A collection of methods for left-censored missing data
imputation
The package contains 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.
R (>= 2.10), tmvtnorm, norm, pcaMethods, impute

The package contains 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.

A Collection of Methods for Left-Censored Missing Data
Imputation
c(
person("Cosmin", "Lazar", email = "vcosminlazar@gmail.com", role = c("aut")),
person("Thomas", "Burger", email = "thomas.burger@cea.fr", role = c("aut")),
person("Samuel", "Wieczorek", email = "samuel.wieczorek@cea.fr", role = c("cre", "ctb"))
)
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.
Cosmin Lazar [aut],
Thomas Burger [aut],
Samuel Wieczorek [cre, ctb]