All functions

add_exprs()

Partially bold a string

br_charts_theme()

BR charts theme

brdata

Example effects table

calculate_diff_bin()

CI for absolute risk for binary outcomes

calculate_diff_con()

CI for treatment difference in continuous outcomes

calculate_diff_rates()

CI for treatment difference in exposure-adjusted rates

calculate_log_odds_ratio_bin()

CI for log odds ratio for binary outcomes

calculate_log_rel_risk_bin()

CI for log relative risk for binary outcomes

calculate_odds_ratio_bin()

CI for odds ratio for binary outcomes

calculate_rel_risk_bin()

CI for relative risk for binary outcomes

check_effects_table()

Check if data contains required features to run a specific plot

check_feature()

intermediate function used to display log messages check if a specific feature exist in the data

check_feature_string()

intermediate function used to display log messages check if a specific feature exist in the data

colfun()

Function for colors

comorbidities

Example comorbidities data used for Figure 4

control_fonts()

Control Fonts

cumexcess

Example cumulative excess plot data used for Figure 13

demography

Example demography data used for Figure 2

effects_table

Example effects table data used for Figure 6 and Figure 7

generate_tradeoff_plot()

Trade-off plot

gensurv()

Simulate data (utilized for function tests)

gensurv_combined()

Combine the cumulative excess plot and corresponding table into one figure

gensurv_plot()

Create a cumulative excess plot from a given dataframe

gensurv_table()

Create a table that corresponds to the cumulative excess plot

ggsave_custom()

Wrapper to ggsave: Save a ggplot (or other grid object) with sensible defaults

grouped_barchart()

Grouped Bar Chart

labs_bold()

Create expression

prepare_br_calculated_ci()

Prepare data analysis for binary and continuous outcomes with Calculated interval confidence identifies whether the dataframe is for Benefit or Risk analysis

prepare_br_supplied_ci()

Prepare data analysis for binary and continuous outcomes with Supplied interval confidence identifies whether the dataframe is for Benefit or Risk analysis

prepare_tradeoff_data()

Prepare data for the tradeoff plot

prepare_tradeoff_plot()

Prepare trade-off plot

pyramid_chart()

Pyramid Chart

relmax()

Derive maximum boundary value for axis Derive boundary value to include all values

relmin()

Derive minimum boundary value for axis Derive boundary value to include all values

save_mermaid()

Save DiagrameR::mermaid object

scatter_plot()

Create a scatterplot from a given dataframe.

scatterplot

Example scatterplot data used for Figure 11

value_tree()

Create Value Tree