Package: missForest
Type: Package
Title: Nonparametric Missing Value Imputation using Random Forest
Version: 1.3-1
Date: 2013-12-10
Author: Daniel J. Stekhoven <stekhoven@stat.math.ethz.ch>
Maintainer: Daniel J. Stekhoven <stekhoven@stat.math.ethz.ch>
Depends: randomForest
Description: The function 'missForest' in this package is used to
impute missing values particularly in the case of mixed-type
data. It uses a random forest trained on the observed values of
a data matrix to predict the missing values. It can be used to
impute continuous and/or categorical data including complex
interactions and non-linear relations. It yields an out-of-bag
(OOB) imputation error estimate without the need of a test set
or elaborate cross-validation.
License: GPL (>= 2)
URL: http://www.r-project.org, https://github.com/stekhoven/missForest
Packaged: 2013-12-10 14:30:17 UTC; DSQuantik
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-12-10 15:48:33

Package: missForest
Type: Package
Title: Nonparametric Missing Value Imputation using Random Forest
Version: 1.4
Date: 2013-12-31
Author: Daniel J. Stekhoven <stekhoven@stat.math.ethz.ch>
Maintainer: Daniel J. Stekhoven <stekhoven@stat.math.ethz.ch>
Depends: randomForest,foreach,itertools
Description: The function 'missForest' in this package is used to
impute missing values particularly in the case of mixed-type
data. It uses a random forest trained on the observed values of
a data matrix to predict the missing values. It can be used to
impute continuous and/or categorical data including complex
interactions and non-linear relations. It yields an out-of-bag
(OOB) imputation error estimate without the need of a test set
or elaborate cross-validation. It can be run in parallel to
save computation time.
License: GPL (>= 2)
URL: http://www.r-project.org, https://github.com/stekhoven/missForest
Packaged: 2013-12-31 14:28:06 UTC; DSQuantik
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-12-31 16:17:04

Title: Handling missing values with/in multivariate data analysis
Maintainer: Francois Husson <husson@agrocampus-ouest.fr>, Julie Josse
Description: Imputation of incomplete continuous or categorical
datasets; Missing values are imputed with a principal component
analysis (PCA) or a multiple correspondence analysis (MCA)
model; Perform multiple imputation with and in PCA