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PLNmodels (version 1.2.0)

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

386

Version

1.2.0

License

GPL (>= 3)

Maintainer

Julien Chiquet

Last Published

March 5th, 2024

Functions in PLNmodels (1.2.0)

An R6 Class to represent a collection of PLNPCAfit

An R6 Class to virtually represent a collection of network fits

Poisson lognormal model

An R6 Class to represent a PLNfit in a LDA framework

Poisson lognormal model towards Principal Component Analysis

PLNLDAfit_diagonal

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

Poisson lognormal model towards Linear Discriminant Analysis

Control of a PLNLDA fit

Control of PLNPCA fit

An R6 Class to represent a PLNfit in a PCA framework

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 collection of PLNfit

Poisson lognormal mixture model

PLNfit_diagonal

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

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

PLNfit_spherical

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

Control of a PLN fit

PLNmixture_param

Control of a PLNmixture fit

An R6 Class to represent a PLNfit in a mixture framework

PLNmixturefamily

An R6 Class to represent a collection of PLNmixturefit

Zero Inflated Poisson lognormal model

PLNnetworkfamily

An R6 Class to represent a collection of PLNnetworkfits

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

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

An R6 Class to represent a ZIPLNfit

ZIPLNfit_diagonal

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

Control of a ZIPLN fit

PLNmodels-package

PLNmodels: Poisson Lognormal Models

Sparse Poisson lognormal model for network inference

PLNnetwork_param

Control of PLNnetwork fit

ZIPLNnetworkfamily

An R6 Class to represent a collection of ZIPLNnetwork

Extract model coefficients

Extract model coefficients

ZIPLNfit_spherical

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

ZIPLNnetwork_param

Control of ZIPLNnetwork fit

coef.PLNmixturefit

Extract model coefficients

ZIPLNfit_sparse

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

Barents fish data set

Zero Inflated Sparse Poisson lognormal model for network inference

Extracts model coefficients from objects returned by PLNLDA()

coefficient_path

Extract the regularization path of a PLNnetwork fit

getModel.PLNPCAfamily

Model extraction from a collection of models

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

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

Mollusk data set

Extract edge selection frequency in bootstrap subsamples

Oaks amplicon data set

plot.Networkfamily

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

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

getBestModel.PLNPCAfamily

Best model extraction from a collection of models

fitted.ZIPLNfit

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

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.PLNmixturefit

Mixture visualization of a PLNmixturefit object

compute_PLN_starting_point

Helper function for PLN initialization.

plot.PLNnetworkfit

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

Prepare data for use in PLN models

predict.PLNmixturefit

Prediction for a PLNmixturefit object

plot.PLNPCAfamily

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

Predict counts of a new sample

predict.ZIPLNfit

Predict counts of a new sample

plot.ZIPLNfit_sparse

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

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

Predict counts conditionally

predict.PLNLDAfit

Predict group of new samples

Single cell RNA-seq data

stability_selection

Compute the stability path by stability selection

standard_error.PLNPCAfit

Component-wise standard errors of B

Extract variance-covariance of residuals 'Sigma'

Trichoptera data set

Extract variance-covariance of residuals 'Sigma'

sigma.PLNmixturefit

Extract variance-covariance of residuals 'Sigma'

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