{ "_id": "6ac204df773a0037682c09ad", "Package": "ViralEntropR", "Title": "A Computational Pipeline for Entropy-Informed Detection of\nEmerging Viral Variants", "Version": "0.6.2", "Authors@R": "c(\nperson(\"Vadim\", \"Tyuryaev\", ,\"vadim.tyuryaev@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0008-1361-6265\")),\nperson(\"Jane\", \"Heffernan\", , \"jmheffer@yorku.ca\",\nrole = c(\"aut\")),\nperson(\"Hanna\", \"Jankowski\", , \"hkj@yorku.ca\",\nrole = c(\"aut\"))\n)", "Description": "Implements an entropy-informed pipeline for detecting\nemerging variants in viral amino acid sequence data, extending\nprior clustering-based approaches including hemagglutinin\nclustering methods (Li et al., 2015)\n. Provides a fully vectorized\nFASTA preprocessing toolkit covering header parsing, two-pass\ndate and country extraction, ambiguous-residue filtering, and\ninteger encoding under a 25-symbol amino acid alphabet.\nComputes per-site Shannon entropy across user-defined\ncumulative, sliding, or disjoint temporal partitions and\nclusters per-site entropy values using Gaussian mixture models\nvia 'mclust' (Scrucca et al., 2016) .\nQuantifies temporal distributional shifts between partitions\nusing the Hellinger distance (van der Vaart, 1998)\n, and detects temporal change\npoints non-parametrically using energy statistics (Matteson and\nJames, 2014) via 'ecp' or\nwild binary segmentation (Fryzlewicz, 2014)\n via 'HDcpDetect'. Per-site amino-acid\nfrequency tables and entropy trajectory plots characterize\nsequence composition and evolutionary dynamics across time. A\nconfigurable multi-variant simulation engine generates\nsynthetic sequence time series with known ground truth for\nbenchmarking detection pipelines. A curated dataset of\nSARS-CoV-2 Variants of Concern and Variants of Interest with\nassociated lineage and surveillance metadata is included, along\nwith a bundled National Center for Biotechnology Information\n(NCBI) Spike protein sample and vignettes demonstrating the\nfull workflow.", "License": "MIT + file LICENSE", "Language": "en-GB", "Date": "2026-05-07", "URL": "https://github.com/vadimtyuryaev/ViralEntropR,\nhttps://doi.org/10.5281/zenodo.19040165,\nhttps://vadimtyuryaev.github.io/ViralEntropR/", "BugReports": "https://github.com/vadimtyuryaev/ViralEntropR/issues", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "VignetteBuilder": "knitr, R.rsp", "Config/pak/sysreqs": "cmake libfontconfig1-dev libfreetype6-dev\nlibfribidi-dev make libharfbuzz-dev libicu-dev libpng-dev\nlibuv1-dev libxml2-dev", "Repository": "https://vadimtyuryaev.r-universe.dev", "Date/Publication": "2026-05-31 07:23:55 UTC", "RemoteUrl": "https://github.com/vadimtyuryaev/viralentropr", "RemoteRef": "HEAD", "RemoteSha": "405acdac3ec36c078f7ca963dc047b1961de2674", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-04 07:32:08 UTC", "User": "root" }, "Author": "Vadim Tyuryaev [aut, cre] (ORCID:\n),\nJane Heffernan [aut],\nHanna Jankowski [aut]", "Maintainer": "Vadim Tyuryaev ", "_user": "vadimtyuryaev", "_type": "src", "_file": "ViralEntropR_0.6.2.tar.gz", "_fileid": "https://r2.ropensci.org/341094c0d2b487e11cf4c624e08594f38eeced5f60ad6b98deb8e97855fe4521", "_filesize": 2142163, "_sha256": "341094c0d2b487e11cf4c624e08594f38eeced5f60ad6b98deb8e97855fe4521", "_expires": "2027-01-12T07:48:45.000Z", "_created": "2026-10-04T07:32:08.000Z", "_published": "2026-10-04T07:48:47.033Z", "_jobs": [ { "job": 111388229025, "time": 160, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11296649001" }, { "job": 111388229012, "time": 201, "config": "linux-release-x86_64", "r": "4.6.1", "check": 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My research specializes in big data clustering of genomic data, math modeling, and machine learning." }, "_downloads": { "count": 327, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/ViralEntropR" }, "_devurl": "https://github.com/vadimtyuryaev/viralentropr", "_pkgdown": "https://vadimtyuryaev.github.io/ViralEntropR/", "_searchresults": 0, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "extra/ViralEntropR.html", "LICENSE" ], "_homeurl": "https://github.com/vadimtyuryaev/viralentropr", "_realowner": "vadimtyuryaev", "_cranurl": true, "_releases": [ { "version": "0.6.2", "date": "2026-05-30" } ], "_exports": [ "calculate_entropy", "calculate_hellinger_matrix", "cluster_sites_by_entropy", "decode_aa_sequence", "detect_changepoints_ecp", "detect_changepoints_hdcp", 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Time Windows", "topics": [ "partition_time_windows" ] }, { "page": "plot_entropy_trajectories", "title": "Plot Shannon Entropy Trajectories", "topics": [ "plot_entropy_trajectories" ] }, { "page": "plot_site_class_trajectory", "title": "Plot GMM Entropy Class Trajectory for a Single Site", "topics": [ "plot_site_class_trajectory" ] }, { "page": "relabel_entropy_classes", "title": "Relabel Entropy Classes", "topics": [ "relabel_entropy_classes" ] }, { "page": "sarscov2_sample", "title": "SARS-CoV-2 Surface Glycoprotein Sequences - NCBI Demo Sample", "topics": [ "sarscov2_sample" ] }, { "page": "sarscov2_variants", "title": "SARS-CoV-2 VOC/VOI Curated Variant Metadata", "topics": [ "sarscov2_variants" ] }, { "page": "simulate_variant_evolution", "title": "Simulate Viral Variant Evolution", "topics": [ "simulate_variant_evolution" ] }, { "page": "tabulate_site_evolution", "title": "Tabulate Site Frequency Evolution", "topics": [ "tabulate_site_evolution" ] } ], "_readme": 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Hellinger Distance, and\nChange Point Analysis for Emerging Viral Variant Detection: A Simulation\nStudy", "1 Introduction", "2 Setup", "3 Reference Sequence", "4 Simulation via\nsimulate_variant_evolution()", "4.1 Simulate Data", "4.2 Check the Pool", "4.3 Emergence Periods and\nSites", "4.4 Deleterious Periods\nand Sites", "4.5 Inspecting the\nSimulation Output", "4.6 Reference Phase 1\n(month 1)", "4.7 Reference Phase 4\n(month 4)", "4.8\nVariant 1 Phase 1 (month 5): sites 12, 15", "4.9\nVariant 1 Phase 2 (month 6): sites 12, 15", "4.10\nVariant 1 Phase 4 (month 8): sites 12, 15", "4.11\nVariant 2 Phase 1 (month 9): sites 6, 13, 23", "4.12\nVariant 2 Phase 5 (month 13): sites 6, 13, 23", "4.13\nVariant 3 Phase 1 (month 14): sites 5, 14, 16, 24", "5 Sites Detection and\nChange Point Detection", "5.1 Encoding", "5.2 Partitioning", "5.3 Hellinger Distance\n(HD)", "5.4 Change Point\nDetection", "6 Conclusion", "References" ], "created": "2026-05-05 03:45:10", "modified": "2026-05-08 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Matrix", "7.2 Filter Ambiguous\nResidues", "7.3 Encode Amino Acids to\nIntegers", "8 Step 6: Assemble the\nFinal Data Frame", "8.1 Date Distribution\n(After Filtering)", "8.2 Filter\nUS Sequences and Save", "9 Downstream\nAnalysis", "10 Pipeline Summary", "References" ], "created": "2026-05-05 03:45:10", "modified": "2026-05-08 02:45:29", "commits": 2 }, { "source": "clustering_accuracy.html.asis", "filename": "clustering_accuracy.html", "title": "Unsupervised Recovery of SARS-CoV-2 Variant Structure via Entropy-Driven Site Selection and PAM Clustering: Precision, Recall, and F1 Evaluation Across Wild-Type and Delta-Dominated Surveillance Periods", "engine": "R.rsp::asis", "headings": [ "Unsupervised Recovery of SARS-CoV-2 Variant\nStructure via Entropy-Driven Site Selection and PAM Clustering:\nPrecision, Recall, and F1 Evaluation Across Wild-Type and\nDelta-Dominated Surveillance Periods", "1 Introduction", "1.1 Definitions", "1.2 Accuracy Study\nLogic", "2 Libraries", "3 Global Parameters", "4 Load Preprocessed\nData", "4.1 Country Filter", "5 Step 1: Data\nPartitioning", "5.1 Partition into Two\nTime Windows (Wild-Type vs Delta)", "5.2 Per-Period Entropy\nClustering", "6 Step 2: Combine Both\nPartitions", "6.1 Analyze the Selected\nSites", "6.2 Cross-Reference with\nKnown Variant Sites", "7 Step 3: PAM Clustering\nwith Gower Distance", "7.1 Prepare Feature\nMatrix", "7.2 Gower Distance\nMatrix", "7.3 Silhouette\nAnalysis", "8 Step 4: PAM Fitting and\nt-SNE Visualisation", "8.1 t-SNE Embedding", "8.2 VOC SNP Matching\nHelpers", "8.3 t-SNE Plot\nFunction", "8.4 PAM and Medoid\nAnalysis", "8.5 PAM k = 2", "8.6 PAM k = 3", "8.7 PAM k = 4", "8.8 PAM k = 9", "9 Step 5: Modal Amino\nAcid Profile per Cluster", "10 Step 6: Precision,\nRecall and F1 Score", "11 Step 7: Site\nDifferentiation — Wild-Type Cluster vs Later Clusters", "11.1 Annotation\nhelper", "11.2 Contrasts\ncalculation", "11.3 Biological\nInterpretation of Key Differentiating Sites", "12 Step 8: Borderline\nPeriod 1 Sequences", "13 Step 9: Full-Dataset\nClustering (All 109,536 Sequences)", "13.1 Site Selection on\nFull Dataset", "13.2 Gower Distance", "13.3 Silhouette\nAnalysis", "13.4 PAM Fitting", "13.5 t-SNE\nVisualisation", "13.6 Full Dataset PAM k =\n6", "13.7 Full Dataset PAM k =\n7", "13.8 Full Dataset PAM k =\n9", "14 Summary (Clustering\nAccuracy)", "15 Conclusions", "15.1 Clustering Accuracy\nStudy (Steps 1–8)", "15.2 Full-Dataset\nClustering: Global Separation Patterns (Step 9)", "References" ], "created": "2026-05-05 03:45:10", "modified": "2026-05-08 02:45:29", "commits": 2 } ], "_score": 5.2552725051033065, "_indexed": true, "_nocasepkg": "viralentropr", "_universes": [ "vadimtyuryaev", "vadimtyu" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.6.2", "date": "2026-10-04T07:34:41.000Z", "distro": "resolute", "commit": "405acdac3ec36c078f7ca963dc047b1961de2674", "fileid": 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