A Collection of Methods for Left-Censored Missing Data Imputation
Generate expression data
Generate roll up map
Imputation under MCAR and MNAR hypothesis
imputation under MAR/MCAR hypothesis
Imputation with min value
Imputation by random draws
imputation based on quantile regression
Imputation with KNN
imputation using the EM algorithm
imputation based on SVD algorithm
Imputation by 0.
Generates missing values in data.
Identifies row in the data matrix affected by a MNAR missingness mecha...
peptide to protein roll-up
A collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR) mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random. * New functionality has been added: a hybrid method that allows the imputation of missing values in a more complex scenario where the missing data are both MAR and MNAR.