```
#' Test based on additive, varying coefficients model
#'
#' @param data [numeric vector] with the values of the response evaluated at the times in 'timepoints' argument.
#' @param group [numeric vector] of 1s and 2s as the grouping variable.
#' @param timepoints [numeric vector] with time points corresponding to measurements in 'data'.
#' @param id [numeric vector] with integers corresponding to the individual curves.
#' @param points [Integer] number of points used when discretizing functions.
#' @param nboot [Integer] number of Boostrap repetitions.
#' @import fda
#' @importFrom mgcv gam
#' @import checkmate
#' @export
gam_test <- function(data, group, timepoints, id, plot = FALSE) {
if(any(missing(data), missing(group), missing(timepoints), missing(id)) ||
any(is.null(data), is.null(group), is.null(timepoints), is.null(id)))
stop("Missing inputs")
checkmate::assertNumeric(data, all.missing = FALSE, min.len = 10)
if (length(group) != length(data) || !is.numeric(group) || any(!group %in% 1:2))
stop("Group has to be a grouping vector of 1s and 2s of same length as data.")
checkmate::assertNumeric(timepoints, all.missing = FALSE, len = length(data))
checkmate::assertInteger(id, lower = 0, all.missing = FALSE, len = length(data))
# make data.frame
data <- data.frame(cbind(id, group, timepoints, data))
g1 <- mgcv::summary.gam(m1 <- mgcv::gam(data ~ s(time, k = 9) + s(time, by = group, k = 9), data = data))
# plot difference curve with confidence bands
if(plot)
plot(m1, page = 1)
# return pvalue
setNames(g1$s.pv[2], nm = "pvalue")
}
```

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