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    "fast_apply_sum_na_rm_T",
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    "translatedRandomBeta"
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    {
      "page": "imp4p-package",
      "title": "Introduction to the IMP4P package",
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        "imp4p-package",
        "imp4p"
      ]
    },
    {
      "page": "estim_bound",
      "title": "Estimation of lower and upper bounds for missing values.",
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      ]
    },
    {
      "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"
      ]
    },
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        "fast_apply_nb_na"
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        "fast_apply_nb_not_na"
      ]
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    },
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      ]
    },
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      "topics": [
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      "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": [
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      ]
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    {
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      "title": "Imputation of data sets containing peptide intensities with a multiple imputation strategy.",
      "topics": [
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      ]
    },
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      "title": "Imputation using a decision rule under an assumption of a mixture of MCAR and MNAR values.",
      "topics": [
        "impute.mix"
      ]
    },
    {
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      "title": "Imputing missing values using a maximum likelihood estimation (MLE).",
      "topics": [
        "impute.mle"
      ]
    },
    {
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      "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"
      ]
    },
    {
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      "title": "Multiple imputation from a matrix of probabilities of being MCAR for each missing value.",
      "topics": [
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      ]
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      "title": "Estimating the MCAR mechanism in a sample.",
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      ]
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}