easyViz: 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). Approximate confidence
intervals for rlm(), gls(), nls(), and betareg() models use delta-method variance
propagation. Optional sandwich covariance estimators can be specified for compatible
model classes (Zeileis 2004 <doi:10.18637/jss.v011.i10>). Plots are rendered using base
R graphics with extensive customization options. For beta regression using 'betareg',
see Cribari-Neto and Zeileis (2010) <doi:10.18637/jss.v034.i02>. For mixed-effects
models with 'lme4', see Bates et al. (2015) <doi:10.18637/jss.v067.i01>. For models
using 'glmmTMB', see Brooks et al. (2017) <doi:10.32614/RJ-2017-066>. For generalized
additive models using 'mgcv', see Wood (2017) <doi:10.1201/9781315370279>.
| Version: |
2.2.0 |
| Imports: |
stats, utils, graphics, grDevices |
| Suggests: |
MASS, sandwich, nlme, numDeriv, reformulas, betareg, statmod, survival, lme4, glmmTMB, mgcv |
| Published: |
2026-11-15 |
| DOI: |
10.32614/CRAN.package.easyViz |
| Author: |
Luca Corlatti [aut, cre] |
| Maintainer: |
Luca Corlatti <lucac1980 at yahoo.it> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
easyViz results |
Documentation:
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