Hmisc

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)

Keywords for this software

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References in zbMATH (referenced in 14 articles )

Showing results 1 to 14 of 14.
Sorted by year (citations)

  1. Antoine Filipovic-Pierucci, Kevin Zarca, Isabelle Durand-Zaleski: Markov Models for Health Economic Evaluations: The R Package heemod (2017) arXiv
  2. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  3. Gerhart, Christoph: A multiple-curve Lévy forward rate model in a two-price economy (2016)
  4. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
  5. Harrell, Frank E. jun.: Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis (2015)
  6. Heiberger, Richard M.; Holland, Burt: Statistical analysis and data display. An intermediate course with examples in R (2015)
  7. Gruber, Susan; van der Laan, Mark J.: An application of targeted maximum likelihood estimation to the meta-analysis of safety data (2013)
  8. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  9. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2013)
  10. Cano, Emilio L.; Moguerza, Javier M.; Redchuk, Andrés: Six Sigma with R. Statistical engineering for process improvement. (2012)
  11. Williams, Graham: Data Mining with Rattle and R. The art of excavating data for knowledge discovery. (2011)
  12. Wollschläger, Daniel: Foundations of data analysis with R. An application oriented introduction. (2010)
  13. Ambrogi, Federico; Biganzoli, Elia; Boracchi, Patrizia: Estimating crude cumulative incidences through multinomial logit regression on discrete cause-specific hazards (2009)
  14. Ambrogi, Federico; Lama, Nicola; Boracchi, Patrizia; Biganzoli, Elia: Selection of artificial neural network models for survival analysis with genetic algorithms (2007)