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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 dependents

cran

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 dependents

alexanderrobitzsch

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 dependents

markvanderloo

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

paulocortez4

rminer:Data Mining Classification and Regression Methods

Facilitates the use of data mining algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.4.8 improved help, several warning and error code fixes (more stable version, all examples run correctly); 1.4.7 improved Importance function and examples, minor error fixes; 1.4.6 / 1.4.5 / 1.4.4 new automated machine learning (AutoML) and ensembles, via improved fit(), mining() and mparheuristic() functions, and new categorical preprocessing, via improved delevels() function; 1.4.3 new metrics (e.g., macro precision, explained variance), new "lssvm" model and improved mparheuristic() function; 1.4.2 new "NMAE" metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics; 1.2 - new input importance methods via improved Importance() function; 1.0 - first version.

Maintained by Paulo Cortez. Last updated 5 months ago.

1.7 match 3 stars 4.58 score 536 scripts

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