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.

Some utility functions for validation and data manipulation. These functions can be helpful to reduce internal codes everywhere in package development.

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(),
 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>.