VGAM

R package VGAM: Vector Generalized Linear and Additive Models , Vector generalized linear and additive models, and associated models (Reduced-Rank VGLMs, Quadratic RR-VGLMs, Reduced-Rank VGAMs). This package fits many models and distribution by maximum likelihood estimation (MLE) or penalized MLE. Also fits constrained ordination models in ecology. (Source: http://cran.r-project.org/web/packages)

Keywords for this software

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References in zbMATH (referenced in 93 articles , 3 standard articles )

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  1. Gregor Zens, Sylvia Frühwirth-Schnatter, Helga Wagner: Efficient Bayesian Modeling of Binary and Categorical Data in R: The UPG Package (2021) arXiv
  2. Justine Lequesne, Philippe Regnault: vsgoftest: An R Package for Goodness-of-Fit Testing Based on Kullback-Leibler Divergence (2020) not zbMATH
  3. Martin, Bryan D.; Witten, Daniela; Willis, Amy D.: Modeling microbial abundances and dysbiosis with beta-binomial regression (2020)
  4. Puth, Marie-Therese; Tutz, Gerhard; Heim, Nils; Münster, Eva; Schmid, Matthias; Berger, Moritz: Tree-based modeling of time-varying coefficients in discrete time-to-event models (2020)
  5. Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
  6. Torsten Hothorn: Most Likely Transformations: The mlt Package (2020) not zbMATH
  7. Berger, Moritz; Welchowski, Thomas; Schmitz-Valckenberg, Steffen; Schmid, Matthias: A classification tree approach for the modeling of competing risks in discrete time (2019)
  8. De Micheaux, Pierre Lafaye; Liquet, Benoît; Sutton, Matthew: PLS for Big Data: a unified parallel algorithm for regularised group PLS (2019)
  9. Espinosa, Javier; Hennig, Christian: A constrained regression model for an ordinal response with ordinal predictors (2019)
  10. Karavarsamis, Natalie: Estimating occupancy and fitting models with the two-stage approach (2019)
  11. Karavarsamis, N.; Huggins, R. M.: Two-stage approaches to the analysis of occupancy data. II: The heterogeneous model and conditional likelihood (2019)
  12. Martínez-Rodríguez, Ana María; Conde-Sánchez, Antonio; Olmo-Jiménez, María José: A new approach to truncated regression for count data (2019)
  13. Miranda-Soberanis, V. F.; Yee, T. W.: New link functions for distribution-specific quantile regression based on vector generalized linear and additive models (2019)
  14. Raya, Rasas; Saidi, La Ode; La, Gubu; Mukhsar: Analyzing of job opportunities in Southeast Sulawesi Indonesia using Bayesian binary logistic regression (2019)
  15. Wilson, Paul; Einbeck, Jochen: A new and intuitive test for zero modification (2019)
  16. Bura, Efstathia; Duarte, S.; Forzani, L.; Smucler, E.; Sued, M.: Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalized linear models (2018)
  17. Gallardo, Diego I.; Gómez, Yolanda M.; de Castro, Mário: A flexible cure rate model based on the polylogarithm distribution (2018)
  18. Powers, Scott; Hastie, Trevor; Tibshirani, Robert: Nuclear penalized multinomial regression with an application to predicting at bat outcomes in Baseball (2018)
  19. Tsagris, Michail; Stewart, Connie: A Dirichlet regression model for compositional data with zeros (2018)
  20. Crackel, Roberto; Flegal, James: Bayesian inference for a flexible class of bivariate beta distributions (2017)

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