rms

R package rms: Regression Modeling Strategies , Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. rms is a collection of 229 functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. rms works with almost any regression model, but it was especially written to work with binary or ordinal logistic regression, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression. (Source: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 96 articles )

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  1. Benjamin Christoffersen: dynamichazard: Dynamic Hazard Models Using State Space Models (2021) not zbMATH
  2. Deswal, Sumit; Bulusu, Krishna C.; Agapow, Paul-Michael; Khan, Faisal M.: Precision medicine (2021)
  3. Ejike R. Ugba: serp: An R package for smoothing in ordinal regression (2021) not zbMATH
  4. Michael J. Wurm, Paul J. Rathouz, Bret M. Hanlon: Regularized Ordinal Regression and the ordinalNet R Package (2021) not zbMATH
  5. Sy Han Chiou, Gongjun Xu, Jun Yan, Chiung-Yu Huang: Regression Modeling for Recurrent Events Using R Package reReg (2021) arXiv
  6. Wang, Rui; Xiu, Naihua; Toh, Kim-Chuan: Subspace quadratic regularization method for group sparse multinomial logistic regression (2021)
  7. Wei, Zheng; Kim, Daeyoung: On exploratory analytic method for multi-way contingency tables with an ordinal response variable and categorical explanatory variables (2021)
  8. Gianluca Baio: survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling (2020) not zbMATH
  9. Hara, Akane; Iwasa, Yoh: Autoimmune diseases initiated by pathogen infection: mathematical modeling (2020)
  10. Kristensen, Simon Bang; Sandberg, Kristian; Bibby, Bo Martin: Regression methods for metacognitive sensitivity (2020)
  11. Li, Chenlu; Li, Baibing; Tee, Kai-Hong: Measuring liquidity commonality in financial markets (2020)
  12. Park, Young Woong; Klabjan, Diego: Subset selection for multiple linear regression via optimization (2020)
  13. Piironen, Juho; Paasiniemi, Markus; Vehtari, Aki: Projective inference in high-dimensional problems: prediction and feature selection (2020)
  14. Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
  15. Yang, Yunli; Chen, Baiyu; Yang, Zhouwang: An algorithm for ordinal classification based on pairwise comparison (2020)
  16. Hirk, Rainer; Hornik, Kurt; Vana, Laura: Multivariate ordinal regression models: an analysis of corporate credit ratings (2019)
  17. Alicja Gosiewska; Przemyslaw Biecek: auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic (2018) arXiv
  18. Daniela Dunkler; Meinhard Ploner; Michael Schemper; Georg Heinze: Weighted Cox Regression Using the R Package coxphw (2018) not zbMATH
  19. Heinze, Georg; Wallisch, Christine; Dunkler, Daniela: Variable selection -- a review and recommendations for the practicing statistician (2018)
  20. Mankad, Shawn; Hu, Shengli; Gopal, Anandasivam: Single stage prediction with embedded topic modeling of online reviews for mobile app management (2018)

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