This workshop provides a comprehensive introduction to Vector Generalized Linear and Additive Models (VGLMs and VGAMs), equipping researchers with advanced skills for analyzing complex data structures often encountered in the social, health, and natural sciences. Participants will gain practical experience using the VGAM package in R for data analysis, enhancing their ability to apply sophisticated yet easy-to-use methods for their own research projects.

Topics Covered
Parameter link functions and xij , Constraint matrices , Fisher scoring , Common arguments and methods , Multiple responses , VGAMdata package , Data sets , Some regression models , VGLM/VGAM framework overview , Quick revision of glm() , The VGAM package (especially vglm()) , Smoothing , VGAMs , VGAM examples , RR-VGLMs , RR-VGLM examples , Doubly constrained RR-VGLMs , Constrained ordination , Stereotype and Goodman's RC association model , Model assumptions checking , Model validation techniques , Interpretation of outputs , Model selection strategies , Additive model insights , Hauck-Donner effect , Tobit regression , Other VGAM-related R packages , Nominal and ordinal responses , The multinomial distribution , Five important models , Multinomial logit model , Cumulative link model , Adjacent categories model , Continuation and stopping ratio models , Latent variables , Using the VGAM package , Partial proportional odds model , Stereotype model , Goodman's RC association model , Biplots , Marginal effects , Genetic models , Reduced-rank VGLMs , Examples , The xij argument , Loglinear models for 2 or 3 binary responses , New VGAM family functions , Model diagnostics , Workshop wrap-up , Count data characteristics , Poisson regression assumptions , VGAM package overview , Interpreting results , Under- and over-dispersion , Negative binomial , Generalized Poisson , Other count models , 5 NB variants , 3 GP variants , What is GAITD regression? , The Generally Truncated Expansion method , Spikeplots , Shiny app , Analyzing output , Heaped and seeped data , Using VGAM , The four operators and PMF , Joint under- and over-dispersion analysis , Choosing the right model , Diagnosing model fit , Test and training data , Model building strategies , Artificial right-censoring , 0 as the special value , Advanced examples , Under- and over-fitting , (Extended) beta-binomial regression