Easy Visualization of Conditional Effects from Regression Models

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(), betareg(), and gam() (from 'mgcv'); nonlinear models via nls(); generalized least squares via gls(); and survival models via coxph() (from 'survival'). 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() and betareg() models are computed using the delta method. Robust standard errors for rlm() are computed using the sandwich estimator (Zeileis 2004) . For beta regression using 'betareg', see Cribari-Neto and Zeileis (2010) . For mixed-effects models with 'lme4', see Bates et al. (2015) . For models using 'glmmTMB', see Brooks et al. (2017) . Methods for generalized additive models using 'mgcv' follow Wood (2017) .