A meta-package that installs and loads a set of packages from
    'easystats' ecosystem in a single step. This collection of packages provide
    a unifying and consistent framework for statistical modeling, visualization,
    and reporting. Additionally, it provides articles targeted at instructors for
    teaching 'easystats', and a dashboard targeted at new R users for easily
    conducting statistical analysis by accessing summary results, model fit indices,
    and visualizations with minimal programming.

Inspect survival data, plot Kaplan-Meier curves, assess the
    proportional hazards assumption, fit parametric survival models,
    predict and plot survival and hazards, and export the outputs to
    'Excel'.  A simple interface for fitting survival models using 
    flexsurv::flexsurvreg(), flexsurv::flexsurvspline(), 
    flexsurvcure::flexsurvcure(), and survival::survreg().

This SVG elements generator can easily generate 
    SVG elements such as rect, line, circle, ellipse, polygon, 
    polyline, text and group. Also, it can combine and 
    output SVG elements into a SVG file.

Set of tools to simplify application of atomic forecast
    verification metrics for (comparative) verification of ensemble forecasts
    to large data sets. The forecast metrics are imported from the
    'SpecsVerification' package, and additional forecast metrics are provided
    with this package. Alternatively, new user-defined forecast scores can be
    implemented using the example scores provided and applied using the
    functionality of this package.

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>.