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  "Description": "Functions to analyse missing value mechanisms and to\nimpute data sets in the context of bottom-up MS-based\nproteomics.",
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    "fast_apply_sum_na_rm_T",
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    "impute.mi",
    "impute.mix",
    "impute.mle",
    "impute.pa",
    "impute.PCA",
    "impute.rand",
    "impute.RF",
    "impute.slsa",
    "mi.mix",
    "miss.mcar.process",
    "miss.total.process",
    "pi.mcar.karpievitch",
    "pi.mcar.logit",
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    "sim.data",
    "translatedRandomBeta"
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      "page": "imp4p-package",
      "title": "Introduction to the IMP4P package",
      "topics": [
        "imp4p-package",
        "imp4p"
      ]
    },
    {
      "page": "estim_bound",
      "title": "Estimation of lower and upper bounds for missing values.",
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        "estim.bound"
      ]
    },
    {
      "page": "estim_mix",
      "title": "Estimation of a mixture model of MCAR and MNAR values in each column of a data matrix.",
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        "estim.mix"
      ]
    },
    {
      "page": "fast_apply_nb_na",
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      "topics": [
        "fast_apply_nb_na"
      ]
    },
    {
      "page": "fast_apply_nb_not_na",
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      "topics": [
        "fast_apply_nb_not_na"
      ]
    },
    {
      "page": "fast_apply_sd_na_rm_T",
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      "topics": [
        "fast_apply_sd_na_rm_T"
      ]
    },
    {
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      "topics": [
        "fast_apply_sum_na_rm_T"
      ]
    },
    {
      "page": "fast_sim",
      "title": "Function to compute similarity measures between a vector and each row of a matrix.",
      "topics": [
        "fast_sim"
      ]
    },
    {
      "page": "gen_cond",
      "title": "Function allowing to create a vector indicating the membership of each sample to a condition.",
      "topics": [
        "gen.cond"
      ]
    },
    {
      "page": "impute_igcda",
      "title": "Imputing missing values by assuming that the distribution of complete values is Gaussian in each column of an input matrix. This algorithm is named \"Imputation under a Gaussian Complete Data Assumption\" (IGCDA).",
      "topics": [
        "impute.igcda"
      ]
    },
    {
      "page": "impute_mi",
      "title": "Imputation of data sets containing peptide intensities with a multiple imputation strategy.",
      "topics": [
        "impute.mi"
      ]
    },
    {
      "page": "impute_mix",
      "title": "Imputation using a decision rule under an assumption of a mixture of MCAR and MNAR values.",
      "topics": [
        "impute.mix"
      ]
    },
    {
      "page": "impute_mle",
      "title": "Imputing missing values using a maximum likelihood estimation (MLE).",
      "topics": [
        "impute.mle"
      ]
    },
    {
      "page": "impute_pa",
      "title": "Imputation of peptides having no value in a biological condition (present in a condition / absent in another).",
      "topics": [
        "impute.pa"
      ]
    },
    {
      "page": "impute_PCA",
      "title": "Imputing missing values using Principal Components Analysis.",
      "topics": [
        "impute.PCA"
      ]
    },
    {
      "page": "impute_rand",
      "title": "Imputation of peptides with a random value.",
      "topics": [
        "impute.rand"
      ]
    },
    {
      "page": "impute_RF",
      "title": "Imputing missing values using Random Forest.",
      "topics": [
        "impute.RF"
      ]
    },
    {
      "page": "impute_slsa",
      "title": "Imputing missing values using an adaptation of the LSimpute algorithm (Bo et al. (2004)) to experimental designs. This algorithm is named \"Structured Least Squares Algorithm\" (SLSA).",
      "topics": [
        "impute.slsa"
      ]
    },
    {
      "page": "mi_mix",
      "title": "Multiple imputation from a matrix of probabilities of being MCAR for each missing value.",
      "topics": [
        "mi.mix"
      ]
    },
    {
      "page": "miss_mcar_process",
      "title": "Estimating the MCAR mechanism in a sample.",
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      ]
    },
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      "topics": [
        "miss.total.process"
      ]
    },
    {
      "page": "pi_mcar_karpievitch",
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