ChungHa Sung, TaeJoon Song, Jae W. Lee, and Junghoon Lee
RIGHT enables interactive data visualization and analysis using
HTML5 canvas and JavaScript technology to help users easily explore data
and gain valuable insight. Its R API is designed to create a seamless
analysis-visualization workflow for interactive graphics. Since HTML5
canvas and JavaScript is supported in virtually all modern web browsers,
the visualizations can be delivered to almost any device/platform for
presentation purposes.
'RIGHT.R' 'body.R' 'common.R' 'head.R' 'plot_RIGHT.R'
'points_RIGHT.R' 'lines_RIGHT.R' 'pie_RIGHT.R' 'hist_RIGHT.R'

'RIGHT.R' 'body.R' 'common.R' 'head.R' 'plot_RIGHT.R'
'points_RIGHT.R' 'lines_RIGHT.R' 'pie_RIGHT.R' 'hist_RIGHT.R'
'boxplot_RIGHT.R'

'RIGHT.R' 'body.R' 'common.R' 'head.R' 'plot_RIGHT.R'
'points_RIGHT.R' 'lines_RIGHT.R' 'pie_RIGHT.R' 'hist_RIGHT.R'
'boxplot_RIGHT.R' 'search_RIGHT.R' 'table_RIGHT.R'

ChungHa Sung, TaeJoon Song, JongHyun Bae, SangGi Hong, Jae W. Lee, and
Junghoon Lee
Interactive data visualization supporting linked graphs can help users easily
explore data and gain valuable insights and enables such visualization and interactive
re-analysis using HTML5 canvas and JavaScript. Its R API is designed to create a seamless
analysis, visualization, and re-analysis workflow for interactive visualization and analysis.
Since HTML5 canvas and JavaScript is supported in virtually all modern web browsers, the
visualizations can be delivered to almost any device/platform for presentation purposes.

Estimation of the relative index of inequality for interval-censored data using natural cubic splines

Causal Inference and Prediction in Cohort-Based Analyses
date, graphics, nlme, MASS, mvtnorm, statmod, parallel,
doParallel, foreach
c(
person("Yohann", "Foucher", email = "Yohann.Foucher@univ-nantes.fr", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-0330-7457")),
person("Florent", "Le Borgne", email = "fleborgne@idbc.fr", role = "aut"),
person("Etienne", "Dantan", email = "Etienne.Dantan@univ-nantes.fr", role = "aut"),
person("Florence", "Gillaizeau", email = "Florence.Gillaizeau@univ-nantes.fr", role = "aut"),
person("Arthur", "Chatton", email = "arthur.chatton@etu.univ-nantes.fr", role = "aut"),
person("Christophe", "Combescure", email = "christophe.combescure@hcuge.ch", role = "aut"))
We propose numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events (Le Borgne, 2016, <doi:10.1002/sim.6777>), competing events (Trebern-Launay, 2018, <doi: 10.1007/s10654-017-0322-3>), and multi-state data (Gillaizeau, 2018, <doi: 10.1002/sim.7550>). For multistate data, semi-Markov model with interval censoring (Foucher, 2008, <doi: 10.1177/0962280208093889>) may be considered and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables (Gillaizeau, 2017, <doi: 10.1177/0962280215586456>). For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders (Le Borgne, 2018,  <doi: 10.1177/0962280217702416>). Finally, several functions are available to assess time-dependent ROC curves (Combescure, 2017, <doi: 10.1177/0962280212464542>) or survival curves (Combescure, 2014, <doi: 10.1002/sim.6111>) from aggregated data.
Yohann Foucher [aut, cre] (<https://orcid.org/0000-0003-0330-7457>),
Florent Le Borgne [aut],
Etienne Dantan [aut],
Florence Gillaizeau [aut],
Arthur Chatton [aut],
Christophe Combescure [aut]