The Multifactor Dimensionality Reduction (MDR) Analysis for Imcomplete Data
Junghyun Namkung, Taeyoung Hwang, MinSeok Kwon, Sunggon Yi and Wonil Chung
This provides various approaches to handling missing values for the MDR analysis

The Multifactor Dimensionality Reduction (MDR) Analysis for
Imcomplete Data
Junghyun Namkung, Taeyoung Hwang, MinSeok Kwon, Sunggon Yi and
Wonil Chung
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
studies

The Multifactor Dimensionality Reduction (MDR) Analysis for
Incomplete Data

Impute Missing Values in a Predictive Context
Matthijs Meire, Michel Ballings, Dirk Van den Poel
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.

Imputation Methods for Multivariate Multinomial Data
person("Alex", "Whitworth", , "whitworth.alex@gmail.com", c("aut", "cre"))
Implements imputation methods using EM and Data Augmentation for
multinomial data following the work of Schafer 1997 <ISBN: 978-0-412-04061-0>.
gtools (>= 3.3), methods, parallel, Rcpp

gtools (>= 3.3), methods, parallel, Rcpp, DBI (>= 0.4),
RSQLite (>= 1.0)

'RcppExports.R' 'class_imputeMulti.R' 'data-tract2221.R'
'data_dep_prior_multi.R' 'int-count_levels.R'
'int-impute_multinomial.R' 'int-search_z_Os_y.R'
'int-splitRows.R' 'merge_imputed.R' 'methods_imputeMulti.R'
'multinomial_data_aug.R' 'multinomial_em.R'
'multinomial_impute.R' 'multinomial_stats.R'

gtools (>= 3.3), methods, parallel, Rcpp (>= 0.11.4),
data.table (>= 1.14.2)
'RcppExports.R' 'class_imputeMulti.R' 'data-tract2221.R'
'data_dep_prior_multi.R' 'imputeMulti-package.R'
'int-count_levels.R' 'int-impute_multinomial.R'
'int-search_z_Os_y.R' 'int-splitRows.R' 'merge_imputed.R'
'methods_imputeMulti.R' 'multinomial_data_aug.R'
'multinomial_em.R' 'multinomial_impute.R' 'multinomial_stats.R'

Impute Missing Data in Time Series Data with PSF Based Method
Imputes the missing values in time series data with PSF algorithm based approach.
The details about PSF algorithm are available at:
<https://cran.r-project.org/package=PSF>.