probcure(x, t, d, dataset, x0, h, local = TRUE, conflevel = 0L,
bootpars = if (conflevel == 0 && !missing(h)) NULL else controlpars())

## Some artificial data
set.seed(123)
n <- 50
x <- runif(n, -2, 2) ## Covariate values
y <- rweibull(n, shape = 0.5 * (x + 4)) ## True lifetimes
c <- rexp(n) ## Censoring values
p <- exp(2*x)/(1 + exp(2*x)) ## Probability of being susceptible
u <- runif(n)
t  <- ifelse(u < p, pmin(y, c), c) ## Observed times
d  <- ifelse(u < p, ifelse(y < c, 1, 0), 0) ## Uncensoring indicator
data <- data.frame(x = x, t = t, d = d)

## Covariate values where cure probability is estimated 
x0 <- seq(-1.5, 1.5, by = 0.1) 

## Nonparametric estimates of cure probability at 'x0'...

## ... (a) with global bandwidths 1, 1.5, 2  
q1 <- probcure(x, t, d, data, x0 = x0, h = c(1, 1.5, 2), local = FALSE)

#### Plot predicted cure probabilities at 'x0' for each bandwidth in 'h'
#### (the true cure probability is displayed for reference)
plot(q1$x0, q1$q$h1, type = "l", xlab = "Covariate", ylab =
"Cure probability", ylim = c(0, 1))
lines(q1$x0, q1$q$h1.5, lty = 2)
lines(q1$x0, q1$q$h2, lty = 3)
lines(q1$x0, 1 - exp(2*q1$x0)/(1 + exp(2*q1$x0)), col = 2)
legend("topright", c(paste("Estimate, ", c("h = 1", "h = 1.5",
"h = 2")), "True"), lty = c(1, 2, 3, 1), col = c(1, 1, 1, 2))

## ... (b) with local bandwidths (default)
#### (the vectors passed to 'x0' and 'h' must have the same length)
q2 <- probcure(x, t, d, data, x0 = x0, h = seq(1, 2.5, along = x0)) 

## ... (c) with local bootstrap bandwidths (based on 1999 booostrap
#### resamples). Besides, 95% confidence intervals are computed  and
#### smoothed (with a 15-th order moving average)
set.seed(1) ## Not needed, just for reproducibility.
hb <- probcurehboot(x, t, d, data, x0 = x0, bootpars = controlpars(B =
1999, hsmooth = 15))
q3 <- probcure(x, t, d, data, x0 = x0, h = hb$hsmooth, conflevel = .95,
bootpars = controlpars(B = 1999))


## ... (d) If the bandwidth is not specified, the local bootstrap
#### bandwidth is used (same results as in (c))
set.seed(1) ## Not needed, just for reproducibility.
q4 <- probcure(x, t, d, data, x0 = x0, conflevel = .95, bootpars =
controlpars(B = 1999, hsmooth = 15))

#### Plot of the estimated cure probabilities evaluated at 'x0'
#### (true cure rate displayed as reference)
plot (q4$x0, q4$q, type = "l", ylim = c(0, 1), xlab = "Covariate X",
ylab = "Cure probability")
lines(q4$x0, q4$conf$lower, lty = 2)
lines(q4$x0, q4$conf$upper, lty = 2)
lines(q4$x0, 1-exp(2 * q4$x0)/(1 + exp(2 * q4$x0)), col = 2)
legend("topright", c("Estimate", "95% CI limits", "True"),
lty = c(1, 2, 1), col = c(1, 1, 2))

## Example with the dataset 'bmt' in the 'KMsurv' package
## to study the probability of cure as a function of the age (z1).
data("bmt", package = "KMsurv")
x0 <- seq(quantile(bmt$z1, .05), quantile(bmt$z1, .95), length.out = 100)
q.age <- probcure(z1, t2, d3, bmt, x0 = x0, conflevel = .95, bootpars =
controlpars(B = 1999, hsmooth = 10)) 

## Plot of estimated cure probability and confidence intervals
par(mar = c(5, 4, 4, 5) + .1)
plot(q.age$x0, q.age$q, type = "l", ylim = c(0, 1), xlab =
"Patient age (years)", ylab = "Cure probability")
lines(q.age$x0, q.age$conf$lower, lty = 2)
lines(q.age$x0, q.age$conf$upper, lty = 2)
## The estimated density of age (z1) is added for reference
par(new = TRUE)
d.age <- density(bmt$z1)
plot(d.age, xaxt = "n", yaxt = "n", xlab = "", ylab = "", col = 2,
main = "", zero.line = FALSE)
mtext("Density", side = 4, col = 2, line = 3) 
axis(4, ylim = c(0, max(d.age$y)), col = 2, col.axis = 2)
legend("topright", c("Estimate", "95% CI limits", "Covariate density"),
lty = c(1, 2, 1), col = c(1, 1, 2))