easyViz: Easy Visualization of Conditional Effects from Regression Models (original) (raw)

Offers a flexible and user-friendly interface for visualizing conditional effects from a broad range of regression models, including mixed-effects and generalized additive (mixed) models. Compatible model types include lm(), rlm(), glm(), glm.nb(), and gam() (from 'mgcv'); nonlinear models via nls(); and generalized least squares via gls(). Mixed-effects models with random intercepts and/or slopes can be fitted using lmer(), glmer(), glmer.nb(), glmmTMB(), or gam() (from 'mgcv', via smooth terms). Plots are rendered using base R graphics with extensive customization options. Approximate confidence intervals for nls() models are computed using the delta method. Robust standard errors for rlm() are computed using the sandwich estimator (Zeileis 2004) <doi:10.18637/jss.v011.i10>. Methods for generalized additive models follow Wood (2017) <doi:10.1201/9781315370279>. For linear mixed-effects models with 'lme4', see Bates et al. (2015) <doi:10.18637/jss.v067.i01>. For mixed models using 'glmmTMB', see Brooks et al. (2017) <doi:10.32614/RJ-2017-066>.

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