--- title: "0. Overview of the package" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{0. Overview of the package} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` This vignette provides an overview of `BayesERtools`. ## 0. Analysis workflow & supported model types Analysis can be performed in the following simple steps. {width=70%} <br /> Supported model types are as follows: ```{r, echo = FALSE, warning=FALSE, message=FALSE} library(gt) library(dplyr) set.seed(1234) # Needed to stablize div id # Saved csv from README.Rmd tab_mod <- read.csv("data/supported_models.csv") tab_mod |> select(-.row_id) |> gt() |> fmt_markdown() |> fmt_url( columns = !1, rows = 2, label = "🔗", show_underline = FALSE ) |> tab_spanner( label = "Binary endpoint", columns = c(lin_logit, emax_logit) ) |> tab_spanner( label = "Continuous endpoint", columns = c(linear, emax) ) |> cols_label( feature_name = "", lin_logit = "Linear \n(logit)", emax_logit = md("E<sub/>max</sub> (logit)"), linear = "Linear", emax = md("E<sub/>max</sub>"), ) |> tab_style( style = cell_text(v_align = "top", align = "center"), locations = cells_column_labels() ) |> tab_style( style = cell_text(v_align = "middle", align = "center"), locations = cells_body() ) |> tab_style( style = cell_text(v_align = "middle", align = "right"), locations = cells_body(columns = feature_name) ) |> tab_footnote( footnote = paste( "✅ Available", "🟡 In plan/under development", "⌠Not in a current plan", sep = ", " ) ) ``` ## 1. ER model development The package provides a set of functions to develop ER models. The following functions are available: - Binary endpoint, linear logistic regression: `dev_ermod_bin()`, `dev_ermod_bin_exp_sel()`, `dev_ermod_bin_cov_sel()` - Continuous endpoint, Emax: `dev_ermod_emax()` {width=100%} <br /> ## 2. Simulation from developed ER model The following functions are available for simulation from developed ER models: - `sim_er()` - `sim_er_new_exp()`, `sim_er_curve()` - `sim_er_new_exp_marg()`, `sim_er_curve_marg()` {width=100%} <br /> ## 3. Plot simulated ER curve Simulated ER curve can be visualized with the following functions: - `plot_er()` - `plot_er_exp_sel()` {width=100%} <br /> ## Acknowledgement Figure created in https://www.biorender.com