07 Sep 2020
07 Sep 2020
GeoChronR – an R package to model, analyze and visualize age-uncertain paleoscientific data
- 1School of Earth and Sustainability, Northern Arizona University, Flagstaff, AZ 86011, USA
- 2Department of Earth Sciences, University of Southern California, Los Angeles, CA, 90089, USA
- 3Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA
- 1School of Earth and Sustainability, Northern Arizona University, Flagstaff, AZ 86011, USA
- 2Department of Earth Sciences, University of Southern California, Los Angeles, CA, 90089, USA
- 3Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA
Abstract. Chronological uncertainty is a hallmark of the paleosciences. While many tools have been made available to researchers to quantify age uncertainties suitable for various settings and assumptions, disparate tools and output formats often discourage integrative approaches. In addition, associated tasks like propagating age model uncertainties to subsequent analyses, and visualizing the results, have received comparatively little attention in the literature and available software. Here we describe GeoChronR, an open-source R package to facilitate these tasks. GeoChronR is built around emerging data standards for the paleosciences (Linked PaleoData, or LiPD), and offers access to four popular age modeling techniques (Bacon, BChron, Oxcal, BAM). The output of these models is used to support ensemble-aware analyses, quantifying the impact of chronological uncertainties on common analyses like age-uncertain correlation, regression, principal component, and spectral analyses. We present five real-world use cases to illustrate how GeoChronR may be used to facilitate these tasks, to visualize the results in intuitive ways, and to store the results for further analysis, promoting transparency and reusability.
Nicholas P. McKay et al.


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SC1: 'Suggestion for more in-depth description of the codes used in the MS to broaden its potential user base', István Gábor Hatvani, 16 Sep 2020
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AC1: 'Authors' response to SC1', Nicholas McKay, 07 Oct 2020
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AC1: 'Authors' response to SC1', Nicholas McKay, 07 Oct 2020
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RC1: 'reviewer comment', Anonymous Referee #1, 27 Sep 2020
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AC4: 'Response to RC1', Nicholas McKay, 16 Nov 2020
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AC4: 'Response to RC1', Nicholas McKay, 16 Nov 2020
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RC2: 'Accept pending some additional references', Anonymous Referee #2, 11 Oct 2020
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AC3: 'Response to RC2', Nicholas McKay, 16 Nov 2020
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AC3: 'Response to RC2', Nicholas McKay, 16 Nov 2020
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SC2: 'Some remarks from a climate time series analyst for meticulous researchers', Manfred Mudelsee, 17 Oct 2020
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AC2: 'Author response', Nicholas McKay, 16 Nov 2020
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AC2: 'Author response', Nicholas McKay, 16 Nov 2020


-
SC1: 'Suggestion for more in-depth description of the codes used in the MS to broaden its potential user base', István Gábor Hatvani, 16 Sep 2020
-
AC1: 'Authors' response to SC1', Nicholas McKay, 07 Oct 2020
-
AC1: 'Authors' response to SC1', Nicholas McKay, 07 Oct 2020
-
RC1: 'reviewer comment', Anonymous Referee #1, 27 Sep 2020
-
AC4: 'Response to RC1', Nicholas McKay, 16 Nov 2020
-
AC4: 'Response to RC1', Nicholas McKay, 16 Nov 2020
-
RC2: 'Accept pending some additional references', Anonymous Referee #2, 11 Oct 2020
-
AC3: 'Response to RC2', Nicholas McKay, 16 Nov 2020
-
AC3: 'Response to RC2', Nicholas McKay, 16 Nov 2020
-
SC2: 'Some remarks from a climate time series analyst for meticulous researchers', Manfred Mudelsee, 17 Oct 2020
-
AC2: 'Author response', Nicholas McKay, 16 Nov 2020
-
AC2: 'Author response', Nicholas McKay, 16 Nov 2020
Nicholas P. McKay et al.
Nicholas P. McKay et al.
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