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 80 articles )

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  1. Kristensen, Simon Bang; Sandberg, Kristian; Bibby, Bo Martin: Regression methods for metacognitive sensitivity (2020)
  2. Park, Young Woong; Klabjan, Diego: Subset selection for multiple linear regression via optimization (2020)
  3. Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
  4. Hirk, Rainer; Hornik, Kurt; Vana, Laura: Multivariate ordinal regression models: an analysis of corporate credit ratings (2019)
  5. Alicja Gosiewska; Przemyslaw Biecek: auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic (2018) arXiv
  6. Daniela Dunkler; Meinhard Ploner; Michael Schemper; Georg Heinze: Weighted Cox Regression Using the R Package coxphw (2018) not zbMATH
  7. Heinze, Georg; Wallisch, Christine; Dunkler, Daniela: Variable selection -- a review and recommendations for the practicing statistician (2018)
  8. Mankad, Shawn; Hu, Shengli; Gopal, Anandasivam: Single stage prediction with embedded topic modeling of online reviews for mobile app management (2018)
  9. 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)
  10. Bilton, Penny; Jones, Geoff; Ganesh, Siva; Haslett, Steve: Classification trees for poverty mapping (2017)
  11. Cipolli, William III; Hanson, Timothy: Computationally tractable approximate and smoothed polya trees (2017)
  12. Contucci, Pierluigi; Luzi, Rachele; Vernia, Cecilia: Inverse problem for the mean-field monomer-dimer model with attractive interaction (2017)
  13. Treppmann, Tabea; Ickstadt, Katja; Zucknick, Manuela: Integration of multiple genomic data sources in a Bayesian Cox model for variable selection and prediction (2017)
  14. Vanegas, Luis Hernando; Paula, Gilberto A.: Log-symmetric regression models under the presence of non-informative left- or right-censored observations (2017)
  15. Balzer, Laura; Ahern, Jennifer; Galea, Sandro; van der Laan, Mark: Estimating effects with rare outcomes and high dimensional covariates: knowledge is power (2016)
  16. Faraway, Julian J.: Extending the linear model with R. Generalized linear, mixed effects and nonparametric regression models. (2016)
  17. Luo, Sirong; Kong, Xiao; Nie, Tingting: Spline based survival model for credit risk modeling (2016)
  18. Ma, Shaohui; Fildes, Robert; Huang, Tao: Demand forecasting with high dimensional data: the case of SKU retail sales forecasting with intra- and inter-category promotional information (2016)
  19. Shepherd, Bryan E.; Li, Chun; Liu, Qi: Probability-scale residuals for continuous, discrete, and censored data (2016)
  20. Steyerberg, E. W.: Book review of: F. E. Harrel jun., Regression modeling strategies. With applications to linear models, logistic and ordinal regression, and survival analysis. 2nd ed. (2016)

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