{
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  "Package": "topolow",
  "Title": "Force-Directed Euclidean Embedding of Dissimilarity Data",
  "Version": "2.1.0",
  "Authors@R": "person(\"Omid\", \"Arhami\", , \"omid.arhami@uga.edu\", role = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0009-0005-2681-6598\"))",
  "Maintainer": "Omid Arhami <omid.arhami@uga.edu>",
  "Description": "A robust implementation of Topolow algorithm. It embeds\nobjects into a low-dimensional Euclidean space from a matrix of\npairwise dissimilarities, even when the data do not satisfy\nmetric or Euclidean axioms. The package is particularly\nwell-suited for sparse, incomplete, and censored (thresholded)\ndatasets such as antigenic relationships. The core is a\nphysics-inspired, gradient-free optimization framework that\nmodels objects as particles in a physical system, where\nobserved dissimilarities define spring rest lengths and\nunobserved pairs exert repulsive forces. The package also\nprovides functions specific to antigenic mapping to transform\ncross-reactivity and binding affinity measurements into\naccurate spatial representations in a phenotype space. Key\nfeatures include: * Robust Embedding from Sparse Data:\nEffectively creates complete and consistent maps (in optimal\ndimensions) even with high proportions of missing data (e.g.,\n>95%). * Physics-Inspired Optimization: Models objects (e.g.,\nantigens, landmarks) as particles connected by springs (for\nmeasured dissimilarities) and subject to repulsive forces (for\nmissing dissimilarities), and simulates the physical system\nusing laws of mechanics, reducing the need for complex gradient\ncomputations. * Automatic Dimensionality Detection: Employs a\nlikelihood-based approach to determine the optimal number of\ndimensions for the embedding/map, avoiding distortions common\nin methods with fixed low dimensions. * Noise and Bias\nReduction: Naturally mitigates experimental noise and bias\nthrough its network-based, error-dampening mechanism. *\nAntigenic Velocity Calculation (for antigenic data): Introduces\nand quantifies \"antigenic velocity,\" a vector that describes\nthe rate and direction of antigenic drift for each pathogen\nisolate. This can help identify cluster transitions and\npotential lineage replacements. * Broad Applicability: Analyzes\ndata from various objects that their dissimilarity may be of\ninterest, ranging from complex biological measurements such as\ncontinuous and relational phenotypes, antibody-antigen\ninteractions, and protein folding to abstract concepts, such as\ncustomer perception of different brands. Methods are described\nin the context of bioinformatics applications in Arhami and\nRohani (2025a) <doi:10.1093/bioinformatics/btaf372>, and\nmathematical proofs and Euclidean embedding details are in\nArhami and Rohani (2025b) <doi:10.48550/arXiv.2508.01733>.",
  "License": "BSD_3_clause + file LICENSE",
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  "Repository": "https://omid-arhami.r-universe.dev",
  "Date/Publication": "2026-05-21 10:34:35 UTC",
  "RemoteUrl": "https://github.com/omid-arhami/topolow",
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  "Packaged": {
    "Date": "2026-05-21 11:00:34 UTC",
    "User": "root"
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  "Author": "Omid Arhami [aut, cre, cph] (ORCID:\n<https://orcid.org/0009-0005-2681-6598>)",
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    "message": "Fix Windows BLAS linking: add src/Makevars.win with LAPACK/BLAS/FLIBS flags\n\nResolves undefined reference to ddot_ (BLAS) when building on Windows\nwith Rtools45. Linux builds are unaffected (Makevars.win is Windows-only).\n",
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    "plot_ll_improvement",
    "plot_mcmc_diagnostics",
    "plot_network_structure",
    "plot_performance_trace",
    "plot_temporal_mapping",
    "process_antigenic_data",
    "profile_likelihood",
    "prune_sparse_matrix",
    "run_adaptive_sampling",
    "sanity_check_subsample",
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        "color"
      ],
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      "table": true,
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        "cluster",
        "color"
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      "title": "Analyze Network Structure",
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      "topics": [
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      "title": "Calculate Prediction Interval for Dissimilarity Estimates",
      "topics": [
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    },
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      "page": "calculate_weighted_marginals",
      "title": "Calculate Weighted Marginal Distributions",
      "topics": [
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    },
    {
      "page": "check_gaussian_convergence",
      "title": "Check Multivariate Gaussian Convergence",
      "topics": [
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    },
    {
      "page": "check_matrix_connectivity",
      "title": "Check Dissimilarity Matrix Connectivity",
      "topics": [
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    {
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      "title": "Clean Data by Removing MAD-based Outliers",
      "topics": [
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    {
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      "title": "Color Palettes",
      "topics": [
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        "color_palettes"
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      "title": "Convert Coordinates to a Distance Matrix",
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      "title": "Create Summary Diagnostic Report",
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      "title": "Dengue Virus (DENV) Titer Data",
      "topics": [
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      "title": "Error calculation and validation metrics for topolow Calculate Comprehensive Error Metrics",
      "topics": [
        "error_calculator_comparison"
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      "title": "Main topolow algorithm implementation",
      "topics": [
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      "title": "Automatic Euclidean Embedding with Parameter Optimization",
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      "topics": [
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      "page": "ggsave_white_bg",
      "title": "Save ggplot with white background",
      "topics": [
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      "title": "H3N2 Influenza HI Assay Data from Smith et al. 2004",
      "topics": [
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      "topics": [
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      "topics": [
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      "title": "topolow Data Preprocessing Functions",
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      "title": "Log Transform Parameter Samples",
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      "topics": [
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      "topics": [
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      "source": "Comprehensive_Evaluation.Rmd",
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      "title": "Comparative Analysis of Embedding Methods for Non-Euclidean Dissimilarity Data: A Comprehensive Evaluation of Topolow",
      "author": "Omid Arhami",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Abstract",
        "Introduction",
        "Methodological Framework",
        "Data Generation Methodologies",
        "Method 1: Synthesized non-Euclidean Space",
        "Method 2: Swiss Roll Manifold",
        "Experimental Design and Analysis Pipeline",
        "Results: Synthesized non-Euclidean Data",
        "Eigenvalue Spectrum Analysis - Synthesized non-Euclidean",
        "Performance Comparison - Synthesized non-Euclidean",
        "Results: Swiss Roll Manifold Data",
        "Eigenvalue Spectrum Analysis - Swiss Roll",
        "Performance Comparison - Swiss Roll",
        "Comparative Analysis Across Dataset Types",
        "Combined Performance Analysis",
        "Statistical Comparisons",
        "Distance Preservation Analysis",
        "Discussion",
        "Key Findings",
        "1. Non-Euclidean Character Assessment",
        "2. Method Performance Characteristics",
        "3. Distance Preservation Quality",
        "Methodological Implications",
        "For Sparse Data",
        "For Non-Euclidean Data",
        "Parameter Optimization",
        "Limitations and Future Directions",
        "Current Limitations",
        "Future Research Directions",
        "Conclusions",
        "For Practitioners",
        "For Method Developers",
        "Scientific Impact",
        "Session Information"
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      "created": "2025-08-04 09:24:26",
      "modified": "2025-08-30 23:27:30",
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