Author: ChungHa Sung, TaeJoon Song, Jae W. Lee, and Junghoon Lee
Description: 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
Collate: 'RIGHT.R' 'body.R' 'common.R' 'head.R' 'plot_RIGHT.R'

Author: ChungHa Sung, TaeJoon Song, JongHyun Bae, SangGi Hong, Jae W. Lee, and
Description: 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.

Title: Relative index of inequality estimation
Description: Estimation of the relative index of inequality for interval-censored data using natural cubic splines
Packaged: Thu Jan  6 23:46:05 2005; SergeantJ

Title: Causal Inference and Prediction in Cohort-Based Analyses
Depends: R (>= 2.10), splines, survival, relsurv
Imports: date, graphics, nlme, MASS, mvtnorm, statmod, parallel,
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"))
Description: 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.
Author: Yohann Foucher [aut, cre] (<https://orcid.org/0000-0003-0330-7457>),