Poisson Lognormal Models
Extracts model fitted values from objects returned by ZIPLN() and it...
Best model extraction from a collection of models
Display the criteria associated with a collection of PLN fits (a PLNfa...
Extract model coefficients
Extracts model coefficients from objects returned by PLNLDA()
Extract model coefficients
Extract model coefficients
Extract the regularization path of a PLNnetwork fit
Compute offsets from a count data using one of several normalization s...
Helper function for PLN initialization.
Extract edge selection frequency in bootstrap subsamples
Extracts model fitted values from objects returned by PLN() and its ...
Extracts model fitted values from objects returned by PLNmixture() a...
Model extraction from a collection of models
An R6 Class to virtually represent a collection of network fits
Pipe operator
Poisson lognormal model
Control of a PLN fit
An R6 Class to represent a collection of PLNfit
An R6 Class to represent a PLNfit in a standard, general framework
An R6 Class to represent a PLNfit in a standard, general framework, wi...
An R6 Class to represent a PLNfit in a standard, general framework, wi...
An R6 Class to represent a PLNfit in a standard, general framework, wi...
Poisson lognormal model towards Linear Discriminant Analysis
Control of a PLNLDA fit
An R6 Class to represent a PLNfit in a LDA framework
An R6 Class to represent a PLNfit in a LDA framework with diagonal cov...
Poisson lognormal mixture model
Control of a PLNmixture fit
An R6 Class to represent a collection of PLNmixturefit
An R6 Class to represent a PLNfit in a mixture framework
PLNmodels: Poisson Lognormal Models
Sparse Poisson lognormal model for network inference
Control of PLNnetwork fit
An R6 Class to represent a collection of PLNnetworkfits
An R6 Class to represent a PLNfit in a sparse inverse covariance frame...
Poisson lognormal model towards Principal Component Analysis
Control of PLNPCA fit
An R6 Class to represent a collection of PLNPCAfit
An R6 Class to represent a PLNfit in a PCA framework
Display various outputs (goodness-of-fit criteria, robustness, diagnos...
LDA visualization (individual and/or variable factor map(s)) for a `PL...
Display the criteria associated with a collection of PLNmixture fits (...
Mixture visualization of a PLNmixturefit object
Extract and plot the network (partial correlation, support or inverse ...
Display the criteria associated with a collection of PLNPCA fits (a PL...
PCA visualization (individual and/or variable factor map(s)) for a `PL...
Extract and plot the network (partial correlation, support or inverse ...
Predict counts of a new sample
Predict group of new samples
Prediction for a PLNmixturefit object
Predict counts of a new sample
Predict counts conditionally
Prepare data for use in PLN models
PLN RNG
Extract variance-covariance of residuals 'Sigma'
Extract variance-covariance of residuals 'Sigma'
Extract variance-covariance of residuals 'Sigma'
Compute the stability path by stability selection
Component-wise standard errors of B
Calculate Variance-Covariance Matrix for a fitted PLN() model object
Zero Inflated Poisson lognormal model
Control of a ZIPLN fit
An R6 Class to represent a ZIPLNfit
An R6 Class to represent a ZIPLNfit in a standard, general framework, ...
An R6 Class to represent a ZIPLNfit in a standard, general framework, ...
An R6 Class to represent a ZIPLNfit in a standard, general framework, ...
An R6 Class to represent a ZIPLNfit in a standard, general framework, ...
Zero Inflated Sparse Poisson lognormal model for network inference
Control of ZIPLNnetwork fit
An R6 Class to represent a collection of ZIPLNnetwork
The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) 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.