Toplevel fitting functionsSet of functions to fit variants of the Poisson lognormal model 


PLNmodels: Poisson Lognormal Models 

Poisson lognormal model 

Zero Inflated Poisson lognormal model 

Poisson lognormal model towards Linear Discriminant Analysis 

Poisson lognormal model towards Principal Component Analysis 

Sparse Poisson lognormal model for network inference 

Zero Inflated Sparse Poisson lognormal model for network inference 

Poisson lognormal mixture model 

Poisson lognormal fitDescription of the PLNfit object and methods for its manipulation. Any PLN variant in the package inherits from this class (PLNPCAfit, PLNnetworkfit, PLNLDAfit). 

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

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, with fixed (inverse) residual covariance 

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

Control of a PLN fit 

Extract model coefficients 

Calculate VarianceCovariance Matrix for a fitted 

Extract variancecovariance of residuals 'Sigma' 

Predict counts of a new sample 

Predict counts conditionally 

Extracts model fitted values from objects returned by 

Componentwise standard errors of B 

Zero Inflated Poisson lognormal fitDescription of the ZIPLNfit object and methods for its manipulation. 

An R6 Class to represent a ZIPLNfit 

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

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

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

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

Control of a ZIPLN fit 

Extract model coefficients 

Extract variancecovariance of residuals 'Sigma' 

Predict counts of a new sample 

Extracts model fitted values from objects returned by 

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

Linear discriminant analysis via a Poisson lognormal fitDescription of the PLNLDAfit object and methods for its manipulation. 

An R6 Class to represent a PLNfit in a LDA framework 

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

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

Control of a PLNLDA fit 

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

Predict group of new samples 

Extracts model coefficients from objects returned by 

Poisson Lognormal PCA fitDescription of the PLNPCAfit and PLNPCAfamily objects and methods for their manipulation. 

An R6 Class to represent a PLNfit in a PCA framework 

Control of PLNPCA fit 

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

An R6 Class to represent a collection of PLNPCAfit 

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

Best model extraction from a collection of models 

Model extraction from a collection of models 

Mixture of Poisson Lognormal fitsDescription of the PLNmixturefit and PLNmixturefamily objects and methods for their manipulation. 

An R6 Class to represent a PLNfit in a mixture framework 

Control of a PLNmixture fit 

Mixture visualization of a 

An R6 Class to represent a collection of PLNmixturefit 

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

Extract model coefficients 

Prediction for a 

Extract variancecovariance of residuals 'Sigma' 

Extracts model fitted values from objects returned by 

Best model extraction from a collection of models 

Model extraction from a collection of models 

Sparse Poisson lognormal fit and network, w/o Zero Inflated componentDescription of the (ZI)PLNnetworkfit and (ZI)PLNnetworkfamily objects and methods for their manipulation. 

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

Control of PLNnetwork fit 

Control of ZIPLNnetwork fit 

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

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

An R6 Class to virtually represent a collection of network fits 

An R6 Class to represent a collection of ZIPLNnetwork 

An R6 Class to represent a collection of 


Display various outputs (goodnessoffit criteria, robustness, diagnostic) associated with a collection of network fits (either 
Best model extraction from a collection of models 

Model extraction from a collection of models 

Extract the regularization path of a PLNnetwork fit 

Compute the stability path by stability selection 

Extract edge selection frequency in bootstrap subsamples 

Other functions and objects 

Prepare data for use in PLN models 

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

An R6 Class to represent a collection of PLNfit 

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

PLN RNG 

Helper function for PLN initialization. 

Data sets 

Trichoptera data set 

Oaks amplicon data set 

Barents fish data set 

Mollusk data set 

Single cell RNAseq data 