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### mfData #
library(roahd)
N = 10
P= 5
grid = seq( 0, 1, length.out = P )
C = exp_cov_function( grid, alpha = 0.3, beta = 0.4 )
Data_1 = generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = C )
Data_2 = generate_gauss_fdata( N, centerline = log(1+ 2 * pi * grid ), Cov = C )
mfD=mfData( grid, list( Data_1, Data_2 ) )
x0=list(as.list(grid))
fun=mean_lists()
final.mfData = conformal.fun.split(NULL,NULL, mfD,NULL, x0, fun$train.fun, fun$predict.fun,
alpha=0.2,
split=NULL, seed=FALSE, randomized=FALSE,seed.rand=FALSE,
verbose=TRUE, rho=0.5,s.type="identity")
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