VGAMdata

R package VGAMdata. Data sets to accompany the VGAM package and the book ”Vector Generalized Linear and Additive Models: With an Implementation in R” (Yee, 2015) &lt;<a href=”http://dx.doi.org/10.1007/978-1-4939-2818-7”>doi:10.1007/978-1-4939-2818-7</a>&gt;. These are used to illustrate vector generalized linear and additive models (VGLMs/VGAMs), and associated models (Reduced-Rank VGLMs, Quadratic RR-VGLMs, Row-Column Interaction Models, and constrained and unconstrained ordination models in ecology).


References in zbMATH (referenced in 11 articles )

Showing results 1 to 11 of 11.
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  1. Abid, Rahma; Kokonendji, Célestin C.; Masmoudi, Afif: Geometric Tweedie regression models for continuous and semicontinuous data with variation phenomenon (2020)
  2. Bu, Xianwei; Majumdar, Dibyen; Yang, Jie: D-optimal designs for multinomial logistic models (2020)
  3. Liu, Nan-Ting; Lin, Feng-Chang; Shih, Yu-Shan: Count regression trees (2020)
  4. Wood, Simon N.: Inference and computation with generalized additive models and their extensions (2020)
  5. Forzani, Liliana; Rodriguez, Daniela; Smucler, Ezequiel; Sued, Mariela: Sufficient dimension reduction and prediction in regression: asymptotic results (2019)
  6. Karavarsamis, N.; Huggins, R. M.: Two-stage approaches to the analysis of occupancy data. II: The heterogeneous model and conditional likelihood (2019)
  7. Miranda-Soberanis, V. F.; Yee, T. W.: New link functions for distribution-specific quantile regression based on vector generalized linear and additive models (2019)
  8. Ghosh, I.; Hamedani, G. G.; Bansal, N.; Maadooliat, M.: On the mixtures of Weibull and Pareto (IV) distribution: an alternative to Pareto distribution (2018)
  9. Leisen, Fabrizio; Rossini, Luca; Villa, Cristiano: A note on the posterior inference for the Yule-Simon distribution (2017)
  10. Smith, Barry; Wang, Steven; Wong, Augustine; Zhou, Xiaofeng: A penalized likelihood approach to parameter estimation with integral reliability constraints (2015)
  11. Yee, Thomas W.: Vector generalized linear and additive models. With an implementation in R (2015)