## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(dineR)

## -----------------------------------------------------------------------------
# Number of observations in sample 1
n_X <- 100 

# Number of observations in sample 2
n_Y <- 150

# Number of features in each of the samples
p <- 50

# The form of the precision covariance matrices
case <- "sparse"

# The seed of the simulation process to ensure reproducibility of results
seed <- 123

## -----------------------------------------------------------------------------
data <- data_generator(n_X = n_X, n_Y = n_Y, p = p, case = case, seed = seed)

## -----------------------------------------------------------------------------
# Extract the first sample
X <- data$X
cat("The number of observations in the first sample is:", nrow(X))
cat("The number of features/dimensions in the first sample is:", ncol(X))

# Extract the second sample
Y <- data$Y
cat("The number of observations in the second sample is:", nrow(Y))
cat("The number of features/dimensions in the second sample is:", ncol(Y))

## -----------------------------------------------------------------------------
# Extract the first sample's covariance matrix
Sigma_X <- data$Sigma_X

# Extract the second sample's covariance matrix
Sigma_Y <- data$Sigma_Y

## -----------------------------------------------------------------------------
# Extract the first sample's precision matrix
Omega_X <- data$Omega_X

# Extract the second sample's precision matrix
Omega_Y <- data$Omega_Y

## -----------------------------------------------------------------------------
# Extract the differential network
Delta <- data$Delta