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 29 articles )

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  1. Faraway, Julian J.: Extending the linear model with R. Generalized linear, mixed effects and nonparametric regression models. (2016)
  2. 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)
  3. Thapa, Ram; Burkhart, Harold E.; Li, Jie; Hong, Yili: Modeling clustered survival times of loblolly pine with time-dependent covariates and shared frailties (2016)
  4. Zhou, Mai: Empirical likelihood method in survival analysis (2016)
  5. Cohen, Andrew L.; Staub, Adrian: Within-subject consistency and between-subject variability in Bayesian reasoning strategies (2015)
  6. Harrell, Frank E. jun.: Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis (2015)
  7. Carvalho, João B.; Valença, Dione M.; Singer, Julio M.: Prediction of failure probability of oil wells (2014)
  8. Gorfine, Malka; Hsu, Li; Zucker, David M.; Parmigiani, Giovanni: Calibrated predictions for multivariate competing risks models (2014)
  9. Huang, Tao; Fildes, Robert; Soopramanien, Didier: The value of competitive information in forecasting FMCG retail product sales and the variable selection problem (2014)
  10. Bollandsås, Ole Martin; Gregoire, Timothy G.; Næsset, Erik; Øyen, Bernt-Håvard: Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data (2013)
  11. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  12. Bantis, Leonidas E.; Tsimikas, John V.; Georgiou, Stelios D.: Survival estimation through the cumulative hazard function with monotone natural cubic splines (2012)
  13. Rizopoulos, Dimitris: Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule (2012)
  14. Trapp, Allan II.; Dixon, Philip; Widrlechner, Mark P.; Kovach, David A.: Scheduling viability tests for seeds in long-term storage based on a Bayesian multi-level model (2012)
  15. Gao, Feng; Miller, J.Philip; Xiong, Chengjie; Beiser, Julia A.; Gordon, Mae; The Ocular Hypertension Study (OHTS) Group: A joint-modeling approach to assess the impact of biomarker variability on the risk of developing clinical outcome (2011)
  16. Koru, Gunes; Liu, Hongfang; Zhang, Dongsong; El Emam, Khaled: Testing the theory of relative defect proneness for closed-source software (2010)
  17. Wollschläger, Daniel: Foundations of data analysis with R. An application oriented introduction. (2010)
  18. O’Connell, Rachel L.; Hudson, H.Malcolm: Risk of mortality after acute myocardial infarction: performance of model updating methods for application in different geographical regions (2009)
  19. Koru, A.Güneş; Emam, Khaled El; Zhang, Dongsong; Liu, Hongfang; Mathew, Divya: Theory of relative defect proneness (2008)
  20. van Buuren, Stef: Multiple imputation of discrete and continuous data by fully conditional specification (2007)

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