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| imp4p-package | Introduction to the IMP4P package |
| 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 | Introduction to the IMP4P package |
| 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.mnar.process | Estimating the MNAR 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 |