{
  "_id": "6a570fd4ff0138dec5d6d2a7",
  "Package": "hahmmr",
  "Title": "Haplotype-Aware Hidden Markov Model for RNA",
  "Version": "1.0.0",
  "Authors@R": "c(person(\"Teng\", \"Gao\", email = \"tgaoteng@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-0196-689X\")),\nperson(\"Evan\", \"Biederstedt\", email = \"evan.biederstedt@gmail.com\", role=\"aut\"),\nperson(\"Peter\", \"Kharchenko\", email = \"peter_kharchenko@hms.harvard.edu\", role = \"aut\"))",
  "Description": "Haplotype-aware Hidden Markov Model for RNA (HaHMMR) is a\nmethod for detecting copy number variations (CNVs) from bulk\nRNA-seq data. Additional examples, documentations, and details\non the method are available at\n<https://github.com/kharchenkolab/hahmmr/>.",
  "biocViews": "",
  "NeedsCompilation": "yes",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "RoxygenNote": "7.2.3",
  "LazyData": "true",
  "Packaged": {
    "Date": "2026-07-15 04:36:07 UTC",
    "User": "root"
  },
  "Author": "Teng Gao [aut, cre] (<https://orcid.org/0000-0002-0196-689X>),\nEvan Biederstedt [aut], Peter Kharchenko [aut]",
  "Maintainer": "Teng Gao <tgaoteng@gmail.com>",
  "Config/pak/sysreqs": "libicu-dev",
  "Repository": "https://teng-gao.r-universe.dev",
  "Date/Publication": "2023-10-25 19:30:39 UTC",
  "RemoteUrl": "https://github.com/cran/hahmmr",
  "RemoteRef": "HEAD",
  "RemoteSha": "466187fcffa94fb4d50aebc23512c4ce8739d1ae",
  "_user": "teng-gao",
  "_type": "src",
  "_file": "hahmmr_1.0.0.tar.gz",
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  "_sha256": "c3bbab83e459ef604f77112f2049e525933e0b74027e87ef580227fa8303b864",
  "_expires": "2026-10-23T04:42:58.000Z",
  "_created": "2026-07-15T04:36:07.000Z",
  "_published": "2026-07-15T04:43:00.211Z",
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  "_host": "GitHub-Actions",
  "_buildurl": "https://github.com/r-universe/teng-gao/actions/runs/29389236172",
  "_status": "success",
  "_upstream": "https://github.com/cran/hahmmr",
  "_commit": {
    "id": "466187fcffa94fb4d50aebc23512c4ce8739d1ae",
    "author": "Teng Gao <tgaoteng@gmail.com>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 1.0.0\n",
    "time": 1698262239
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  "_maintainer": {
    "name": "Teng Gao",
    "email": "tgaoteng@gmail.com",
    "login": "teng-gao",
    "twitter": "@tGaoTeng",
    "description": "Postdoctoral fellow at Boston Children’s Hospital and Harvard Medical School",
    "uuid": 13375875,
    "orcid": "0000-0002-0196-689X"
  },
  "_distro": "resolute",
  "_registered": true,
  "_dependencies": [
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      "package": "R",
      "version": ">= 4.1.0",
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    },
    {
      "package": "Rcpp",
      "role": "LinkingTo"
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    {
      "package": "RcppArmadillo",
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    {
      "package": "roptim",
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    {
      "package": "data.table",
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    },
    {
      "package": "dplyr",
      "role": "Imports"
    },
    {
      "package": "GenomicRanges",
      "role": "Imports"
    },
    {
      "package": "ggplot2",
      "role": "Imports"
    },
    {
      "package": "glue",
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    {
      "package": "IRanges",
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    {
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  "_owner": "cran",
  "_selfowned": true,
  "_usedby": 1,
  "_updates": [],
  "_tags": [],
  "_stars": 0,
  "_contributors": [
    {
      "user": "teng-gao",
      "count": 1,
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  ],
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    "type": "user",
    "name": "Teng Gao",
    "followers": 55,
    "description": "Postdoctoral fellow at Boston Children’s Hospital and Harvard Medical School"
  },
  "_downloads": {
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/hahmmr"
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  "_searchresults": 6,
  "_topics": [
    "cpp"
  ],
  "_rbuild": "4.6.1",
  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/hahmmr.html",
    "extra/readme.html",
    "extra/readme.md",
    "LICENSE",
    "manual.pdf"
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  "_releases": [
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      "version": "1.0.0",
      "date": "2023-10-25"
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  ],
  "_exports": [
    "analyze_allele",
    "analyze_joint",
    "dbbinom",
    "dpoilog",
    "fit_lnpois_cpp",
    "forward_back_allele",
    "get_allele_bulk",
    "get_bulk",
    "l_bbinom",
    "l_lnpois",
    "likelihood_allele",
    "logSumExp",
    "plot_bulks",
    "plot_psbulk",
    "run_allele_hmm_s5",
    "run_joint_hmm_s15"
  ],
  "_datasets": [
    {
      "name": "acen_hg19",
      "title": "centromere regions (hg19)",
      "object": "acen_hg19",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "CHROM",
        "start",
        "end"
      ],
      "rows": 22,
      "table": true,
      "tojson": true
    },
    {
      "name": "acen_hg38",
      "title": "centromere regions (hg38)",
      "object": "acen_hg38",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "CHROM",
        "start",
        "end"
      ],
      "rows": 22,
      "table": true,
      "tojson": true
    },
    {
      "name": "bulk_example",
      "title": "example pseudobulk dataframe",
      "object": "bulk_example",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "cell",
        "snp_id",
        "CHROM",
        "POS",
        "cM",
        "REF",
        "ALT",
        "AD",
        "DP",
        "GT",
        "gene",
        "AR",
        "snp_index",
        "pBAF",
        "pAD",
        "R",
        "inter_snp_cm",
        "p_s",
        "Y_obs",
        "lambda_obs",
        "lambda_ref",
        "d_obs",
        "gene_index",
        "logFC",
        "lnFC",
        "sse",
        "gene_start",
        "gene_end",
        "state",
        "cnv_state",
        "diploid",
        "mu",
        "sig",
        "theta_min",
        "boundary",
        "seg",
        "seg_start",
        "seg_end",
        "seg_start_index",
        "seg_end_index",
        "n_genes",
        "n_snps",
        "cnv",
        "phi_mle",
        "phi_mle_sig",
        "p_amp",
        "p_del",
        "theta_mle",
        "theta_mle_sig",
        "LLR_y",
        "LLR_x",
        "LLR",
        "cnv_state_post",
        "state_post",
        "p_up",
        "haplo_post",
        "nu",
        "gamma"
      ],
      "rows": 10321,
      "table": true,
      "tojson": true
    },
    {
      "name": "chrom_sizes_hg19",
      "title": "chromosome sizes (hg19)",
      "object": "chrom_sizes_hg19",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "CHROM",
        "size"
      ],
      "rows": 22,
      "table": true,
      "tojson": true
    },
    {
      "name": "chrom_sizes_hg38",
      "title": "chromosome sizes (hg38)",
      "object": "chrom_sizes_hg38",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "CHROM",
        "size"
      ],
      "rows": 22,
      "table": true,
      "tojson": true
    },
    {
      "name": "df_allele_example",
      "title": "example allele count dataframe",
      "object": "df_allele_example",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "cell",
        "snp_id",
        "CHROM",
        "POS",
        "cM",
        "REF",
        "ALT",
        "AD",
        "DP",
        "GT",
        "gene"
      ],
      "rows": 9957,
      "table": true,
      "tojson": true
    },
    {
      "name": "gaps_hg19",
      "title": "genome gap regions (hg19)",
      "object": "gaps_hg19",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "CHROM",
        "start",
        "end"
      ],
      "rows": 28,
      "table": true,
      "tojson": true
    },
    {
      "name": "gaps_hg38",
      "title": "genome gap regions (hg38)",
      "object": "gaps_hg38",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "CHROM",
        "start",
        "end"
      ],
      "rows": 30,
      "table": true,
      "tojson": true
    },
    {
      "name": "gene_counts_example",
      "title": "example gene expression counts matrix",
      "object": "gene_counts_example",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "MN-5_TUMOR"
      ],
      "rows": 1758,
      "table": true,
      "tojson": true
    },
    {
      "name": "gtf_hg19",
      "title": "gene model (hg19)",
      "object": "gtf_hg19",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "gene",
        "gene_start",
        "gene_end",
        "CHROM",
        "gene_length"
      ],
      "rows": 26841,
      "table": true,
      "tojson": true
    },
    {
      "name": "gtf_hg38",
      "title": "gene model (hg38)",
      "object": "gtf_hg38",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "gene",
        "gene_start",
        "gene_end",
        "CHROM",
        "gene_length"
      ],
      "rows": 26807,
      "table": true,
      "tojson": true
    },
    {
      "name": "gtf_mm10",
      "title": "gene model (mm10)",
      "object": "gtf_mm10",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "gene",
        "gene_start",
        "gene_end",
        "CHROM",
        "gene_length"
      ],
      "rows": 30336,
      "table": true,
      "tojson": true
    },
    {
      "name": "pre_likelihood_hmm",
      "title": "HMM object for unit tests",
      "object": "pre_likelihood_hmm",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "ref_hca",
      "title": "reference expression magnitudes from HCA",
      "object": "ref_hca",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "NK",
        "Fibroblast",
        "Macrophage",
        "CD4+T",
        "CD8+T",
        "Endothelial",
        "Myeloid",
        "Monocyte",
        "Dendritic",
        "Plasma",
        "B",
        "Epithelial"
      ],
      "rows": 24756,
      "table": true,
      "tojson": true
    },
    {
      "name": "ref_hca_counts",
      "title": "reference expression counts from HCA",
      "object": "ref_hca_counts",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "NK",
        "Fibroblast",
        "Macrophage",
        "CD4+T",
        "CD8+T",
        "Endothelial",
        "Myeloid",
        "Monocyte",
        "Dendritic",
        "Plasma",
        "B",
        "Epithelial"
      ],
      "rows": 24857,
      "table": true,
      "tojson": true
    },
    {
      "name": "segs_example",
      "title": "example CNV segments dataframe",
      "object": "segs_example",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "sample",
        "CHROM",
        "seg",
        "cnv_state",
        "cnv_state_post",
        "seg_start",
        "seg_end",
        "seg_start_index",
        "seg_end_index",
        "theta_mle",
        "theta_sigma",
        "phi_mle",
        "phi_sigma",
        "p_loh",
        "p_del",
        "p_amp",
        "p_bamp",
        "p_bdel",
        "LLR",
        "LLR_y",
        "LLR_x",
        "n_genes",
        "n_snps",
        "component",
        "LLR_sample",
        "seg_length",
        "seg_cons",
        "n_states",
        "cnv_states",
        "group"
      ],
      "rows": 27,
      "table": true,
      "tojson": true
    },
    {
      "name": "vcf_meta",
      "title": "example VCF header",
      "object": "vcf_meta",
      "class": [
        "character"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "acen_hg19",
      "title": "centromere regions (hg19)",
      "topics": [
        "acen_hg19"
      ]
    },
    {
      "page": "acen_hg38",
      "title": "centromere regions (hg38)",
      "topics": [
        "acen_hg38"
      ]
    },
    {
      "page": "analyze_allele",
      "title": "Analyze allele profile",
      "topics": [
        "analyze_allele"
      ]
    },
    {
      "page": "analyze_joint",
      "title": "Analyze allele and expression profile",
      "topics": [
        "analyze_joint"
      ]
    },
    {
      "page": "bulk_example",
      "title": "example pseudobulk dataframe",
      "topics": [
        "bulk_example"
      ]
    },
    {
      "page": "chrom_sizes_hg19",
      "title": "chromosome sizes (hg19)",
      "topics": [
        "chrom_sizes_hg19"
      ]
    },
    {
      "page": "chrom_sizes_hg38",
      "title": "chromosome sizes (hg38)",
      "topics": [
        "chrom_sizes_hg38"
      ]
    },
    {
      "page": "dbbinom",
      "title": "Beta-binomial distribution density function A distribution is beta-binomial if p, the probability of success, in a binomial distribution has a beta distribution with shape parameters alpha > 0 and beta > 0 For more details, see extraDistr::dbbinom",
      "topics": [
        "dbbinom"
      ]
    },
    {
      "page": "df_allele_example",
      "title": "example allele count dataframe",
      "topics": [
        "df_allele_example"
      ]
    },
    {
      "page": "dpoilog",
      "title": "Returns the density for the Poisson lognormal distribution with parameters mu and sig",
      "topics": [
        "dpoilog"
      ]
    },
    {
      "page": "fit_lnpois_cpp",
      "title": "Fit MLE of log-normal Poisson model",
      "topics": [
        "fit_lnpois_cpp"
      ]
    },
    {
      "page": "forward_back_allele",
      "title": "Forward-backward algorithm for allele HMM",
      "topics": [
        "forward_back_allele"
      ]
    },
    {
      "page": "gaps_hg19",
      "title": "genome gap regions (hg19)",
      "topics": [
        "gaps_hg19"
      ]
    },
    {
      "page": "gaps_hg38",
      "title": "genome gap regions (hg38)",
      "topics": [
        "gaps_hg38"
      ]
    },
    {
      "page": "gene_counts_example",
      "title": "example gene expression counts matrix",
      "topics": [
        "gene_counts_example"
      ]
    },
    {
      "page": "get_allele_bulk",
      "title": "Aggregate into pseudobulk alelle profile",
      "topics": [
        "get_allele_bulk"
      ]
    },
    {
      "page": "get_bulk",
      "title": "Produce combined bulk expression and allele profile",
      "topics": [
        "get_bulk"
      ]
    },
    {
      "page": "gtf_hg19",
      "title": "gene model (hg19)",
      "topics": [
        "gtf_hg19"
      ]
    },
    {
      "page": "gtf_hg38",
      "title": "gene model (hg38)",
      "topics": [
        "gtf_hg38"
      ]
    },
    {
      "page": "gtf_mm10",
      "title": "gene model (mm10)",
      "topics": [
        "gtf_mm10"
      ]
    },
    {
      "page": "l_bbinom",
      "title": "calculate joint likelihood of allele data",
      "topics": [
        "l_bbinom"
      ]
    },
    {
      "page": "l_lnpois",
      "title": "calculate joint likelihood of a PLN model",
      "topics": [
        "l_lnpois"
      ]
    },
    {
      "page": "likelihood_allele",
      "title": "Only compute total log likelihood from an allele HMM",
      "topics": [
        "likelihood_allele"
      ]
    },
    {
      "page": "logSumExp",
      "title": "logSumExp function",
      "topics": [
        "logSumExp"
      ]
    },
    {
      "page": "plot_bulks",
      "title": "Plot a group of pseudobulk HMM profiles",
      "topics": [
        "plot_bulks"
      ]
    },
    {
      "page": "plot_psbulk",
      "title": "Plot a pseudobulk HMM profile",
      "topics": [
        "plot_psbulk"
      ]
    },
    {
      "page": "pre_likelihood_hmm",
      "title": "HMM object for unit tests",
      "topics": [
        "pre_likelihood_hmm"
      ]
    },
    {
      "page": "ref_hca",
      "title": "reference expression magnitudes from HCA",
      "topics": [
        "ref_hca"
      ]
    },
    {
      "page": "ref_hca_counts",
      "title": "reference expression counts from HCA",
      "topics": [
        "ref_hca_counts"
      ]
    },
    {
      "page": "run_allele_hmm_s5",
      "title": "Run a 5-state allele-only HMM - two theta levels",
      "topics": [
        "run_allele_hmm_s5"
      ]
    },
    {
      "page": "run_joint_hmm_s15",
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