Package: easyViz
Title: Easy Visualization of Conditional Effects from Regression Models
Version: 2.1.0
Authors@R: person("Luca", "Corlatti", role = c("aut", "cre"), email="lucac1980@yahoo.it")
Description: 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) <doi:10.18637/jss.v011.i10>. 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>. 
 Methods for generalized additive models using 'mgcv' follow Wood (2017)
 <doi:10.1201/9781315370279>.
Maintainer: Luca Corlatti <lucac1980@yahoo.it>
Imports: stats, utils, graphics, grDevices
Suggests: MASS, sandwich, nlme, numDeriv, reformulas, betareg, statmod,
        survival, lme4, glmmTMB, mgcv
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-03-05 19:22:06 UTC; lucacorlatti
Author: Luca Corlatti [aut, cre]
Repository: CRAN
Date/Publication: 2026-03-05 19:40:19 UTC
Built: R 4.6.0; ; 2026-04-26 16:57:16 UTC; unix
