R package MASS: Support Functions and Datasets for Venables and Ripley’s MASS , Functions and datasets to support Venables and Ripley, ’Modern Applied Statistics with S’ (4th edition, 2002). (Source:

References in zbMATH (referenced in 303 articles , 1 standard article )

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  1. Bak, Kwan-Young; Jhong, Jae-Hwan; Lee, JungJun; Shin, Jae-Kyung; Koo, Ja-Yong: Penalized logspline density estimation using total variation penalty (2021)
  2. Chesneau, Christophe; Sharma, Vikas Kumar; Bakouch, Hassan S.: Extended Topp-Leone family of distributions as an alternative to beta and Kumaraswamy type distributions: application to glycosaminoglycans concentration level in urine (2021)
  3. Conde, David; Fernández, Miguel A.; Rueda, Cristina; Salvador, Bonifacio: Isotonic boosting classification rules (2021)
  4. Dierckx, Goedele; Goegebeur, Yuri; Guillou, Armelle: Local robust estimation of Pareto-type tails with random right censoring (2021)
  5. Engin, Ayşegül: The cognitive ability and working memory framework: interpreting cognitive reflection test results in the domain of the cognitive experiential theory (2021)
  6. Jensen, Jens Ledet: On the use of saddlepoint approximations in high dimensional inference (2021)
  7. Nguyen K. Huynh, Sergio Bejar, Vineeta Yadav, Bumba Mukherjee: IDCeMPy: Python Package for Inflated Discrete Choice Models (2021) not zbMATH
  8. Pospiech, Solveig; Tolosana-Delgado, Raimon; van den Boogaart, K. Gerald: Discriminant analysis for compositional data incorporating cell-wise uncertainties (2021)
  9. Subedi, Sanjeena; McNicholas, Paul D.: A variational approximations-DIC rubric for parameter estimation and mixture model selection within a family setting (2021)
  10. Van Belle, Jente; Guns, Tias; Verbeke, Wouter: Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains (2021)
  11. Aaron Cochrane: TEfits: Nonlinear regression for time-evolving indices (2020) not zbMATH
  12. Brouste, Alexandre; Dutang, Christophe; Rohmer, Tom: Closed-form maximum likelihood estimator for generalized linear models in the case of categorical explanatory variables: application to insurance loss modeling (2020)
  13. Dattner, Itai; Ship, Harold; Voit, Eberhard O.: Separable nonlinear least-squares parameter estimation for complex dynamic systems (2020)
  14. Derek Beaton: Generalized eigen, singular value, and partial least squares decompositions: The GSVD package (2020) arXiv
  15. Fuino, Michel; Wagner, Joël: Duration of long-term care: socio-economic factors, type of care interactions and evolution (2020)
  16. González, Miguel; Martínez, Rodrigo; Minuesa, Carmen; del Puerto, Inés: Approximate Bayesian computation in controlled branching processes: the role of summary statistics (2020)
  17. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  18. Irizarry, Rafael A.: Introduction to data science. Data analysis and prediction algorithms with R (2020)
  19. Jhwueng, Dwueng-Chwuan: Modeling rate of adaptive trait evolution using Cox-Ingersoll-Ross process: an approximate Bayesian computation approach (2020)
  20. Jobst, Lisa J.; Heck, Daniel W.; Moshagen, Morten: A comparison of correlation and regression approaches for multinomial processing tree models (2020)

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