### ZIPLNfit Example

Source: https://pln-team.github.io/PLNmodels/reference/ZIPLNfit.html

Example code showing how to fit a ZIPLN model and inspect the resulting ZIPLNfit object.

```APIDOC
## ZIPLNfit Example

### Description
This example demonstrates how to fit a Zero-Inflated Poisson Lognormal (ZIPLN) model using the `ZIPLN` function and then inspect the resulting `ZIPLNfit` object.

### Usage
```r
if (FALSE) { # \dontrun{
# See other examples in function ZIPLN
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera)
class(myPLN)
print(myPLN)
} # }
```

### Details
- The example uses the `trichoptera` dataset.
- `prepare_data` is used to format the data.
- `ZIPLN` fits the model with the formula `Abundance ~ 1`.
- `class` and `print` are used to inspect the resulting object.
```

--------------------------------

### Fit PLNnetwork and get best model

Source: https://pln-team.github.io/PLNmodels/reference/PLNnetworkfit.html

Example wrapped in \dontrun that loads the trichoptera dataset, prepares abundance and covariate data, fits a PLNnetwork model with formula Abundance ~ 1, retrieves the best model with getBestModel, and prints its class and details.

```r
if (FALSE) { # \dontrun{
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
nets <- PLNnetwork(Abundance ~ 1, data = trichoptera)
myPLNnet <- getBestModel(nets)
class(myPLNnet)
print(myPLNnet)
} # }
```

--------------------------------

### Example: compute_PLN_starting_point with barents data

Source: https://pln-team.github.io/PLNmodels/reference/compute_PLN_starting_point.html

Example wrapped in \dontrun, demonstrating both default LM and GLM methods. Requires the barents dataset and constructs the model matrix with covariates Latitude, Longitude, Depth, and Temperature.

```R
if (FALSE) { # \dontrun{
data(barents)
Y <- barents$Abundance
X <- model.matrix(Abundance ~ Latitude + Longitude + Depth + Temperature, data = barents)
O <- log(barents$Offset)
w <- rep(1, nrow(Y))
compute_PLN_starting_point(Y, X, O, w)
compute_PLN_starting_point(Y, X, O, w, method = "GLM")
} # }
```

--------------------------------

### PLNfit Example

Source: https://pln-team.github.io/PLNmodels/reference/PLNfit.html

Example of fitting a PLN model and inspecting the resulting PLNfit object.

```APIDOC
## PLNfit Example

### Description
Example of fitting a PLN model and inspecting the resulting PLNfit object.

### Usage
```r
if (FALSE) { # \dontrun{
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- PLN(Abundance ~ 1, data = trichoptera)
class(myPLN)
print(myPLN)
} # }
```

### Arguments
None.

### Details
This example uses the trichoptera dataset, prepares the data with `prepare_data`, fits a PLN model with `PLN`, and then prints the class and the object.

### Value
No return value; the example is for demonstration.

### Examples
```r
if (FALSE) { # \dontrun{
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- PLN(Abundance ~ 1, data = trichoptera)
class(myPLN)
print(myPLN)
} # }
```
```

--------------------------------

### Example: Fit ZIPLN model and extract coefficients

Source: https://pln-team.github.io/PLNmodels/reference/coef.ZIPLNfit.html

Example from the documentation that fits a ZIPLN model on a subset of the scRNA dataset and prints initialization and adjustment messages. The resulting model object can then be used with coef() to extract coefficients.

```r
data(scRNA)
# data subsample: only 100 random cell and the 50 most varying transcript
subset <- sample.int(nrow(scRNA), 100)
myPLN  <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), subset = subset, data = scRNA)
#> 
#>  Initialization...
#>  Adjusting a ZI-PLN model with full covariance model and single specific parameter(s) in Zero inflation component.
#>  DONE!
```

--------------------------------

### Example: Plot network from PLNnetworkfit

Source: https://pln-team.github.io/PLNmodels/reference/plot.PLNnetworkfit.html

Example workflow: load trichoptera data, prepare it, fit a PLNnetwork model, select the best model, and plot the network. The plot call is wrapped in \dontrun{}.

```R
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
fits <- PLNnetwork(Abundance ~ 1, data = trichoptera)
#> 
#>  Initialization...
#>  Adjusting 30 PLN with sparse inverse covariance estimation
#> 	Joint optimization alternating gradient descent and graphical-lasso
#> 	sparsifying penalty = 0.5246191 
	sparsifying penalty = 0.4845754 
	sparsifying penalty = 0.4475881 
	sparsifying penalty = 0.4134241 
	sparsifying penalty = 0.3818678 
	sparsifying penalty = 0.3527202 
	sparsifying penalty = 0.3257973 
	sparsifying penalty = 0.3009295 
	sparsifying penalty = 0.2779598 
	sparsifying penalty = 0.2567434 
	sparsifying penalty = 0.2371464 
	sparsifying penalty = 0.2190452 
	sparsifying penalty = 0.2023257 
	sparsifying penalty = 0.1868823 
	sparsifying penalty = 0.1726178 
	sparsifying penalty = 0.159442 
	sparsifying penalty = 0.1472719 
	sparsifying penalty = 0.1360308 
	sparsifying penalty = 0.1256477 
	sparsifying penalty = 0.1160571 
	sparsifying penalty = 0.1071986 
	sparsifying penalty = 0.09901618 
	sparsifying penalty = 0.09145836 
	sparsifying penalty = 0.08447742 
	sparsifying penalty = 0.07802933 
	sparsifying penalty = 0.07207342 
	sparsifying penalty = 0.06657212 
	sparsifying penalty = 0.06149072 
	sparsifying penalty = 0.05679719 
	sparsifying penalty = 0.05246191 

#>  Post-treatments
#>  DONE!
myNet <- getBestModel(fits)
if (FALSE) { # \dontrun{
plot(myNet)
} # }
```

--------------------------------

### Example: Fit ZIPLN with fixed Omega

Source: https://pln-team.github.io/PLNmodels/reference/ZIPLNfit_fixed.html

Example from the documentation (wrapped in \dontrun) that prepares the trichoptera data and fits a ZIPLN model with a fixed diagonal Omega matrix. It prints the class and the fitted model.

```r
if (FALSE) { # \dontrun{
# See other examples in function ZIPLN
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera,
    control = ZIPLN_param(Omega = diag(ncol(trichoptera$Abundance))))
class(myPLN)
print(myPLN)
} # }
```

--------------------------------

### Example: Fitting and Inspecting a PLNnetworkfit Model

Source: https://pln-team.github.io/PLNmodels/reference/PLNnetworkfit.html

Demonstrates how to prepare data, fit a PLNnetwork model, retrieve the best model, and inspect its class and print output.

```APIDOC
## Example: Fitting and Inspecting a PLNnetworkfit Model

### Description
This example shows how to use the PLNnetworkfit class by preparing data, fitting a PLNnetwork model, and retrieving the best model.

### Usage
```r
if (FALSE) { # \dontrun{
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
nets <- PLNnetwork(Abundance ~ 1, data = trichoptera)
myPLNnet <- getBestModel(nets)
class(myPLNnet)
print(myPLNnet)
} # }
```

### Details
- `data(trichoptera)` loads the trichoptera dataset.
- `prepare_data()` prepares the abundance and covariate data.
- `PLNnetwork()` fits a PLN network model with the formula `Abundance ~ 1`.
- `getBestModel()` extracts the best model according to the selection criterion.
- `class()` and `print()` are used to inspect the resulting PLNnetworkfit object.
```

--------------------------------

### compute_ZIPLN_starting_point

Source: https://pln-team.github.io/PLNmodels/reference/compute_ZIPLN_starting_point.html

Computes a fast starting point for ZIPLN model initialization using linear models and empirical zero rates.

```APIDOC
## compute_ZIPLN_starting_point

### Description
Computes a fast LM-based starting point for ZIPLN: one multivariate `lm.fit` for the PLN component and empirical zero rates / binomial GLMs for the ZI component. Replaces the previous per-species `pscl::zeroinfl` loop.

### Usage
```r
compute_ZIPLN_starting_point(Y, X, X0, O, w = NULL)
```

### Arguments
- **Y** (matrix) - Required - Response count matrix (n × p)
- **X** (matrix) - Required - Design matrix for the PLN component (n × d)
- **X0** (matrix) - Required - Design matrix for the ZI component (n × d0, empty `matrix(NA,0,0)` when unused)
- **O** (matrix) - Required - Offset matrix in log-scale (n × p)
- **w** (numeric vector) - Optional - Weight vector of length n (defaults to uniform weights)

### Value
Named list: `B` (d × p), `M` (n × p), `S2` (n × p), `R` (n × p), `B0` (d0 × p)
```

--------------------------------

### Example: PCA visualization with trichoptera data

Source: https://pln-team.github.io/PLNmodels/reference/plot.PLNPCAfit.html

Complete example showing data preparation, model fitting with PLNPCA, selecting the best model, and plotting individual, variable, and both factor maps. The plotting calls are wrapped in \dontrun{} and are not executed.

```r
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPCAs <- PLNPCA(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, ranks = 1:5)
#> 
#>  Initialization...
#> 
#>  Adjusting 5 PLN models for PCA analysis.
#> 	 Rank approximation = 1 
	 Rank approximation = 2 
	 Rank approximation = 3 
	 Rank approximation = 4 
	 Rank approximation = 5 

#>  Post-treatments
#>  DONE!
myPCA <- getBestModel(myPCAs)
if (FALSE) { # \dontrun{
plot(myPCA, map = "individual", nb_axes=2, ind_cols = trichoptera$Group)
plot(myPCA, map = "variable", nb_axes=2)
plot(myPCA, map = "both", nb_axes=2, ind_cols = trichoptera$Group)
} # }
```

--------------------------------

### Example: Prepare data and fit PLNnetwork

Source: https://pln-team.github.io/PLNmodels/reference/extract_probs.html

Loads the trichoptera dataset, prepares it, and fits a PLNnetwork model with an offset. The output shows the optimization progress with sparsifying penalties.

```r
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
nets <- PLNnetwork(Abundance ~ 1 + offset(log(Offset)), data = trichoptera)
#> 
#>  Initialization...
#>  Adjusting 30 PLN with sparse inverse covariance estimation
#> 	Joint optimization alternating gradient descent and graphical-lasso
#> 	sparsifying penalty = 0.3985143 
	sparsifying penalty = 0.368096 
	sparsifying penalty = 0.3399996 
	sparsifying penalty = 0.3140477 
	sparsifying penalty = 0.2900767 
	sparsifying penalty = 0.2679354 
	sparsifying penalty = 0.2474841 
	sparsifying penalty = 0.2285939 
	sparsifying penalty = 0.2111455 
	sparsifying penalty = 0.1950289 
	sparsifying penalty = 0.1801425 
	sparsifying penalty = 0.1663924 
	sparsifying penalty = 0.1536918 
	sparsifying penalty = 0.1419607 
	sparsifying penalty = 0.1311249 
	sparsifying penalty = 0.1211163 
	sparsifying penalty = 0.1118716 
	sparsifying penalty = 0.1033325 
	sparsifying penalty = 0.09544523 
	sparsifying penalty = 0.08815998 
	sparsifying penalty = 0.0814308 
	sparsifying penalty = 0.07521526 
	sparsifying penalty = 0.06947414 
	sparsifying penalty = 0.06417124 
	sparsifying penalty = 0.0592731 
	sparsifying penalty = 0.05474884 
	sparsifying penalty = 0.05056991 
	sparsifying penalty = 0.04670995 
	sparsifying penalty = 0.04314462 
	sparsifying penalty = 0.03985143 

#>  Post-treatments
#>  DONE!
```

--------------------------------

### ZIPLNfit_sparse Example

Source: https://pln-team.github.io/PLNmodels/reference/ZIPLNfit_sparse.html

Example usage of the ZIPLN function to create a ZIPLNfit_sparse object, with printing and plotting.

```APIDOC
## ZIPLNfit_sparse

### Description
Example of fitting a sparse zero-inflated Poisson lognormal model using the ZIPLN function and inspecting the resulting ZIPLNfit_sparse object.

### Usage
```r
if (FALSE) { # \dontrun{
# See other examples in function ZIPLN
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control=  ZIPLN_param(penalty = 1))
class(myPLN)
print(myPLN)
plot(myPLN)
} # }
```

### Arguments
- **data** (data.frame) - Required - The dataset containing abundance and covariate information.
- **formula** (formula) - Required - Model formula, e.g., `Abundance ~ 1`.
- **control** (list) - Optional - Control parameters, e.g., `ZIPLN_param(penalty = 1)` for sparse regularization.

### Details
This example demonstrates the typical workflow: load the trichoptera dataset, prepare it, fit a ZIPLN model with a penalty, and then examine the resulting object's class, print summary, and generate plots.

### Value
An object of class `ZIPLNfit_sparse` (inheriting from `ZIPLNfit`), which contains the fitted model parameters and can be used for further analysis.

### Examples
```r
if (FALSE) { # \dontrun{
# See other examples in function ZIPLN
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control=  ZIPLN_param(penalty = 1))
class(myPLN)
print(myPLN)
plot(myPLN)
} # }
```
```

--------------------------------

### PLNPCAfit Example

Source: https://pln-team.github.io/PLNmodels/reference/PLNPCAfit.html

Example of fitting a PLNPCA model and accessing the resulting PLNPCAfit object, including its fields and S3 methods.

```APIDOC
## PLNPCAfit

### Description
PLNPCAfit is an R6 class representing a fitted Poisson Lognormal model with rank-constrained PCA. It extends PLNfit and PCA classes.

### Usage
```r
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPCAs <- PLNPCA(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, ranks = 1:5)
myPCA <- getBestModel(myPCAs)
```

### Fields
- `$model_par` - Model parameters
- `$latent` - Latent variables
- `$latent_pos` - Latent variables (positive)
- `$var_par` - Variational parameters
- `$optim_par` - Optimization parameters
- `$loglik` - Log-likelihood
- `$BIC` - Bayesian Information Criterion
- `$ICL` - Integrated Completed Likelihood
- `$loglik_vec` - Log-likelihood vector
- `$nb_param` - Number of parameters
- `$criteria` - Model selection criteria
- `$percent_var` - Percentage of variance explained
- `$corr_circle` - Correlation circle
- `$scores` - PCA scores
- `$rotation` - PCA rotation
- `$eig` - Eigenvalues
- `$var` - Variance
- `$ind` - Individual coordinates

### S3 Methods
- `print()` - Print the model
- `coef()` - Extract coefficients
- `sigma()` - Extract standard deviation
- `vcov()` - Variance-covariance matrix
- `fitted()` - Fitted values
- `predict()` - Predictions
- `predict_cond()` - Conditional predictions
- `standard_error()` - Standard errors
- `plot.PLNPCAfit()` - Plot the model

### Example Output
```
#> Poisson Lognormal with rank constrained for PCA (rank = 3)
#> ==================================================================
#>  nb_param    loglik       BIC       AIC       ICL
#>        65 -1056.865 -1183.349 -1121.865 -1241.534
#> ==================================================================
```
```

--------------------------------

### Example: Fit ZIPLN with diagonal covariance

Source: https://pln-team.github.io/PLNmodels/reference/ZIPLNfit_diagonal.html

Example code (not run) showing how to fit a ZIPLN model with diagonal covariance using the trichoptera dataset. It prepares the data, fits the model, and prints the class and the model object.

```r
if (FALSE) { # \dontrun{
# See other examples in function ZIPLN
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "diagonal"))
class(myPLN)
print(myPLN)
} # }
```

--------------------------------

### Example: Fit PLNPCA on trichoptera data with parallel computation

Source: https://pln-team.github.io/PLNmodels/reference/PLNPCA.html

Example from the documentation showing how to fit PLNPCA models on the trichoptera dataset. It sets up parallel computation with mc.cores, prepares the data, and runs PLNPCA with ranks 1:5. The output shows the initialization and adjustment progress.

```R
## Use parallel to dispatch the computations on 2 workers
if (FALSE) { # \dontrun{
options(mc.cores = 2)
} # }

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPCA <- PLNPCA(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, ranks = 1:5)
#> 
#>  Initialization...
#> 
#>  Adjusting 5 PLN models for PCA analysis.
#> 	 Rank approximation = 1 
	 Rank approximation = 2 
	 Rank approximation = 3 
	 Rank approximation = 4 
	 Rank approximation = 5 

#>  Post-treatments
#>  DONE!

if (FALSE) { # \dontrun{
options(mc.cores = 1)
} # }
```

--------------------------------

### Fit ZIPLN model on trichoptera data

Source: https://pln-team.github.io/PLNmodels/reference/ZIPLNfit.html

This example shows how to fit a ZIPLN model using the ZIPLN function. It prepares the trichoptera dataset and fits a model with an intercept-only formula. The example is wrapped in a dontrun block, so it is not executed by default.

```r
if (FALSE) { # \dontrun{
# See other examples in function ZIPLN
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera)
class(myPLN)
print(myPLN)
} # }
```

--------------------------------

### Example: predict groups with PLNLDA on trichoptera data

Source: https://pln-team.github.io/PLNmodels/reference/predict.PLNLDAfit.html

Demonstrates fitting a PLNLDA model and predicting posterior probabilities and response groups. The example is wrapped in a dontrun block, so it is not executed by default. It shows how to compute posterior probabilities and predicted groups, then compare them to true groups.

```r
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myLDA <- PLNLDA(Abundance ~ 0 + offset(log(Offset)),
                grouping = Group,
                data = trichoptera)
#> 
#>  Performing discriminant Analysis...
#>  DONE!
if (FALSE) { # \dontrun{
post_probs <- predict(myLDA, newdata = trichoptera, type = "posterior", scale = "prob")
head(round(post_probs, digits = 3))
predicted_group <- predict(myLDA, newdata = trichoptera, type = "response")
table(predicted_group, trichoptera$Group, dnn = c("predicted", "true"))
} # }
```

--------------------------------

### Example: stability_selection with trichoptera data

Source: https://pln-team.github.io/PLNmodels/reference/stability_selection.html

Example code that prepares the trichoptera dataset, fits a PLNnetwork model, and runs stability_selection with 10 subsamples. The example is wrapped in a dontrun block.

```r
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
fits <- PLNnetwork(Abundance ~ 1, data = trichoptera)
#> 
#>  Initialization...
#>  Adjusting 30 PLN with sparse inverse covariance estimation
#> 	Joint optimization alternating gradient descent and graphical-lasso
#> 	sparsifying penalty = 0.5246191 
	sparsifying penalty = 0.4845754 
	sparsifying penalty = 0.4475881 
	sparsifying penalty = 0.4134241 
	sparsifying penalty = 0.3818678 
	sparsifying penalty = 0.3527202 
	sparsifying penalty = 0.3257973 
	sparsifying penalty = 0.3009295 
	sparsifying penalty = 0.2779598 
	sparsifying penalty = 0.2567434 
	sparsifying penalty = 0.2371464 
	sparsifying penalty = 0.2190452 
	sparsifying penalty = 0.2023257 
	sparsifying penalty = 0.1868823 
	sparsifying penalty = 0.1726178 
	sparsifying penalty = 0.159442 
	sparsifying penalty = 0.1472719 
	sparsifying penalty = 0.1360308 
	sparsifying penalty = 0.1256477 
	sparsifying penalty = 0.1160571 
	sparsifying penalty = 0.1071986 
	sparsifying penalty = 0.09901618 
	sparsifying penalty = 0.09145836 
	sparsifying penalty = 0.08447742 
	sparsifying penalty = 0.07802933 
	sparsifying penalty = 0.07207342 
	sparsifying penalty = 0.06657212 
	sparsifying penalty = 0.06149072 
	sparsifying penalty = 0.05679719 
	sparsifying penalty = 0.05246191 

#>  Post-treatments
#>  DONE!
if (FALSE) { # \dontrun{
n <- nrow(trichoptera)
subs <- replicate(10, sample.int(n, size = n/2), simplify = FALSE)
stability_selection(nets, subsamples = subs)
} # }
```

--------------------------------

### getModel example with PLNPCA

Source: https://pln-team.github.io/PLNmodels/reference/getModel.html

Example usage of getModel to extract a model from a PLNPCAfamily. The example is wrapped in a dontrun block, so it is not executed by default. It prepares the trichoptera data, fits a PLNPCA model with ranks 1:5, and extracts the model with rank 2.

```R
if (FALSE) { # \dontrun{
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPCA <- PLNPCA(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, ranks = 1:5)
myModel <- getModel(myPCA, 2)
} # }
```

--------------------------------

### Example: Plot individual factor map for PLNLDAfit

Source: https://pln-team.github.io/PLNmodels/reference/plot.PLNLDAfit.html

This example loads the trichoptera dataset, prepares the data, fits a PLNLDA model, and plots the individual factor map with two axes. The plot call is wrapped in a dontrun block, so it is not executed in examples.

```r
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLNLDA <- PLNLDA(Abundance ~ 1, grouping = Group, data = trichoptera)
#> 
#>  Performing discriminant Analysis...
#>  DONE!
if (FALSE) { # \dontrun{
plot(myPLNLDA, map = "individual", nb_axes = 2)
} # }
```

--------------------------------

### PLNLDA example

Source: https://pln-team.github.io/PLNmodels/reference/PLNLDAfit.html

Example of fitting a PLNLDA model using the trichoptera dataset, including data preparation and printing the fitted object.

```APIDOC
## PLNLDA Example

### Description
This example demonstrates how to fit a PLNLDA model using the trichoptera dataset.

### Usage
```r
if (FALSE) { # \dontrun{
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLNLDA <- PLNLDA(Abundance ~ 1, grouping = Group, data = trichoptera)
class(myPLNLDA)
print(myPLNLDA)
} # }
```

### Arguments
- **formula** (formula) - Required - A formula specifying the model, e.g., `Abundance ~ 1`.
- **grouping** (factor) - Required - The grouping variable for the data.
- **data** (data.frame) - Required - The prepared data frame containing abundance and covariates.

### Details
This example is wrapped in `\dontrun{}` and is not executed during package checks.

### Value
An object of class `PLNLDAfit`.

### Examples
```r
if (FALSE) { # \dontrun{
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLNLDA <- PLNLDA(Abundance ~ 1, grouping = Group, data = trichoptera)
class(myPLNLDA)
print(myPLNLDA)
} # }
```
```

--------------------------------

### compute_PLN_starting_point()

Source: https://pln-team.github.io/PLNmodels/reference/index.html

Helper function for PLN initialization.

```APIDOC
## compute_PLN_starting_point()

### Description
Helper function for PLN initialization.

### Usage
`compute_PLN_starting_point()`

### Arguments
No arguments documented.

### Value
No return value documented.

```

--------------------------------

### Function usage signature for compute_PLN_starting_point

Source: https://pln-team.github.io/PLNmodels/reference/compute_PLN_starting_point.html

Shows the required arguments and default method for compute_PLN_starting_point. The method argument accepts either "LM" (default) or "GLM".

```R
compute_PLN_starting_point(Y, X, O, w, method = c("LM", "GLM"))
```

--------------------------------

### Install PLNmodels from CRAN and GitHub

Source: https://pln-team.github.io/PLNmodels/index.html

Install the stable version from CRAN, the development version from GitHub, or a specific tagged release using remotes.

```R
install.packages("PLNmodels")             # last stable version, from CRAN
remotes::install_github("pln-team/PLNmodels")            # development version, from GitHub
remotes::install_github("pln-team/PLNmodels@tag_number")  # a specific tagged release
```

--------------------------------

### Example: Extract best model from PLNPCA

Source: https://pln-team.github.io/PLNmodels/reference/getBestModel.html

Example usage of getBestModel on a PLNPCAfamily object. The example is wrapped in 'if (FALSE)' to prevent execution during documentation checks. It prepares the trichoptera data, fits a PLNPCA model with ranks 1:4, and extracts the best model using the default criterion.

```r
if (FALSE) { # \dontrun{
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPCA <- PLNPCA(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, ranks = 1:4)
myModel <- getBestModel(myPCA)
} # }
```

--------------------------------

### PLNLDA example with diagonal covariance

Source: https://pln-team.github.io/PLNmodels/reference/PLNLDAfit_diagonal.html

Example code showing how to fit a PLNLDA model with diagonal covariance using the trichoptera dataset, and how to inspect the resulting object class and print it.

```APIDOC
## PLNLDA example with diagonal covariance

### Description
This example demonstrates fitting a PLNLDA model with diagonal covariance using the `PLNLDA` function on the trichoptera dataset, and then inspecting the fitted model object.

### Usage
```r
if (FALSE) { # \dontrun{
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLNLDA <- PLNLDA(Abundance ~ 1, grouping = Group, data = trichoptera,
                   control = PLNLDA_param(covariance = "diagonal"))
class(myPLNLDA)
print(myPLNLDA)
} # }
```

### Arguments
- `Abundance ~ 1` (formula) - Required - The model formula for the abundance response.
- `grouping = Group` (factor) - Required - The grouping variable for discriminant analysis.
- `data = trichoptera` (data.frame) - Required - The dataset containing the variables.
- `control = PLNLDA_param(covariance = "diagonal")` (list) - Required - Control parameters specifying diagonal covariance.

### Details
- The `prepare_data` function is used to prepare the abundance and covariate data.
- The `PLNLDA` function fits the model.
- `class(myPLNLDA)` returns the class of the fitted object.
- `print(myPLNLDA)` prints the fitted model summary.

### Value
The fitted PLNLDA model object of class `PLNLDAfit` (or subclass).
```

--------------------------------

### PLNPCAfit$warm_start_from()

Source: https://pln-team.github.io/PLNmodels/reference/PLNPCAfit.html

Reinitializes parameters for sequential warm-starting from a lower-rank fit, carrying over fitted loadings, scores, variances, and regression coefficients, and padding new columns using the inception SVD or zeros/0.1.

```APIDOC
## PLNPCAfit$warm_start_from()

### Description
Reinitialize parameters for sequential warm-starting from a lower-rank fit. Fitted loadings C, scores M, variances S, and regression coefficients B from `prev_fit` are carried over; new columns are padded using the inception SVD (C) or zeros/0.1 (M/S).

### Usage
```
PLNPCAfit$warm_start_from(prev_fit, svdM)
```

### Arguments
- **prev_fit** - a converged `PLNPCAfit` of rank `self$rank - k` (k >= 1)
- **svdM** - the inception SVD (from `PLNPCAfamily`)
```

--------------------------------

### PLNfit_genpop$new()

Source: https://pln-team.github.io/PLNmodels/reference/PLNfit_genpop.html

Initializes a PLNfit_genpop model with responses, covariates, offsets, weights, formula, and control parameters. The control list must include a fixed correlation matrix C.

```APIDOC
## PLNfit_genpop$new()

### Description
Initialize a `PLNfit_genpop` model.

### Method
Constructor

### Usage
```
PLNfit_genpop$new(responses, covariates, offsets, weights, formula, control)
```

### Parameters
#### Arguments
- **responses** (matrix) - Required - The matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class.
- **covariates** (matrix) - Required - Design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class.
- **offsets** (matrix) - Required - Offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class.
- **weights** (vector) - Optional - An optional vector of observation weights to be used in the fitting process.
- **formula** (formula) - Required - Model formula used for fitting, extracted from the formula in the upper-level call.
- **control** (list) - Required - A list for controlling the optimization, must include a field `C` (the fixed p x p correlation matrix). See details.

### Details
Sigma = sigma2 * (rho * C + (1 - rho) * I_p), where C is a fixed p x p correlation matrix supplied by the user (`control$C`) and (sigma2, rho) are estimated. See `GeneticCovTraits` in `src/covariance_pln.h` for the C++ side.

### Example
```r
if (FALSE) { # \dontrun{
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
p <- ncol(trichoptera$Abundance)
C <- 0.5^abs(outer(1:p, 1:p, "-")); diag(C) <- 1
myPLN <- PLN(Abundance ~ 1, data = trichoptera, control = PLN_param(covariance = "genpop", C = C))
class(myPLN)
print(myPLN)
} # }
```
```

--------------------------------

### Example: plot network from ZIPLNfit_sparse

Source: https://pln-team.github.io/PLNmodels/reference/plot.ZIPLNfit_sparse.html

Fits a ZIPLN model with sparse covariance and plots the network. The example is wrapped in \dontrun{} and includes data preparation and model fitting with a penalty of 0.1.

```r
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
fit <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(penalty = 0.1))
#> ℹ The penalty now applies on the correlation scale by default: the penalties
#>   given are taken as such, between 0 and 1. Until version 1.3.2 they were on
#>   the covariance scale.
#> ℹ Set `penalty_scale = "covariance"` in the control parameters to get the
#>   former behavior back, or `penalty_scale = "correlation"` to keep the
#>   penalties as they are, without this message.
#> This message is displayed once per session.
#> 
#>  Initialization...
#>  Adjusting a ZI-PLN model with sparse covariance model and single specific parameter(s) in Zero inflation component.
#>  DONE!
if (FALSE) { # \dontrun{
plot(fit)
} # }
```

--------------------------------

### ZIPLNfit$show() usage

Source: https://pln-team.github.io/PLNmodels/reference/ZIPLNfit.html

User-friendly print method with a customizable first line.

```r
ZIPLNfit$show(
  model = paste("A multivariate Zero Inflated Poisson Lognormal fit with",
    self$vcov_model, "covariance model.\n")
)
```

--------------------------------

### Example: Stability selection and extract probabilities as vector

Source: https://pln-team.github.io/PLNmodels/reference/extract_probs.html

Performs stability selection on the fitted network and extracts edge selection probabilities as a named vector. Wrapped in \dontrun{} to avoid execution in examples.

```r
if (FALSE) { # \dontrun{
stability_selection(nets)
probs <- extract_probs(nets, crit = "StARS", format = "vector")
probs
} # }
```

--------------------------------

### ZIPLNfit$show()

Source: https://pln-team.github.io/PLNmodels/reference/ZIPLNfit.html

User-friendly print method for the ZIPLNfit object.

```APIDOC
## ZIPLNfit$show()

### Description
User-friendly print method for the ZIPLNfit object.

### Method
R6 method

### Usage
```r
ZIPLNfit$show(
  model = paste("A multivariate Zero Inflated Poisson Lognormal fit with",
    self$vcov_model, "covariance model.\n")
)
```

### Parameters
- **model** (character) - Optional - First line of the print output.
```

--------------------------------

### ZIPLNfit_spherical$new()

Source: https://pln-team.github.io/PLNmodels/reference/ZIPLNfit_spherical.html

Initializes a ZIPLNfit_spherical model with the provided data and control parameters.

```APIDOC
## ZIPLNfit_spherical$new()

### Description
Initialize a `ZIPLNfit_spherical` model.

### Usage
```
ZIPLNfit_spherical$new(data, control)
```

### Arguments
- **data** (named list) - Required - A named list used internally to carry the data matrices.
- **control** (list) - Required - A list for controlling the optimization. See details.

```

--------------------------------

### PLNmixturefit$new()

Source: https://pln-team.github.io/PLNmodels/reference/PLNmixturefit.html

Initializes a PLNmixturefit model with the provided responses, covariates, offsets, posterior probabilities, formula, and control parameters.

```APIDOC
## PLNmixturefit$new()

### Description
Initializes a PLNmixturefit model.

### Usage
```r
PLNmixturefit$new(
  responses,
  covariates,
  offsets,
  posteriorProb,
  formula,
  control
)
```

### Arguments
- **responses** (matrix) - Required - The matrix of responses common to every model.
- **covariates** (matrix) - Required - The matrix of covariates common to every model.
- **offsets** (matrix) - Required - The matrix of offsets common to every model.
- **posteriorProb** (matrix) - Required - Matrix of posterior probability for cluster belonging.
- **formula** (formula) - Required - Model formula used for fitting, extracted from the formula in the upper-level call.
- **control** (list) - Required - A list for controlling the optimization.
```

--------------------------------

### ZIPLNnetworkfamily$new()

Source: https://pln-team.github.io/PLNmodels/reference/ZIPLNnetworkfamily.html

Initializes all models in the collection with given penalties, data, and control parameters.

```APIDOC
## ZIPLNnetworkfamily$new()

### Description
Initializes all models in the collection with given penalties, data, and control parameters.

### Usage
```
ZIPLNnetworkfamily$new(penalties, data, control)
```

### Arguments
- **penalties** (vector of positive real numbers) - Required - A vector of positive real numbers controlling the level of sparsity of the underlying network.
- **data** (named list) - Required - A named list used internally to carry the data matrices.
- **control** (list) - Required - A list for controlling the optimization.

### Returns
Updates current `PLNnetworkfit` with smart starting values.
```

--------------------------------

### Example: Fit PLN mixture and plot PCA and matrix

Source: https://pln-team.github.io/PLNmodels/reference/plot.PLNmixturefit.html

Fits a PLN mixture model on the trichoptera data and plots the results using 'pca' and 'matrix' types. The example is wrapped in \dontrun{} and includes the model fitting output.

```r
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- PLNmixture(Abundance ~ 1 + offset(log(Offset)),
           data = trichoptera, control = PLNmixture_param(smoothing = "none"))  %>% getBestModel()
#> 
#>  Initialization...
#> 
#>  Adjusting 5 PLN mixture models.
#> 	number of cluster = 1 
	number of cluster = 2 
	number of cluster = 3 
	number of cluster = 4 
	number of cluster = 5 

#>  Post-treatments
#>  DONE!
if (FALSE) { # \dontrun{
plot(myPLN, "pca")
plot(myPLN, "matrix")
} # }
```

--------------------------------

### PLNmixturefit$show()

Source: https://pln-team.github.io/PLNmodels/reference/PLNmixturefit.html

User friendly print method.

```APIDOC
## PLNmixturefit$show()

### Description
User friendly print method.

### Method
R6 method

### Usage
```
PLNmixturefit$show()
```
```