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(),
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>.
| Version: |
1.1.0 |
| Imports: |
stats, utils, graphics, grDevices |
| Suggests: |
nlme, lme4, MASS, glmmTMB, mgcv, numDeriv, sandwich |
| Published: |
2025-08-21 |
| 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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