R package pscl: Political Science Computational Laboratory, Stanford University. Bayesian analysis of item-response theory (IRT) models, roll call analysis; computing highest density regions; maximum likelihood estimation of zero-inflated and hurdle models for count data; goodness-of-fit measures for GLMs; data sets used in writing and teaching at the Political Science Computational Laboratory; seats-votes curves.

References in zbMATH (referenced in 25 articles , 1 standard article )

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  1. Achim Zeileis, Susanne Köll, Nathaniel Graham: Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R (2020) not zbMATH
  2. Martínez-Rodríguez, Ana María; Conde-Sánchez, Antonio; Olmo-Jiménez, María José: A new approach to truncated regression for count data (2019)
  3. Zoe Meers, Robert Hickman, Thomas J. Leeper: ggparliament: A ggplot2 extension for parliament plotsin R (2019) not zbMATH
  4. Cantoni, Eva; Auda, Marie: Stochastic variable selection strategies for zero-inflated models (2018)
  5. Möller, Tobias A.; Weiß, Christian H.; Kim, Hee-Young; Sirchenko, Andrei: Modeling zero inflation in count data time series with bounded support (2018)
  6. Hagar, Y.; Hayden, M.; Wiedinmyer, C.; Dukic, V.: Comparison of models analyzing a small number of observed meningitis cases in Navrongo, Ghana (2017)
  7. Liu, Xueyan; Winter, Bryan; Tang, Li; Zhang, Bo; Zhang, Zhiwei; Zhang, Hui: Simulating comparisons of different computing algorithms fitting zero-inflated Poisson models for zero abundant counts (2017)
  8. Martin, Jacob; Hall, Daniel B.: Marginal zero-inflated regression models for count data (2017)
  9. Neelon, Brian; Chung, Dongjun: The LZIP: a Bayesian latent factor model for correlated zero-inflated counts (2017)
  10. Rafael Moral; John Hinde; Clarice Demétrio: Half-Normal Plots and Overdispersed Models in R: The hnp Package (2017) not zbMATH
  11. Sáez-Castillo, Antonio J.; Conde-Sánchez, Antonio: Detecting over- and under-dispersion in zero inflated data with the hyper-Poisson regression model (2017)
  12. Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
  13. Wilson, Paul: The misuse of the Vuong test for non-nested models to test for zero-inflation (2015)
  14. Kosmidis, Ioannis: Improved estimation in cumulative link models (2014)
  15. Yee, Thomas W.: Reduced-rank vector generalized linear models with two linear predictors (2014)
  16. Zeng, Ping; Wei, Yongyue; Zhao, Yang; Liu, Jin; Liu, Liya; Zhang, Ruyang; Gou, Jianwei; Huang, Shuiping; Chen, Feng: Variable selection approach for zero-inflated count data via adaptive lasso (2014)
  17. Birgit Erni; Bo Bonnevie; Hans-Dieter Oschadleus; Res Altwegg; Les Underhill: moult: An R Package to Analyze Moult in Birds (2013) not zbMATH
  18. Schlittgen, Rainer: Regression analyses with R (2013)
  19. Loeys, Tom; Moerkerke, Beatrijs; De Smet, Olivia; Buysse, Ann: The analysis of zero-inflated count data: beyond zero-inflated Poisson regression. (2012)
  20. Zhu, Fukang: Zero-inflated Poisson and negative binomial integer-valued GARCH models (2012)

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