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 91 articles )

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  1. Sy Han Chiou, Gongjun Xu, Jun Yan, Chiung-Yu Huang: Regression Modeling for Recurrent Events Using R Package reReg (2021) arXiv
  2. Wang, Rui; Xiu, Naihua; Toh, Kim-Chuan: Subspace quadratic regularization method for group sparse multinomial logistic regression (2021)
  3. Gianluca Baio: survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling (2020) not zbMATH
  4. Hara, Akane; Iwasa, Yoh: Autoimmune diseases initiated by pathogen infection: mathematical modeling (2020)
  5. Kristensen, Simon Bang; Sandberg, Kristian; Bibby, Bo Martin: Regression methods for metacognitive sensitivity (2020)
  6. Li, Chenlu; Li, Baibing; Tee, Kai-Hong: Measuring liquidity commonality in financial markets (2020)
  7. Park, Young Woong; Klabjan, Diego: Subset selection for multiple linear regression via optimization (2020)
  8. Piironen, Juho; Paasiniemi, Markus; Vehtari, Aki: Projective inference in high-dimensional problems: prediction and feature selection (2020)
  9. Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
  10. Yang, Yunli; Chen, Baiyu; Yang, Zhouwang: An algorithm for ordinal classification based on pairwise comparison (2020)
  11. Hirk, Rainer; Hornik, Kurt; Vana, Laura: Multivariate ordinal regression models: an analysis of corporate credit ratings (2019)
  12. Alicja Gosiewska; Przemyslaw Biecek: auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic (2018) arXiv
  13. Daniela Dunkler; Meinhard Ploner; Michael Schemper; Georg Heinze: Weighted Cox Regression Using the R Package coxphw (2018) not zbMATH
  14. Heinze, Georg; Wallisch, Christine; Dunkler, Daniela: Variable selection -- a review and recommendations for the practicing statistician (2018)
  15. Mankad, Shawn; Hu, Shengli; Gopal, Anandasivam: Single stage prediction with embedded topic modeling of online reviews for mobile app management (2018)
  16. Maxild Mortensen, Lotte; Hansen, Camilla Plambeck; Overvad, Kim; Lundbye-Christensen, Søren; Parner, Erik T.: The pseudo-observation analysis of time-to-event data. Example from the Danish Diet, Cancer and Health Cohort illustrating assumptions, model validation and interpretation of results (2018)
  17. Bilton, Penny; Jones, Geoff; Ganesh, Siva; Haslett, Steve: Classification trees for poverty mapping (2017)
  18. Cipolli, William III; Hanson, Timothy: Computationally tractable approximate and smoothed polya trees (2017)
  19. Contucci, Pierluigi; Luzi, Rachele; Vernia, Cecilia: Inverse problem for the mean-field monomer-dimer model with attractive interaction (2017)
  20. Treppmann, Tabea; Ickstadt, Katja; Zucknick, Manuela: Integration of multiple genomic data sources in a Bayesian Cox model for variable selection and prediction (2017)

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