• impute

  • Referenced in 99 articles [sw14376]
  • missing values. Methods for imputing missing data are needed, therefore, to minimize the effect ... estimation of missing values in gene microarray data. We implemented and evaluated three methods ... robustness of the imputation methods to the amount of missing data over the range...
  • Amelia

  • Referenced in 36 articles [sw04533]
  • Missing Data , Amelia II ”multiply imputesmissing data in a single cross-section (such ... from a time-series-cross-sectional data set (such as collected by years for each ... Unlike Amelia I and other statistically rigorous imputation software, it virtually never crashes (but please ... have about the values of missing cells in their data. Amelia II also includes useful...
  • StatMatch

  • Referenced in 22 articles [sw11933]
  • also be used to impute missing values in data sets through hot deck imputation methods...
  • missForest

  • Referenced in 29 articles [sw19483]
  • used to impute missing values particularly in the case of mixed-type data. It uses ... missing values. It can be used to impute continuous and/or categorical data including complex interactions...
  • VIM

  • Referenced in 17 articles [sw06776]
  • exploring the data and the structure of the missing and/or imputed values. Depending on this ... explore the data including missing values. In addition, the quality of imputation can be visually...
  • TASSEL

  • Referenced in 5 articles [sw11446]
  • statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components. Availability...
  • ice

  • Referenced in 13 articles [sw24700]
  • Multiple Imputation by Chained Equations (MICE): Implementation in Stata. Missing data are a common occurrence ... multiple imputation is becoming the standard route to estimating models with missing covariate data under ... missing-at-random assumption. We describe ice, an implementation in Stata ... MICE approach to multiple imputation. Real data from an observational study in ovarian cancer...
  • Imputation

  • Referenced in 4 articles [sw14374]
  • package Imputation. Missing data imputation (also known as matrix completion) is an extremely difficult science ... that tries to fill in missing values of a dataset with the best guess. Recently ... their movie ratings were presented to the data science community to see if algorithms could...
  • missMDA

  • Referenced in 15 articles [sw08142]
  • missMDA: Handling missing values with/in multivariate data analysis (principal component methods). Imputation of incomplete continuous...
  • Hmisc

  • Referenced in 41 articles [sw04530]
  • Hmisc library contains many functions useful for data analysis, high-level graphics, utility operations, functions ... sample size and power, importing datasets, imputing missing values, advanced table making, variable clustering, character...
  • XGobi

  • Referenced in 32 articles [sw11752]
  • projections. Missing data are accommodated and their patterns can be examined; multiple imputations...
  • mitools

  • Referenced in 6 articles [sw23419]
  • package mitools: Tools for multiple imputation of missing data. Tools to perform analyses and combine...
  • MDI

  • Referenced in 2 articles [sw35028]
  • Missing Data Imputation Toolbox for MATLAB. Here we introduce a graphical user-friendly interface ... called Missing Data Imputation (MDI) Toolbox. This MATLAB toolbox allows imputing missing values, following missing ... Toolbox presents a general procedure to impute missing data, thus can be used to infer ... missing data, to estimate the covariance structure of incomplete data matrices, or to impute...
  • RegEM

  • Referenced in 17 articles [sw04943]
  • climate data: Estimation of mean values and covariance matrices and imputation of missing values. Journal ... algorithm for Gaussian data is based on iterated linear regression analyses. In the regularized...
  • imputeTS

  • Referenced in 5 articles [sw20442]
  • univariate time series. Offers several imputation functions and missing data plots. Available imputation algorithms include...
  • iVAR

  • Referenced in 2 articles [sw31224]
  • iVAR: A program for imputing missing data in multivariate time series using vector autoregressive models ... introduces iVAR, an R program for imputing missing data in multivariate time series ... with three methods for handling missing data: listwise deletion, imputation with sample means and variances...
  • Rphylopars

  • Referenced in 3 articles [sw26123]
  • data. Performs ancestral state reconstruction and missing data imputation on the estimated evolutionary model, which...
  • GenForImp

  • Referenced in 2 articles [sw32731]
  • Sequential Distance-Based Approach for Imputing Missing Data. Two methods based on the Forward Imputation ... approach are implemented for the imputation of quantitative missing data. One method alternates Nearest Neighbour...
  • PROC MI

  • Referenced in 5 articles [sw12081]
  • procedure performs multiple imputation of missing data. Missing values are an issue in a substantial...
  • FLIM

  • Referenced in 2 articles [sw26176]
  • Farewell’s Linear Increments Model for Missing Data (the FLIM package), which can be used ... observed increments of longitudinal processes and impute missing data. The method is valid for data...