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:

References in zbMATH (referenced in 99 articles , 3 standard articles )

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  1. Ejike R. Ugba: serp: An R package for smoothing in ordinal regression (2021) not zbMATH
  2. Gregor Zens, Sylvia Frühwirth-Schnatter, Helga Wagner: Efficient Bayesian Modeling of Binary and Categorical Data in R: The UPG Package (2021) arXiv
  3. Michael J. Wurm, Paul J. Rathouz, Bret M. Hanlon: Regularized Ordinal Regression and the ordinalNet R Package (2021) not zbMATH
  4. Wei, Zheng; Kim, Daeyoung: On exploratory analytic method for multi-way contingency tables with an ordinal response variable and categorical explanatory variables (2021)
  5. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2021)
  6. Zhao, Jun; Kim, SungBum; Kim, Hyoung-Moon: Closed-form estimators and bias-corrected estimators for the Nakagami distribution (2021)
  7. Justine Lequesne, Philippe Regnault: vsgoftest: An R Package for Goodness-of-Fit Testing Based on Kullback-Leibler Divergence (2020) not zbMATH
  8. Martin, Bryan D.; Witten, Daniela; Willis, Amy D.: Modeling microbial abundances and dysbiosis with beta-binomial regression (2020)
  9. 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)
  10. Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
  11. Tchorbadjieff, Assen; Mayster, Penka: Geometric branching reproduction Markov processes (2020)
  12. Torsten Hothorn: Most Likely Transformations: The mlt Package (2020) not zbMATH
  13. Berger, Moritz; Welchowski, Thomas; Schmitz-Valckenberg, Steffen; Schmid, Matthias: A classification tree approach for the modeling of competing risks in discrete time (2019)
  14. De Micheaux, Pierre Lafaye; Liquet, Benoît; Sutton, Matthew: PLS for Big Data: a unified parallel algorithm for regularised group PLS (2019)
  15. Espinosa, Javier; Hennig, Christian: A constrained regression model for an ordinal response with ordinal predictors (2019)
  16. Karavarsamis, Natalie: Estimating occupancy and fitting models with the two-stage approach (2019)
  17. Karavarsamis, N.; Huggins, R. M.: Two-stage approaches to the analysis of occupancy data. II: The heterogeneous model and conditional likelihood (2019)
  18. Martínez-Rodríguez, Ana María; Conde-Sánchez, Antonio; Olmo-Jiménez, María José: A new approach to truncated regression for count data (2019)
  19. Miranda-Soberanis, V. F.; Yee, T. W.: New link functions for distribution-specific quantile regression based on vector generalized linear and additive models (2019)
  20. Raya, Rasas; Saidi, La Ode; La, Gubu; Mukhsar: Analyzing of job opportunities in Southeast Sulawesi Indonesia using Bayesian binary logistic regression (2019)

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