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 329 articles , 1 standard article )

Showing results 1 to 20 of 329.
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  1. Combes, Catherine; Ng, Hon Keung Tony: On parameter estimation for amoroso family of distributions (2022)
  2. Dutang, Christophe; Guibert, Quentin: An explicit split point procedure in model-based trees allowing for a quick fitting of GLM trees and GLM forests (2022)
  3. A. Grant Schissler, Edward J. Bedrick, Alexander D. Knudson, Tomasz J. Kozubowski, Tin Nguyen, Juli Petereit, Walter W. Piegorsch, Duc Tran: Simulating High-Dimensional Multivariate Data using the bigsimr R Package (2021) arXiv
  4. Aromi, Lloyd L.; Katz, Yuri A.; Vives, Josep: Topological features of multivariate distributions: dependency on the covariance matrix (2021)
  5. Bak, Kwan-Young; Jhong, Jae-Hwan; Lee, JungJun; Shin, Jae-Kyung; Koo, Ja-Yong: Penalized logspline density estimation using total variation penalty (2021)
  6. Benjamin Säfken, David Rügamer, Thomas Kneib, Sonja Greven: Conditional Model Selection in Mixed-Effects Models with cAIC4 (2021) not zbMATH
  7. Berger, Moritz; Tutz, Gerhard: Transition models for count data: a flexible alternative to fixed distribution models (2021)
  8. 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)
  9. Conde, David; Fernández, Miguel A.; Rueda, Cristina; Salvador, Bonifacio: Isotonic boosting classification rules (2021)
  10. Dierckx, Goedele; Goegebeur, Yuri; Guillou, Armelle: Local robust estimation of Pareto-type tails with random right censoring (2021)
  11. Engin, Ayşegül: The cognitive ability and working memory framework: interpreting cognitive reflection test results in the domain of the cognitive experiential theory (2021)
  12. Fasiolo, M., Wood, S. N., Zaffran, M., Nedellec, R., Goude, Y. : qgam: Bayesian Nonparametric Quantile Regression Modeling in R (2021) not zbMATH
  13. Henckaerts, Roel; Côté, Marie-Pier; Antonio, Katrien; Verbelen, Roel: Boosting insights in insurance tariff plans with tree-based machine learning methods (2021)
  14. Jantre, S. R.; Bhattacharya, S.; Maiti, T.: Quantile regression neural networks: a Bayesian approach (2021)
  15. Jensen, Jens Ledet: On the use of saddlepoint approximations in high dimensional inference (2021)
  16. Matthew Trupiano: The R Package knnwtsim: Nonparametric Forecasting With a Tailored Similarity Measure (2021) arXiv
  17. Mickael Binois, Robert B. Gramacy: hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R (2021) not zbMATH
  18. Mikhail Zhelonkin, Elvezio Ronchetti: Robust Analysis of Sample Selection Models through the R Package ssmrob (2021) not zbMATH
  19. Mulder, J., Williams, D. R., Gu, X., Tomarken, A., Böing-Messing, F., Olsson-Collentine, A., Meijerink-Bosman, M., Menke, J., van Aert, R., Fox, J.-P., Hoijtink, H., Rosseel, Y., Wagenmakers, E.-J., van Lissa, C.: BFpack: Flexible Bayes Factor Testing of Scientific Theories in R (2021) not zbMATH
  20. Nguyen K. Huynh, Sergio Bejar, Vineeta Yadav, Bumba Mukherjee: IDCeMPy: Python Package for Inflated Discrete Choice Models (2021) not zbMATH

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