The analysis of conflicting claims arises when an amount has
    to be divided among a set of agents with claims that exceed what is
    available. A rule is a way of selecting a division among the
    claimants. This package computes the main rules introduced in the
    literature from ancient times to the present. The inventory of rules
    covers the proportional and the adjusted proportional rules, the
    constrained equal awards and the constrained equal losses rules, the
    constrained egalitarian, the Piniles’ and the minimal overlap rules,
    the random arrival and the Talmud rules. Besides, the Dominguez and
    Thomson and the average-of-awards rules are also included.  All of
    them can be found in the book by W. Thomson (2019), How to divide when
    there isn't enough. From Aristotle, the Talmud, and Maimonides to the
    axiomatics of resource allocation', except for the average-of-awards
    rule, introduced by Mirás Calvo et al. (2022),
    <doi:10.1007/s00355-022-01414-6>.  In addition, graphical diagrams
    allow the user to represent, among others, the set of awards, the
    paths of awards, the schedules of awards of a rule, and some indexes.
    A good understanding of the similarities and differences between the
    rules is useful for better decision-making. Therefore, this package
    could be helpful to students, researchers, and managers alike.  For a
    more detailed explanation of the package, see Mirás Calvo et al.
    (2023), <doi:10.1016/j.dajour.2022.100160>.

Performs 'classical' age-depth modelling of dated sediment deposits - prior to applying more sophisticated techniques such as Bayesian age-depth modelling. Any radiocarbon dated depths are calibrated. Age-depth models are constructed by sampling repeatedly from the dated levels, each time drawing age-depth curves. Model types include linear interpolation, linear or polynomial regression, and a range of splines. See Blaauw (2010) <doi:10.1016/j.quageo.2010.01.002>.

Implements the model-free multiscale idealisation approaches: Jump-Segmentation by MUltiResolution Filter (JSMURF), Hotz et al. (2013) <doi:10.1109/TNB.2013.2284063>, JUmp Local dEconvolution Segmentation filter (JULES), Pein et al. (2018) <doi:10.1109/TNB.2018.2845126>, and Heterogeneous Idealization by Local testing and DEconvolution (HILDE), Pein et al. (2021) <doi:10.1109/TNB.2020.3031202>. Further details on how to use them are given in the accompanying vignette.