MASS (R)

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: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 292 articles , 1 standard article )

Showing results 21 to 40 of 292.
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  1. Torsten Hothorn: Most Likely Transformations: The mlt Package (2020) not zbMATH
  2. van Ginkel, Joost R.: Standardized regression coefficients and newly proposed estimators for (R^2) in multiply imputed data (2020)
  3. van Meegen, Carmen; Schnackenberg, Sarah; Ligges, Uwe: Unequal priors in linear discriminant analysis (2020)
  4. Virta, Joni; Li, Bing; Nordhausen, Klaus; Oja, Hannu: Independent component analysis for multivariate functional data (2020)
  5. Agostinelli, Claudio; Valdora, Marina; Yohai, Victor J.: Initial robust estimation in generalized linear models (2019)
  6. Angelov, Angel G.; Ekström, Magnus; Kriström, Bengt; Nilsson, Mats E.: Four-decision tests for stochastic dominance, with an application to environmental psychophysics (2019)
  7. Baíllo, Amparo; Cárcamo, Javier; Getman, Konstantin: New distance measures for classifying X-ray astronomy data into stellar classes (2019)
  8. Barbeito, Inés; Cao, Ricardo: Smoothed bootstrap bandwidth selection for nonparametric hazard rate estimation (2019)
  9. Biernacki, Christophe; Lourme, Alexandre: Unifying data units and models in (co-)clustering (2019)
  10. Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
  11. Cerqueira, Vitor; Torgo, Luís; Pinto, Fábio; Soares, Carlos: Arbitrage of forecasting experts (2019)
  12. David Ardia; Kris Boudt; Leopoldo Catania: Generalized Autoregressive Score Models in R: The GAS Package (2019) not zbMATH
  13. Dedduwakumara, Dilanka S.; Prendergast, Luke A.; Staudte, Robert G.: A simple and efficient method for finding the closest generalized lambda distribution to a specific model (2019)
  14. del Rosario, Zachary; Lee, Minyong; Iaccarino, Gianluca: Lurking variable detection via dimensional analysis (2019)
  15. Dureh, Nurin; Tongkumchum, Phattrawan: A comparison of logistic regression and machine learning algorithms applied to zero counts data in contingency tables (2019)
  16. Gallaugher, Michael P. B.; McNicholas, Paul D.: On fractionally-supervised classification: weight selection and extension to the multivariate (t)-distribution (2019)
  17. García, Oscar: Estimating reducible stochastic differential equations by conversion to a least-squares problem (2019)
  18. Goegebeur, Yuri; Guillou, Armelle; Qin, Jing: Bias-corrected estimation for conditional Pareto-type distributions with random right censoring (2019)
  19. Górecki, Tomasz; Smaga, Łukasz: fdANOVA: an R software package for analysis of variance for univariate and multivariate functional data (2019)
  20. Gu, Jiaying; Fu, Fei; Zhou, Qing: Penalized estimation of directed acyclic graphs from discrete data (2019)

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