In nature, each mineral grain (quartz or feldspar) receives a dose rate (Dr) specific to its environment. The dose-rate distribution therefore reflects the micro-dosimetric context of grains of similar size. If all the grains were well bleached at deposition, this distribution is assumed to correspond, within uncertainties, with the distribution of equivalent doses (De). The combination of the De and Dr distributions in the De_Dr model proposed here would then allow calculation of the true depositional age. If grains whose De values are not representative of this age (hereafter called “outliers”) are present in the De distribution, this model allows them to be identified before the age is calculated, enabling their exclusion. As the De_Dr approach relies only on the Dr distribution to describe the De distribution, the model avoids any assumption about the shape of the De distribution, which can be difficult to justify. Herein, we outline the mathematical concepts of the De_Dr approach (more details are given in Galharret et al., 2021) and the exploitation of this Bayesian modelling based on an R code available in the R package “Luminescence”. We also present a series of tests using simulated Dr and De distributions with and without outliers and show that the De_Dr approach can be an alternative to available models for interpreting De distributions.

Past environmental information is typically inferred from proxy data contained in accretionary sediments. The validity of proxy data and analysis workflows are usually assumed implicitly, with systematic tests and uncertainty estimates restricted to modern analogue studies or reduced-complexity case studies. However, a more generic and consistent approach to exploring the validity and variability of proxy functions would be to translate a sediment section into a model scenario: a “virtual twin”. Here, we introduce a conceptual framework and numerical tool set that allows the definition and analysis of synthetic sediment sections. The R package sandbox describes arbitrary stratigraphically consistent deposits by depth-dependent rules and grain-specific parameters, allowing full scalability and flexibility. Virtual samples can be taken, resulting in discrete grain mixtures with defined parameters. These samples can be virtually prepared and analysed, for example to test hypotheses. We illustrate the concept of sandbox, explain how a sediment section can be mapped into the model and explore geochronological research questions related to the effects of sample geometry and grain-size-specific age inheritance. We summarise further application scenarios of the model framework, relevant for but not restricted to the broader geochronological community.

Kinetic models have been used extensively for modeling and numerical simulation of luminescence phenomena and dating techniques for various dosimetric materials. Several comprehensive models have been implemented for quartz, which allow simulation of complex sequences of irradiation and thermal/optical events in nature and in the laboratory. In this paper, we present a simple and accurate way of simulating similarly complex sequences in feldspars. We introduce the open-access R scripts Feldspar Simulation Functions (FSF) for kinetic model simulation of luminescence phenomena in feldspars. These R functions offer useful numerical tools to perform luminescence simulations in a user-friendly manner. The mathematical framework of four different types of previously published models is presented in a uniform way, and the models are simulated with FSF. While previously published versions of these four models require numerical integration of the differential equations, the FSF circumvent the need for numerical integration by using accurate summations over the finite range of the model parameters. The simulation process can be understood easily by creating transparent sequences of events consisting of these compact R functions. The key physical concept of the FSF is that irradiation and thermal/optical treatments of feldspars change the distribution of nearest neighbor (NN) distances in donor-acceptor pairs. These changes are described using analytical equations within the four models examined in this paper. The NN distribution at the end of one simulation stage becomes the initial distribution for the next stage in the sequences of events being simulated. Several practical examples and possible applications and extensions of the FSF are discussed.

Abstract The luminescence phenomena of insulators and semiconductors (e.g., natural minerals such as quartz) have various application domains. For instance, Earth Sciences and archaeology exploit luminescence as a dating method. Herein, we present the R package RLumCarlo implementing sets of luminescence models to be simulated with Monte Carlo (MC) methods. MC methods make a powerful ally to all kinds of simulation attempts involving stochastic processes. Luminescence production is such a stochastic process in the form of charge (electron-hole pairs) interaction within insulators and semiconductors. To simulate luminescence-signal curves, we distribute single and independent MC processes to virtual MC clusters. RLumCarlo comes with a modularized design and consistent user interface: (1) C++ functions represent the modeling core and implement models for specific stimulations modes. (2) R functions give access to combinations of models and stimulation modes, start the simulation and render terminal and graphical feedback. The combination of MC clusters supports the simulation of complex luminescence phenomena.

In situ gamma spectrometry is a useful technique used by the Luminescence and ESR dating community to improve the representativity of dose rate measurements in the context of gamma dose rate heterogeneities around dated material. This paper presents gamma, a new package and its graphical user interface gammaShiny, allowing a reliable and reproducible workflow for in situ gamma spectrometry data analysis in the context of luminescence and ESR dating.

During the last decades, luminescence dating has entered a phase of diversification. This evolution is strongly linked to a series of technical advances, including but not limited to the improvement of EMCCD cameras and the use of violet and yellow LEDs. New measurement techniques have been developed, such as post infra-red infra-red stimulated luminescence (postIR-IRSL) measurement protocols and thermally-transferred OSL (TT-OSL), and the luminescence properties of new materials, such as gypsum and apatite, have been reinvestigated. The appropriate configuration of detection equipment is key to the design of new experimental investigations. The R-package LumReader has been designed to simulate both the detection properties of luminescence readers and the emission properties of materials. It allows optimisation of the experimental detection window for a given filter and detector (PMT or EMCCD camera) combination through comparison of the detection window, the stimulation signal and the luminescence emission of the studied material. With the development of a reference database and its flexibility for accommodating user defined functions, this package will allow the spectral information of any luminescence experiment to be simulated, also facilitating experimental design.

Geochemical conditions (e.g., pH-value, tem- perature, availability of CO2) in carbonate-rich sedimentary environments lead to cementation processes, i.e., air or water in the pore space are substituted by mineral phases. Consequently, in such environments the conventional formalism of estimating the environmental dose rate from U, Th and K concentrations (pores are filling by air or water) cannot be overall correct. In 2008, Nathan & Mauz (2008) presented a model to account for dose-rate changes occurring when carbonate minerals replace air and water in the pore space between mineral grains. The underlying MATLAB⃝R code (Carb) was later published by Mauz & Hoffmann (2014). Here we present an implementation of this tool using the statistical programming environment R. Our implementation does not alter the under- lying model and its assumption but comes with an updated code basis published as R package under GPL-3 licence conditions.

Whilst optically stimulated luminescence (OSL) is commonly more suitable for sediment dating because of faster signal resetting, thermoluminescence (TL) remains important for dating burnt material, e.g. in archaeological contexts, or for studying the luminescence properties of different materials. A lack of user-optimized analysis software for TL data has exacerbated the decline of TL dating in comparison to OSL. However, exciting developments in TL dating of flint and calcite indicate a rise in application of this underused method. R is a programming language and environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques and is highly extendable. A package specifically designed for luminescence data analysis is available. However, it mainly includes functions for the analysis of OSL data. The TLdating package is a new R package specifically dedicated to TL dating. This package is designed to be fully compatible with the existing Luminescence package and is user-friendly. It includes functions for TL data pretreatment and palaeodose estimation using the MAAD and the SAR protocols. The functionality of the TLdating package is evaluated using heated flints from Taibeh, Jordan.

The relevance of luminescence dating is reflected by the steadily growing quantity of published data. At the same time, the amount of data available for analysis has increased due to technological and methodological advances. Routinely, luminescence data are analysed using a mixture of commercially available software, self-written tools and specific solutions. Based on a luminescence dating literature screening we show how rarely articles report on the software used for the data analysis and we discuss potential problems arising from this. We explore the growing importance of the statistical programming language R in general and especially its reflection in recent software developments in the context of luminescence dating. Specifically, for the R package “Luminescence” we show how the transparency, flexibility and reliability of tools used for the data analysis have been improved. We finally advocate for more transparency if unvalidated software solutions are used and we emphasise that more attention should be paid to the tools used for analysing the data.

A function named analyse_baSAR() was written using the statistical programming language R and its code is now available within theR package ’Luminescence’. The function allows the application of the Bayesian hierarchical model ’baSAR’ proposed by Combés et al. (2015) and comes with additional features to analyse luminescence data in a straight forward way. Example scripts are provided showing the possible numerical and graphical outputs.

Kinetic models of quartz luminescence have gained an important role for predicting experimental results and for understanding charge transfers in (natural) quartz. Here we present and discuss the new R package “RLumModel”, offering an easy-to-use tool for simulating quartz luminescence signals (TL, OSL, LM-OSL and RF) based on five integrated and published parameter sets. Simulation commands can be created (a) using the Risø Sequence Editor, (b) a built-in SAR sequence generator or (c) self-explanatory keywords for customised sequences. Results can be analysed seamlessly using the R package “Luminescence” along with a visualisation of concentrations of electrons and holes in every trap/centre as well as in the valence and conduction band during all stages of the simulation. Examples of simulated luminescence phenomena and dating protocols are given, and the presented code snippets are available in the supplementary material. Package and source code are provided under the General Public Licence (GPL-3) conditions.

Numerical dating methods in Quaternary science are faced with the need to adequately visualise data consisting of estimates that have differing standard errors. Recent approaches either focus on the display of age frequency distributions that ignore the standard errors or on radial plots, that support comparisons between estimates allowing for their differing precisions, but without giving an explicit picture of the age frequency distribution. Hence, visualising both aspects requires at least two plots. Here, an alternative is introduced: The abanico plot. It combines both aspects and therefore allows comprehensive presentation of chronometric data with individual standard errors. It extends the radial plot by a kernel density estimate plot, histogram or dot plot and contains elements that link both plot types. As part of the R package "Luminescence"" (version >= 0.4.5), the abanico plot is designed as the final part of a comprehensive analysis chain of luminescence data but is open to a wide range of other Quaternary dating communities, as illustrated by several examples.

Since the release of the R package ’Luminescence’ in 2012 the functionality of the package has been greatly enhanced by implementing further functions for measurement data processing, statistical analysis and graphical output. Along with the accompanying increase in complexity of the package, working with the command-line interface of R can be tedious, especially for users without previous experience in programming languages. Here, we present a collection of interactive web applications that provide a user-friendly graphical user interface for the ’Luminescence’ package. These applications can be accessed over the internet or used on a local computer using the R package ’RLumShiny’. A short installation and usage guide is accompanied by the presentation of two exemplary applications.

A new function (calc IEU) is now available in the latest version of the R Luminescence package (version 0.4.2). The calc IEU function can be used to calculate an equivalent dose (De) value for a given dose distribution using the Internal External Uncertainty (IEU) model. The IEU model is used in luminescence dating to determine a De value for a partially-bleached sample by calculating the weighted mean from the well bleached part of a partially-bleached population. The new calc IEU function au- tomates the calculation of the IEU model so that the results are produced rapidly and reproducibly. This is advantageous as the user can easily perform sensitivity tests of the model in response to changing input parameters.

The first version of the R package “Luminescence” was released and published in 2012. Since then, the package has been continuously improved by implementing further measurement protocols, adding age models, and extending functions. In geoscientific applications, luminescence dating requires a series of data processing procedures. A comprehensive and replicate analysis of luminescence data using the R package “Luminescence”, therefore, suggests combining selected functions. With this contribution, we provide a practical example of a workflow from reading measurement data to age modelling. The exemplary data processing routine is applied to an OSL data set of a fluvial sediment sample from the Pamir Mountains.

A practical guide for the R package “Luminescence” is provided. An introduction on data types in R is given first, followed by a guideline on how to import, analyse and visualise typical SAR- OSL measurement data.

For routine luminescence dating applications the commonly used Risø readers are bundled with analysis software, such as Viewer or Analyst. These software solutions are appropriate for most of the regular dating and publication jobs and enable assessment of luminescence characteristics and provide basic statistical data treatment. However, for further statistical analysis and data treatments, this software may reach its limits. In such cases, open programming languages are a more appropriate approach. Here, we present the R package “Luminescence” for a more flexible handling of luminescence data and related plotting purposes using the statistical programming language R. The R language as well as the package and the source code are provided under the General Public License (GPL) conditions and are available for free. The basic functionality of the package is described along with three application examples. This package is not an alternative to the existing software (Analyst, Viewer) but may provide a collection of additional tools to analyse luminescence data and serve as a platform for further contributions.