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  "Package": "fingerPro",
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  "Title": "Unmixing Model Framework",
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  "Date": "2026-04-09",
  "Authors@R": "c(\nperson(\"Borja Latorre (Core Team)\", role = c(\"aut\", \"cre\"), email = \"borja.latorre@csic.es\", comment = c(ORCID = \"0000-0002-6720-3326\")),\nperson(\"Leticia Gaspar (Core Team)\", role = \"aut\", comment = c(ORCID = \"0000-0002-3473-7110\")),\nperson(\"Ivan Lizaga\", role = \"aut\", comment = c(ORCID = \"0000-0003-4372-5901\")),\nperson(\"Leticia Palazon\", role = \"aut\", comment = c(ORCID = \"0000-0002-5773-1723\")),\nperson(\"Vince Q Vu\", role = \"ctb\"),\nperson(\"Ana Navas (Core Team)\", role = c(\"aut\", \"fnd\", \"ths\"), comment = c(ORCID = \"0000-0002-4724-7532\")))",
  "Description": "Quantifies the provenance of sediments by applying a\nmixing model algorithm to end sediment mixtures based on a\ncomprehensive characterization of the sediment sources. The\n'fingerPro' model builds upon the foundational concept of using\nmass balance linear equations for sediment source\nquantification by incorporating several distinct technical\nadvancements. It employs an optimization approach to normalize\ndiscrepancies in tracer ranges and minimize the objective\nfunction. Latin hypercube sampling is used to explore all\npossible combinations of source contributions (0-100%),\nmitigating the risk of local minima. Uncertainty in source\nestimates is quantified through a Monte Carlo routine, and the\nmodel includes additional metrics, such as the normalized error\nof the virtual mixture, to detect mathematical inconsistencies,\nnon-physical solutions, and biases. A new linear variability\npropagation (LVP) method is also included to address and\nquantify potential bias in model outcomes, particularly when\ndealing with dominant or non-contributing sources and high\nsource variability, offering a significant advancement for\nfield studies where direct comparison with theoretical\napportionments is not feasible. In addition to the unmixing\nmodel, a complete framework for tracer selection is included.\nSeveral methods are implemented to evaluate tracer behaviour by\nconsidering both source and mixture information. These include\nthe Consistent Tracer Selection (CTS) method to explore all\ntracer combinations and select the optimal ones improving the\nrobustness and interpretability of the model results. A\nConservative Balance (CB) method is also incorporated to enable\nthe use of isotopic tracers. The package also provides several\ngraphical tools to support data exploration and interpretation,\nincluding box plots, correlation plots, Linear Discriminant\nAnalysis (LDA) and Principal Component Analysis (PCA).",
  "License": "GPL-2",
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  "Author": "Borja Latorre (Core Team) [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-6720-3326>), Leticia Gaspar (Core\nTeam) [aut] (ORCID: <https://orcid.org/0000-0002-3473-7110>),\nIvan Lizaga [aut] (ORCID:\n<https://orcid.org/0000-0003-4372-5901>), Leticia Palazon [aut]\n(ORCID: <https://orcid.org/0000-0002-5773-1723>), Vince Q Vu\n[ctb], Ana Navas (Core Team) [aut, fnd, ths] (ORCID:\n<https://orcid.org/0000-0002-4724-7532>)",
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}