# load gss
library(gss)

# generate data
set.seed(5732)
x <- runif(100)
y <- 1 + 3 * sin(2*pi*x-pi) + rnorm(x)

# fit cubic spline
cubic.fit <- ssanova(y~x,type="cubic",method="v")

# evaluate the fit
range <- max(x) - min(x)
grid <- seq(min(x)-.05*range,max(x)+.05*range,len=51)
est <- predict(cubic.fit,data.frame(x=grid),se.fit=TRUE)

# plot the fit
plot(x,y)
lines(grid,1+3*sin(2*pi*grid-pi),col=6)
lines(grid,est$fit,col=4)
lines(grid,est$fit+1.96*est$se.fit,col=5)
lines(grid,est$fit-1.96*est$se.fit,col=5)