betareg

R package betareg: Beta Regression. Beta regression for modeling beta-distributed dependent variables, e.g., rates and proportions. In addition to maximum likelihood regression (for both mean and precision of a beta-distributed response), bias-corrected and bias-reduced estimation as well as finite mixture models and recursive partitioning for beta regressions are provided.

Keywords for this software

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References in zbMATH (referenced in 51 articles , 1 standard article )

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  1. Calcagnì, Antonio; Lombardi, Luigi: Modeling random and non-random decision uncertainty in ratings data: a fuzzy beta model (2022)
  2. Loliencar, Prachi; Heo, Giseon: Phenotyping OSA: a time series analysis using fuzzy clustering and persistent homology (2022)
  3. Zhou, Haiming; Huang, Xianzheng: Bayesian beta regression for bounded responses with unknown supports (2022)
  4. Barrientos, Andrés F.; Canale, Antonio: A Bayesian goodness-of-fit test for regression (2021)
  5. Hwang, Wook-Yeon: Quantile-based control charts for poisson and gamma distributed data (2021)
  6. Pinheiro, Eliane C.; Ferrari, Silvia L. P.; Medeiros, Francisco M. C.: Higher-order approximate confidence intervals (2021)
  7. Silva, Ana R. S.; Azevedo, Caio L. N.; Bazán, Jorge L.; Nobre, Juvêncio S.: Bayesian inference for zero-and/or-one augmented beta rectangular regression models (2021)
  8. Guedes, Ana C.; Cribari-Neto, Francisco; Espinheira, Patrícia L.: Modified likelihood ratio tests for unit gamma regressions (2020)
  9. Lemonte, Artur J.; Moreno-Arenas, Germán: On a heavy-tailed parametric quantile regression model for limited range response variables (2020)
  10. Lokonon, Bruno Enagnon; Moussa, Freedath Djibril; Diouf, Saliou; Kakaï, Romain Glèlè: Empirical performance of estimation methods in beta mixed models with application to ecological data (2020)
  11. Mazucheli, J.; Menezes, A. F. B.; Fernandes, L. B.; de Oliveira, R. P.; Ghitany, M. E.: The unit-Weibull distribution as an alternative to the Kumaraswamy distribution for the modeling of quantiles conditional on covariates (2020)
  12. Rauber, Cristine; Cribari-Neto, Francisco; Bayer, Fábio M.: Improved testing inferences for beta regressions with parametric mean link function (2020)
  13. Weinhold, Leonie; Schmid, Matthias; Mitchell, Richard; Maloney, Kelly O.; Wright, Marvin N.; Berger, Moritz: A random forest approach for bounded outcome variables (2020)
  14. Alfó, Marco; Nieddu, Luciano; Vitiello, Cecilia: Cluster weighted beta regression: a simulation study (2019)
  15. Amorim, Gustavo Guimarães de Castro: Semiparametric estimator for a secondary analysis with correlated outcomes (2019)
  16. Bonat, Wagner H.; Petterle, Ricardo R.; Hinde, John; Demétrio, Clarice G. B.: Flexible quasi-beta regression models for continuous bounded data (2019)
  17. David Smith; Malcolm Faddy: Mean and Variance Modeling of Under-Dispersed and Over-Dispersed Grouped Binary Data (2019) not zbMATH
  18. Di Caterina, Claudia; Kosmidis, Ioannis: Location-adjusted Wald statistics for scalar parameters (2019)
  19. Lemonte, Artur J.; Moreno-Arenas, Germán: On residuals in generalized Johnson (S_B) regressions (2019)
  20. Magalhães, Tiago M.; Botter, Denise A.; Sandoval, Mônica C.; Pereira, Gustavo H. A.; Cordeiro, Gauss M.: Skewness of maximum likelihood estimators in the varying dispersion beta regression model (2019)

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