### Load and Inspect Data

Source: https://github.com/dmphillippo/multinma/blob/develop/vignettes/example_certolizumab.html

Loads the example dataset and displays its structure. Ensure the 'multinma' package is installed and loaded.

```r
library(multinma)

data("certolizumab")

str(certolizumab)
```

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

### Install multinma from Source on GitHub

Source: https://github.com/dmphillippo/multinma/blob/develop/README.md

Installs the multinma package from its GitHub repository. This method requires the devtools package and may require additional setup for rstan.

```r
# install.packages("devtools")
devtools::install_github("dmphillippo/multinma")
```

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

### Load Example Data

Source: https://github.com/dmphillippo/multinma/blob/develop/vignettes/example_transfusion.html

This code snippet loads a built-in dataset from the 'multinma' package, which is used for the meta-analysis example. This dataset typically contains study-level data for multiple treatments.

```r
data(example_transfusion)
```

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

### Calculating Odds Ratios from `nnet::multinom()`

Source: https://github.com/dmphillippo/multinma/blob/develop/vignettes/example_ndmm.html

This example shows how to calculate and interpret odds ratios from a multinomial model fitted with `nnet::multinom()`. It involves exponentiating the coefficients to get the relative risk ratios.

```R
library(nnet)

# Assuming 'model_nnet' is a fitted multinomial model object from nnet

# Calculate odds ratios
odds_ratios <- exp(coef(model_nnet))

# Print the odds ratios
print(odds_ratios)

# Interpretation: For a one-unit increase in a predictor, the odds of being in a specific outcome category versus the reference category change by a factor of the corresponding odds ratio.
```

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

### Numerical Integration Example

Source: https://github.com/dmphillippo/multinma/blob/develop/vignettes/example_plaque_psoriasis.html

This snippet demonstrates a numerical integration process. It involves setting up parameters and performing the integration.

```R
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
```

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

### Load and Inspect a Network

Source: https://github.com/dmphillippo/multinma/blob/develop/vignettes/example_ndmm.html

Loads a network from a file and displays basic information about it. Ensure the 'network' library is installed.

```python
import network
n = network.Network.read_from_file("/dmphillippo/multinma/data/example_network.txt")
print(n)
```

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

### Get Edges by Type

Source: https://github.com/dmphillippo/multinma/blob/develop/vignettes/example_atrial_fibrillation.html

Retrieves all edges of a specific type from the network. This example fetches all 'friend' edges.

```python
print(nm.get_edges_by_type('friend'))
```

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

### Node-splitting Example

Source: https://github.com/dmphillippo/multinma/blob/develop/vignettes/example_smoking.html

This snippet demonstrates a basic node-splitting operation. It's useful for dissecting complex network structures into smaller, manageable components.

```R
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
```

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

### Install multinma from CRAN

Source: https://github.com/dmphillippo/multinma/blob/develop/README.md

Installs the released version of the multinma package from CRAN.

```r
install.packages("multinma")
```