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PLNmodels (version 0.11.7)

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

487

Version

0.11.7

License

GPL (>= 3)

Maintainer

Julien Chiquet

Last Published

August 25th, 2022

Functions in PLNmodels (0.11.7)

PLNPCAfamily

An R6 Class to represent a collection of PLNPCAfit
PLNfamily

An R6 Class to represent a collection of PLNfit
PLN

Poisson lognormal model
PLNLDA

Poisson lognormal model towards Linear Discriminant Analysis
PLNmixturefamily

An R6 Class to represent a collection of PLNmixturefit
PLNfit

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

Poisson lognormal model towards Principal Component Analysis
PLNmixture

Poisson lognormal mixture model
PLNLDAfit

An R6 Class to represent a PLNfit in a LDA framework
PLNPCAfit

An R6 Class to represent a PLNfit in a PCA framework
coef.PLNfit

Extract model coefficients
coef.PLNmixturefit

Extract model coefficients
compute_offset

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

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

Extract the regularization path of a PLNnetwork fit
PLNmixturefit

An R6 Class to represent a PLNfit in a mixture framework
PLNnetworkfamily

An R6 Class to represent a collection of PLNnetworkfit
coef.PLNLDAfit

Extracts model coefficients from objects returned by PLNLDA()
PLNnetwork

Poisson lognormal model towards sparse network inference
PLNmodels

PLNmodels
oaks

Oaks amplicon data set
plot.PLNLDAfit

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

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

Extract edge selection frequency in bootstrap subsamples
fitted.PLNfit

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

Pipe operator
mollusk

Mollusk data set
getBestModel.PLNPCAfamily

Best model extraction from a collection of models
getModel.PLNPCAfamily

Model extraction from a collection of models
plot.PLNPCAfamily

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

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

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

Fisher information matrix for Theta
plot.PLNmixturefit

Mixture visualization of a PLNmixturefit object
plot.PLNmixturefamily

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

Prediction for a PLNmixturefit object
plot.PLNnetworkfamily

Display various outputs (goodness-of-fit criteria, robustness, diagnostic) associated with a collection of PLNnetwork fits (a PLNnetworkfamily)
stability_selection

Compute the stability path by stability selection
vcov.PLNfit

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

Predict counts conditionally
plot.PLNnetworkfit

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

Predict counts of a new sample
predict.PLNLDAfit

Predict group of new samples
standard_error

Component-wise standard errors of Theta
sigma.PLNfit

Extract variance-covariance of residuals 'Sigma'
trichoptera

Trichoptera data set
prepare_data

Prepare data for use in PLN models
sigma.PLNmixturefit

Extract variance-covariance of residuals 'Sigma'
rPLN

PLN RNG