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 54 articles )

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  1. Cipolli, William III; Hanson, Timothy: Computationally tractable approximate and smoothed polya trees (2017)
  2. Contucci, Pierluigi; Luzi, Rachele; Vernia, Cecilia: Inverse problem for the mean-field monomer-dimer model with attractive interaction (2017)
  3. Faraway, Julian J.: Extending the linear model with R. Generalized linear, mixed effects and nonparametric regression models. (2016)
  4. Luo, Sirong; Kong, Xiao; Nie, Tingting: Spline based survival model for credit risk modeling (2016)
  5. 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)
  6. Shepherd, Bryan E.; Li, Chun; Liu, Qi: Probability-scale residuals for continuous, discrete, and censored data (2016)
  7. 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)
  8. Thapa, Ram; Burkhart, Harold E.; Li, Jie; Hong, Yili: Modeling clustered survival times of loblolly pine with time-dependent covariates and shared frailties (2016)
  9. Zhou, Mai: Empirical likelihood method in survival analysis (2016)
  10. Cohen, Andrew L.; Staub, Adrian: Within-subject consistency and between-subject variability in Bayesian reasoning strategies (2015) MathEduc
  11. Harrell, Frank E. jun.: Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis (2015)
  12. Sauerbrei, Willi; Buchholz, Anika; Boulesteix, Anne-Laure; Binder, Harald: On stability issues in deriving multivariable regression models (2015)
  13. Steyerberg, Ewout W.; Vedder, Moniek M.; Leening, Maarten J.G.; Postmus, Douwe; D’Agostino, Ralph B.sen.; Van Calster, Ben; Pencina, Michael J.: Graphical assessment of incremental value of novel markers in prediction models: from statistical to decision analytical perspectives (2015)
  14. Carvalho, João B.; Valença, Dione M.; Singer, Julio M.: Prediction of failure probability of oil wells (2014)
  15. Gorfine, Malka; Hsu, Li; Zucker, David M.; Parmigiani, Giovanni: Calibrated predictions for multivariate competing risks models (2014)
  16. Huang, Tao; Fildes, Robert; Soopramanien, Didier: The value of competitive information in forecasting FMCG retail product sales and the variable selection problem (2014) ioport
  17. Kruppa, Jochen; Liu, Yufeng; Biau, Gérard; Kohler, Michael; König, Inke R.; Malley, James D.; Ziegler, Andreas: Probability estimation with machine learning methods for dichotomous and multicategory outcome: theory (2014)
  18. Lu, Yun; Hannenhalli, Sridhar; Cappola, Tom; Putt, Mary: An evaluation of Monte Carlo logic and logicFS motivated by a study of the regulation of gene expression in heart failure (2014)
  19. Steyerberg, Ewout W.; van der Ploeg, Tjeerd; Van Calster, Ben: Risk prediction with machine learning and regression methods (2014)
  20. Ziegler, Andreas: Rejoinder (2014)

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