library(Cyclops)
library(IterativeHardThresholding)
library(survival)

## data dimension
p <- 20    # number of covariates
n <- 300   # sample size

## Cox model parameters
true.beta <- c(1, 0.1, 0, -1, 1, rep(0, p - 5))

## simulate data from an exponential model
x        <- matrix(rnorm(p * n, mean = 0, sd = 1), ncol = p)
ti       <- rweibull(n, shape = 1, scale = exp(-x%*%true.beta))
ui       <- runif(n, 0, 10) # Controls censoring
ci       <- rweibull(n, shape = 1, scale = ui * exp(-x%*%true.beta))
survtime <- pmin(ti, ci)
delta    <- ti == survtime; mean(delta)

cyclopsData <- createCyclopsData(Surv(survtime, delta) ~ x, modelType = "cox")
ihtPrior    <- createIhtPrior(K = 3, penalty = "bic")

cyclopsFit <- fitCyclopsModel(cyclopsData,
                             prior = ihtPrior)
coef(cyclopsFit)

library(Cyclops)
library(IterativeHardThresholding)

## data dimension
p <- 20    # number of covariates
n <- 300   # sample size

## logistic model parameters
itcpt     <- 0.2 # intercept
true.beta <- c(1, 0.3, 0, -1, 1, rep(0, p - 5))

## simulate data from logistic model
x <- matrix(rnorm(p * n, mean = 0, sd = 1), ncol = p)
y <- rbinom(n, 1, 1 / (1 + exp(-itcpt - x%*%true.beta)))


# fit BAR model
cyclopsData <- createCyclopsData(y ~ x, modelType = "lr")
ihtPrior    <- createIhtPrior(K  = 3, penalty = "bic", exclude = c("(Intercept)"))

cyclopsFit <- fitCyclopsModel(cyclopsData,
                             prior = ihtPrior)
coef(cyclopsFit)

install.packages("Cyclops")
install.packages("IterativeHardThresholding")

library(IterativeHardThresholding)
cyclopsData <- createCyclopsData(formula, modelType = "modelType") ## TODO: Update
ihtPrior    <- createIhtPrior(K = 5, penalty = "bic")
cyclopsFit  <- fitCyclopsModel(cyclopsData, prior = ihtPrior)
coef(cyclopsFit) #Extract coefficients