{
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  "Package": "animalcules",
  "Title": "Interactive microbiome analysis toolkit",
  "Version": "1.29.0",
  "Authors@R": "c(person(given=c(\"W.\", \"Evan\"), family=\"Johnson\", email=\"wej@bu.edu\",\nrole=c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-6247-6595\")),\nperson(given=\"Yue\", family=\"Zhao\", email=\"yuezh@bu.edu\",\nrole=c(\"aut\"), comment = c(ORCID = \"0000-0001-5257-5103\")),\nperson(given=\"Anthony\", family=\"Federico\", email=\"anfed@bu.edu\",\nrole=c(\"aut\"), comment = c(ORCID = \"0000-0002-9200-1689\")),\nperson(given=\"Jessica\", family=\"Anderson\",\nemail=\"anderson.jessica@rutgers.edu\", role=c(\"ctr\"),\ncomment = c(ORCID = \"0000-0002-0542-9872\")))",
  "Description": "animalcules is an R package for utilizing up-to-date data\nanalytics, visualization methods, and machine learning models\nto provide users an easy-to-use interactive microbiome analysis\nframework. It can be used as a standalone software package or\nusers can explore their data with the accompanying interactive\nR Shiny application. Traditional microbiome analysis such as\nalpha/beta diversity and differential abundance analysis are\nenhanced, while new methods like biomarker identification are\nintroduced by animalcules. Powerful interactive and dynamic\nfigures generated by animalcules enable users to understand\ntheir data better and discover new insights.",
  "License": "Artistic-2.0",
  "URL": "https://github.com/wejlab/animalcules",
  "BugReports": "https://github.com/wejlab/animalcules/issues",
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  "biocViews": "Microbiome, Metagenomics, Coverage, Visualization",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:50:11 UTC",
  "RemoteUrl": "https://github.com/bioc/animalcules",
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  "Packaged": {
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  "Author": "W. Evan Johnson [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-6247-6595>),\nYue Zhao [aut] (ORCID: <https://orcid.org/0000-0001-5257-5103>),\nAnthony Federico [aut] (ORCID: <https://orcid.org/0000-0002-9200-1689>),\nJessica Anderson [ctr] (ORCID: <https://orcid.org/0000-0002-0542-9872>)",
  "Maintainer": "W. Evan Johnson <wej@bu.edu>",
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    "counts_to_relabu",
    "df_char_to_factor",
    "differential_abundance",
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    "diversities",
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    "diversity_beta_NMDS",
    "diversity_beta_test",
    "do_alpha_div_test",
    "filter_categorize",
    "filter_summary_bar_density",
    "filter_summary_pie_box",
    "find_biomarker",
    "find_taxon_mat",
    "find_taxonomy",
    "find_taxonomy_300",
    "gini_simpson",
    "grep_tid",
    "inverse_simpson",
    "is_categorical",
    "is_integer0",
    "is_integer1",
    "mae_pick_organisms",
    "mae_pick_samples",
    "pct2str",
    "percent",
    "read_pathoscope_data",
    "relabu_barplot",
    "relabu_boxplot",
    "relabu_heatmap",
    "run_animalcules",
    "shannon",
    "simpson_index",
    "upsample_counts",
    "write_to_biom"
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    {
      "page": "alpha_div_boxplot",
      "title": "Alpha diversity boxplot",
      "topics": [
        "alpha_div_boxplot"
      ]
    },
    {
      "page": "alpha_div_test",
      "title": "Get alpha diversity",
      "topics": [
        "alpha_div_test"
      ]
    },
    {
      "page": "counts_to_logcpm",
      "title": "Covert a counts table to a relative abundances table",
      "topics": [
        "counts_to_logcpm"
      ]
    },
    {
      "page": "counts_to_relabu",
      "title": "Covert a counts table to a relative abundances table",
      "topics": [
        "counts_to_relabu"
      ]
    },
    {
      "page": "df_char_to_factor",
      "title": "Factorize all categorical columns",
      "topics": [
        "df_char_to_factor"
      ]
    },
    {
      "page": "differential_abundance",
      "title": "Differential abundance analysis",
      "topics": [
        "differential_abundance"
      ]
    },
    {
      "page": "dimred_pca",
      "title": "Dimensionality reduction through PCA",
      "topics": [
        "dimred_pca"
      ]
    },
    {
      "page": "dimred_pcoa",
      "title": "Dimensionality reduction through PCoA",
      "topics": [
        "dimred_pcoa"
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    },
    {
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      "title": "Dimensionality reduction through t-SNE",
      "topics": [
        "dimred_tsne"
      ]
    },
    {
      "page": "dimred_umap",
      "title": "Dimensionality reduction through PCA",
      "topics": [
        "dimred_umap"
      ]
    },
    {
      "page": "diversities",
      "title": "Get alpha diversity",
      "topics": [
        "diversities"
      ]
    },
    {
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      "title": "Get alpha diversity",
      "topics": [
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      ]
    },
    {
      "page": "diversity_beta_boxplot",
      "title": "Beta diversity boxplot",
      "topics": [
        "diversity_beta_boxplot"
      ]
    },
    {
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      "title": "Beta diversity heatmap",
      "topics": [
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    {
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      "title": "Beta diversity NMDS plot",
      "topics": [
        "diversity_beta_NMDS"
      ]
    },
    {
      "page": "diversity_beta_test",
      "title": "Perform a beta diversity test",
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      "topics": [
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      ]
    },
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      "title": "Data visualization by pie chart / box plot",
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    {
      "page": "find_biomarker",
      "title": "Identify biomarkers",
      "topics": [
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      ]
    },
    {
      "page": "find_taxon_mat",
      "title": "Find the Taxonomy Information Matrix",
      "topics": [
        "find_taxon_mat"
      ]
    },
    {
      "page": "find_taxonomy",
      "title": "Find the taxonomy for unlimited tids",
      "topics": [
        "find_taxonomy"
      ]
    },
    {
      "page": "find_taxonomy_300",
      "title": "Find the taxonomy for maximum 300 tids",
      "topics": [
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    {
      "page": "gini_simpson",
      "title": "Get alpha diversity using gini",
      "topics": [
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    {
      "page": "grep_tid",
      "title": "Greps the tid from the given identifier string",
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      "page": "inverse_simpson",
      "title": "Get alpha diversity using inverse simpson",
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      "page": "is_categorical",
      "title": "Check if object is categorical",
      "topics": [
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      "title": "check if integer(0)",
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      "title": "Format decimals to percentages",
      "topics": [
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      "page": "read_pathoscope_data",
      "title": "Reads the data from PathoScope reports and returns a list of final guess relative abundance and count data",
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    },
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      "page": "relabu_barplot",
      "title": "Plot bar plots of sample and group level relative abundance",
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    {
      "page": "relabu_boxplot",
      "title": "Plot boxplots comparing different organism prevalence across conditions",
      "topics": [
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    {
      "page": "relabu_heatmap",
      "title": "Plot heatmap of sample level counts in logcpm",
      "topics": [
        "relabu_heatmap"
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    {
      "page": "run_animalcules",
      "title": "Run animalcules shiny app",
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      "page": "shannon",
      "title": "Get alpha diversity using shannon",
      "topics": [
        "shannon"
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      "page": "simpson_index",
      "title": "Get alpha diversity using simpson",
      "topics": [
        "simpson_index"
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      "title": "Upsample a counts table to a higher taxon level",
      "topics": [
        "upsample_counts"
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    },
    {
      "page": "write_to_biom",
      "title": "Output biom",
      "topics": [
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    }
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      "title": "animalcules",
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