library(Cyclops)
library(BrokenAdaptiveRidge)

## data dimension
p <- 30    # number of covariates
n <- 200   # sample size

## logistic model parameters 
itcpt     <- 0.2 # intercept
true.beta <- c(1, 0, 0, -1, 1, rep(0, p - 5))

## simulate data from logistic model
set.seed(100)

x <- matrix(rnorm(p * n, mean = 0, sd = 1), ncol = p)
x <- ifelse(abs(x) > 1., 1, 0)
y <- rbinom(n, 1, 1 / (1 + exp(-itcpt - x%*%true.beta)))


# fit BAR model
cyclopsData <- createCyclopsData(y ~ x, modelType = "lr")
barPrior    <- createBarPrior(penalty = 0.1, exclude = c("(Intercept)"), 
                             initialRidgeVariance = 1) 

cyclopsFit <- fitCyclopsModel(cyclopsData,
                             prior = barPrior)
fit1 <- coef(cyclopsFit) 

# fit BAR using sparse-represented covariates
tmp <- apply(x, 1, function(x) which(x != 0))

y.df <- data.frame(rowId = 1:n, y = y)
x.df <- data.frame(rowId = rep(1:n, lengths(tmp)), covariateId = unlist(tmp), covariateValue = 1)

cyclopsData <- convertToCyclopsData(outcomes = y.df, covariates = x.df, modelType = "lr")
barPrior    <- createFastBarPrior(penalty = 0.1, exclude = c("(Intercept)"), 
                                 initialRidgeVariance = 1) 

fit2 <- coef(cyclopsFit) 

# fit BAR using cyclic algorithm
cyclopsData <- createCyclopsData(y ~ x, modelType = "lr")
barPrior    <- createFastBarPrior(penalty = 0.1, exclude = c("(Intercept)"), 
                             initialRidgeVariance = 1) 

cyclopsFit <- fitCyclopsModel(cyclopsData,
                             prior = barPrior)
fit3 <- coef(cyclopsFit) 

fit1
fit2
fit3

install.packages("BrokenAdaptiveRidge")