Imports: shiny, stargazer, compare, BBmisc, rms

Description: Demonstrate the results of a statistical model object as a dynamic nomogram in an 'RStudio' panel or web browser. Also, the generic DNbuilder function in this package provides a simple and straightforward way to build and publish a dynamic nomogram on the web to use the app independent of R. DynNom supports a variety of model objects; 'lm', 'glm', 'coxph' and also 'Ols', 'Glm', 'lrm', 'cph' models.

Description: Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. Also, the generic DNbuilder() function in this package provides a simple and straightforward way to build and publish a dynamic nomogram on the web to use the app independent of R. 'DynNom' supports a variety of model objects; lm(), glm(), coxph() models and also ols(), Glm(), lrm(), cph() models in the 'rms' package.
Depends: survival (>= 2.38-3), rms, plotly
Imports: shiny, ggplot2 (> 2.1.0), stargazer, compare, BBmisc

Imports: shiny, ggplot2 (> 2.1.0), plotly, stargazer, compare, BBmisc

Title: Visualising Statistical Models using Dynamic Nomograms
Description: Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. The package provides two generics functions: DynNom, which display statistical model objects as a dynamic nomogram; DNbuilder, which builds required scripts to publish a dynamic nomogram on a web server such as the <https://www.shinyapps.io/>. Current version of 'DynNom' supports stats::lm, stats::glm, survival::coxph, rms::ols, rms::Glm, rms::lrm, rms::cph, mgcv::gam and gam::gam model objects.
Imports: survival (>= 2.38-3), shiny, ggplot2 (> 2.1.0), plotly,
stargazer, prediction, rms, dplyr, compare, BBmisc

Packaged: 2022-08-29 09:21:32 UTC; amirjalali

Title: Methods for Estimating Dynamic Treatment Regimes
Author: S. T. Holloway, E. B. Laber, K. A. Linn, B. Zhang, M. Davidian, and A. A. Tsiatis
Description: A comprehensive toolkit for estimating Dynamic Treatment Regimes. Available methods include Interactive Q-Learning, Q-Learning, and value-search methods based on Augmented Inverse Probability Weighted estimators and Inverse Probability Weighted estimators.