Implement sampling from a long-format density fit

[osofr]

• sample_value() will not work when using pool = TRUE.
• The approach needs to somehow replicate the functionality in

  condensier/R/SummariesModelClass.R

  Lines 205 to 215 in 9e177e5

  	for (k_i in seq_along(private$PsAsW.models)) {
  	sampleA_newcat <- private$PsAsW.models[[k_i]]$sampleA(newdata = newdata, ...)
  	if (k_i == 1L) sampleA_mat[, k_i] <- sampleA_newcat
  	# carry forward all previously sampled 1's (degenerate ones a bin a chosen for the first time)
  	if (k_i > 1) {
  	# if you succeeded at the previous bin, your 1L is carried through till the end:
  	sampleA_mat[(sampleA_mat[, k_i - 1] == 1L), k_i] <- 1L
  	# if you haven't succeeded at the previous bin, you get a chance to succeed at this category:
  	sampleA_mat[(sampleA_mat[, k_i - 1] == 0L), k_i] <- sampleA_newcat[(sampleA_mat[, k_i - 1] == 0L)]
  	}
  	}

  but based on a single (pooled) model fit for all bin hazards.
• This needs to be done directly inside BinOutModel$sampleA. One potential approach is to re-create a loop over nbins, inside the loop mutate newdata with a new bin indicator, then keep call predict for the same fit.