• missForest

  • Referenced in 22 articles [sw19483]
  • package missForest: Nonparametric Missing Value Imputation using Random Forest. The function ’missForest’ in this package ... missing values particularly in the case of mixed-type data. It uses a random forest...
  • REALCOM

  • Referenced in 8 articles [sw18415]
  • assumption that the data are missing at random. However, many medical and social datasets...
  • MissMech

  • Referenced in 4 articles [sw24416]
  • Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random. To test whether the missing data ... observed data, is one of missing completely at random (MCAR). For detailed description see Jamshidian ... Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR),” Journal of Statistical Software...
  • ice

  • Referenced in 5 articles [sw24700]
  • with missing covariate data under a missing-at-random assumption. We describe ice, an implementation...
  • VarSelLCM

  • Referenced in 4 articles [sw15183]
  • values by assuming that values are missing at random. The one-dimensional marginals ... This package also performs the imputation of missing values...
  • YalSAT

  • Referenced in 9 articles [sw31644]
  • winner in the random track in the SAT competition 2016 was missing...
  • PROC CALIS

  • Referenced in 1 article [sw12071]
  • used. If your data sets contain random missing data, the full information maximum likelihood (FIML...
  • eigenmodel

  • Referenced in 1 article [sw26199]
  • assumption that the data are missing at random. The marginal distribution of the relational data...
  • BradleyTerry2

  • Referenced in 17 articles [sw09554]
  • quasi-likelihood (for models which involve a random effect), or by bias-reduced maximum likelihood ... simple and efficient approach to handling missing covariate data, and suitably-defined residuals for diagnostic...
  • experiment

  • Referenced in 1 article [sw24908]
  • randomized experiments with noncompliance, and randomized experiments with missing data...
  • sanon

  • Referenced in 1 article [sw24138]
  • Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable ... measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment...
  • ui

  • Referenced in 1 article [sw32122]
  • probit) parameters when outcome is missing not at random (non-ingorable missingness); (ii) for double...
  • speff2trial

  • Referenced in 0 articles [sw15614]
  • treatment effect in a 2-group randomized clinical trial with a quantitative, dichotomous, or right ... unbiased estimation when the endpoint is missing at random...
  • rrp

  • Referenced in 2 articles [sw25704]
  • Data Matching. Random Recursive Partitiong and Rank-based proximities for data matching, missing data imputation...
  • mirf

  • Referenced in 2 articles [sw26718]
  • random forests (MIRF) for unobservable, high-dimensional data. This package applies a combination of missing ... modelling trait-haplotype associations via the Random Forest algorithm. The EM algorithm is implemented...
  • SAPPER

  • Referenced in 5 articles [sw18894]
  • large database graph with (possible) missing edges. The SAPPER method is proposed to solve this ... index, SAPPER takes advantage of pre-generated random spanning trees and a carefully designed graph...
  • bild

  • Referenced in 2 articles [sw24688]
  • observations from a given individual and a random intercept term. Estimation is via maximization ... exact likelihood of a suitably defined model. Missing values and unbalanced data are allowed, with...
  • ArpEgg

  • Referenced in 1 article [sw14213]
  • solve this, most arpeggiators have a “random” setting. While this produces more complex results ... musician is extremely hit-and-miss, forgoing any form of control over the process itself ... artist must rely on luck that a random sequence will be suitable. Neither extreme...
  • MissingDataGUI

  • Referenced in 2 articles [sw23665]
  • Provides numeric and graphical summaries for the missing values from both categorical and quantitative variables ... including the univariate imputations like fixed or random values, multivariate imputations like the nearest neighbors...