{
  "_id": "6a43f4678696d7565c1695d2",
  "Package": "NeighborFinder",
  "Title": "Find Neighbor Species of a Bacteria of Interest in the Human Gut\nMicrobiota",
  "Version": "1.0.1",
  "Authors@R": "c(\nperson(\"Mathilde\", \"Sola\", , \"mathilde.sola@inrae.fr\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0009-0436-5078\")),\nperson(\"Mahendra\", \"Mariadassou\", , \"mahendra.mariadassou@inrae.fr\", role = \"aut\",\ncomment = c(ORCID = \"0000-0003-2986-354X\")),\nperson(\"Magali\", \"Berland\", , \"magali.berland@inrae.fr\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-6762-5350\"))\n)",
  "Description": "Implementation of the local approach described in Sola et\nal., 2026 <doi:10.64898/2025.12.05.692507> to identify\ncompanion species of a bacteria of interest. From several\nabundance tables of metagenomic data, 'NeighborFinder' suggests\na shortlist of companion species based on the integration of\nresults. A visualization via a network is proposed.",
  "License": "MIT + file LICENSE",
  "VignetteBuilder": "knitr",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-30 16:49:28 UTC",
    "User": "root"
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  "Author": "Mathilde Sola [aut, cre] (ORCID:\n<https://orcid.org/0009-0009-0436-5078>), Mahendra Mariadassou\n[aut] (ORCID: <https://orcid.org/0000-0003-2986-354X>), Magali\nBerland [aut] (ORCID: <https://orcid.org/0000-0002-6762-5350>)",
  "Maintainer": "Mathilde Sola <mathilde.sola@inrae.fr>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-06-30 12:20:32 UTC",
  "RemoteUrl": "https://github.com/cran/NeighborFinder",
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  "_user": "cran",
  "_type": "src",
  "_file": "NeighborFinder_1.0.1.tar.gz",
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  "_created": "2026-06-30T16:49:28.000Z",
  "_published": "2026-06-30T16:52:55.344Z",
  "_distro": "resolute",
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    "author": "Mathilde Sola <mathilde.sola@inrae.fr>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 1.0.1\n",
    "time": 1782822032
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    "name": "Mathilde Sola",
    "email": "mathilde.sola@inrae.fr"
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    "extra/contents.json",
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  "_exports": [
    "%>%",
    "apply_NeighborFinder",
    "apply_NF_simple",
    "choose_params_values",
    "compute_precision",
    "compute_recall",
    "cvglm_to_coeffs_by_object",
    "final_step",
    "find_all_module_neighbors",
    "find_module_neighbors",
    "get_count_table",
    "graph_step",
    "identify_module",
    "intersections_network",
    "intersections_table",
    "mclr",
    "module_to_node",
    "new_synth_data",
    "norm_data",
    "prev_for_selected_nodes",
    "simulate_by_prevalence",
    "simulate_from_ecdf",
    "test_filter",
    "truth_by_prevalence",
    "visualize_network"
  ],
  "_datasets": [
    {
      "name": "data",
      "title": "data",
      "object": "data",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "graphs",
      "title": "graphs",
      "object": "graphs",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "metadata",
      "title": "metadata",
      "object": "metadata",
      "class": [
        "list"
      ],
      "fields": [],
      "table": true,
      "tojson": true
    },
    {
      "name": "result_example",
      "title": "result_example",
      "object": "result_example",
      "class": [
        "list"
      ],
      "fields": [],
      "table": true,
      "tojson": true
    },
    {
      "name": "taxo",
      "title": "taxo",
      "object": "taxo",
      "class": [
        "data.frame"
      ],
      "fields": [
        "msp_id",
        "species",
        "genus",
        "catalogue"
      ],
      "rows": 2537,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "apply_NeighborFinder",
      "title": "Apply NeighborFinder on raw data",
      "topics": [
        "apply_NeighborFinder"
      ]
    },
    {
      "page": "apply_NF_simple",
      "title": "Apply NeighborFinder simplest version on raw data",
      "topics": [
        "apply_NF_simple"
      ]
    },
    {
      "page": "choose_params_values",
      "title": "Render a table to give an indication of the values to choose for the prevalence level and the top filtering percentage",
      "topics": [
        "choose_params_values"
      ]
    },
    {
      "page": "compute_precision",
      "title": "Compute precision rate",
      "topics": [
        "compute_precision"
      ]
    },
    {
      "page": "compute_recall",
      "title": "Compute recall rate",
      "topics": [
        "compute_recall"
      ]
    },
    {
      "page": "cvglm_to_coeffs_by_object",
      "title": "Apply cv.glmnet() for a list of module IDs and for each prevalence level",
      "topics": [
        "cvglm_to_coeffs_by_object"
      ]
    },
    {
      "page": "data",
      "title": "data",
      "topics": [
        "data"
      ]
    },
    {
      "page": "final_step",
      "title": "Gather lists of neighbors of true ones from the graph and detected ones from cv.glmnet()",
      "topics": [
        "final_step"
      ]
    },
    {
      "page": "find_all_module_neighbors",
      "title": "Apply cv.glmnet() for a list of module IDs",
      "topics": [
        "find_all_module_neighbors"
      ]
    },
    {
      "page": "find_module_neighbors",
      "title": "Apply cv.glmnet() for a given mmodule ID",
      "topics": [
        "find_module_neighbors"
      ]
    },
    {
      "page": "get_count_table",
      "title": "Conversion to count table function with prevalence filter",
      "topics": [
        "get_count_table"
      ]
    },
    {
      "page": "graph_step",
      "title": "Generate a graph with a \"cluster-like\" structure, only needed for simulation purposes",
      "topics": [
        "graph_step"
      ]
    },
    {
      "page": "graphs",
      "title": "graphs",
      "topics": [
        "graphs"
      ]
    },
    {
      "page": "identify_module",
      "title": "List the modules corresponding to a given object of interest",
      "topics": [
        "identify_module"
      ]
    },
    {
      "page": "intersections_network",
      "title": "Display the intersection network from 2 or more datasets",
      "topics": [
        "intersections_network"
      ]
    },
    {
      "page": "intersections_table",
      "title": "Display the intersection table summarizing the results from 2 or more datasets",
      "topics": [
        "intersections_table"
      ]
    },
    {
      "page": "mclr",
      "title": "Modified central log ratio (mclr) transformation extracted from the SPRING package",
      "topics": [
        "mclr"
      ]
    },
    {
      "page": "metadata",
      "title": "metadata",
      "topics": [
        "metadata"
      ]
    },
    {
      "page": "module_to_node",
      "title": "Correspondence between the module ID (msp or functional module) and its name (bacteria or function)",
      "topics": [
        "module_to_node"
      ]
    },
    {
      "page": "new_synth_data",
      "title": "Simulate data from some empirical count dataset with a \"cluster-like\" structure",
      "topics": [
        "new_synth_data"
      ]
    },
    {
      "page": "norm_data",
      "title": "Normalize data and filters it by prevalence level",
      "topics": [
        "norm_data"
      ]
    },
    {
      "page": "prev_for_selected_nodes",
      "title": "Extract edges in graph involving any module in object_of_interest set",
      "topics": [
        "prev_for_selected_nodes"
      ]
    },
    {
      "page": "result_example",
      "title": "result_example",
      "topics": [
        "result_example"
      ]
    },
    {
      "page": "simulate_by_prevalence",
      "title": "List the simulated count tables by level of prevalence",
      "topics": [
        "simulate_by_prevalence"
      ]
    },
    {
      "page": "simulate_from_ecdf",
      "title": "Simulate data Generates synthetic count data based on empirical cumulative distribution (ecdf) of real count data",
      "topics": [
        "simulate_from_ecdf"
      ]
    },
    {
      "page": "taxo",
      "title": "taxo",
      "topics": [
        "taxo"
      ]
    },
    {
      "page": "test_filter",
      "title": "Render a table gathering precision and recall rates before and after filtering on coefficient values",
      "topics": [
        "test_filter"
      ]
    },
    {
      "page": "truth_by_prevalence",
      "title": "Give true neighbors by level of prevalence",
      "topics": [
        "truth_by_prevalence"
      ]
    },
    {
      "page": "visualize_network",
      "title": "Display network after applying NeighborFinder",
      "topics": [
        "visualize_network"
      ]
    }
  ],
  "_readme": "https://github.com/cran/NeighborFinder/raw/HEAD/README.md",
  "_rundeps": [
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  "_vignettes": [
    {
      "source": "NeighborFinder_vignette.Rmd",
      "filename": "NeighborFinder_vignette.html",
      "title": "Detailed example",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Presentation of dataset: CRC Example",
        "Preview of the data",
        "1) The abundance table",
        "2) The metadata",
        "3) The taxonomy",
        "4) The graph",
        "Aim of this use case",
        "Test if the default parameters of NeighborFinder are suitable for your species of interest & dataset",
        "Apply NeighborFinder & look for Escherichia coli neighbors in CRC patients",
        "Visualize the corresponding network",
        "Apply NeighborFinder with covariate option",
        "Look at the intersection of neighbors found in the 3 subgroups"
      ],
      "created": "2026-06-30 12:20:32",
      "modified": "2026-06-30 12:20:32",
      "commits": 1
    },
    {
      "source": "NeighborFinder_technical_report.Rmd",
      "filename": "NeighborFinder_technical_report.html",
      "title": "Technical Report",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Why use NeighborFinder?",
        "How to use it?",
        "Input dataframe format",
        "What is behind apply_NeighborFinder() ?",
        "1)\tPre-processing: Counts & Normalization",
        "a) Prevalence filter & shotgun pre-treatment",
        "b) Normalization",
        "2)\tRegularized linear regressions",
        "a)\tSimple case: no covariates",
        "b)\tHandling covariates",
        "3)\tPost-processing",
        "a)\tFiltering the results",
        "b)\tIncreasing robusteness",
        "How to calibrate the parameters values ?"
      ],
      "created": "2026-06-30 12:20:32",
      "modified": "2026-06-30 12:20:32",
      "commits": 1
    }
  ],
  "_score": 3,
  "_indexed": true,
  "_nocasepkg": "neighborfinder",
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