## clustNet: Network-based clustering with covariate adjustment

`clustNet`

is an R package for network-based clustering of categorical data using a Bayesian network mixture model and optional covariate adjustment.

## Installation

The package requires `Rgraphviz`

and `RBGL`

, which can be installed from Bioconductor as follows:

`{r eval=FALSE} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install(c("Rgraphviz", "RBGL"))`

The latest stable version of clustNet is available on CRAN and can be installed with

`{r eval=FALSE} install.packages("clustNet")`

from within an R session. On a normal computer, this should take around 5-60 seconds, depending on how many of the required packages are already installed.

BiocManager::install(“remotes”)

Being hosted on GitHub, it is also possible to use the `install_github`

tool from an R session to install the latest development version:

`{r eval=FALSE} library("devtools") install_github("cbg-ethz/clustNet")`

`clustNet`

requires R `>= 3.5`

.

## Example

```{r eval=FALSE} library(clustNet)

# Simulate data

k_clust <- 3 # numer of clusters ss <- c(400, 500, 600) # samples in each cluster simulation_data <- sampleData(k_clust = k_clust, n_vars = 20, n_samples = ss) sampled_data <- simulation_data$sampled_data

# Network-based clustering

cluster_results <- get_clusters(sampled_data, k_clust = k_clust)

# Load additional pacakges to visualize the networks

library(ggplot2) library(ggraph) library(igraph) library(ggpubr)

# Visualize networks

plot_clusters(cluster_results)

# Load additional pacakges to create a 2d dimensionality reduction

library(car) library(ks) library(graphics) library(stats)

# Plot a 2d dimensionality reduction

density_plot(cluster_results)

```

On a normal computer, the clustering should take around 2-4 minutes.