Preliminaries | Requirements | Data set | Mathematical background | Covariates and offsets | Alternating optimization | Analysis of trichoptera data with a PLNnetwork model | Adjusting a collection of network - a.k.a. a regularization path | Structure of PLNnetworkfamily | Exploring the path of networks | Model selection issue: choosing a network | Structure of a PLNnetworkfit | References

Preliminaries | Requirements | Data set | Mathematical background | Analysis of microcosm with ZIPLN | Comparing zero-inflation parameterizations | Inspecting the fit | Sparse network inference with ZIPLNnetwork | References

Preliminaries | Format description | Computing offsets | Offset methods | Dealing with sparse data | Offsets scales | Building data frame using prepare_data | Importing data from biom and phyloseq objects using prepare_data_from_[phyloseq|biom] | Reading from a biom file | Reading from a phyloseq-class object | Mathematical details about the offsets | Total Sum Scaling | Cumulative Sum Scaling | Relative Log Expression | Geometric Mean of Pairwise Ratio | Wrench normalisation | References

Motivation | Setup | Simulation: a mixed sparse + dense signal | Fitting with lava() | Regularization paths by component | Model Selection | Model extraction and decomposition | Comparing estimators | Cross-validation | Group LAVA: group-sparse + dense decomposition | Simulation with a group-sparse signal | Fitting | Paths of the sparse and dense components | Model selection | Group identification | Reference

Motivation | Setup | Simulation | Community graph and its Laplacian | Lasso: a baseline without structural knowledge | Fused Lasso: penalizing differences between consecutive predictors | Fused Lasso with the community graph | Structured Ridge: shrinking toward block-constant solutions | Standard ridge | Structured ridge ($S = L_2$) | Structured Elastic-net: sparsity + graph-Laplacian regularization | Debiased structured Elastic-net | Coefficient recovery: a side-by-side comparison

Preliminaries | Mathematical background | The data | A known clustering | Letting the model infer the clustering | A collection over the number of clusters | Reading the clusters | Choosing the shape of the residual covariance | Sparsifying the residual covariance | References

Preliminaries | Requirements | Data simulation | Fix simulation parameters | Simulation 1 (no zero-inflation) | Simulation 2 (with zero-inflation) | Prepare the data for a Normal-Block analysis | Run a Normal-Block analysis | With non-zero-inflated data | Fixed clustering | Fixed number of clusters | Unknown number of clusters | Playing with sparsity | With zero-inflated data | References

Preliminaries | Requirements | Mathematical background | The data | A known clustering of the proteins | Letting the model infer its own clustering | Fitting a collection over a range of cluster counts | Model selection: criteria to fix the number of clusters | Refining the clustering | Sparsifying the network of the selected clustering | References

Preliminaries | Mathematical background | The Normal-Block model | Zero-inflation extension | The zero-inflation probabilities $\kappa$ | Requirements | The data | Preparing the data | Fitting a zero-inflated Normal-Block model | Refining the collection | Selecting a model and inspecting the clustering | Inspecting the zero-inflation probabilities | Sparsifying the association network | References

Prerequisites | The war network | Generating missing data | Estimation with missing data | Estimation on fully observed network | Taking covariates into account | Military power | Trade data | References

Preliminaries | Requirements | Data set | Mathematical background | Covariates and offsets | Optimization by Variational inference | Analysis of trichoptera data with a PLN model | A PLN model with latent main effects | Adjusting a fit | The PLNfit object | Field access | GLM-like interface | Observation weights | Accounting for covariates and offsets | Covariance models (full, diagonal, spherical) | References

Preliminaries | Requirements | Data set | Mathematical background | Covariates and offsets | Parametrization of the covariance of the mixture models | Optimization by Variational inference | Analysis of trichoptera data with a PLN-mixture model | A mixture model with a latent main effects for the Trichoptera data set | Adjusting a collection of fits | Structure of PLNmixturefamily | Model selection | Structure of PLNmixturefit | Specific fields | plot method | predict method | References

Preliminaries | The trichoptera data set | Formatting | Table of counts (abundancies) | Covariates (external meteorological effect, groups) | Offsets and the compositionality issue | References

Preliminaries | Requirements | Data set | Mathematical background | Covariates and offsets | Optimization by Variational inference | Analysis of trichoptera data with a PLNPCA model | A model with latent main effects for the Trichoptera data set | Adjusting a collection of fits | Structure of PLNPCAfamily | Model selection of rank $q$ | Structure of PLNPCAfit | Additional visualization | Projecting new data in the PCA space | A model accounting for meteorological covariates | References

Preliminaries | Requirements | Data set | Mathematical background | Covariates and offsets | Prediction | Optimization by Variational inference | Analysis of trichoptera data with a PLN-LDA model | A model with main effects and no covariates | Structure of PLNLDAfit | Specific fields | plot method | predict method | A model with latent main effects and meteorological covariates | References

Setup | Fitting group-sparse linear models | Group Lasso | Cooperative Lasso | Sparse Group Lasso | Fit objects | Regularization paths | Information criteria | Model extraction | Cross-validation | Cross-validation on $\lambda_1 \times \lambda_2$ | Prediction | Stability selection | Debiased Estimator

Setup | Fitting sparse linear models | Lasso | SCAD | MCP | Elastic-Net | Fit objects | Regularization paths | Information criteria | Model extraction | Cross-validation | Cross-validation on $\lambda_1 \times \lambda_2$ | Prediction | Stability selection | Debiased Estimators

Preliminaries | Requirements | Data set | Data manipulation | Fitting a multiplex SBM model where the two layers are assumed to be independent | Fitting a multiplex SBM model where the two layers are assumed to be dependent | References

Preliminaries | Requirements | Dataset | Formatting the data | Mathematical Background | Inference | Plots | References

Preliminaries | Requirements | Data set: antagonistic tree/fungus interaction network | Mathematical Background | Analysis of the tree/tree data | Tree-tree binary interaction networks | About model selection and choice of the number of blocks | Analysis of the weighted interaction network | Introduction of covariates | Analysis of the tree/fungi data | References

Preliminaries | Requirements | Multiplex network data | Stochastic Block models for multiplex networks | General formulation of the model | Dependent and independent layers conditionally to $Z$ | Bipartite multiplex networks | Inference | Implementation | Data simulation | References