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)


References in zbMATH (referenced in 41 articles )

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  1. Herwartz, Helmut; Maxand, Simone: Nonparametric tests for independence: a review and comparative simulation study with an application to malnutrition data in India (2020)
  2. Jonas M. B. Haslbeck, Lourens J. Waldorp: mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2020) not zbMATH
  3. Mateusz Staniak, Przemyslaw Biecek: The Landscape of R Packages for Automated Exploratory Data Analysis (2019) arXiv
  4. Saul, Bradley C.; Hudgens, Michael G.; Mallin, Michael A.: Downstream effects of upstream causes (2019)
  5. Sayan Putatunda, Kiran Rama, Dayananda Ubrangala, Ravi Kondapalli: SmartEDA: An R Package for Automated Exploratory Data Analysis (2019) arXiv
  6. Zheng, Dayuan; Zhang, Zhaogong; Zhang, Yuting: TNT: an effective method for finding correlations between two continuous variables (2019)
  7. Imbert, Alyssa; Vialaneix, Nathalie: Exploring, handling, imputing and evaluating missing data in statistical analyses: a review of existing approaches (2018)
  8. 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
  9. Alexandra Kuznetsova; Per Brockhoff; Rune Christensen: lmerTest Package: Tests in Linear Mixed Effects Models (2017) not zbMATH
  10. Antoine Filipovic-Pierucci, Kevin Zarca, Isabelle Durand-Zaleski: Markov Models for Health Economic Evaluations: The R Package heemod (2017) arXiv
  11. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  12. 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)
  13. Vélez, Jorge I.; Marmolejo-Ramos, Fernando: Extension of a graphical diagnostic test for contingency tables (2017)
  14. Alexander Kowarik; Matthias Templ: Imputation with the R Package VIM (2016) not zbMATH
  15. 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
  16. De Jong, Roel; van Buuren, Stef; Spiess, Martin: Multiple imputation of predictor variables using generalized additive models (2016)
  17. Gerhart, Christoph: A multiple-curve Lévy forward rate model in a two-price economy (2016)
  18. Ugarte, María Dolores; Militino, Ana F.; Arnholt, Alan T.: Probability and statistics with R (2016)
  19. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
  20. Harrell, Frank E. jun.: Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis (2015)

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