#' @title Analyse IRSAR RF measurements
#'
#' @description
#' The function analyses IRSAR RF measurements on K-feldspar samples performed
#' using the protocol according to Erfurt et al. (2003) and beyond.
#'
#' @details The function performs an IRSAR analysis described for K-feldspar samples by
#' Erfurt et al. (2003) assuming a negligible sensitivity change of the RF
#' signal.
#'
#' **General Sequence Structure** (according to Erfurt et al., 2003)
#'
#' 1. Measuring IR-RF intensity of the natural dose for a few seconds (\eqn{RF_{nat}})
#' 2. Bleach the samples under solar conditions for at least 30 min without changing the geometry
#' 3. Waiting for at least one hour
#' 4. Regeneration of the IR-RF signal to at least the natural level (measuring (\eqn{RF_{reg}})
#' 5. Fitting data with a stretched exponential function
#' 6. Calculate the palaeodose \eqn{D_{e}} using the parameters from the fitting
#'
#' Three methods are supported to obtain the \eqn{D_{e}}:
#' `method = "FIT"`, `method = "SLIDE"` and `method = "VSLIDE"`:
#'
#' **`method = "FIT"`**
#'
#' The principle is described above and follows the original suggestions by
#' Erfurt et al., 2003. For the fitting, the mean count value of the `RF_nat`
#' curve is used.
#'
#' Function used for the fitting (according to Erfurt et al. (2003)):
#'
#' \deqn{\phi(D) = \phi_{0} - \Delta\phi(1 - \exp(-\lambda D))^\beta}
#'
#' with
#' \eqn{\phi(D)} the dose dependent IR-RF flux,
#' \eqn{\phi_{0}} the initial IR-RF flux,
#' \eqn{\Delta\phi} the dose dependent change of the IR-RF flux,
#' \eqn{\lambda} the exponential parameter, \eqn{D} the dose and
#' \eqn{\beta} the dispersive factor.
#'
#' To obtain the palaeodose
#' \eqn{D_{e}} the function is changed to:
#'
#' \deqn{D_{e} = \ln(-(\phi(D) - \phi_{0})/(-\lambda\phi)^{1/\beta}+1)/-\lambda}
#'
#' The fitting is done using the `port` algorithm of the [nls] function.
#'
#'
#' **`method = "SLIDE"`**
#'
#' For this method, the natural curve is slid along the x-axis until
#' congruence with the regenerated curve is reached. Unlike fitting, this
#' allows for working with the original data without the need for any physical
#' model. This approach was introduced for RF curves by Buylaert et al., 2012
#' and Lapp et al., 2012.
#'
#' Here the sliding is done by searching for the minimum of the sum of
#' squared residuals.
#' For the mathematical details of the implementation see Frouin et al., 2017
#'
#' **`method = "VSLIDE"`**
#'
#' Same as `"SLIDE"` but searching also vertically for the best match (i.e. in xy-direction.)
#' See Kreutzer et al. (2017) and Murari et al. (2021). By default, the vertical sliding
#' range is set automatically, but can be set manually by changing the
#' `vslide_range` parameter (see `method_control`).
#'
#' **`method_control`**
#'
#' To keep the generic argument list as clear as possible, parameters to control
#' the methods for De estimation are preset with meaningful default values,
#' which can however be modified using the `method_control` argument, e.g.,
#' `method_control = list(trace = TRUE)`. Supported parameters are:
#'
#' *For FIT*
#' - `trace` ([logical], default: `FALSE`): as in [nls]; shows sum of squared
#' residuals.
#' - `maxiter` ([integer], default: 500): as in [nls].
#' - `warnOnly` ([logical], default: `FALSE`): as in [nls].
#' - `minFactor` ([numeric], default: `1 / 4096`): as in [nls].
#'
#' *For SLIDE or VSLIDE*
#' - `trace` ([logical], default: `FALSE`): as in [nls]; shows sum of squared
#' residuals.
#' - `trace_vslide` ([logical], default: `FALSE`): enable/disable the tracing
#' of the vertical sliding.
#' - `correct_onset` ([logical], default: `TRUE`): whether the curves should
#' be shifted along the x-axis by the first channel, as light is expected in
#' the first channel.
#' - `show_density` ([logical], default: `TRUE`): enable/disable KDE plots for
#' MC run results. Nothing is shown if the distribution is too narrow.
#' - `show_fit` ([logical], default: `FALSE`): enable/disable the plot of the
#' fitted curve routinely obtained during the evaluation.
#' - `n.MC` ([integer], default: 1000): number of Monte Carlo runs within the
#' sliding (assessing the possible minimum values). **Note:** This parameter
#' is not the same as the function argument `n.MC`.
#' - `vslide_range` ([numeric] or [character], default: "auto"): boundaries
#' for the vertical curve sliding. The argument expects a vector with absolute
#' minimum and maximum (e.g., `c(-1000,1000)`). The default "auto" mode
#' detects the reasonable vertical sliding range (*recommended*). `NULL`
#' disables the vertical sliding.
#' - `num_slide_windows` ([integer], default: 3): number of  differently-sized
#' windows tested when sliding: the higher the value (up to a maximum of 10),
#' the more time is spent in searching the global optimum. The default setting
#' attempts to strike a balance between quality of the fit and computation
#' speed.
#' - `cores` ([numeric] or [character], default: `NULL`): number of cores
#' allocated for a parallel processing of the Monte-Carlo runs. The default
#' value corresponds to single-threaded computation; the recommended values is
#' `"auto"`, which assigns all but two of the available cores.
#'
#' **Error estimation**
#'
#' For **`method = "FIT"`**, the \eqn{D_{e}} error range is obtained by using
#' the 2.5 % (lower) and the 97.5 % (upper) quantiles of the \eqn{RF_{nat}}
#' curve.
#'
#' For **`method = "SLIDE"`** and **`method = "VSLIDE"`**, the error is obtained
#' by bootstrapping the residuals of the slid curve to construct new natural
#' curves for a Monte Carlo simulation. The error is returned in two ways:
#' (a) the standard deviation of the \eqn{D_{e}} obtained from the MC runs and
#' (b) the confidence interval using the 2.5 % (lower) and the 97.5 % (upper)
#' quantiles. The results of the MC runs are returned with the function output.
#'
#' **Test parameters**
#'
#' The argument `test_parameters` allows to pass thresholds for several test
#' parameters, which will be evaluated during the function run. If a threshold
#' is set and it is exceeded, the test parameter status will be set to
#' `"FAILED"`. This argument is intentionally not termed `'rejection criteria'`
#' as not all test parameters are evaluated for both methods and some parameters
#' are calculated but not evaluated by default.
#'
#' `NA` and `NULL` are the allowed values for all parameters. If the parameter
#' is set to `NA`, the value is calculated but the result will not be evaluated,
#' therefore it will have no effect on the status (`"OK"` or `"FAILED"`) of
#' the parameter. Setting the parameter to `NULL` disables the parameter
#' entirely and the parameter will also be removed from the function output.
#' This might be useful in cases where a particular parameter requires a long
#' computation time. Currently supported parameters are:
#'
#' - `curves_ratio` ([numeric], default: `1.001`):
#' the ratio of \eqn{RF_{nat}} to \eqn{RF_{reg}} is calculated over the range
#' spanned by \eqn{RF_{nat}}, and should not exceed the threshold value.
#'
#' - `intersection_ratio` ([numeric], default: `NA`):
#' calculated as absolute difference from 1 of the ratio of the integral of
#' the normalised RF-curves. This value indicates intersection of the RF-curves
#' and should be close to 0 if the curves have a similar shape. For this
#' calculation first the corresponding time-count pair value on the RF_reg
#' curve is obtained using the maximum count value of the `RF_nat` curve and
#' only this segment (fitting to the `RF_nat` curve) on the RF_reg curve is
#' taken for further calculating this ratio. If nothing is found at all, `Inf`
#' is returned.
#'
#' - `residuals_slope` ([numeric], default: `NA`; only for `method = "SLIDE"`
#' and `"VSLIDE`"):
#' a linear function is fitted on the residuals after sliding.
#' The corresponding slope can be used to discard values as a high (positive,
#' negative) slope may indicate that both curves are fundamentally different
#' and the method cannot be applied at all.
#' By default, the value of this parameter is calculated but not evaluated.
#'
#' - `curves_bounds` ([numeric], default: \eqn{\max(RF_{reg_counts})}):
#' this measure uses the maximum time (x) value of the regenerated curve.
#' The maximum time (x) value of the natural curve cannot be larger than this
#' value. However, although this is not recommended the value can be changed
#' or disabled.
#'
#' - `dynamic_ratio` ([numeric], default: `NA`):
#' the dynamic ratio of the regenerated curve is calculated as ratio of the
#' minimum and maximum count values.
#'
#' - `lambda`, `beta` and `delta.phi` [numeric] (default: `NA`):
#' the stretched exponential function suggested by Erfurt et al. (2003)
#' describing the decay of the RF signal, comprises several parameters that
#' might be useful to evaluate the shape of the curves. For `method = "FIT"`,
#' this parameter is obtained during the fitting; for `method = "SLIDE"` a
#' rather rough estimation is made using the function [minpack.lm::nlsLM] and
#' the equation given above.
#' **Note:** As this procedure requests more computation time, it is performed
#' only if all three parameters are set.
#'
#' @param object [Luminescence::RLum.Analysis-class] or a [list] of [Luminescence::RLum.Analysis-class]-objects (**required**):
#' input object containing data for protocol analysis. The function expects to
#' find at least two curves in the [Luminescence::RLum.Analysis-class] object: (1) `RF_nat`, (2) `RF_reg`.
#' If a `list` is provided as input all other parameters can be provided as
#' `list` as well to gain full control.
#'
#' @param sequence_structure [vector] [character] (*with default*):
#' specifies the general sequence structure. Allowed steps are `NATURAL` and
#' `REGENERATED`, and at least one of each must appear. In addition, any other
#' character is allowed in the sequence structure; such curves will be ignored
#' during the analysis. If `sequence_structure` is shorter than the number of
#' curves in `object`, it is recycled. If multiple `NATURAL` and `REGENERATED`
#' steps are specified, the corresponding measurements are stacked.
#'
#' @param RF_nat.lim [vector] (*with default*):
#' set minimum and maximum channel range for natural signal fitting and sliding.
#' If only one value is provided this will be treated as minimum value and the
#' maximum limit will be added automatically.
#'
#' @param RF_reg.lim [vector] (*with default*):
#' set minimum and maximum channel range for regenerated signal fitting and sliding.
#' If only one value is provided this will be treated as minimum value and the
#' maximum limit will be added automatically.
#'
#' @param method [character] (*with default*): select method applied for the
#' data analysis. Possible options are `"FIT"`, `"SLIDE"`, `"VSLIDE"`;
#' `"NONE"` can be used to disable the analysis and plot the natural points
#' at their original position.
#'
#' @param method_control [list] (*optional*):
#' parameters to control the method, that can be passed to the chosen method.
#' These are for (1) `method = "FIT"`: `'trace'`, `'maxiter'`, `'warnOnly'`, `'minFactor'` and for
#' (2) `method = "SLIDE"`: `'correct_onset'`, `'show_density'`,  `'show_fit'`, `'trace'`.
#' See details.
#'
#' @param test_parameters [list] (*with default*):
#' set test parameters. Supported parameters are: `curves_ratio`,
#' `residuals_slope` (only for `method = "SLIDE"` and `"VSLIDE"`),
#' `curves_bounds`, `dynamic_ratio`, `lambda`, `beta` and `delta.phi`.
#' All input: [numeric] values, `NA` and `NULL` (see Details for further
#' information).
#'
#' @param n.MC [numeric] (*with default*):
#' number of Monte Carlo runs for the estimation of the start parameter
#' (`method = "FIT"`) or of the error (`method = "SLIDE"` and `"VSLIDE"`).
#' This value can be set to `NULL` to skip the MC runs.
#' Note: Large values will significantly increase the computation time.
#'
#' @param txtProgressBar [logical] (*with default*):
#' enable/disable the progress bar during MC runs.
#'
#' @param plot [logical] (*with default*):
#' enable/disable the plot output.
#'
#' @param plot_reduced [logical] (*optional*):
#' provides a reduced plot output if enabled to allow common R plot combinations,
#' e.g., `par(mfrow(...))`. If `TRUE` no residual plot
#' is returned; it has no effect if `plot = FALSE`
#'
#' @param ... further arguments that will be passed to the plot output.
#' Currently supported arguments are `main`, `mtext`, `xlab`, `ylab`,
#' `xlim`, `ylim`, `log`, `legend` (`TRUE/FALSE`),
#' `legend.pos`, `legend.text` (passes argument to x,y in
#' [graphics::legend]), `xaxt`, `verbose` (`TRUE/FALSE`).
#'
#' @return
#' The function returns numerical output and an (*optional*) plot.
#'
#' -----------------------------------\cr
#' `[ NUMERICAL OUTPUT ]`\cr
#' -----------------------------------\cr
#'
#' **`RLum.Results`**-object
#'
#' **slot:** **`@data`**
#'
#' `[.. $data : data.frame]`
#'
#' \tabular{lll}{
#'  **Column** \tab **Type** \tab **Description**\cr
#'  `DE` \tab `numeric` \tab the obtained equivalent dose\cr
#'  `DE.ERROR` \tab `numeric` \tab (only `method = "SLIDE"`) standard deviation obtained from MC runs \cr
#'  `DE.LOWER` \tab `numeric`\tab 2.5% quantile for De values obtained by MC runs \cr
#'  `DE.UPPER` \tab `numeric`\tab 97.5% quantile for De values obtained by MC runs  \cr
#'  `DE.STATUS`  \tab `character`\tab test parameter status\cr
#'  `RF_NAT.LIM`  \tab `character`\tab used `RF_nat` curve limits \cr
#'  `RF_REG.LIM` \tab `character`\tab used `RF_reg` curve limits\cr
#'  `POSITION` \tab `integer`\tab (*optional*) position of the curves\cr
#'  `DATE` \tab `character`\tab (*optional*) measurement date\cr
#'  `SEQUENCE_NAME` \tab `character`\tab (*optional*) sequence name\cr
#'  `UID` \tab `character`\tab unique data set ID
#' }
#'
#' `[.. $De.MC : numeric]`
#'
#' A `numeric` vector with all the De values obtained by the MC runs.
#'
#' `[.. $test_parameters : data.frame]`
#'
#' \tabular{lll}{
#'  **Column** \tab **Type** \tab **Description**\cr
#'  `POSITION` \tab `numeric` \tab aliquot position \cr
#'  `PARAMETER` \tab `character` \tab test parameter name \cr
#'  `THRESHOLD` \tab `numeric` \tab set test parameter threshold value \cr
#'  `VALUE` \tab `numeric` \tab the calculated test parameter value (to be compared with the threshold)\cr
#'  `STATUS` \tab `character` \tab test parameter status either `"OK"` or `"FAILED"` \cr
#'  `SEQUENCE_NAME` \tab `character` \tab name of the sequence, so far available \cr
#'  `UID` \tab `character`\tab unique data set ID
#' }
#'
#' `[.. $fit : data.frame]`
#'
#' An [nls] object produced by the fitting.
#'
#' `[.. $slide : list]`
#'
#' A [list] with data produced during the sliding. Some elements are previously
#' reported with the summary object data. List elements are:
#'
#' \tabular{lll}{
#'  **Element** \tab **Type** \tab **Description**\cr
#'  `De` \tab `numeric` \tab the final De obtained with the sliding approach \cr
#'  `De.MC` \tab `numeric` \tab all De values obtained by the MC runs \cr
#'  `residuals` \tab `numeric` \tab the obtained residuals for each channel of the curve \cr
#'  `trend.fit` \tab `lm` \tab fitting results produced by the fitting of the residuals \cr
#'  `RF_nat.slid` \tab `matrix` \tab the slid `RF_nat` curve \cr
#'  `t_n.id` \tab `numeric` \tab the index of the t_n offset \cr
#'  `I_n` \tab `numeric` \tab the vertical intensity offset if a vertical slide was applied \cr
#'  `algorithm_error` \tab `numeric` \tab the vertical sliding suffers from a systematic effect induced by the used
#'  algorithm. The returned value is the standard deviation of all obtained De values while expanding the
#'  vertical sliding range. It can be added as systematic error to the final De error; so far wanted.\cr
#'  `vslide_range` \tab `numeric` \tab the range used for the vertical sliding \cr
#'  `num_slide_windows` \tab `integer` \tab the number of windows used for the vertical sliding \cr
#'  `squared_residuals` \tab `numeric` \tab the squared residuals (horizontal sliding)
#' }
#'
#'
#' **slot:** **`@info`**
#'
#' The original function call ([methods::language-class]-object)
#'
#' The output (`data`) should be accessed using the function [Luminescence::get_RLum].
#'
#' ------------------------\cr
#' `[ PLOT OUTPUT ]`\cr
#' ------------------------\cr
#'
#' The slid IR-RF curves with the finally obtained De
#'
#' @note
#' This function assumes that there is no sensitivity change during the
#' measurements (natural vs. regenerated signal), which is in contrast to the
#' findings by Buylaert et al. (2012).
#'
#' @section Function version: 0.7.10
#'
#' @author Sebastian Kreutzer, F2.1 Geophysical Parametrisation/Regionalisation, LIAG - Institute for Applied Geophysics (Germany)
#'
#' @seealso [Luminescence::RLum.Analysis-class], [Luminescence::RLum.Results-class], [Luminescence::get_RLum],
#' [nls], [minpack.lm::nlsLM], [parallel::mclapply], [Luminescence::ExampleData.RF70Curves]
#'
#'
#' @references
#' Buylaert, J.P., Jain, M., Murray, A.S., Thomsen, K.J., Lapp, T.,
#' 2012. IR-RF dating of sand-sized K-feldspar extracts: A test of accuracy.
#' Radiation Measurements 44 (5-6), 560-565. \doi{10.1016/j.radmeas.2012.06.021}
#'
#' Erfurt, G., Krbetschek, M.R., 2003. IRSAR - A single-aliquot
#' regenerative-dose dating protocol applied to the infrared radiofluorescence
#' (IR-RF) of coarse- grain K-feldspar. Ancient TL 21, 35-42.
#' \doi{10.26034/la.atl.2003.358}
#'
#' Erfurt, G., 2003. Infrared luminescence of Pb+ centres in potassium-rich
#' feldspars. physica status solidi (a) 200, 429-438. \doi{10.1002/pssa.200306700}
#'
#' Erfurt, G., Krbetschek, M.R., 2003. Studies on the physics of the infrared
#' radioluminescence of potassium feldspar and on the methodology of its
#' application to sediment dating. Radiation Measurements 37, 505-510.
#' \doi{10.1016/s1350-4487(03)00058-1}
#'
#' Erfurt, G., Krbetschek, M.R., Bortolot, V.J., Preusser, F., 2003. A fully
#' automated multi-spectral radioluminescence reading system for geochronometry
#' and dosimetry. Nuclear Instruments and Methods in Physics Research Section
#' B: Beam Interactions with Materials and Atoms 207, 487-499.
#' \doi{10.1016/s0168-583x(03)01121-2}
#'
#' Frouin, M., Huot, S., Kreutzer, S., Lahaye, C., Lamothe, M., Philippe, A., Mercier, N., 2017.
#' An improved radiofluorescence single-aliquot regenerative dose protocol for K-feldspars.
#' Quaternary Geochronology 38, 13-24. \doi{10.1016/j.quageo.2016.11.004}
#'
#' Kreutzer, S., Murari, M.K., Frouin, M., Fuchs, M., Mercier, N., 2017.
#' Always remain suspicious: a case study on tracking down a technical artefact while measuring IR-RF.
#' Ancient TL 35, 20–30. \doi{10.26034/la.atl.2017.510}
#'
#' Murari, M.K., Kreutzer, S., Fuchs, M., 2018. Further investigations on IR-RF:
#' Dose recovery and correction. Radiation Measurements 120, 110–119.
#' doi: 10.1016/j.radmeas.2018.04.017 \doi{10.1016/j.radmeas.2018.04.017}
#'
#' Lapp, T., Jain, M., Thomsen, K.J., Murray, A.S., Buylaert, J.P., 2012. New
#' luminescence measurement facilities in retrospective dosimetry. Radiation
#' Measurements 47, 803-808.
#' \doi{10.1016/j.radmeas.2012.02.006}
#'
#' Trautmann, T., 2000. A study of radioluminescence kinetics of natural
#' feldspar dosimeters: experiments and simulations. Journal of Physics D:
#' Applied Physics 33, 2304-2310.
#' \doi{10.1088/0022-3727/33/18/315}
#'
#' Trautmann, T., Krbetschek, M.R., Dietrich, A., Stolz, W., 1998.
#' Investigations of feldspar radioluminescence: potential for a new dating
#' technique. Radiation Measurements 29, 421-425.
#' \doi{10.1016/s1350-4487(98)00012-2}
#'
#' Trautmann, T., Krbetschek, M.R., Dietrich, A., Stolz, W., 1999. Feldspar
#' radioluminescence: a new dating method and its physical background. Journal
#' of Luminescence 85, 45-58.
#' \doi{10.1016/s0022-2313(99)00152-0}
#'
#' Trautmann, T., Krbetschek, M.R., Stolz, W., 2000. A systematic study of the
#' radioluminescence properties of single feldspar grains. Radiation
#' Measurements 32, 685-690.
#' \doi{10.1016/s1350-4487(00)00077-9}
#'
#' ** Further reading**
#'
#' Murari, M.K., Kreutzer, S., King, G.E., Frouin, M., Tsukamoto, S., Schmidt, C., Lauer, T.,
#' Klasen, N., Richter, D., Friedrich, J., Mercier, N., Fuchs, M., 2021.
#' Infrared radiofluorescence (IR-RF) dating: A review. Quaternary Geochronology 64,
#' 101155. \doi{10.1016/j.quageo.2021.101155}
#'
#' @keywords datagen
#'
#' @examples
#'
#' ##load data
#' data(ExampleData.RLum.Analysis, envir = environment())
#'
#' ##(1) perform analysis using the method 'FIT'
#' results <- analyse_IRSAR.RF(object = IRSAR.RF.Data)
#'
#' ##show De results and test parameter results
#' get_RLum(results, data.object = "data")
#' get_RLum(results, data.object = "test_parameters")
#'
#' ##(2) perform analysis using the method 'SLIDE'
#' data(ExampleData.RF70Curves, envir = environment())
#' results <- analyse_IRSAR.RF(
#' object = RF70Curves,
#' method = "SLIDE",
#'  n.MC = 1)
#'
#' \dontrun{
#' ##(3) perform analysis using the method 'VSLIDE' and method control option
#' ## 'trace
#' results <- analyse_IRSAR.RF(
#'  object = RF70Curves,
#'  method = "VSLIDE",
#'  method_control = list(trace = TRUE))
#' }
#'
#' @export
analyse_IRSAR.RF<- function(
  object,
  sequence_structure = c("NATURAL", "REGENERATED"),
  RF_nat.lim = NULL,
  RF_reg.lim = NULL,
  method = c("FIT", "SLIDE", "VSLIDE", "NONE"),
  method_control = NULL,
  test_parameters = NULL,
  n.MC = 10,
  txtProgressBar = TRUE,
  plot = TRUE,
  plot_reduced = FALSE,
  ...
) {
  .set_function_name("analyse_IRSAR.RF")
  on.exit(.unset_function_name(), add = TRUE)

  ##TODO
  ## - if a file path is given, the function should try to find out whether an XSYG-file or
  ##   a BIN-file is provided
  ##  - update documentary ... if it works as expected.

  extraArgs <- list(...)

  # SELF CALL ---------------------------------------------------------------
  if (inherits(object, "list")) {
    ## expand input arguments
    rep.length <- length(object)

    sequence_structure <- .listify(sequence_structure, rep.length)
    RF_nat.lim <- .listify(RF_nat.lim, rep.length)
    RF_reg.lim <- .listify(RF_reg.lim, rep.length)
    method <- .listify(method, rep.length)
    test_parameters <- .listify(test_parameters, rep.length)
    n.MC <- .listify(n.MC, rep.length)

    ##main
    if ("main" %in% names(extraArgs)) {
      temp_main <- .listify(extraArgs$main, rep.length)
    }else{
      originator <- if (length(object) > 0) {
                      .validate_class(object[[1]], "RLum.Analysis",
                                      name = "All elements of 'object'")
                      object[[1]]@originator } else NA
      if (!is.na(originator) && originator == "read_RF2R") {
        temp_main <- lapply(object, function(x) x@info$ROI)
      } else {
        temp_main <- as.list(paste0("ALQ #",1:length(object)))
      }
    }

    ##run analysis
    temp <- lapply(seq_along(object), function(x) {
      analyse_IRSAR.RF(
        object = object[[x]],
        sequence_structure = sequence_structure[[x]],
        RF_nat.lim = RF_nat.lim[[x]],
        RF_reg.lim = RF_reg.lim[[x]],
        method = method[[x]],
        method_control = method_control,
        test_parameters = test_parameters[[x]],
        n.MC = n.MC[[x]],
        txtProgressBar = txtProgressBar,
        plot = plot,
        plot_reduced = plot_reduced,
        main = temp_main[[x]],
        ...)
    })

    ##combine everything to one RLum.Results object as this as what was written ... only
    ##one object

    ##merge results and check if the output became NULL
    results <- merge_RLum(temp)

    if (length(results) == 0)
      return(NULL)
    return(results)
  }

  ## Integrity checks -------------------------------------------------------
  .validate_class(object, "RLum.Analysis", extra = "a 'list' of such objects")
  .validate_class(sequence_structure, "character")
  .validate_not_empty(object)
  if (length(sequence_structure) < 2) {
    .throw_error("'sequence_structure' should contain at least two elements")
  }
  if (!all(c("NATURAL", "REGENERATED") %in% sequence_structure)) {
    .throw_error("'sequence_structure' must contain one each of 'NATURAL' ",
                 "and 'REGENERATED'")
  }
  .validate_class(RF_nat.lim, c("numeric", "integer"), null.ok = TRUE)
  .validate_class(RF_reg.lim, c("numeric", "integer"), null.ok = TRUE)
  method <- .validate_args(toupper(method), c("FIT", "SLIDE", "VSLIDE", "NONE"))
  .validate_class(method_control, "list", null.ok = TRUE)
  .validate_positive_scalar(n.MC, int = TRUE, null.ok = TRUE)

  ##SELECT ONLY MEASURED CURVES
  ## (this is not really necessary but rather user friendly)
  if (length(suppressWarnings(get_RLum(object, curveType = "measured"))) > 0) {
    object <- get_RLum(object, curveType= "measured", drop = FALSE)
  }

  ##INVESTIGATE SEQUENCE OBJECT STRUCTURE

  ##grep object structure
  temp.sequence_structure <- structure_RLum(object)

  ##check whether both curve have the same length, in this case we cannot proceed (sliding
  ##is not allowed)
  if(length(unique(temp.sequence_structure[["x.max"]])) == 1 &&
     grepl("SLIDE", method) &&
     (is.null(RF_nat.lim) & is.null(RF_reg.lim))) {
    .throw_error("There is no further sliding space left. All curves have ",
                 "the same length and no limitation was set")
  }

  ##grep name of the sequence and the position this will be useful later on
  ##name
  record1 <- get_RLum(object, record.id = 1)
  aliquot.sequence_name <- suppressWarnings(get_RLum(record1,
                                                     info.object = "name")) %||% NA

  ##position
  aliquot.position <- suppressWarnings(get_RLum(record1,
                                                info.object = "position")) %||% NA

  ##date
  aliquot.date <- suppressWarnings(get_RLum(record1,
                                            info.object = "startDate"))
  if (!is.null(aliquot.date)) {
    ##transform so far the format can be identified
    if (nchar(aliquot.date) == 14) {
      aliquot.date <- paste(c(substr(aliquot.date, 1, 4),
                              substr(aliquot.date, 5, 6),
                              substr(aliquot.date, 7, 8)), collapse = "-")
    }
  }else{
    aliquot.date <- NA
  }

  ##set structure values
  temp.sequence_structure$protocol.step <-
    rep(sequence_structure, length_RLum(object))[1:length_RLum(object)]

  ## check that we both natural and regenerated are still there
  nat.idx <- which(temp.sequence_structure$protocol.step == "NATURAL")
  reg.idx <- which(temp.sequence_structure$protocol.step == "REGENERATED")
  if (length(nat.idx) == 0 || length(reg.idx) == 0) {
    .throw_error("'sequence_structure' is missing one of 'NATURAL' or ",
                 "'REGENERATED'")
  }

  ##=========================================================================
  ## SET CURVE LIMITS
  ##=========================================================================

  ##01
  ## get the allowed curve limits
  max.channels.nat <- sum(temp.sequence_structure$n.channels[nat.idx])
  max.channels.reg <- sum(temp.sequence_structure$n.channels[reg.idx])
  if (max.channels.nat > max.channels.reg) {
    .throw_error("The number of data channels for the natural (", max.channels.nat,
                 ") cannot exceed that of the regenerated (", max.channels.reg,
                 "), check your 'sequence_structure'")
  }

  ## 02 - check boundaries
  ##RF_nat.lim
  if (is.null(RF_nat.lim) || anyNA(RF_nat.lim)) {
    RF_nat.lim <- c(1, max.channels.nat)

  }else {
    ##this allows to provide only one boundary and the 2nd will be added automatically
    if (length(RF_nat.lim) == 1) {
      RF_nat.lim <- c(RF_nat.lim, max.channels.nat)
    }

    if (min(RF_nat.lim) < 1 || max(RF_nat.lim) > max.channels.nat) {
      RF_nat.lim <- c(1, max.channels.nat)
      .throw_warning("'RF_nat.lim' out of bounds, reset to c(",
                     .format_range(RF_nat.lim, sep = ", "), ")")
    }
  }

  ##RF_reg.lim
  if (is.null(RF_reg.lim)) {
    RF_reg.lim <- c(1, max.channels.reg)

  }else {
    ##this allows to provide only one boundary and the 2nd will be added automatically
    if (length(RF_reg.lim) == 1) {
      RF_reg.lim <- c(RF_reg.lim, max.channels.reg)
    }

    if (min(RF_reg.lim) < 1 || max(RF_reg.lim) > max.channels.reg) {
      RF_reg.lim <- c(1, max.channels.reg)
      .throw_warning("'RF_reg.lim' out of bounds, reset to c(",
                     .format_range(RF_reg.lim, sep = ", "), ")")
    }
  }

  ## check if intervals make sense at all
  len.RF_reg.lim <- length(RF_reg.lim[1]:RF_reg.lim[2])
  if (len.RF_reg.lim < RF_nat.lim[2]) {
    RF_reg.lim[2] <- RF_reg.lim[2] + abs(len.RF_reg.lim - RF_nat.lim[2]) + 1

    ## check that now we haven't gone beyond the size of RF_reg.lim
    if (RF_reg.lim[2] > max.channels.reg) {
      .throw_error("'RF_reg.lim' defines too short an interval and it's not ",
                   "possible to extend it")
    }

    .throw_warning("'RF_reg.lim' defines too short an interval, reset to c(",
                   .format_range(RF_reg.lim, sep = ", "), ")")
  }

  ## check again that we can actually slide the curve
  if (diff(RF_reg.lim) <= diff(RF_nat.lim)) {
    .throw_error("The range of regenerated channels should be larger than ",
                 "the range of natural channels")
  }

  # Method Control Settings -------------------------------------------------
  ##=========================================================================
  ## SET METHOD CONTROL PARAMETER - FOR BOTH METHODS
  ##=========================================================================
  ##
  ##set supported values with default
  method_control.settings <- list(
    trace = FALSE,
    trace_vslide = FALSE,
    maxiter = 500,
    warnOnly = FALSE,
    minFactor = 1 / 4096,
    correct_onset = TRUE,
    show_density = TRUE,
    show_fit = FALSE,
    n.MC = if(is.null(n.MC)) NULL else 1000,
    vslide_range = if(method[1] == "VSLIDE") "auto" else NULL,
    num_slide_windows = 3,
    cores = NULL
  )

  ## deprecated argument
  if ("method.control" %in% names(extraArgs)) {
    method_control <- extraArgs$method.control
    .deprecated("method.control", "method_control", since = "1.0.0")
  }

  ##modify list if necessary
  if (!is.null(method_control)) {

    ##check whether this arguments are supported at all
    unsupported.idx <- which(!names(method_control) %in%
                             names(method_control.settings))
    if (length(unsupported.idx) > 0) {
      .throw_warning(.collapse(names(method_control)[unsupported.idx]),
                     " not supported for 'method_control'. Supported arguments are: ",
                     .collapse(names(method_control.settings)))
    }

    ## check for odd user input
    temp.vslide_range <- method_control$vslide_range
    if (length(temp.vslide_range) > 0) {
      .validate_class(temp.vslide_range, c("character", "integer", "numeric"),
                      name = "'vslide_range' in 'method_control'")
    }
    if (is.character(temp.vslide_range) && temp.vslide_range[1] != "auto") {
      .throw_error("'vslide_range' in 'method_control' should be either ",
                   "'auto' or a 2-element numeric vector")
    }
    if (length(temp.vslide_range) > 2) {
      method_control$vslide_range <- method_control$vslide_range[1:2]
      .throw_warning("'vslide_range' in 'method_control' has more ",
                     "than 2 elements, only the first two were used")
    }

    temp.num_slide_windows <- method_control$num_slide_windows
    if (length(temp.num_slide_windows) > 0) {
      .validate_positive_scalar(temp.num_slide_windows, int = TRUE,
                                name = "'num_slide_windows' in 'method_control'")
      if (temp.num_slide_windows > 10) {
        method_control$num_slide_windows <- 10
        .throw_warning("'num_slide_windows' in 'method_control' should be ",
                       "between 1 and 10, reset to 10 ")
      }
    }

    ##modify list
    method_control.settings <- modifyList(
      x = method_control.settings,
      val = method_control,
      keep.null = TRUE)
  }

  ## control terminal output
  verbose <- extraArgs$verbose %||% TRUE

  ## don't show the progress bar if not verbose
  if (!verbose)
    txtProgressBar <- FALSE


  ##=========================================================================
  ## SET PLOT PARAMETERS
  ##=========================================================================
  ##get channel resolution (should be equal for all curves, but if not the mean is taken)
  resolution.RF <- round(mean((temp.sequence_structure$x.max/temp.sequence_structure$n.channels)),digits=1)

  plot.settings <- list(
    main = "IR-RF",
    xlab = "Time [s]",
    ylab = paste0("IR-RF [cts/", resolution.RF," s]"),
    log = "",
    cex = 1,
    legend = TRUE,
    legend.text = c("RF_nat","RF_reg"),
    legend.pos = "top",
    xaxt = "s"
    ##xlim and ylim see below as they has to be modified differently
  )

  ##modify list if something was set
  plot.settings <- modifyList(plot.settings, extraArgs)
  plot.settings$mtext.cex <- 0.7 * plot.settings$cex
  xlog <- grepl("x", plot.settings$log, fixed = TRUE)
  ylog <- grepl("y", plot.settings$log, fixed = TRUE)

  ##=============================================================================#
  ## ANALYSIS
  ##=============================================================================#

  ##grep first regenerated curve
  RF_reg <- as.data.frame(rbindlist(lapply(object@records[reg.idx],
                                           function(x) as.data.frame(x@data))))
  ## correct of the onset of detection by using the first time value
  if (grepl("SLIDE", method) && method_control.settings$correct_onset) {
      RF_reg[,1] <- RF_reg[,1] - RF_reg[1,1]
    }

  RF_reg.x <- RF_reg[RF_reg.lim[1]:RF_reg.lim[2],1]
  RF_reg.y <- RF_reg[RF_reg.lim[1]:RF_reg.lim[2],2]

  ##grep values from natural signal
  RF_nat <- as.data.frame(rbindlist(lapply(object@records[nat.idx],
                                           function(x) as.data.frame(x@data))))

  ## correct the onset of detection by using the first time value
  if (grepl("SLIDE", method) && method_control.settings$correct_onset) {
    RF_nat[,1] <- RF_nat[,1] - RF_nat[1,1]
  }

  ##limit values to fit range (at least to the minimum)
  RF_nat.limited<- RF_nat[min(RF_nat.lim):max(RF_nat.lim),]

  ##calculate some useful parameters
  RF_nat.mean <- mean(RF_nat.limited[,2])
  RF_nat.error.lower <- quantile(RF_nat.limited[,2], 0.975, na.rm = TRUE)
  RF_nat.error.upper <- quantile(RF_nat.limited[,2], 0.025, na.rm = TRUE)

  De <- De.error <- De.lower <- De.upper <- De.MC <- De.diff <- NA

  ## parameter boundaries
  lower <- c(
      phi.0 = .Machine$double.xmin,
      delta.phi = .Machine$double.xmin,
      lambda = .Machine$double.xmin,
      beta = .Machine$double.xmin
  )
  upper <- c(
      phi.0 = max(RF_reg.y),
      delta.phi = max(RF_reg.y),
      lambda = 1,
      beta = 100
  )

  ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  ##METHOD FIT
  ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
    ## REGENERATED SIGNAL
    # set function for fitting ------------------------------------------------
    fit.function <- y ~ phi.0 - (delta.phi * ((1 - exp(
        -lambda * x
      )) ^ beta))

    ## stretched exponential function according to Erfurt et al. (2003)
    ## + phi.0 >> initial IR-RF flux
    ## + delta.phi >> dose dependent change of the IR-RF flux
    ## + lambda >> exponential parameter
    ## + beta >> dispersive factor

    # set start parameter estimation ------------------------------------------

    fit.parameters.start <- c(
      phi.0 = max(RF_reg.y),
      delta.phi = 1.5 * (max(RF_reg.y) - min(RF_reg.y)),
      lambda = 0.0001,
      beta = 1
    )

  if(method == "FIT"){

    # start nls fitting -------------------------------------------------------

    ##Monte Carlo approach for fitting
    fit.MC.results <- data.frame()

    ##produce set of start paramters
    n.MC <- n.MC %||% 0
    lambda.MC <- seq(0.0001, 0.001, length = n.MC)
    start.MC <- fit.parameters.start

    ##start fitting loop for MC runs
    for (i in seq_len(n.MC)) {
      start.MC["lambda"] <- lambda.MC[i]
      fit.MC <- try(nls(
        fit.function,
        trace = FALSE,
        data = list(x = RF_reg.x, y = RF_reg.y),
        algorithm = "port",
        stats::nls.control(
          maxiter = 100,
          warnOnly = FALSE,
          minFactor = 1 / 1024
        ),
        start = start.MC,
        lower = lower,
        upper = upper),
      silent = TRUE
      )

      if (!inherits(fit.MC, "try-error")) {
        fit.MC.coefs <- coef(fit.MC)
        for (val in c("phi.0", "delta.phi", "lambda", "beta"))
          fit.MC.results[i, val] <- fit.MC.coefs[val]
      }
    }

    ##FINAL fitting after successful MC
    if (length(stats::na.omit(fit.MC.results)) != 0) {

      ##choose median as final fit version
      fit.MC.results <- sapply(stats::na.omit(fit.MC.results), median)

      ##try final fitting
      fit <- try(nls(
        fit.function,
        trace = method_control.settings$trace,
        data = data.frame(x = RF_reg.x, y = RF_reg.y),
        algorithm = "port",
        stats::nls.control(
          maxiter = method_control.settings$maxiter,
          warnOnly = method_control.settings$warnOnly,
          minFactor = method_control.settings$minFactor
        ),
        start = fit.MC.results,
        lower = lower,
        upper = upper),
      silent = FALSE
      )
    }else{
      fit <- NA
      class(fit) <- "try-error"
    }

    # get parameters ----------------------------------------------------------
    # and with that the final De
    fit.parameters.results <- NA
    if (!inherits(fit,"try-error")) {
      fit.parameters.results <- coef(fit)
      residuals <- residuals(fit)
    }

    ##calculate De value
    if (!is.na(fit.parameters.results[1])) {
      RF_nat.vals <- c(RF_nat.mean, RF_nat.error.lower, RF_nat.error.upper)
      De.vals <- suppressWarnings(round(log(
          -((RF_nat.vals - fit.parameters.results["phi.0"]) /
            -fit.parameters.results["delta.phi"]
        ) ^ (1 / fit.parameters.results["beta"]) + 1
      ) / -fit.parameters.results["lambda"], digits = 2))

      De <- De.vals[1]
      De.lower <- De.vals[2]
      De.upper <- De.vals[3]

      ##This could be solved with a MC simulation, but for this the code has to be adjusted
      ##The question is: Where the parameters are coming from?
      ##TODO
      De.error <- NA
    }
  }

  ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  ## METHOD SLIDE - ANALYSIS
  ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  else if(method == "SLIDE" || method == "VSLIDE"){
    ##convert to matrix (in fact above the matrix data were first transferred to
    ##data.frames ... here
    ##we correct this ... again)
    RF_nat.limited <- as.matrix(RF_nat.limited)
    RF_reg.limited <- matrix(c(RF_reg.x, RF_reg.y), ncol = 2)
    RF_nat <- as.matrix(RF_nat)

    ##DEFINE FUNCTION FOR SLIDING
    ##FIND MINIMUM - this is done in a function so that it can be further used for MC simulations
    # sliding() -----------------------------------------------------------------------------------
    sliding <- function(RF_nat,
                        RF_nat.limited,
                        RF_reg.limited,
                        n.MC = method_control.settings$n.MC,
                        vslide_range = method_control.settings$vslide_range,
                        num_slide_windows = method_control.settings$num_slide_windows,
                        trace = method_control.settings$trace_vslide,
                        numerical.only = FALSE){

      ##(0) set objects ... nomenclature as used in Frouin et al., please note that here the index
      ##is used instead the real time values
      t_max.id <- nrow(RF_reg.limited)
      t_max_nat.id <- nrow(RF_nat.limited)
      t_min <- RF_nat.limited[1,1]

      ##(1) calculate sum of residual squares using internal Rcpp function

      ##initialise slide range for specific conditions, namely NULL and "auto"
      compute_algorithm_error <- TRUE
      if (is.null(vslide_range)) {
        vslide_range <- 0

      } else if (vslide_range[1] == "auto") {
        range <- max(RF_reg.limited[, 2]) - min(RF_reg.limited[, 2])
        vslide_range <- -range:range
      } else{
        vslide_range <- vslide_range[1]:vslide_range[2]
        compute_algorithm_error <- FALSE
      }

      ##problem: the optimisation routine slightly depends on the chosen input sliding vector
      ##and it might get trapped in a local minimum
      ##therefore we run the algorithm by expanding the sliding vector
      algorithm_error <- NA
      if(!is.null(vslide_range) && any(vslide_range != 0)){

        ##construct list of vector ranges we want to check for, this should avoid that we
        ##got trapped in a local minimum
        mid.idx <- floor((length(vslide_range) + 1) / 2)
        steps <- as.integer(seq(min(t_max_nat.id / 2, mid.idx / 16), mid.idx,
                                length.out = num_slide_windows))
        vslide_range.list <- lapply(steps, function(x) mid.idx + c(-x, x))

        ##correct for out of bounds problem; it might occur
        vslide_range.list[[num_slide_windows]] <- c(0, length(vslide_range))

        ##TODO ... this is not really optimal, but ok for the moment, better would be
        ##the algorithm finds sufficiently the global minimum.
        ##now run it in a loop and expand the range from the inner to the outer part
        ##at least this is considered for the final error range ...
        temp_minimum_list <- lapply(vslide_range.list, function(range) {
          src_analyse_IRSARRF_SRS(
            values_regenerated_limited =  RF_reg.limited[,2],
            values_natural_limited = RF_nat.limited[,2],
            vslide_range = vslide_range[range[1]:range[2]],
            n_MC = 0, #we don't need MC runs here, so make it quick
            trace = trace)[c("sliding_vector_min_index","vslide_minimum", "vslide_index")]
        })

        ##get all horizontal index value for the local minimum (corresponding to the vslide)
        temp_hslide_indices <- vapply(temp_minimum_list, function(x) {
          x$sliding_vector_min_index}, FUN.VALUE = numeric(length = 1))

        ##get all the minimum values
        temp_minimum <- vapply(temp_minimum_list, function(x) {
          x$vslide_minimum}, FUN.VALUE = numeric(length = 1))

        ##get minimum and set it to the final range
        opt.range <- vslide_range.list[[which.min(temp_minimum)]]
        vslide_range <- vslide_range[opt.range[1]:opt.range[2]]

        ##get all possible t_n values for the range expansion ... this can be considered
        ##as somehow systematic uncertainty, but it will be only calculated if the full range
        ##is considered, otherwise it is too biased by the user's choice
        ##ToDo: So far the algorithm error is not sufficiently documented
        if (compute_algorithm_error) {
          algorithm_error <- sd(vapply(temp_hslide_indices, function(k) {
            temp.sliding.step <- RF_reg.limited[k] - t_min
            ## return the offset of the t_n values
            RF_nat[1, 1] + temp.sliding.step
          }, FUN.VALUE = numeric(1)))
        }
      }

      ##now run the final sliding with the identified range that corresponds to the minimum value
      temp.sum.residuals <-
        src_analyse_IRSARRF_SRS(
          values_regenerated_limited =  RF_reg.limited[,2],
          values_natural_limited = RF_nat.limited[,2],
          vslide_range = vslide_range,
          n_MC = n.MC %||% 0,
          trace = trace
      )

      #(2) get minimum value (index and time value)
      index_min <- which.min(temp.sum.residuals$sliding_vector)
      t_n.id <- if (length(index_min) == 0) 1 else index_min

      I_n <- vslide_range[temp.sum.residuals$vslide_index] %||% 0
      temp.sliding.step <- RF_reg.limited[t_n.id] - t_min

      ##(3) slide curve graphically ... full data set we need this for the plotting later
      RF_nat.slid <- matrix(data = c(RF_nat[,1] + temp.sliding.step, RF_nat[,2] + I_n), ncol = 2)
      t_n <- RF_nat.slid[1,1]

      ##the same for the MC runs of the minimum values
      t_n.MC <- NA_integer_
      if(!is.null(n.MC)) {
        t_n.MC <-
          vapply(
            X = temp.sum.residuals$sliding_vector_min_MC,
            FUN = function(x) {
              ##get minimum for MC
              t_n.id.MC <- which(temp.sum.residuals$sliding_vector == x)

              ## there is a non-zero chance that we have got two indices for
              ## the minimun, so we take the mean
              RF_nat[1, 1] + mean(RF_reg.limited[t_n.id.MC]) - t_min
            },
            FUN.VALUE = numeric(1))
      }

      ##(4) get residuals (needed to be plotted later)
      ## they cannot be longer than the RF_reg.limited curve
      reg.limited.idx <- t_n.id:t_max.id
      len.shorter <- min(nrow(RF_nat.limited), length(reg.limited.idx))
      residuals <- (RF_nat.limited[1:len.shorter, 2] + I_n) -
        RF_reg.limited[reg.limited.idx[1:len.shorter], 2]

      ##(4.1) calculate De from the first channel ... which is t_n here
      De <- round(t_n, digits = 2)
      De.MC <- round(t_n.MC, digits = 2)

      ##(5) calculate trend fit
      max.rows <- min(nrow(RF_nat.limited), length(residuals))
      temp.fit <- stats::lm(y ~ x, data.frame(x = RF_nat.limited[1:max.rows, 1],
                                              y = residuals))
      temp.trend.fit <- coef(temp.fit)

      ##return values and limited if they are not needed
      if (numerical.only) {
        return(list(De = De, De.MC = De.MC))
      }
      list(De = De,
            De.MC = De.MC,
            residuals = residuals,
            trend.fit = temp.trend.fit,
            RF_nat.slid = RF_nat.slid,
            t_n.id = t_n.id,
            I_n = I_n,
            algorithm_error = algorithm_error,
            vslide_range = if(is.null(vslide_range)){NA}else{range(vslide_range)},
            num_slide_windows = num_slide_windows,
            squared_residuals = temp.sum.residuals$sliding_vector
          )
    }##end of function sliding()

    ##PERFORM sliding and overwrite values
    slide <-  sliding(
      RF_nat = RF_nat,
      RF_nat.limited = RF_nat.limited,
      RF_reg.limited = RF_reg.limited
    )

    ##write results in variables
    De <- slide$De
    residuals <- slide$residuals
    RF_nat.slid <-  slide$RF_nat.slid
    I_n <- slide$I_n

    # ERROR ESTIMATION
    # MC runs for error calculation -----------------------------------------

    ##set residual matrix for MC runs, i.e. set up list of pseudo RF_nat curves as function
    ##(i.e., bootstrap from the natural curve distribution)

    if(!is.null(n.MC)){
      slide.MC.list <- lapply(1:n.MC, function(dummy) {
        reg.limited.idx <- slide$t_n.id:nrow(RF_reg.limited)
        len.shorter <- min(nrow(RF_nat.limited), length(reg.limited.idx))
        cbind(RF_nat.limited[1:len.shorter, 1],
              RF_reg.limited[reg.limited.idx[1:len.shorter], 2] +
              sample(residuals, len.shorter, replace = TRUE))
      })

      ##set parallel calculation if wanted
      if (is.null(method_control.settings$cores)) {
        cores <- 1

      } else {
        available.cores <- parallel::detectCores()
        requested.cores <- method_control.settings$cores[1]

        ##case 'auto'
        if (requested.cores == "auto") {
          cores <- max(available.cores - 2, 1) # nocov

        } else if (is.numeric(requested.cores)) {
          .validate_positive_scalar(requested.cores, int = TRUE,
                                    name = "method_control.settings$cores")
          if (requested.cores > available.cores) {
            ##assign all they have, it is not our problem
            # nocov start
            .throw_warning("Number of cores limited to the maximum ",
                           "available (", available.cores, ")")
            # nocov end
          }
          cores <- min(requested.cores, available.cores)

        }else{
          .throw_message("Invalid value for control argument 'cores', ",
                         "value set to 1")
          cores <- 1
        }

        if (verbose)
          .throw_message("Using ", cores, ifelse(cores == 1, " core", " cores"),
                         " ...", error = FALSE)
      }

      ## SINGLE CORE -----
      if (cores[1] == 1) {
        if(txtProgressBar){
          ##progress bar
          cat("\n\t Run Monte Carlo loops for error estimation\n")
          pb <- txtProgressBar(min = 0, max = n.MC, initial = 0, char = "=", style = 3)
        }

        De.MC <- sapply(1:n.MC, function(i) {
          # update progress bar
          if (txtProgressBar) setTxtProgressBar(pb, i)

          sliding(
            RF_nat = RF_nat,
            RF_reg.limited = RF_reg.limited,
            RF_nat.limited = slide.MC.list[[i]],
            numerical.only = TRUE
          )[[2]]
        })

        ## close progress bar
        if (txtProgressBar) close(pb)

      ## MULTICORE -----
      } else {
        ## Create the determined number of R copies
        cl <- parallel::makeCluster(cores)

        ##run MC runs
        De.MC <- parallel::parSapply(cl, X = slide.MC.list,
                                     FUN = function(x){
                                       sliding(
                                         RF_nat = RF_nat,
                                         RF_reg.limited = RF_reg.limited,
                                         RF_nat.limited = x,
                                         numerical.only = TRUE
                                       )[[2]]
                                     })
        ##destroy multicore cluster
        parallel::stopCluster(cl)
      }
      ##calculate absolute deviation between De and the here newly calculated De.MC
      ##this is, e.g. ^t_n.1* - ^t_n in Frouin et al.
      De.diff <- diff(x = c(De, De.MC))
      De.error <- round(sd(De.MC), digits = 2)
      De.lower <- De - quantile(De.diff, 0.975, na.rm = TRUE)
      De.upper <- De - quantile(De.diff, 0.025, na.rm = TRUE)
    }
  }

  ##=========================================================================
  ## TEST PARAMETER
  ##=========================================================================
  ## Test parameter are evaluated after all the calculations have been done as
  ## it should be up to the user to decide whether a value should be taken into account or not.

  ##(0)
  ##set default values and overwrite them if there was something new
  ##set defaults
  TP <- list(
    curves_ratio = 1.001,
    intersection_ratio = NA,
    residuals_slope = NA,
    curves_bounds = ceiling(max(RF_reg.x)),
    dynamic_ratio = NA,
    lambda = NA,
    beta = NA,
    delta.phi = NA
  )

    ##modify default values by given input
    if(!is.null(test_parameters)){TP <- modifyList(TP, test_parameters)}

    ##remove NULL elements from list
    TP <- .rm_NULL_elements(TP)

    ##set list with values we want to evaluate
    TP <- lapply(TP, function(x){
      data.frame(THRESHOLD = as.numeric(x), VALUE = NA, STATUS = "OK", stringsAsFactors = TRUE)
    })


  ##(1) check if RF_nat > RF_reg, considering the fit range
  ##TP$curves_ratio
    if ("curves_ratio" %in% names(TP)) {
      TP$curves_ratio$VALUE <-
        sum(RF_nat.limited[,2]) / sum(RF_reg[RF_nat.lim[1]:RF_nat.lim[2], 2])

      if (!is.na(TP$curves_ratio$THRESHOLD)) {
        TP$curves_ratio$STATUS <-
          ifelse(TP$curves_ratio$VALUE > TP$curves_ratio$THRESHOLD, "FAILED", "OK")
      }
    }

   ##(1.1) check if RF_nat > RF_reg, considering the fit range
   ##TP$intersection_ratio
    if ("intersection_ratio" %in% names(TP)) {

      ##It is, as always, a little bit more complicated ...
      ##We cannot just normalise both curves and compare ratios. With increasing De the curve
      ##shape of the RF_nat curve cannot be the same as the RF_reg curve at t = 0. Therefore we
      ##have to find the segment in the RF_reg curve that fits to the RF_nat curve
      ##
      ##(1) get maximum count value for RF_nat
      IR_RF_nat.max <- max(RF_nat.limited[,2])

      ##(2) find corresponding time value for RF_reg (here no limited)
      IR_RF_reg.corresponding_id <- which.min(abs(RF_reg[,2] - IR_RF_nat.max))

      ##(3) calculate ratio, but just starting from the point where both curves correspond
      ##in terms of intensity, otherwise the ratio cannot be correct

      ##the boundary check is necessary to avoid errors
      len.RF_nat.lim <- length(RF_nat.lim[1]:RF_nat.lim[2])
      if (IR_RF_reg.corresponding_id + len.RF_nat.lim > length(RF_reg[, 2])) {
        TP$intersection_ratio$VALUE <- Inf

      }else{
      this.idx <- IR_RF_reg.corresponding_id + (1:len.RF_nat.lim) - 1
      TP$intersection_ratio$VALUE <-
        abs(1 - sum(RF_nat.limited[, 2] / IR_RF_nat.max) /
            sum(RF_reg[this.idx, 2] / max(RF_reg[this.idx, 2])))

      if (!is.na(TP$intersection_ratio$THRESHOLD)) {
        TP$intersection_ratio$STATUS <-
          ifelse(TP$intersection_ratio$VALUE > TP$intersection_ratio$THRESHOLD, "FAILED", "OK")
      }
      }
    }

  ##(2) check slop of the residuals using a linear fit
  ##TP$residuals_slope
  if ("residuals_slope" %in% names(TP) && exists("slide")) {
        TP$residuals_slope$VALUE <- abs(slide$trend.fit[2])

        if (!is.na(TP$residuals_slope$THRESHOLD)) {
          TP$residuals_slope$STATUS <- ifelse(
            TP$residuals_slope$VALUE > TP$residuals_slope$THRESHOLD, "FAILED", "OK")
        }
  }

  ##(3) calculate dynamic range of regenrated curve
  ##TP$dynamic_ratio
  if ("dynamic_ratio" %in% names(TP)) {
    TP.dynamic_ratio <- subset(temp.sequence_structure,
                               temp.sequence_structure$protocol.step == "REGENERATED")
    TP$dynamic_ratio$VALUE <- min(TP.dynamic_ratio$y.max / TP.dynamic_ratio$y.min)

    if (!is.na(TP$dynamic_ratio$THRESHOLD)){
      TP$dynamic_ratio$STATUS  <- ifelse(
        TP$dynamic_ratio$VALUE < TP$dynamic_ratio$THRESHOLD , "FAILED", "OK")
    }
  }

  ##(4) decay parameter
  ##TP$lambda
  if (all(c("lambda", "beta", "delta.phi") %in% names(TP))) {

    fit.lambda <- try(minpack.lm::nlsLM(
        fit.function,
        data = data.frame(x = RF_reg.x, y = RF_reg.y),
        algorithm = "LM",
        start = fit.parameters.start,
        lower = lower,
        upper = upper),
    silent = TRUE
    )

    if(!inherits(fit.lambda, "try-error")){
       temp.coef <- coef(fit.lambda)

       TP$lambda$VALUE <- temp.coef["lambda"]
       TP$beta$VALUE <- temp.coef["beta"]
       TP$delta.phi$VALUE <- temp.coef["delta.phi"]

       if (!is.na( TP$lambda$THRESHOLD)){
        TP$lambda$STATUS <- ifelse(TP$lambda$VALUE <= TP$lambda$THRESHOLD, "FAILED", "OK")
       }

       if (!is.na( TP$beta$THRESHOLD)){
         TP$beta$STATUS <- ifelse(TP$beta$VALUE <= TP$beta$THRESHOLD, "FAILED", "OK")
       }

       if (!is.na( TP$delta.phi$THRESHOLD)){
         TP$delta.phi$STATUS <- ifelse(TP$delta.phi$VALUE <= TP$delta.phi$THRESHOLD, "FAILED", "OK")
       }
    }
  }

  ##(99) check whether after sliding the
  ##TP$curves_bounds
  if (!is.null(TP$curves_bounds)) {
    if(exists("slide")){
      ## add one channel on the top to make sure that it works
      TP$curves_bounds$VALUE <- max(RF_nat.slid[RF_nat.lim,1]) + (RF_nat[2,1] - RF_nat[1,1])

       if (!is.na(TP$curves_bounds$THRESHOLD)){
        TP$curves_bounds$STATUS <- ifelse(TP$curves_bounds$VALUE >= floor(max(RF_reg.x)), "FAILED", "OK")
       }

    }else if(exists("fit")){
      TP$curves_bounds$VALUE <- De.upper

      if (!is.na(TP$curves_bounds$THRESHOLD)){
        TP$curves_bounds$STATUS <- ifelse(TP$curves_bounds$VALUE  >= max(RF_reg.x), "FAILED", "OK")
      }
    }
  }

  ##Combine everything in a data.frame
  De.status <- "OK"
  TP.data.frame <- NULL
  if (length(TP) != 0) {
      TP.data.frame <- as.data.frame(
        cbind(
          POSITION =  as.integer(aliquot.position),
          PARAMETER = names(TP),
          do.call(data.table::rbindlist, args = list(l = TP)),
          SEQUENCE_NAME = aliquot.sequence_name,
          UID = NA
        )
      )

      ##set De.status to indicate whether there is any problem with the De according to the test parameter
      if ("FAILED" %in% TP.data.frame$STATUS) {
        De.status <- "FAILED"
      }
  }

  ##=========================================================================
  ## Plotting
  ##=========================================================================
  if (plot) {
    par.default <- .par_defaults()
    on.exit(par(par.default), add = TRUE)

    ##get internal colour definition
    col <- get("col", pos = .LuminescenceEnv)

    if (!plot_reduced && method != "NONE") {
        graphics::layout(matrix(c(1, 2), 2, 1, byrow = TRUE), 2, c(1.3, 0.4), TRUE)
        par(mar = c(0, 4, 3, 1))
    }
    par(cex = plot.settings[["cex"]])

    ##here control xlim and ylim behaviour
    xlim <- extraArgs$xlim %||% c(if (xlog)
                                    min(temp.sequence_structure$x.min) else 0,
                                  max(temp.sequence_structure$x.max))
    ylim <- extraArgs$ylim %||% c(min(temp.sequence_structure$y.min),
                                  max(temp.sequence_structure$y.max))

    ##open plot area
    plot(
      NA,NA,
      xlim = xlim,
      ylim = ylim,
      xlab = ifelse(method == "NONE" || plot_reduced, plot.settings$xlab, " "),
      xaxt = ifelse(method == "NONE" || plot_reduced, plot.settings$xaxt, "n"),
      yaxt = "n",
      ylab = plot.settings$ylab,
      main = plot.settings$main,
      log = plot.settings$log
    )

    if(De.status == "FAILED"){

      ##build list of failed TP
      mtext.message <- paste0(
        "Threshold exceeded for: ",
        .collapse(TP.data.frame$PARAMETER[TP.data.frame$STATUS == "FAILED"]),
                  ". For details see manual.")

      ##print mtext
      mtext(text = mtext.message,
            side = 3, outer = TRUE, col = "red",
            cex = plot.settings$mtext.cex)
      .throw_warning(mtext.message)
    }

    ##use scientific format for y-axis
    labels <- axis(2, labels = FALSE)
    axis(side = 2, at = labels, labels = format(labels, scientific = TRUE))

    ##(1) plot points that have been not selected
    points(RF_reg[-(min(RF_reg.lim):max(RF_reg.lim)),1:2], pch=3, col=col[19])

    ##(2) plot points that has been used for the fitting
    points(RF_reg.x,RF_reg.y, pch=3, col=col[10])

    ##show natural points if no analysis was done
    if (method == "NONE") {
      ##add points
      points(RF_nat, pch = 20, col = "grey")
      points(RF_nat.limited, pch = 20, col = "red")

      ## subtitle
      if ("mtext" %in% names(extraArgs)) {
        mtext(side = 3, extraArgs$mtext, cex = plot.settings$mtext.cex)
      }

      ##legend
      if (plot.settings$legend) {
        legend(
          plot.settings$legend.pos,
          legend = plot.settings$legend.text,
          pch = c(19, 3),
          col = c("red", col[10]),
          horiz = TRUE,
          bty = "n",
          cex = 0.9
        )
      }
    }

    ##Add fitted curve, if possible. This is a graphical control that might be considered
    ##as useful before further analysis will be applied
    if (method_control.settings$show_fit) {
      if (!inherits(fit.lambda, "try-error")) {
        fit.lambda_coef <- coef(fit.lambda)
        curve(fit.lambda_coef[[1]]-
                (fit.lambda_coef[[2]]*
                   ((1-exp(-fit.lambda_coef[[3]]*x))^fit.lambda_coef[[4]])),
              add=TRUE,
              lty = 2,
              col="red")

        rm(fit.lambda_coef)
      }else{
        .throw_warning("No fit possible, no fit shown.")
      }
    }

    ## ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
    ## PLOT - METHOD FIT
    ## ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
    if(method == "FIT"){
      ## silence notes raised by R CMD check
      x <- NULL

      ##plot fitted curve
      curve(fit.parameters.results["phi.0"]-
              (fit.parameters.results["delta.phi"]*
                 ((1-exp(-fit.parameters.results["lambda"]*x))^fit.parameters.results["beta"])),
            add=TRUE,
            from = RF_reg[min(RF_reg.lim), 1],
            to = RF_reg[max(RF_reg.lim), 1],
            col="red")

      ##plotting to show the limitations if RF_reg.lim was chosen
      ## show fitted curve GREY (before red curve)
      curve(fit.parameters.results["phi.0"]-
              (fit.parameters.results["delta.phi"]*
                 ((1-exp(-fit.parameters.results["lambda"]*x))^fit.parameters.results["beta"])),
            add=TRUE,
            from = min(RF_reg[, 1]),
            to = RF_reg[min(RF_reg.lim), 1],
            col="grey")

      ##show fitted curve GREY (after red curve)
      curve(fit.parameters.results["phi.0"]-
              (fit.parameters.results["delta.phi"]*
                 ((1-exp(-fit.parameters.results["lambda"]*x))^fit.parameters.results["beta"])),
            add=TRUE,
            from = RF_reg[max(RF_reg.lim), 1],
            to = max(RF_reg[, 1]),
            col="grey")

      ##add points
      points(RF_nat, pch = 20, col = col[19])
      points(RF_nat.limited, pch = 20, col = col[2])

      ##legend
      if (plot.settings$legend) {
        legend(
          plot.settings$legend.pos,
          legend = plot.settings$legend.text,
          pch = c(19, 3),
          col = c("red", col[10]),
          horiz = TRUE,
          bty = "n",
          cex = 0.9
        )
      }

      ## plot range chosen for fitting
      abline(v=RF_reg[min(RF_reg.lim), 1], lty=2)
      abline(v=RF_reg[max(RF_reg.lim), 1], lty=2)

      ##plot De if De was calculated
      if (!is.na(De)) {
        lines(c(0,De.lower), c(RF_nat.error.lower,RF_nat.error.lower), lty=2, col="grey")
        lines(c(0,De), c(RF_nat.mean,RF_nat.mean), lty=2, col="red")
        lines(c(0,De.upper), c(RF_nat.error.upper,RF_nat.error.upper), lty=2, col="grey")

        lines(c(De.lower, De.lower),
              c(0,RF_nat.error.lower), lty=2, col="grey")
        lines(c(De,De), c(0, RF_nat.mean), lty=2, col="red")
        lines(c(De.upper, De.upper),
              c(0,RF_nat.error.upper), lty=2, col="grey")
      }

      ##Insert fit and result
      mtext.txt <-  substitute(D[e] == De,
                               list(De = paste0(De, " [", De.lower,
                                                " ; ", De.upper,"]")))
      if (!is.na(De) && max(De, De.upper) > max(RF_reg.x)) {
        try(mtext(side=3, mtext.txt,
                  line = 0, cex = plot.settings$mtext.cex, col = "red"),
            silent = TRUE)
        De.status <- "VALUE OUT OF BOUNDS"

      } else{
        if ("mtext" %in% names(extraArgs)) {
          mtext(side = 3, extraArgs$mtext, cex = plot.settings$mtext.cex)
        }else{
          try(mtext(side = 3, mtext.txt,
                    line = 0, cex = plot.settings$mtext.cex), silent = TRUE)
        }

        De.status <- "OK"
      }
    }

    ## ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
    ## PLOT - METHOD SLIDE
    ## ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
    else if (method == "SLIDE" || method == "VSLIDE") {
      ##(0) density plot
      if (method_control.settings$show_density && !is.null(n.MC)) {
        ##showing the density makes only sense when we see at least 10 data points
        if (!anyNA(De.MC) && length(unique(De.MC)) >= 15) {

          ##calculate density De.MC
          density.De.MC <- density(De.MC)

          ##calculate transformation function
          x.1 <- max(density.De.MC$y)
          x.2 <- min(density.De.MC$y)

          ## limit the scaling a little bit
          threshold.value <- max(RF_reg.limited[, 2]) -
            (max(RF_reg.limited[, 2]) - min(RF_reg.limited[, 2])) * 0.5
          y.1 <- min(RF_nat.limited[1, 2], threshold.value)
          y.2 <- par("usr")[3]

          m <- (y.1 - y.2) / (x.1 + x.2)
          n <- y.1 - m * x.1

          density.De.MC$y <- m * density.De.MC$y + n
          rm(x.1,x.2,y.1,y.2,m,n)

          polygon(density.De.MC$x,
                  density.De.MC$y,
                  col = rgb(0,0.4,0.8,0.5))
        }else{
          .throw_warning("Narrow density distribution, ",
                         "no density distribution plotted")
        }
      }

      ##(1) plot unused points in grey ... unused points are points outside of the set limit
      points(
        matrix(RF_nat.slid[-(min(RF_nat.lim):max(RF_nat.lim)),1:2], ncol = 2),
        pch = 21, col = col[19]
      )

      ##(2) add used points
      points(RF_nat.slid[min(RF_nat.lim):max(RF_nat.lim),], pch = 21, col = col[2],
             bg = col[2])

      ##(3) add line to show the connection between the first point and the De
      lines(x = c(RF_nat.slid[1,1], RF_nat.slid[1,1]),
            y = c(.Machine$double.xmin,RF_nat.slid[1,2]),
            lty = 2,
            col = col[2]
      )

      ##(4) add arrow at the lowest y-coordinate possible to show the sliding
      if (!ylog) {
        shape::Arrows(
          x0 = 0,
          y0 = ylim[1],
          y1 = ylim[1],
          x1 = RF_nat.slid[1,1],
          arr.type = "triangle",
          arr.length = 0.3 * par()[["cex"]],
          code = 2,
          col = col[2],
          arr.adj = 1,
          arr.lwd = 1
        )
      }

      ##(5) add vertical shift as arrow; show nothing if nothing was shifted
      if (!ylog && I_n != 0) {
        shape::Arrows(
          x0 = (0 + par()$usr[1])/2,
          y0 = RF_nat[1,2],
          y1 = RF_nat[1,2] + I_n,
          x1 = (0 + par()$usr[1])/2,
          arr.type = "triangle",
          arr.length = 0.3 * par()[["cex"]],
          code = 2,
          col = col[2],
          arr.adj = 1,
          arr.lwd = 1
        )
      }

      ##TODO
      ##uncomment here to see all the RF_nat curves produced by the MC runs
      ##could become a polygone for future versions
      #lapply(1:n.MC, function(x){lines(slide.MC.list[[x]], col = rgb(0,0,0, alpha = 0.2))})

      ## plot range chosen for fitting
      abline(v=RF_reg[min(RF_reg.lim), 1], lty=2)
      abline(v=RF_reg[max(RF_reg.lim), 1], lty=2)

      if (plot.settings$legend) {
        legend(
          plot.settings$legend.pos,
          legend = plot.settings$legend.text,
          pch = c(19, 3),
          col = c("red", col[10]),
          horiz = TRUE,
          bty = "n",
          cex = 0.9
        )
      }

      ##write information on the De in the plot
      if("mtext" %in% names(extraArgs)) {
        mtext(side = 3, extraArgs$mtext, cex = plot.settings$mtext.cex)
      }else{
        try(mtext(side=3,
                  substitute(D[e] == De, list(De=paste0(De," [", De.lower, " ; ", De.upper, "]"))),
                  line = 0, cex = plot.settings$mtext.cex),
            silent=TRUE)
      }
    }

    if (!plot_reduced && method != "NONE") {
      ## lower plot of residuals
      par(mar = c(4.2, 4, 0, 1))

      fit.error <- method == "FIT" && is.na(fit.parameters.results[1])
      plot(
          NA,
          NA,
          xlim = xlim,
          ylim = if (fit.error) c(-1, 1) else range(residuals),
          xlab = plot.settings$xlab,
          ylab = "E",
          xaxt = plot.settings$xaxt,
          yaxt = "n",
          type = "p",
          log = gsub("y", "", plot.settings$log)
      )

      if (!fit.error) {
        ## add axis for 0 ... means if the 0 is not visible there is labelling
        axis(side = 4, at = 0, labels = 0)

        ## add residual points
        if (method == "FIT") {
          points(RF_reg.x, residuals, pch = 20, col = "grey")
        } else {
          temp.points.diff <- max(length(min(RF_nat.lim):max(RF_nat.lim)) -
                                  length(residuals), 0)
          points(RF_nat.slid[c(min(RF_nat.lim):(max(RF_nat.lim) - temp.points.diff)), 1],
                 residuals, pch = 20, col = rgb(0, 0, 0, 0.4))

          ## add residual indicator (should circle around 0)
          col.ramp <- grDevices::colorRampPalette(c(col[19], "white", col[19]))
          col.polygon <- col.ramp(100)

          xright <- if (xlog) 10^par("usr")[2] else par("usr")[2]
          shape::filledrectangle(
                     mid = c((xlim[2] + xright) / 2,
                             max(residuals) - diff(range(residuals)) / 2),
                     wx = xright - xlim[2],
                     wy = diff(range(residuals)),
                     col = col.polygon
                 )

          ## 0-line indicator and arrows if this is not visible
          ## red colouring here only if the 0 point is not visible to avoid too much colouring
          if (max(residuals) < 0) {
            ## draw arrow pointing down
            angle <- 270
            y0 <- max(residuals)
          } else if (min(residuals) > 0) {
            ## draw arrow pointing up
            angle <- 90
            y0 <- min(residuals)
          } else { # min < 0, max > 0
            ## draw no arrow, only a + sign
            angle <- NA
            points(xlim[2], 0, pch = 3)
          }
          if (!is.na(angle)) {
            shape::Arrowhead(
                       x0 = xlim[2] + (par("usr")[2] - xlim[2]) / 2,
                       y0 = y0,
                       angle = angle,
                       lcol = col[2],
                       arr.length = 0.4,
                       arr.type = "triangle",
                       arr.col = col[2]
                   )
          }

          ## add 0 line
          abline(h = 0, lty = 3)

          ## add vertical line to mark De (t_n)
          abline(v = De, lty = 2, col = col[2])

          ## add numeric value of De ... t_n
          axis(side = 1,
               at = De,
               labels = De,
               cex.axis = 0.8,
               col = "blue",
               padj = -1.55)
        }
      } else {
        text(x = (par()$usr[2] - par()$usr[1]) / 2,
             y = 0, "Fitting Error!")
      }
    }

    ## TODO: CONTROL PLOT! can be implemented in appropriate form in a later version
    if (method %in% c("SLIDE", "VSLIDE") && method_control.settings$trace) {
        par(new = TRUE)
        plot(
            RF_reg.limited[1:length(slide$squared_residuals),1],
            slide$squared_residuals,
            ylab = "",
            type = "l",
            xlab = "",
            xaxt = plot.settings$xaxt,
            axes = FALSE,
            xlim = xlim,
            ylim = ylim,
            log = "y"
        )
    }
  }#endif::plot

  # Return ------------------------------------------------------------------
  ##catch up worst case scenarios ... means something went wrong
  if (!exists("fit")) {
    fit  <- list()
    if (exists("fit.lambda")) {
      fit <- fit.lambda
    }
  }
  if(!exists("slide")){slide <- list()}

  ##generate unique identifier
  UID <- create_UID()

  ##combine values for De into a data frame
  De.values <- data.frame(
      DE = De,
      DE.ERROR = De.error,
      DE.LOWER = De.lower,
      DE.UPPER = De.upper,
      DE.STATUS = De.status,
      RF_NAT.LIM = .format_range(RF_nat.lim),
      RF_REG.LIM = .format_range(RF_reg.lim),
      POSITION =  as.integer(aliquot.position),
      DATE = aliquot.date,
      SEQUENCE_NAME = aliquot.sequence_name,
      UID = UID,
    row.names = NULL,
    stringsAsFactors = FALSE
  )

    if(!is.null(TP.data.frame)){
      TP.data.frame$UID <- UID
    }

  ##produce results object
  set_RLum(
      class = "RLum.Results",
      data = list(
        data = De.values,
        De.MC = De.MC,
        test_parameters = TP.data.frame,
        fit = fit,
        slide = slide
      ),
      info = list(call = sys.call())
  )
}
