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PLNmodels (version 1.2.1)

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.

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Version

Install

install.packages('PLNmodels')

Monthly Downloads

388

Version

1.2.1

License

GPL (>= 3)

Maintainer

Julien Chiquet

Last Published

March 10th, 2025

Functions in PLNmodels (1.2.1)

An R6 Class to virtually represent a collection of network fits

Poisson lognormal model towards Principal Component Analysis

Control of PLNPCA fit

Poisson lognormal model

Poisson lognormal model towards Linear Discriminant Analysis

An R6 Class to represent a PLNfit in a LDA framework

An R6 Class to represent a collection of PLNPCAfit

Control of a PLNLDA fit

PLNLDAfit_diagonal

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

An R6 Class to represent a PLNfit in a PCA framework

Control of a PLN fit

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

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

Poisson lognormal mixture model

PLNfit_diagonal

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

PLNmixturefamily

An R6 Class to represent a collection of PLNmixturefit

PLNmixture_param

Control of a PLNmixture fit

PLNfit_spherical

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

PLNmodels-package

PLNmodels: Poisson Lognormal Models

Control of a ZIPLN fit

An R6 Class to represent a PLNfit in a mixture framework

PLNnetwork_param

Control of PLNnetwork fit

ZIPLNfit_diagonal

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

Sparse Poisson lognormal model for network inference

coef.PLNmixturefit

Extract model coefficients

Extract model coefficients

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

getModel.PLNPCAfamily

Model extraction from a collection of models

An R6 Class to represent a ZIPLNfit

Zero Inflated Sparse Poisson lognormal model for network inference

Extracts model coefficients from objects returned by PLNLDA()

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

Zero Inflated Poisson lognormal model

Mollusk data set

plot.Networkfamily

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

ZIPLNnetworkfamily

An R6 Class to represent a collection of ZIPLNnetwork

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

Oaks amplicon data set

Single cell RNA-seq data

Extract variance-covariance of residuals 'Sigma'

ZIPLNnetwork_param

Control of ZIPLNnetwork fit

Barents fish data set

Extract model coefficients

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

coefficient_path

Extract the regularization path of a PLNnetwork fit

fitted.ZIPLNfit

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

getBestModel.PLNPCAfamily

Best model extraction from a collection of models

plot.PLNmixturefit

Mixture visualization of a PLNmixturefit object

plot.PLNnetworkfit

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

Predict counts of a new sample

Extract edge selection frequency in bootstrap subsamples

sigma.PLNmixturefit

Extract variance-covariance of residuals 'Sigma'

plot.PLNPCAfamily

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

compute_PLN_starting_point

Helper function for PLN initialization.

predict.ZIPLNfit

Predict counts of a new sample

Extract variance-covariance of residuals 'Sigma'

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

stability_selection

Compute the stability path by stability selection

predict.PLNmixturefit

Prediction for a PLNmixturefit object

PLNnetworkfamily

An R6 Class to represent a collection of PLNnetworkfits

ZIPLNfit_spherical

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

ZIPLNfit_sparse

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

standard_error.PLNPCAfit

Component-wise standard errors of B

Predict counts conditionally

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

fitted.PLNmixturefit

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

Prepare data for use in PLN models

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)

plot.ZIPLNfit_sparse

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

predict.PLNLDAfit

Predict group of new samples

Trichoptera data set

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