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 43 articles )

Showing results 21 to 40 of 43.
Sorted by year (citations)
  1. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
  2. Harrell, Frank E. jun.: Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis (2015)
  3. Heiberger, Richard M.; Holland, Burt: Statistical analysis and data display. An intermediate course with examples in R (2015)
  4. Pierre Bunouf; Geert Molenberghs; Jean-Marie Grouin; Herbert Thijs: A SAS Program Combining R Functionalities to Implement Pattern-Mixture Models (2015) not zbMATH
  5. Scott Fortmann-Roe: Consistent and Clear Reporting of Results from Diverse Modeling Techniques: The A3 Method (2015) not zbMATH
  6. Xiaoyue Cheng and Dianne Cook and Heike Hofmann: Visually Exploring Missing Values in Multivariable Data Using a Graphical User Interface (2015) not zbMATH
  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. Broström, Göran: Event history analysis with R (2012)
  11. Cano, Emilio L.; Moguerza, Javier M.; Redchuk, Andrés: Six Sigma with R. Statistical engineering for process improvement. (2012)
  12. Ulla Mogensen; Hemant Ishwaran; Thomas Gerds: Evaluating Random Forests for Survival Analysis Using Prediction Error Curves (2012) not zbMATH
  13. Abrahantes, José Cortiñas; Sotto, Cristina; Molenberghs, Geert; Vromman, Geert; Bierinckx, Bart: A comparison of various software tools for dealing with missing data via imputation (2011)
  14. Recai Yucel: State of the Multiple Imputation Software (2011) not zbMATH
  15. Williams, Graham: Data Mining with Rattle and R. The art of excavating data for knowledge discovery. (2011)
  16. Kojadinovic, Ivan: Hierarchical clustering of continuous variables based on the empirical copula process and permutation linkages (2010)
  17. Wollschläger, Daniel: Foundations of data analysis with R. An application oriented introduction. (2010)
  18. Ambrogi, Federico; Biganzoli, Elia; Boracchi, Patrizia: Estimating crude cumulative incidences through multinomial logit regression on discrete cause-specific hazards (2009)
  19. Juned Siddique; Ofer Harel: MIDAS: A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors (2009) not zbMATH
  20. Péter Sólymos: Processing Ecological Data in R with the mefa Package (2009) not zbMATH