Title: A Collection of Methods for Left-Censored Missing Data
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"))
Description: 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.

Title: The Multifactor Dimensionality Reduction (MDR) Analysis for Imcomplete Data
Author: Junghyun Namkung, Taeyoung Hwang, MinSeok Kwon, Sunggon Yi and Wonil Chung
Description: This provides various approaches to handling missing values for the MDR analysis
Packaged: Thu Oct  2 15:28:34 2008; zippyjh

Title: The Multifactor Dimensionality Reduction (MDR) Analysis for
Author: Junghyun Namkung, Taeyoung Hwang, MinSeok Kwon, Sunggon Yi and
Description: This package provides various approaches to handling
missing values for the MDR analysis to identify gene-gene
interactions using biallelic marker data in genetic association

Packaged: Thu May 20 09:59:13 2010; jh.namkung

Title: Impute Missing Values in a Predictive Context
Author: Matthijs Meire, Michel Ballings, Dirk Van den Poel
Description: Compute missing values on a training data set and impute them on a new data set. Current available options are median/mode and random forest.