R package Hmisc: Harrell Miscellaneous , The Hmisc library contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of S objects to LaTeX code, and recoding variables. Please submit bug reports to ’’. (Source:

References in zbMATH (referenced in 24 articles )

Showing results 1 to 20 of 24.
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  1. Alexandra Kuznetsova; Per Brockhoff; Rune Christensen: lmerTest Package: Tests in Linear Mixed Effects Models (2017)
  2. Antoine Filipovic-Pierucci, Kevin Zarca, Isabelle Durand-Zaleski: Markov Models for Health Economic Evaluations: The R Package heemod (2017) arXiv
  3. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  4. Cho, Sun-Joo; Goodwin, Amanda P.: Modeling learning in doubly multilevel binary longitudinal data using generalized linear mixed models: an application to measuring and explaining word learning (2017)
  5. Alexander Kowarik; Matthias Templ: Imputation with the R Package VIM (2016)
  6. Anders Bilgrau; Poul Eriksen; Jakob Rasmussen; Hans Johnsen; Karen Dybkaer; Martin Boegsted: GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models (2016)
  7. De Jong, Roel; van Buuren, Stef; Spiess, Martin: Multiple imputation of predictor variables using generalized additive models (2016)
  8. Gerhart, Christoph: A multiple-curve Lévy forward rate model in a two-price economy (2016)
  9. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
  10. Harrell, Frank E. jun.: Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis (2015)
  11. Heiberger, Richard M.; Holland, Burt: Statistical analysis and data display. An intermediate course with examples in R (2015)
  12. Pierre Bunouf; Geert Molenberghs; Jean-Marie Grouin; Herbert Thijs: A SAS Program Combining R Functionalities to Implement Pattern-Mixture Models (2015)
  13. Scott Fortmann-Roe: Consistent and Clear Reporting of Results from Diverse Modeling Techniques: The A3 Method (2015)
  14. Xiaoyue Cheng and Dianne Cook and Heike Hofmann: Visually Exploring Missing Values in Multivariable Data Using a Graphical User Interface (2015)
  15. Gruber, Susan; Van der Laan, Mark J.: An application of targeted maximum likelihood estimation to the meta-analysis of safety data (2013)
  16. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  17. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2013)
  18. Cano, Emilio L.; Moguerza, Javier M.; Redchuk, Andrés: Six Sigma with R. Statistical engineering for process improvement. (2012)
  19. Ulla Mogensen; Hemant Ishwaran; Thomas Gerds: Evaluating Random Forests for Survival Analysis Using Prediction Error Curves (2012)
  20. Recai Yucel: State of the Multiple Imputation Software (2011)

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