sg_apply(dt_prep_sets): add extra "_row<row-number>" string to prep_label column of dt_prep_sets input when combining with Savitzky-Golay parameter sets. This modification makes sure that cartesian products of existing preprocessing sets supplied as input are correctly formed with the repeated Savitzky-Golay plans.snv_apply(X): add prep_params as list-column with a single-row data.table (snv = NA) to the output when X is provided. This makes binding outputs to other (pre)processed collections of spectra possible without further intervention; also, append_rows = TRUE will work with other methods, when output of snv_apply() is used as input of other *_apply() functions.sg_apply(): allow joins of Savitzky-Golay plans and preprocessing labels, and then also prepared Savitzky-Golay plans with inputted dt_prep_sets, when there is duplicated is. This is the case when dt_prep_sets input has already multiple rows (multiple collections) of spectra. Now, the desired duplicate joins are explicitly allowed by setting allow.cartesian = TRUE for respective data.table joins inside the sg_make_dt_prep() helper.colmean_group_apply() and group label constructor ids_apply.R(). This is to apply column means to to spectral collections, each by a group label.sg_apply() so that the extra "-snv" that got accidentally added to both prep_set and prep_label is not there anymore.sg_apply(), so that it can be run after e.g. snv_apply() (via dt_prep_sets input argument).snv_apply() to compute the standard normal variate (SNV) of spectral collections (#15).sg_apply() to process spectral collections with Savitzky-Golay smoothers with different parameter sets (derivative order, window size, polynomial degree).