Top-level fitting functionsSet of functions to fit variants of the Poisson lognormal model |
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PLNmodels: Poisson Lognormal Models |
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Poisson lognormal model |
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Zero Inflated Poisson lognormal model |
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Poisson lognormal model towards Linear Discriminant Analysis |
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Poisson lognormal model towards Principal Component Analysis |
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Sparse Poisson lognormal model for network inference |
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Zero Inflated Sparse Poisson lognormal model for network inference |
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Poisson lognormal mixture model |
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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). |
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An R6 Class to represent a PLNfit in a standard, general framework |
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An R6 Class to represent a PLNfit in a standard, general framework, with diagonal residual covariance |
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An R6 Class to represent a PLNfit in a standard, general framework, with fixed (inverse) residual covariance |
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An R6 Class to represent a PLNfit in a standard, general framework, with spherical residual covariance |
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Control of a PLN fit |
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Extract model coefficients |
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Calculate Variance-Covariance Matrix for a fitted |
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Extract variance-covariance of residuals 'Sigma' |
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Predict counts of a new sample |
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Predict counts conditionally |
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Extracts model fitted values from objects returned by |
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Component-wise standard errors of B |
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Zero Inflated Poisson lognormal fitDescription of the ZIPLNfit object and methods for its manipulation. |
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An R6 Class to represent a ZIPLNfit |
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An R6 Class to represent a ZIPLNfit in a standard, general framework, with diagonal residual covariance |
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An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance |
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An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance |
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An R6 Class to represent a ZIPLNfit in a standard, general framework, with spherical residual covariance |
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Control of a ZIPLN fit |
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Extract model coefficients |
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Extract variance-covariance of residuals 'Sigma' |
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Predict counts of a new sample |
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Extracts model fitted values from objects returned by |
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Extract and plot the network (partial correlation, support or inverse covariance) from a |
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Linear discriminant analysis via a Poisson lognormal fitDescription of the PLNLDAfit object and methods for its manipulation. |
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An R6 Class to represent a PLNfit in a LDA framework |
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An R6 Class to represent a PLNfit in a LDA framework with diagonal covariance |
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An R6 Class to represent a PLNfit in a standard, general framework, with diagonal residual covariance |
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Control of a PLNLDA fit |
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LDA visualization (individual and/or variable factor map(s)) for a |
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Predict group of new samples |
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Extracts model coefficients from objects returned by |
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Poisson Lognormal PCA fitDescription of the PLNPCAfit and PLNPCAfamily objects and methods for their manipulation. |
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An R6 Class to represent a PLNfit in a PCA framework |
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Control of PLNPCA fit |
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PCA visualization (individual and/or variable factor map(s)) for a |
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An R6 Class to represent a collection of PLNPCAfit |
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Display the criteria associated with a collection of PLNPCA fits (a PLNPCAfamily) |
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Best model extraction from a collection of models |
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Model extraction from a collection of models |
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Mixture of Poisson Lognormal fitsDescription of the PLNmixturefit and PLNmixturefamily objects and methods for their manipulation. |
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An R6 Class to represent a PLNfit in a mixture framework |
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Control of a PLNmixture fit |
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Mixture visualization of a |
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An R6 Class to represent a collection of PLNmixturefit |
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Display the criteria associated with a collection of PLNmixture fits (a PLNmixturefamily) |
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Extract model coefficients |
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Prediction for a |
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Extract variance-covariance of residuals 'Sigma' |
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Extracts model fitted values from objects returned by |
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Best model extraction from a collection of models |
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Model extraction from a collection of models |
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Sparse Poisson lognormal fit and network, w/o Zero Inflated componentDescription of the (ZI)PLNnetworkfit and (ZI)PLNnetworkfamily objects and methods for their manipulation. |
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An R6 Class to represent a PLNfit in a sparse inverse covariance framework |
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Control of PLNnetwork fit |
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Control of ZIPLNnetwork fit |
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Extract and plot the network (partial correlation, support or inverse covariance) from a |
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Extract and plot the network (partial correlation, support or inverse covariance) from a |
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An R6 Class to virtually represent a collection of network fits |
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An R6 Class to represent a collection of ZIPLNnetwork |
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An R6 Class to represent a collection of |
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Display various outputs (goodness-of-fit criteria, robustness, diagnostic) associated with a collection of network fits (either |
Best model extraction from a collection of models |
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Model extraction from a collection of models |
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Extract the regularization path of a PLNnetwork fit |
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Compute the stability path by stability selection |
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Extract edge selection frequency in bootstrap subsamples |
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Other functions and objects |
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Prepare data for use in PLN models |
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Compute offsets from a count data using one of several normalization schemes |
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An R6 Class to represent a collection of PLNfit |
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Display the criteria associated with a collection of PLN fits (a PLNfamily) |
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PLN RNG |
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Helper function for PLN initialization. |
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Data sets |
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Trichoptera data set |
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Oaks amplicon data set |
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Barents fish data set |
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Mollusk data set |
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Single cell RNA-seq data |