Hmisc

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 ’http://biostat.mc.vanderbilt.edu/trac/Hmisc’. (Source: http://cran.r-project.org/web/packages)

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References in zbMATH (referenced in 38 articles )

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  1. Jonas M. B. Haslbeck, Lourens J. Waldorp: mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2020) not zbMATH
  2. Mateusz Staniak, Przemyslaw Biecek: The Landscape of R Packages for Automated Exploratory Data Analysis (2019) arXiv
  3. Saul, Bradley C.; Hudgens, Michael G.; Mallin, Michael A.: Downstream effects of upstream causes (2019)
  4. Sayan Putatunda, Kiran Rama, Dayananda Ubrangala, Ravi Kondapalli: SmartEDA: An R Package for Automated Exploratory Data Analysis (2019) arXiv
  5. Imbert, Alyssa; Vialaneix, Nathalie: Exploring, handling, imputing and evaluating missing data in statistical analyses: a review of existing approaches (2018)
  6. Jeffrey C. Miecznikowski, En-shuo Hsu, Yanhua Chen, Albert Vexler : testforDEP: An R Package for Modern Distribution-free Tests and Visualization Tools for Independence (2018) not zbMATH
  7. Alexandra Kuznetsova; Per Brockhoff; Rune Christensen: lmerTest Package: Tests in Linear Mixed Effects Models (2017) not zbMATH
  8. Antoine Filipovic-Pierucci, Kevin Zarca, Isabelle Durand-Zaleski: Markov Models for Health Economic Evaluations: The R Package heemod (2017) arXiv
  9. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  10. 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)
  11. Alexander Kowarik; Matthias Templ: Imputation with the R Package VIM (2016) not zbMATH
  12. Anders Bilgrau; Poul Eriksen; Jakob Rasmussen; Hans Johnsen; Karen Dybkaer; Martin Boegsted: GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models (2016) not zbMATH
  13. De Jong, Roel; van Buuren, Stef; Spiess, Martin: Multiple imputation of predictor variables using generalized additive models (2016)
  14. Gerhart, Christoph: A multiple-curve Lévy forward rate model in a two-price economy (2016)
  15. Ugarte, María Dolores; Militino, Ana F.; Arnholt, Alan T.: Probability and statistics with R (2016)
  16. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
  17. Harrell, Frank E. jun.: Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis (2015)
  18. Heiberger, Richard M.; Holland, Burt: Statistical analysis and data display. An intermediate course with examples in R (2015)
  19. Pierre Bunouf; Geert Molenberghs; Jean-Marie Grouin; Herbert Thijs: A SAS Program Combining R Functionalities to Implement Pattern-Mixture Models (2015) not zbMATH
  20. Scott Fortmann-Roe: Consistent and Clear Reporting of Results from Diverse Modeling Techniques: The A3 Method (2015) not zbMATH

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