (individual and/or variable factor map(s)) for a PLNPCAfit object

plot.PLNPCAfamily

Display the criteria associated with a collection of PLNPCA fits (a PLNPCAfamily)

Extract log-likelihood of a fitted PLN model

fitted.PLNmixturefit

Extracts model fitted values from objects returned by PLNmixture() and its variants

fitted.ZIPLNfit

Extracts model fitted values from objects returned by ZIPLN() and its variants

Single cell RNA-seq data

Extract variance-covariance of residuals 'Sigma'

predict.PLNLDAfit

Predict group of new samples

plot.ZIPLNfit_sparse

Extract and plot the network (partial correlation, support or inverse covariance) from a ZIPLNfit_sparse object

logLik.ZIPLNfit

Extract log-likelihood of a fitted ZIPLN model

Mollusk data set

compute_PLN_starting_point

Helper function for PLN initialization.

plot.Networkfamily

Display various outputs (goodness-of-fit criteria, robustness, diagnostic) associated with a collection of network fits (either PLNnetworkfamily or ZIPLNnetworkfamily)

getModel.PLNPCAfamily

Model extraction from a collection of models

Cow microbiome data set

plot.PLNnetworkfit

Extract and plot the network (partial correlation, support or inverse covariance) from a PLNnetworkfit object

Display the criteria associated with a collection of PLN fits (a PLNfamily)

plot.PLNmixturefamily

Display the criteria associated with a collection of PLNmixture fits (a PLNmixturefamily)

sigma.PLNmixturefit

Extract variance-covariance of residuals 'Sigma'

Extract variance-covariance of residuals 'Sigma'

Predict counts of a new sample

LDA visualization (individual and/or variable factor map(s)) for a PLNPCAfit object

predict.ZIPLNfit

Predict counts of a new sample

Prepare data for use in PLN models

Oaks amplicon data set

Calculate Variance-Covariance Matrix for a fitted PLN() model object

Trichoptera data set

Predict counts conditionally

standard_error.PLNPCAfit

Component-wise standard errors of B

predict.PLNmixturefit

Prediction for a PLNmixturefit object

stability_selection

Compute the stability path by stability selection

Control of a PLNLDA fit

Integrated Classification Likelihood

An R6 Class to represent a PLNfit in a LDA framework

Akaike Information Criterion for a fitted PLN model

Poisson lognormal model

Poisson lognormal model towards Linear Discriminant Analysis

Akaike Information Criterion for a fitted ZIPLN model

Bayesian Information Criterion for a fitted PLN model

Bayesian Information Criterion for a fitted ZIPLN model

An R6 Class to virtually represent a collection of network fits