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statistikat
VIM:Visualization and Imputation of Missing Values
New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.
Maintained by Matthias Templ. Last updated 7 months ago.
hotdeckimputation-methodsmodel-predictionsvisualizationcpp
14.3 match 85 stars 14.44 score 2.6k scripts 19 dependentscran
StatMatch:Statistical Matching or Data Fusion
Integration of two data sources referred to the same target population which share a number of variables. Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing with data from complex sample surveys are available too.
Maintained by Marcello DOrazio. Last updated 2 months ago.
6.8 match 4.03 score 10 dependentsalexanderrobitzsch
miceadds:Some Additional Multiple Imputation Functions, Especially for 'mice'
Contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, <doi:10.18637/jss.v045.i03>) are implemented. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, <doi:10.1007/BF02294457>), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, Ch.7, <doi:10.1201/9780429492259>), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, <doi:10.1111/1467-9574.00217>), substantive model compatible imputation (Bartlett et al., 2015, <doi:10.1177/0962280214521348>), and features for the generation of synthetic datasets (Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>; Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).
Maintained by Alexander Robitzsch. Last updated 14 days ago.
missing-datamultiple-imputationopenblascpp
2.0 match 16 stars 9.16 score 542 scripts 9 dependentsmarkvanderloo
simputation:Simple Imputation
Easy to use interfaces to a number of imputation methods that fit in the not-a-pipe operator of the 'magrittr' package.
Maintained by Mark van der Loo. Last updated 8 months ago.
data-scienceimputationofficialstatistics
1.1 match 92 stars 8.38 score 350 scripts