{ "_id": "6a1f0001b401979e7341ac09", "Package": "fingerPro", "Type": "Package", "Title": "A Comprehensive Package for Sediment Source Unmixing", "Version": "2.0", "Encoding": "UTF-8", "Date": "2025-08-01", "Authors@R": "c(\nperson(\"Borja\", \"Latorre\", role = c(\"aut\", \"cre\"), email = \"borja.latorre@csic.es\"),\nperson(\"Ivan\", \"Lizaga\", role = \"aut\"),\nperson(\"Leticia\", \"Gaspar\", role = \"aut\"),\nperson(\"Leticia\", \"Palazon\", role = \"aut\"),\nperson(\"Ana\", \"Navas\", role = \"aut\"),\nperson(\"Vince Q\", \"Vu\", role = \"ctb\"))", "Description": "\"This package quantifies the provenance of sediments in a\ncatchment or study area by applying a mixing model algorithm to\nend sediment mixtures based on a comprehensive characterization\nof the sediment sources. The fingerPro model builds upon the\nfoundational concept of using mass balance linear equations for\nsediment source quantification by incorporating several\ndistinct technical advancements. It employs an optimization\napproach to normalize discrepancies in tracer ranges and\nminimize the objective function. Latin hypercube sampling is\nused to explore all possible combinations of source\ncontributions (0-100%), mitigating the risk of local minima.\nUncertainty in source estimates is quantified through a Monte\nCarlo routine, and the model includes additional metrics, such\nas the normalized error of the virtual mixture, to detect\nmathematical inconsistencies, non-physical solutions, and\nbiases. A new linear variability propagation (LVP) method is\nalso included to address and quantify potential bias in model\noutcomes, particularly when dealing with dominant or\nnon-contributing sources and high source variability, offering\na significant advancement for field studies where direct\ncomparison with theoretical apportionments is not feasible. The\npackage includes several graphs to help users with data\nunderstanding, such as box plots, correlation, PCA, and LDA\ngraphs. Furthermore, new methods such as Consensus Ranking (CR)\nand Consistent Tracer Selection (CTS) are included to correctly\napply the fingerprinting technique and increase dataset and\nmodel understanding. A new Conservative Balance (CB) method has\nalso been incorporated to enable the use of isotopic tracers.\"", "License": "GPL (>= 3)", "URL": "https://github.com/eead-csic-eesa/fingerPro", "RoxygenNote": "7.2.3", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-18 09:07:43 UTC", "User": "root" }, "Author": "Borja Latorre [aut, cre], Ivan Lizaga [aut], Leticia Gaspar\n[aut], Leticia Palazon [aut], Ana Navas [aut], Vince Q Vu [ctb]", "Maintainer": "Borja Latorre ", "Config/pak/sysreqs": "cmake libfreetype6-dev libglu1-mesa-dev make\nlibgsl0-dev texlive libicu-dev libpng-dev libuv1-dev\nlibgl1-mesa-dev libssl-dev libx11-dev zlib1g-dev", "Repository": "https://eead-csic-eesa.r-universe.dev", "Date/Publication": "2025-12-19 18:52:56 UTC", "RemoteUrl": "https://github.com/eead-csic-eesa/fingerpro", "RemoteRef": "HEAD", "RemoteSha": "b7268832c423bc1ef4a96cd763be3dec32584fd1", "MD5sum": "dd649d027f5c23d2be7a66a0e986f9ac", "_user": "eead-csic-eesa", "_type": "src", "_file": 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unmixing database", "topics": [ "check_database" ] }, { "page": "CI", "title": "Compute Conservativeness Index (CI) for individual tracers", "topics": [ "CI" ] }, { "page": "correlation_plot", "title": "Correlation matrix chart", "topics": [ "correlation_plot" ] }, { "page": "CR", "title": "Consensus Ranking (CR) method for tracer selection", "topics": [ "CR" ] }, { "page": "CTS_error", "title": "Evaluate the mathematical consistency of a tracer selection for an apportionment solution.", "topics": [ "CTS_error" ] }, { "page": "CTS_seeds", "title": "Extract all possible minimal tracer combinations to identify the most discriminant.", "topics": [ "CTS_seeds" ] }, { "page": "DFA_test", "title": "Discriminant Function Analysis (DFA) Test", "topics": [ "DFA_test" ] }, { "page": "ggbiplot", "title": "Biplot for Principal Components using ggplot2", "topics": [ "ggbiplot" ] }, { "page": "individual_tracer_analysis", "title": "Individual tracer analysis", "topics": [ 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