estim.bound             Estimation of lower and upper bounds for
                        missing values.
estim.mix               Estimation of a mixture model of MCAR and MNAR
                        values in each column of a data matrix.
fast_apply_nb_na        Function similar to the function
                        'apply(X,dim,function(x)sum(is.na(x)))'.
fast_apply_nb_not_na    Function similar to the function
                        'apply(X,dim,function(x)sum(!is.na(x)))'.
fast_apply_sd_na_rm_T   Function similar to the function
                        'apply(X,dim,sd,na.rm=TRUE)'.
fast_apply_sum_na_rm_T
                        Function similar to the function
                        'apply(X,dim,sum,na.rm=TRUE)'.
fast_sim                Function to compute similarity measures between
                        a vector and each row of a matrix.
gen.cond                Function allowing to create a vector indicating
                        the membership of each sample to a condition.
imp4p-package           Introduction to the IMP4P package
impute.PCA              Imputing missing values using Principal
                        Components Analysis.
impute.RF               Imputing missing values using Random Forest.
impute.igcda            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).
impute.mi               Imputation of data sets containing peptide
                        intensities with a multiple imputation
                        strategy.
impute.mix              Imputation using a decision rule under an
                        assumption of a mixture of MCAR and MNAR
                        values.
impute.mle              Imputing missing values using a maximum
                        likelihood estimation (MLE).
impute.pa               Imputation of peptides having no value in a
                        biological condition (present in a condition /
                        absent in another).
impute.rand             Imputation of peptides with a random value.
impute.slsa             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).
mi.mix                  Multiple imputation from a matrix of
                        probabilities of being MCAR for each missing
                        value.
miss.mcar.process       Estimating the MCAR mechanism in a sample.
miss.total.process      Estimating the missing data mechanism in a
                        sample.
pi.mcar.karpievitch     Estimating the proportion of MCAR values in
                        biological conditions using the method of
                        Karpievitch (2009).
pi.mcar.logit           Estimating the proportion of MCAR values in a
                        sample using a logit model.
pi.mcar.probit          Estimating the proportion of MCAR values in a
                        sample using a probit model.
prob.mcar               Estimation of a vector of probabilities that
                        missing values are MCAR.
prob.mcar.tab           Estimation of a matrix of probabilities that
                        missing values are MCAR.
sim.data                Simulation of data sets by controlling the
                        proportion of MCAR values and the distribution
                        of MNAR values.
translatedRandomBeta    Function to generated values following a
                        translated Beta distribution
