MeanEst(Lt, Ly, kern, bw, gridout)

# Generate data
n <- 100
interval <- c(0, 10)
lambda_1 <- 9 #the first eigenvalue
lambda_2 <- 1.5 #the second eigenvalue
eigfun <- list()
eigfun[[1]] <- function(x){cos(pi * x/10)/sqrt(5)}
eigfun[[2]] <- function(x){sin(pi * x/10)/sqrt(5)}
score <- cbind(rnorm(n, 0, sqrt(lambda_1)), rnorm(n, 0, sqrt(lambda_2)))
DataNew <- GenDataKL(n, interval = interval, sparse = 6:8,
                     meanfun = function(x){x}, score = score,
                     eigfun = eigfun, sd = sqrt(0.1))
# Mean function estimate at all observation time points
bwOpt <- GetGCVbw1D(DataNew$Lt, DataNew$Ly, kern = "epan")
meanest <- MeanEst(DataNew$Lt, DataNew$Ly, kern = "epan", bw = bwOpt,
                   gridout = sort(unique(unlist(DataNew$Lt))))
plot(meanest$Grid, meanest$mean)