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The goal of dineR is to enable users of all backgrounds to easily and computationally efficiently perform differential network estimation.
You can install the released version of dineR from CRAN with:
install.packages("dineR")This is a basic example which shows you how to solve a common problem:
library(dineR)
# Data Generation
n_X <- 100
n_Y <- n_X
p_X <- 100
p_Y <- p_X
#case <- "sparse"
case <- "asymsparse"
data <- data_generator(n = n_X, p = p_X, seed = 123)
X <- data$X
Y <- data$Y
diff_Omega <- data$diff_Omega
paste("The number of non-zero entries in the differential network is: ", sum(diff_Omega!=0))
# Estimation Preliminaries (All of the parameters are now optional as the function has pre-specified defaults)
loss <- "lasso"
nlambda <- 50
tuning <- "AIC"
stop_tol <- 1e-4
perturb <- F
correlation <- F
max_iter <- 500
lambda_min_ratio <- 0.5
#gamma <- 1 #Only if we use EBIC
# Estimation
result <- estimation(X, Y, loss = loss, nlambda = nlambda, tuning = tuning, stop_tol = stop_tol,
perturb = perturb, correlation = correlation,
max_iter = max_iter, lambda_min_ratio = lambda_min_ratio)
# Results
print(result$path[[1]][1:5,1:5])
result$elapseThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.